Example 1:
titleEntities: {'Subject': ['Countries'], 'Date': ['2050', '2055']}
title: Countries with the lowest fertility rate globally 2050 to 2055
X_Axis['Country']: ['Singapore', 'Republic_of_Korea', 'United_Arab_Emirates', 'Puerto_Rico', 'Bosnia_and_Herzegovina', 'Saint_Lucia', 'Greece', 'Cyprus', 'Italy', 'Republic_of_Moldova', 'China_Taiwan_Province_of_China', 'Albania', 'Mauritius', 'Thailand', 'Qatar', 'Nepal', 'Croatia', 'Japan', 'Serbia', 'Brazil', 'North_Macedonia', 'Brunei_Darussalam', 'Portugal', 'Spain', 'Canada']
Y_Axis['Average', 'number', 'of', 'children', 'per', 'woman']: ['1.38', '1.44', '1.45', '1.45', '1.47', '1.48', '1.5', '1.51', '1.51', '1.52', '1.53', '1.53', '1.54', '1.54', '1.56', '1.56', '1.56', '1.57', '1.57', '1.58', '1.58', '1.59', '1.59', '1.59', '1.59']

gold: This statistic shows the countries and territories with the lowest projected fertility rate between 2050 and 2055 . Between 2050 and 2055 , Singapore is projected to have the lowest fertility rate , with an average of 1.38 children born per woman .
gold_template: This statistic shows the templateTitleSubject[0] and territories templateTitle[1] the templateTitle[2] projected templateTitle[3] templateTitle[4] between templateTitleDate[min] and templateTitleDate[max] . Between templateTitleDate[min] and templateTitleDate[max] , templateXValue[0] is projected to have the templateTitle[2] templateTitle[3] templateTitle[4] , templateTitle[1] an templateYLabel[0] of templateYValue[min] templateYLabel[2] born templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the 20 templateTitle[0] of the templateTitle[2] in templateTitleDate[0] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateXValue[0] , with a templateYLabel[3] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Countries of the fertility in 2050 .  In 2050 , the Average number of Countries was Singapore , with a per of 1.59 children per .

Example 2:
titleEntities: {'Subject': ['U.S.'], 'Date': []}
title: Expenditure of affluent U.S. households on fees for social , recreation , health club
X_Axis['Year']: ['2013', '2014', '2015', '2016']
Y_Axis['Amount', 'spent', 'in', 'U.S.', 'dollars']: ['46.58', '47.39', '51.52', '56.15']

gold: The statistic shows the expenditure of affluent households , or the so-called highest quintile , in the United States on fees and admissions for social , recreation , health club membership from 2013 to 2016 . In 2016 , affluent Americans spent some 56 U.S. dollars on fees and admissions for social , recreation , health club membership .
gold_template: The statistic shows the templateTitle[0] of templateTitle[1] templateTitle[3] , or the so-called highest quintile , in the templateTitle[2] on templateTitle[4] and admissions templateTitle[5] templateTitle[6] , templateTitle[7] , templateTitle[8] templateTitle[9] membership from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[1] Americans templateYLabel[1] some templateYValue[max] templateYLabel[2] templateYLabel[3] on templateTitle[4] and admissions templateTitle[5] templateTitle[6] , templateTitle[7] , templateTitle[8] templateTitle[9] membership .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[1] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateTitle[1] templateYLabel[0] amounted to approximately templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Amount of the affluent U.S. from 2013 to 2016 .  In 2016 , the U.S. affluent Amount amounted to approximately 56.15 U.S. dollars .

Example 3:
titleEntities: {'Subject': ['New Orleans'], 'Date': ['2016', '2017']}
title: Quarterly average daily rate in hotels in New Orleans 2016 to 2017
Data:
           0    1    2    3
Quarter   Q1   Q2   Q3   Q4
2016     184  194  145  183
2017     189    0    0    0 

gold: This statistic shows the quarterly average daily rate in hotels in New Orleans in 2016 and 2017 . In the first quarter of 2017 , the average daily rate for hotels in New Orleans in the United States was 189 U.S. dollars .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[4] in templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . In the first templateLabel[0][0] of templateTitleDate[max] , the templateTitle[1] templateTitle[2] templateTitle[3] for templateTitle[4] in templateTitleSubject[0] in the country was templateValue[2][max] U.S. dollars .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . In the first templateLabel[0][0] of templateTitleDate[max] , the templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in the country was templateValue[2][max] U.S. dollars .
generated: This statistic shows the Quarterly average daily rate of hotels in New Orleans 2016 and 2017 .  In the first Quarter of 2017 , the average daily rate of hotels in New Orleans the country was 189 U.S. dollars .

Example 4:
titleEntities: {'Subject': ['U.S'], 'Date': []}
title: Population age structure in metropolitan areas in the U.S .
Data:
                                                                       0                                        1                                       2                                   3                                               4                                  5                                           6                                                7                                      8                                  9
Area               New_York_-_Northern_New_Jersey_-_Long_Island_NY-NJ-PA  Los_Angeles_-_Long_Beach_-_Santa_Ana_CA  Chicago_-_Naperville_-_Joliet_IL-IN-WI  Dallas_-_Fort_Worth_-_Arlington_TX  Philadelphia_-_Camden_-_Wilmington_PA-NJ-DE-MD  Houston_-_Sugar_Land_-_Baytown_TX  Miami_-_Fort_Lauderdale_-_Pompano_Beach_FL  Washington_-_Arlington_-_Alexandria_DC-VA-MD-WV  Atlanta_-_Sandy_Springs_-_Marietta_GA  Boston_-_Cambridge_-_Quincy_MA-NH
Under 18 years                                                   4423734                                  3256354                                 2436723                             1806339                                         1399360                            1673274                                     1251716                                          1337831                                1476554                             993673
18 to 44 years                                                   7135783                                  5163207                                 3654096                             2598211                                         2183443                            2296708                                     1991118                                          2152100                                2185701                            1758176
45 to 64 years                                                   5022623                                  3065350                                 2415975                             1489928                                         1593846                            1401077                                     1420578                                          1436495                                1342201                            1245644
65 to 74 years                                                   1293522                                   725875                                  572564                              311959                                          396354                             285072                                      404918                                           310624                                 274841                             300875
75 years and over                                                1194134                                   664011                                  501209                              241178                                          395249                             211358                                      478721                                           239191                                 195916                             290312 

gold: This graph shows the age structure of the population in the 10 largest metropolitan areas in the U.S. in 2009 . 4.42 million people under 18 years of age were living in New York in 2009 .
gold_template: This graph shows the templateTitle[1] templateTitle[2] of the templateTitle[0] in the 10 largest templateTitle[3] templateTitle[4] in the U.S. in 2009 . templateValue[1][0] templateScale people templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] of templateTitle[1] were living in templateValue[0][0] in 2009 .

generated_template: This statistic gives information on the templateScale of the templateTitleSubject[0] templateYLabel[2] templateYLabel[3] as of 2019 , from templateValue[0][0] . According to the source , it was found that templateValue[1][0] templateScale of employees using it .
generated: This statistic gives information on the percentage of the U.S as of 2019 , from New_York_-_Northern_New_Jersey_-_Long_Island_NY-NJ-PA .  According to the source , it was found that 4423734 % of employees using it .

Example 5:
titleEntities: {'Subject': ['Canada'], 'Date': ['2015', '2019']}
title: Average retail price for white sugar in Canada 2015 to 2019
Data:
          0     1     2     3     4     5     6     7     8     9    10    11
Month   Dec   Nov   Oct   Sep   Aug   Jul   Jun   May   Apr   Mar   Feb   Jan
2015   2.57  2.76  2.77  2.83  2.81  2.81  2.79  2.81  2.78  2.81  2.81  2.88
2016   2.64  2.68  2.72   2.8  2.88  2.84  2.75  2.76  2.78  2.69  2.73  2.77
2017   2.69  2.66  2.68  2.74  2.76  2.79  2.77  2.94  2.78  2.83  2.75  2.82
2018   2.56  2.55  2.67  2.61   2.7  2.67  2.58  2.71  2.71  2.58  2.69   2.7
2019   2.41  2.45  2.44  2.52  2.49   2.5   2.5  2.56  2.53  2.59  2.48  2.61 

gold: The average retail price for two kilograms of white sugar in Canada hit an all-time low of 2.41 Canadian dollars in December 2019 . This price has gradually decreased over time , from a monthly average of 2.79 dollars per two kilograms in 2015 . What is white sugar ? White sugar which we buy in stores is a refined sugar .
gold_template: The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateValue[5][min] kilograms of templateTitle[4] templateTitle[5] in templateTitleSubject[0] hit an all-time low of templateValue[5][min] Canadian dollars in 2019 . This templateTitle[2] has gradually templateNegativeTrend over time , from a monthly templateTitle[0] of templateValue[1][6] dollars per templateValue[5][min] kilograms in templateTitleDate[min] . What is templateTitle[4] templateTitle[5] ? templateTitle[4] templateTitle[5] which we buy in stores is a refined templateTitle[5] .

generated_template: The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] one dozen , or 12 , templateTitle[4] in templateTitleSubject[0] was templateValue[2][3] Canadian dollars in 2019 . This templateTitle[2] has remained relatively stable over the given time period , not templatePositiveTrend above templateValue[2][max] or below templateValue[2][min] Canadian dollars . The Canadian egg marketCanada produces an templatePositiveTrend number of templateTitle[4] each year .
generated: The Average retail price for one dozen , or 12 , white in Canada was 2.8 Canadian dollars in 2019 .  This price has remained relatively stable over the given time period , not rising above 2.88 or below 2.64 Canadian dollars .  The Canadian egg marketCanada produces an increasing number of white each year .

Example 6:
titleEntities: {'Subject': ['Mercedes', 'EU'], 'Date': ['2019']}
title: Mercedes : market share of new car registrations in the EU 2019
X_Axis['Month']: ['Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18']
Y_Axis['Market', 'share']: ['5.9', '6.2', '6.4', '6.9', '5.7', '5.6', '4.9', '5.3', '5.5', '4.9', '5.3', '5.3', '6.6', '6.4', '6.8', '7.2', '4.6']

gold: This statistic shows Mercedes 's share of new car registrations in the EU between August 2018 and December 2019 . In December 2019 , Mercedes had a market share of 5.9 percent in the EU . At 71,300 units , the brand 's vehicle sales were about 8.5 percent above the same month one year earlier , mainly thanks to increased demand from German customers .
gold_template: This statistic shows templateTitleSubject[0] 's templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitleSubject[1] between 2018 and 2019 . In 2019 , templateTitleSubject[0] had a templateYLabel[0] templateYLabel[1] of templateYValue[0] templateScale in the templateTitleSubject[1] . At 71,300 units , the brand 's vehicle sales were about 8.5 templateScale above the same templateXLabel[0] one year earlier , mainly thanks to templatePositiveTrend demand from German customers .

generated_template: This statistic illustrates the monthly templateYLabel[0] templateYLabel[1] of the templateTitle[1] manufacturer templateTitleSubject[0] in the templateTitleSubject[1] ( UK ) between 2016 and 2019 . The company 's templateYLabel[0] templateYLabel[1] has gradually templateNegativeTrend from in this time period from templateYValue[29] templateScale in 2016 to templateYValue[0] templateScale in 2019 . templateTitleSubject[0] is one of the leading templateTitle[1] manufacturers in the UK .
generated: This statistic illustrates the monthly Market share of the market manufacturer Mercedes in the EU ( UK ) between 2016 and 2019 .  The company 's Market share has gradually decreased from in this time period from 4.6 % in 2016 to 5.9 % in 2019 .  Mercedes is one of the leading market manufacturers in the UK .

Example 7:
titleEntities: {'Subject': ['Red Bull'], 'Date': ['2011', '2018']}
title: Total workforce of Red Bull worldwide 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'employees']: ['12239', '11886', '11865', '10997', '10410', '9694', '8966', '8294']

gold: This statistic depicts the total number of Red Bull employees worldwide from 2011 to 2018 . According to the report , Red Bull had employed approximately 12,239 people in 171 countries at the end of 2018 .
gold_template: This statistic depicts the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] employees templateTitle[4] from templateXValue[min] to templateXValue[max] . According to the report , templateTitleSubject[0] had employed approximately templateYValue[max] templateYValue[idxmax(X)] in 171 countries at the end of templateXValue[idxmax(Y)] .

generated_template: This statistic outlines the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company employed a total of templateYValue[idxmax(X)] people . The company is one of the largest oil and gas corporation .
generated: This statistic outlines the Number of employees Red Bull worldwide from 2011 to 2018 .  In 2018 , the company employed a total of 12239 people .  The company is one of the largest oil and gas corporation .

Example 8:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Women 's average age at first marriage in Italy 2018 , by region
X_Axis['Month']: ['Aosta_Valley', 'Liguria', 'Emilia-Romagna', 'Tuscany', 'Sardinia', 'Friuli-Venezia_Giulia', 'Lazio', 'Trentino-South_Tyrol', 'Piedmont', 'Lombardy', 'Umbria', 'Marche', 'Veneto', 'Abruzzo', 'Molise', 'Basilicata', 'Apulia', 'Campania', 'Sicily', 'Calabria']
Y_Axis['Average', 'age']: ['34.7', '34.1', '33.9', '33.9', '33.9', '33.6', '33.5', '33.4', '33.3', '33.1', '33.0', '32.8', '32.8', '32.8', '32.2', '32.1', '31.6', '31.0', '30.8', '30.6']

gold: In 2018 , the average age of Italian women walking down the aisle was of 32.5 years . From the perspective of the singular regions , the oldest females to tie the knot were citizens of Aosta Valley and Liguria , where the average age of the bride at the first marriage reached 34.7 years and 34.1 years in 2018 . Aosta Valley was also the region with the oldest grooms in the country – a male inhabitant of the region got married at the average age of 38.2 years .
gold_template: In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of Italian templateTitle[0] walking down the aisle was of 32.5 years . From the perspective of the singular regions , the oldest females to tie the knot were citizens of templateXValue[0] and templateXValue[1] , where the templateYLabel[0] templateYLabel[1] of the bride at the templateTitle[4] templateTitle[5] reached templateYValue[max] years and templateYValue[1] years in templateTitleDate[0] . templateXValue[0] was also the templateTitle[9] with the oldest grooms in the country – a male inhabitant of the templateTitle[9] got married at the templateYLabel[0] templateYLabel[1] of 38.2 years .

generated_template: In templateTitleDate[0] , the highest templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitleSubject[0] was templateXValue[0] , with the highest templateYLabel[0] templateYLabel[1] accounting for about templateYValue[max] templateScale templateYLabel[2] . In the same year , the second highest templateYLabel[0] templateYLabel[1] reached templateYValue[3] templateScale templateYLabel[3] . women in the templateXValue[4] was the highest figure of templateTitle[0] templateTitle[1] templateTitle[2] in the country .
generated: In 2018 , the highest Average of Women 's in the Italy was Aosta_Valley , with the highest Average age accounting for about 34.7 % age .  In the same year , the second highest Average age reached 33.9 % age .  women in the Sardinia was the highest figure of Women 's average in the country .

Example 9:
titleEntities: {'Subject': ['Cleveland Browns'], 'Date': ['2019']}
title: Average regular season home attendance of the Cleveland Browns 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Average', 'attendance']: ['67431', '65765', '63882', '64311', '66186', '67425', '71242', '66632', '65859', '66116', '68888', '72778']

gold: This graph depicts the average regular season home attendance of the Cleveland Browns from 2008 to 2019 . In 2019 , the average attendance at home games of the Cleveland Browns was 67,431 .
gold_template: This graph depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitleSubject[0] was templateYValue[0] templateYValue[idxmax(X)]

generated_template: This graph depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitleSubject[0] was templateYValue[0] templateYValue[idxmax(X)]
generated: This graph depicts the Average regular season home attendance of the Cleveland Browns from 2008 to 2019 .  In 2019 , the Average attendance at home games of the Cleveland Browns was 67431 . 

Example 10:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2017', '2023']}
title: Smartphone penetration as share of population in Indonesia 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population', 'with', 'a', 'smartphone']: ['33', '32', '31', '30', '28', '26', '24']

gold: The statistic depicts the smartphone penetration in Indonesia as share of the population from 2017 to 2023 . In 2017 , 24 percent of the population in Indonesia used a smartphone .
gold_template: The statistic depicts the templateYLabel[3] templateTitle[1] in templateTitleSubject[0] as templateYLabel[0] of the templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the templateYLabel[1] in templateTitleSubject[0] used a templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[3] templateTitle[1] as a templateYLabel[0] of the templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[5] , templateYLabel[2] a forecast up to templateXValue[max] . In templateXValue[5] , around templateYValue[5] templateScale of the templateYLabel[1] in templateTitleSubject[0] used a templateYLabel[3] .
generated: The statistic depicts the smartphone penetration as a Share of the population in Indonesia from 2017 to 2018 , smartphone a forecast up to 2023 .  In 2018 , around 26 % of the population in Indonesia used a smartphone .

Example 11:
titleEntities: {'Subject': ['Millennials'], 'Date': ['2016']}
title: U.S. Millennials : most popular social network 2016 , by age group
Data:
                 0          1        2         3          4         5        6
Platform  Facebook  Instagram  YouTube  LinkedIn  Pinterest  Snapchat  Twitter
18-24           36         24       12         7          9         9        3
25-29           41         17       14        10          8         9        1
30-34           50         13       11        10          8         5        2 

gold: This statistic presents the most popular social network among Millennials in the United States as of August 2016 , by age group . During the survey period , 24 percent of respondents between 18 and 24 years old stated that they used Instagram the most .
gold_template: This statistic presents the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] among templateTitleSubject[0] in the templateTitle[0] as of 2016 , templateTitle[7] templateTitle[8] templateTitle[9] . During the survey period , templateValue[1][1] templateScale of respondents between 18 and templateValue[1][1] years old stated that they used templateValue[0][1] the templateTitle[2] .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateLabel[0][0] templateTitle[3] templateTitle[4] in the templateTitle[5] as of templateTitleSubject[0] templateTitleDate[0] , based on templateTitle[7] . During the survey , templateValue[1][0] templateScale of respondents stated that they went to purchase via templateValue[0][0] .
generated: This statistic presents the U.S. Millennials Platform popular social in the network as of Millennials 2016 , based on by .  During the survey , 36 % of respondents stated that they went to purchase via Facebook .

Example 12:
titleEntities: {'Subject': ['Fast Furious'], 'Date': ['2019']}
title: The Fast and the Furious : domestic and global box office revenue 2019
Data:
                       0                        1                 2                                      3          4                 5                 6                         7                                      8
Movie          Furious_7  The_Fate_of_the_Furious  Fast_&_Furious_6  Fast_&_Furious_Presents:_Hobbs_&_Shaw  Fast_Five  Fast_and_Furious  2_Fast_2_Furious  The_Fast_and_the_Furious  The_Fast_and_the_Furious:_Tokyo_Drift
North America     353.01                   226.01            238.68                                 164.34     209.84            155.06            127.15                    144.53                                  62.51
Worldwide         1516.0                   1236.0             788.7                                 721.04      626.1             363.2             236.4                     207.3                                  158.5 

gold: As of September 2019 , 'Furious 7 ' _ , the seventh installment in the series , reached a gross of 353.01 million U.S. dollars in cinemas across North America . The film had the highest grossing opening weekend of all of the films in the franchise , bringing in a box office gross of 147.2 million U.S. dollars when it was released in April 2015 .
gold_template: As of 2019 , 'Furious templateValue[0][0] ' _ , the seventh installment in the series , reached a gross of templateValue[1][0] templateScale U.S. dollars in cinemas across templateLabel[1][0] templateLabel[1][1] . The film had the highest grossing opening weekend of all of the films in the franchise , bringing in a templateTitle[4] templateTitle[5] gross of 147.2 templateScale U.S. dollars when it was released in 2015 .

generated_template: The statistic presents the templateTitle[5] templateTitle[6] templateTitle[7] of the templateValue[0][0] movie templateTitle[2] in templateLabel[1][0] templateLabel[1][1] and templateLabel[2][0] . As of 2018 , `` templateValue[0][0] '' , the second installment in the templateTitleSubject[0] based on the DC Comics character , reached a gross of templateValue[1][1] templateScale U.S. dollars in 3,555 cinemas across templateLabel[1][0] templateLabel[1][1] .
generated: The statistic presents the office revenue 2019 of the Furious_7 movie domestic in North America and Worldwide .  As of 2018 , `` Furious_7 '' , the second installment in the Fast Furious based on the DC Comics character , reached a gross of 226.01 million U.S. dollars in 3,555 cinemas across North America .

Example 13:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2010', '2018']}
title: University applicants in the United Kingdom ( UK ) 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Number', 'of', 'university', 'applicants']: ['636960', '649700', '674890', '673040', '659030', '635910', '616700', '668150', '658560']

gold: This statistic shows the number of applicants to universities in the United Kingdom ( UK ) from 2010 to 2018 . The number of applicants peaked in 2016 . The lower figures in 2012 and 2013 may be connected to the rise of the tuition fee limit in 2012 to 9,000 British pounds per year .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[2] to universities in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[2] peaked in templateXValue[2] . The lower figures in templateXValue[6] and templateXValue[5] may be connected to the rise of the tuition fee limit in templateXValue[6] to 9,000 British pounds per templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] of the templateYLabel[2] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of the templateYLabel[1] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number of the university of the applicants UK from 2010 to 2018 .  In 2018 , the University of the university of United Kingdom amounted to 636960 applicants .

Example 14:
titleEntities: {'Subject': ['United States'], 'Date': ['2018']}
title: Favorite James Bond actor in the United States 2018
Data:
                                  0               1            2             3               4               5
Actor                  Sean_Connery  Pierce_Brosnan  Roger_Moore  Daniel_Craig  Timothy_Dalton  George_Lazenby
Very favorable                   61              51           42            41              14               7
Somewhat favorable               21              30           32            30              35              24
Somewhat unfavorable              4               6            6             9              16              16
Very unfavorable                  2               2            3             4               5               7
Don't know/no opinion            12              11           18            17              30              46 

gold: The statistic shows the most favorable James Bond actors according to adults in the United States as of November 2018 . The findings reveal that Sean Connery was the favorite among survey respondents , with 61 percent stating that they had a very favorable impression of the actor 's interpretation of James Bond . A total of 51 percent said the same about Pierce Brosnan .
gold_template: The statistic shows the most templateLabel[1][1] templateTitle[1] templateTitle[2] actors according to adults in the templateTitleSubject[0] as of 2018 . The findings reveal that templateValue[0][0] was the templateTitle[0] among survey respondents , with templateValue[1][0] templateScale stating that they had a templateLabel[1][0] templateLabel[1][1] impression of the templateLabel[0][0] 's interpretation of templateTitle[1] templateTitle[2] . A total of templateValue[1][1] templateScale said the same about templateValue[0][1] .

generated_template: This statistic shows the templateScale of online consumers in the templateTitle[0] , sorted templateTitle[5] templateTitle[6] templateTitle[7] . According to the survey findings , templateValue[1][0] templateScale of the respondents stated that they had used a templateLabel[1][0] templateLabel[1][1] .
generated: This statistic shows the percentage of online consumers in the Favorite , sorted States 2018 .  According to the survey findings , 61 % of the respondents stated that they had used a Very favorable .

Example 15:
titleEntities: {'Subject': ['United States'], 'Date': ['1998', '2018']}
title: Natural gas production - United States 1998 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1998']
Y_Axis['Production', 'in', 'billion', 'cubic', 'meters']: ['831.8', '745.8', '727.4', '740.3', '704.7', '655.7', '649.1', '617.4', '575.2', '557.6', '546.1', '521.9', '524.0', '511.1', '526.4', '540.8', '536.0', '555.5', '543.2', '538.7']

gold: Production of natural gas in the United States has been increasing for the past decade and amounted to 831.8 billion cubic meters in 2018 . An increase in production corresponded with rising demand for natural gas in the United States , particularly after the 2008 Recession . Natural gas becomes competitive Since the early 2000s , the price of coal had been going up , and increased more rapidly following the 2008 Recession , which affected the cost of crude oil to an even greater degree .
gold_template: templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitleSubject[0] has been templatePositiveTrend for the past decade and amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . An templatePositiveTrend in templateYLabel[0] corresponded with templatePositiveTrend demand for templateTitle[0] templateTitle[1] in the templateTitleSubject[0] , particularly after the templateXValue[10] Recession . templateTitle[0] templateTitle[1] becomes competitive Since the early 2000s , the price of coal had been going up , and templatePositiveTrend more rapidly following the templateXValue[10] Recession , which affected the cost of crude oil to an even greater degree .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] of the templateYLabel[2] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the data from the templateTitle[0] of the templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Production of the billion of the cubic States 1998 to 2018 .  In 2018 , the data from the Natural of the United States was 831.8 cubic meters .

Example 16:
titleEntities: {'Subject': ['Pokémon GO'], 'Date': ['2016']}
title: Number of Pokémon GO daily users in the U.S. 2016
X_Axis['Month']: ['August_16', 'July_19', 'July_14', 'July_12']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['30', '45', '25', '21']

gold: The graph shows the number of Pokémon GO users in the United States as of July 2016 . Shortly after its release , the game had 21 million daily active users in the U.S. and the user numbers peaked on July 19 , reaching 45 million . On August 16 , 2016 Pokémon GO had 30 million DAU .
gold_template: The graph shows the templateYLabel[0] of templateTitleSubject[0] users in the templateTitle[5] as of templateXValue[1] templateTitleDate[0] . Shortly after its release , the game had templateYValue[min] templateScale templateTitle[3] active templateYLabel[1] in the templateTitle[5] and the user numbers peaked on templateXValue[1] , reaching templateYValue[max] templateScale . On templateXValue[0] , templateTitleDate[0] templateTitleSubject[0] had templateYValue[0] templateScale DAU .

generated_template: As of 2019 , templateXValue[0] accounted for over templateYValue[max] templateScale of templateTitle[2] templateYLabel[1] templateYLabel[2] worldwide , up from templateYValue[1] templateScale in the previous year . The app is one of the most popular social network worldwide .
generated: As of 2019 , August_16 accounted for over 45 millions of GO users millions worldwide , up from 45 millions in the previous year .  The app is one of the most popular social network worldwide .

Example 17:
titleEntities: {'Subject': ['Canada'], 'Date': ['2015', '2019']}
title: Average retail price for butter in Canada 2015 to 2019
Data:
          0     1     2     3     4     5     6     7     8     9    10    11
Month   Dec   Nov   Oct   Sep   Aug   Jul   Jun   May   Apr   Mar   Feb   Jan
2015   4.66   4.7  4.67  4.69  4.56   4.6  4.54  4.62  4.55  4.59  4.61  4.57
2016    4.7  4.66  4.66  4.83  4.92  4.93  4.85  4.93  4.96  4.92  4.93  4.77
2017   4.78  4.78  4.82  4.93  4.91  4.94  4.94  4.88  4.88   4.9  4.78  4.76
2018   4.77  4.81  4.77  4.74  4.69  4.74  4.62  4.66  4.85  4.79  4.86  4.86
2019    5.0  5.05   5.1  4.97  5.03  5.16  4.85  4.97  4.85  4.99  4.98  4.94 

gold: The average retail price for butter in Canada was 5 Canadian dollars per 454 grams in December 2019 . This price has fluctuated between months , yet has gradually increased over the given period , with an average price of 4.99 Canadian dollars in 2019 and 4.61 Canadian dollars in 2015 . The Canadian butter market The volume of butter produced in Canada amounted to around 108.77 million kilograms in 2017 , an increase of over 30 million kilograms over the previous ten years .
gold_template: The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] was templateValue[1][0] Canadian dollars per 454 grams in 2019 . This templateTitle[2] has fluctuated between months , yet has gradually templatePositiveTrend over the given period , with an templateTitle[0] templateTitle[2] of templateValue[5][9] Canadian dollars in templateTitleDate[max] and templateValue[1][10] Canadian dollars in templateTitleDate[min] . The Canadian templateTitle[4] market The volume of templateTitle[4] produced in templateTitleSubject[0] amounted to around 108.77 templateScale kilograms in templateLabel[3][0] , an templatePositiveTrend of over 30 templateScale kilograms over the previous ten years .

generated_template: The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] one dozen , or 12 , templateTitle[4] in templateTitleSubject[0] was templateValue[2][3] Canadian dollars in 2019 . This templateTitle[2] has remained relatively stable over the given time period , not templatePositiveTrend above templateValue[2][max] or below templateValue[2][min] Canadian dollars . The Canadian egg marketCanada produces an templatePositiveTrend number of templateTitle[4] each year .
generated: The Average retail price for one dozen , or 12 , butter in Canada was 4.83 Canadian dollars in 2019 .  This price has remained relatively stable over the given time period , not rising above 4.96 or below 4.66 Canadian dollars .  The Canadian egg marketCanada produces an increasing number of butter each year .

Example 18:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1998', '2018']}
title: U.S. construction : annual spending in public and private sectors 1998 to 2018
Data:
                  0    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18   19   20
Year            '18  '17  '16  '15  '14  '13  '12  '11  '10  '09  '08  '07  '06  '05  '04  '03  '02  '01  '00  '99  '98
Public sector   302  283  292  290  276  271  279  286  304  315  309  289  255  234  220  216  213  202  181  169  155
Private sector  992  963  899  823  730  636  571  502  505  592  769  863  912  870  771  675  634  638  621  575  534 

gold: The statistic reflects the total value of both public and private sector construction spending in the United States between 1993 and 2018 . In 2018 , private sector construction spending is expected to total around 992 billion US dollars .
gold_template: The statistic reflects the total value of both templateTitle[4] and templateTitle[5] templateLabel[1][1] templateTitle[1] templateTitle[3] in the templateTitle[0] between 1993 and templateTitleDate[max] . In templateTitleDate[max] , templateTitle[5] templateLabel[1][1] templateTitle[1] templateTitle[3] is expected to total around templateValue[2][0] templateScale US dollars .

generated_template: This statistic shows the amount of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[0] in templateTitleDate[min] and templateTitleDate[max] . In templateTitleDate[max] , some templateValue[1][0] templateScale of templateTitle[2] templateTitle[3] were employed in the templateTitle[0] .
generated: This statistic shows the amount of annual spending public in the U.S. in 1998 and 2018 .  In 2018 , some 302 % of annual spending were employed in the U.S. .

Example 19:
titleEntities: {'Subject': ['India'], 'Date': ['2024']}
title: Inflation rate in India 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['3.97', '3.98', '4.05', '4.07', '4.09', '3.44', '3.43', '3.6', '4.5', '4.9', '5.8', '9.4', '10', '9.5', '10.53', '12.31', '9.09', '6.2', '6.7', '4.4', '3.82', '3.86', '3.98', '4.31', '3.83', '5.7', '13.13', '6.84', '9.43', '9.96', '10.28', '7.28', '9.86', '13.48', '11.2', '4.57', '7.21', '9.06', '8.89', '6.25', '6.52']

gold: The statistic shows the inflation rate in India from 1984 to 2018 , with projections up until 2024 . The inflation rate is calculated using the price increase of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the year .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price templatePositiveTrend of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in India from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in India amounted to about 3.43 % compared to the previous Year .

Example 20:
titleEntities: {'Subject': ['Congress', 'Democrats'], 'Date': ['2019']}
title: Top hashtags used by U.S. Congress Democrats in 2019
X_Axis['Hashtag']: ['#forthepeople', '#trumpshutdown', '#protectourcare', '#endgunviolence', '#hr8', '#actonclimate', '#endtheshutdown', '#sotu', '#equalityact', '#hr1']
Y_Axis['Total', 'number', 'of', 'mentions']: ['6961', '3992', '2810', '2342', '2206', '2206', '1769', '1767', '1720', '1683']

gold: This statistic presents the top hashtags used by Democratic members of United States Congress in 2019 . According to the findings , the most popular hashtag used by Democratic members of Congress was `` # forthepeople '' . Democratic members of Congress quoted this hashtag a total of 6,961 times .
gold_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] Democratic members of templateTitle[4] templateTitleSubject[0] in templateTitleDate[0] . According to the findings , the most popular templateXLabel[0] templateTitle[2] templateTitle[3] Democratic members of templateTitleSubject[0] was `` # forthepeople '' . Democratic members of templateTitleSubject[0] quoted this templateXLabel[0] a templateYLabel[0] of templateYValue[max] times .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateXValue[0] templateTitle[3] in the templateTitle[4] as of 2019 , ranked by far the largest templateTitle[0] templateYLabel[0] of people . It was templateXValue[0] , with over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: This statistic gives information on the Top hashtags used #forthepeople by in the U.S. as of 2019 , ranked by far the largest Top Total of people .  It was #forthepeople , with over 6961 million mentions in 2019 .

Example 21:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2015']}
title: Leading fast food restaurants in the United Kingdom 2015
X_Axis['Response']: ["McDonald's", 'KFC', 'Subway', 'Burger_King', 'Pizza_Hut', "Domino's", "Nando's", 'Pizza_Express', 'None', 'Pret_A_Manger', 'Krispy_Kreme', 'Wimpy', "Harry_Ramsden's", 'Chicken_Cottage', 'Perfect_Pizza', 'Dixy_chicken', 'Quiznos', 'Other', 'Prefer_not_to_say']
Y_Axis['Share', 'of', 'respondents']: ['57', '42', '35', '33', '28', '26', '23', '22', '19', '12', '9', '6', '6', '5', '4', '4', '2', '10', '1']

gold: This statistic shows the leading fast food ( quick service ) restaurants in the United Kingdom ( UK ) in 2015 . During the survey , 57 percent of respondents said they went to McDonald 's , while 42 percent chose KFC . Despite the popularity McDonald 's enjoy in the United Kingdom , there are far fewer McDonald 's restaurants per million people in the United Kingdom , than in similar nations .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( quick service ) templateTitle[3] in the templateTitleSubject[0] ( UK ) in templateTitleDate[0] . During the survey , templateYValue[max] templateScale of templateYLabel[1] said they went to McDonald 's , while templateYValue[1] templateScale chose templateXValue[1] . Despite the popularity McDonald 's enjoy in the templateTitleSubject[0] , there are far fewer McDonald 's templateTitle[3] per templateScale people in the templateTitleSubject[0] , than in similar nations .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded templateXValue[3] by Harris Interactive in 2014 , templateYValue[max] templateScale of templateYLabel[1] stated that they used templateXValue[0] a templateXValue[1] templateTitle[1] .
generated: This statistic shows the results of a survey among United Kingdom adult fast .  The survey was fielded Burger_King by Harris Interactive in 2014 , 57 % of respondents stated that they used McDonald's a KFC fast .

Example 22:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: U.S. per capita consumption of fresh lettuce ( romaine and leaf ) 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['12.3', '15.0', '14.5', '11.9', '10.8', '11.4', '11.9', '11.7', '12.0', '10.0', '10.4', '11.5', '12.0', '9.7', '12.0', '10.8', '9.6', '8.0', '8.4']

gold: The timeline shows the per capita consumption of fresh romaine and leaf lettuce in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh romaine and leaf lettuce amounted to approximately 12.3 pounds in 2018 .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[6] and templateTitle[7] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[6] and templateTitle[7] templateTitle[5] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of fresh lettuce in the U.S. from 2000 to 2018 .  According to the report , the U.S. Per capita consumption of fresh lettuce amounted to approximately 12.3 pounds in 2018 .

Example 23:
titleEntities: {'Subject': ['Harley-Davidson'], 'Date': []}
title: Harley-Davidson - motorcycle sales by region 2017/2018
Data:
                                         0       1
Year                                  2017    2018
U.S.                                147972  132868
Canada                               10081    9690
Europe, Middle East, Africa (EMEA)   44935   46602
Asia-Pacific                         30348   28724
Latin America                         9452   10167 

gold: Globally , H-D shipped some 228,000 ‘ hogs ' in 2018 . Worldwide retail sales of Harley-Davidson motorcycles declined by 6.1 percent between 2017 and 2018 . Sales are falling in key markets The slump was most severe in traditional Asian and Oceanian markets such as Japan , South Korea , Australia , and New Zealand , where retail sales dropped by almost 14 percent .
gold_template: Globally , H-D shipped some 228,000 ‘ hogs ' in templateValue[0][last] . Worldwide retail templateTitle[2] of templateTitleSubject[0] motorcycles declined templateTitle[3] 6.1 templateScale between templateValue[0][0] and templateValue[0][last] . templateTitle[2] are templateNegativeTrend in key markets The slump was most severe in traditional Asian and Oceanian markets such as Japan , South Korea , Australia , and New Zealand , where retail templateTitle[2] templateNegativeTrend templateTitle[3] almost 14 templateScale .

generated_template: This statistic gives information on the templateScale of templateTitleSubject[0] 's employees templateTitle[4] from templateValue[0][last] to templateValue[0][0] , sorted templateTitle[8] templateTitle[9] . As of 2019 , it was found that templateValue[2][0] templateScale of people in the state deficit .
generated: This statistic gives information on the percentage of Harley-Davidson 's employees region from 2018 to 2017 , sorted 2017/2018 .  As of 2019 , it was found that 10081 % of people in the state deficit .

Example 24:
titleEntities: {'Subject': ['Oman'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Oman 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['90.56', '87.02', '84.05', '81.9', '78.66', '76.61', '79.28', '70.6', '65.48', '68.92', '81.08', '78.78', '76.62', '68.02', '57.05', '48.39', '60.91', '42.09', '37.22', '31.08', '24.76', '21.63', '20.14', '19.45', '19.51', '15.59', '14.0', '15.84', '15.28', '13.8', '12.92', '12.49', '12.45', '11.34', '11.69', '9.37', '8.39', '8.63', '8.23', '10.4', '9.36']

gold: The statistic shows gross domestic product ( GDP ) in Oman from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Oman from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 25:
titleEntities: {'Subject': ['Restasis'], 'Date': ['2014']}
title: Sales forecast for eye care product Restasis in U.S. 2014 to 2025
X_Axis['Year']: ['2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022', '2023', '2024', '2025']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['1084', '1217', '1361', '1524', '1679', '1830', '1975', '2115', '2242', '2332', '1914', '1650']

gold: This statistic represents a sales forecast for eye care product Restasis in the United States between 2014 and 2025 . It is expected that Restasis will generate around 2.3 billion U.S. dollars in 2023 .
gold_template: This statistic represents a templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitleSubject[0] in the templateTitle[7] between templateXValue[min] and templateXValue[max] . It is expected that templateTitleSubject[0] will generate around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] of the templateTitle[1] templateTitle[2] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Sales of the forecast for eye from 2014 to 2020 , with projections up until 2025 .  In 2020 , the Sales of the forecast for in Restasis amounted to about 1975 million U.S. dollars .

Example 26:
titleEntities: {'Subject': ['DVDs U.S.'], 'Date': ['2019']}
title: Top-selling DVDs in the U.S. in 2019
X_Axis['Movies']: ['The_Goonies', 'Aquaman', 'Bohemian_Rhapsody', 'Avengers:_Endgame', 'Captain_Marvel', 'A_Star_is_Born', 'How_to_Train_Your_Dragon:_The_Hidden_World', 'Hocus_Pocus', 'Matilda', 'John_Wick:_Chapter_3_-_Parabellum', 'Fantastic_Beasts:_The_Crimes_of_Grindelwald', 'Bumblebee', "Dr._Seuss'_The_Grinch", 'Pokemon:_Detective_Pikachu', 'Aladdin']
Y_Axis['Number', 'of', 'units', 'sold', 'in', 'thousands']: ['903.47', '868.93', '855.06', '848.49', '840.92', '832.1', '686.49', '656.63', '569.92', '567.68', '542.02', '531.96', '529.48', '509.33', '506.52']

gold: In 2019 , the top-selling DVD in the United States was 1985 movie 'The Goonies ' _ , which sold approximately 903.5 thousand copies in the U.S. that year . In second place was 'Aquaman ' with 868.93 thousand copies sold . The movie also ranked fourth in the 2019 DVD sales list based on consumer spending .
gold_template: In templateTitleDate[0] , the templateTitle[0] DVD in the templateTitle[2] was 1985 movie 'The templateXValue[0] ' _ , which templateYLabel[2] approximately templateYValue[max] thousand copies in the templateTitleSubject[0] that year . In second place was 'Aquaman ' with templateYValue[1] thousand copies templateYLabel[2] . The movie also ranked fourth in the templateTitleDate[0] DVD sales list based on consumer spending .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] in templateTitleSubject[0] as of templateTitleDate[0] , based on goods templateXValue[0] . During the survey period , it was found that the templateScale of the templateYLabel[1] templateYLabel[2] will be the goods and services .
generated: This statistic gives information on the Top-selling DVDs U.S. Movies in DVDs U.S. as of 2019 , based on goods The_Goonies .  During the survey period , it was found that the thousands of the units sold will be the goods and services .

Example 27:
titleEntities: {'Subject': ['Italy'], 'Date': ['2000', '2018']}
title: Average annual wages in Italy 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Average', 'annual', 'wages', 'in', 'euros']: ['29601', '29558', '29843', '29634', '29390', '29277', '29231', '30140', '30620', '30330', '30179', '30148', '30176', '29970', '29636', '29042', '29073', '29272', '29125']

gold: Between 2000 and 2018 , the average wage in Italy was stable , remaining between 29 thousand and 30 thousand euros . In 2018 , male managers represented the group of people with the highest earnings . Their gross salary amounted to 102 thousand euros a year .
gold_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] wage in templateTitleSubject[0] was stable , remaining between templateYValue[4] thousand and templateYValue[0] thousand templateYLabel[3] . In templateXValue[max] , male managers represented the group of people with the highest earnings . Their gross salary amounted to 102 thousand templateYLabel[3] a templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Over this period , the templateYLabel[1] templateYLabel[0] templatePositiveTrend by over 5 thousand templateYLabel[3] , peaking at 13.8 thousand templateYLabel[3] in templateXValue[max] .
generated: This statistic shows the Average annual wages in Italy from 2000 to 2018 .  Over this period , the annual Average increased by over 5 thousand euros , peaking at 13.8 thousand euros in 2018 .

Example 28:
titleEntities: {'Subject': ['Amazon'], 'Date': ['2022']}
title: Projected total revenue of Amazon 2002 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Net', 'revenue', 'in', 'billion', 'U.S.', 'dollars']: ['356$', '316$', '276$', '238$', '201$', '166$', '136$', '107$', '89$', '74$', '61$', '48$', '34$', '25$', '19$', '15$', '11$', '8$', '7$', '5$', '4$']

gold: The time series shows the projected net revenue of Amazon.com 's e-commerce and service from 2002 to 2022 , in billion US dollars . In the last measured year fiscal period , the multinational e-commerce company 's net revenue was 136 billion U.S. dollars , up from 107 billion U.S. dollars in 2015 . Amazon.com , an American electronic commerce company originally founded in 1994 , is the world 's largest online retailer of books , clothing , electronics , music and many more goods .
gold_template: The time series shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of Amazon.com 's e-commerce and service from templateXValue[min] to templateXValue[max] , in templateScale US templateYLabel[4] . In the last measured templateXLabel[0] fiscal period , the multinational e-commerce company 's templateYLabel[0] templateYLabel[1] was 136 templateScale templateYLabel[3] templateYLabel[4] , up from 107 templateScale templateYLabel[3] templateYLabel[4] in templateXValue[7] . Amazon.com , an American electronic commerce company originally founded in 1994 , is the world 's largest online retailer of books , clothing , electronics , music and many more goods .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the templateTitleSubject[0] amounted to templateYValue[3] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net revenue of the Amazon of from 2002 to 2022 .  In 2022 , the Net revenue of the Amazon of the Amazon amounted to 238$ billion U.S. dollars .

Example 29:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2015']}
title: Average hours spent watching TV weekly in the United Kingdom ( UK ) in 2015
X_Axis['Year']: ['5_hours_or_less', '6_to_10_hours', '11_to_20_hours', 'More_than_20_hours', "I_don't_watch_TV"]
Y_Axis['Share', 'of', 'respondents']: ['9', '18', '31', '39', '2']

gold: This survey details a distribution of the average number of hours spent on watching TV in the United Kingdom ( UK ) in 2015 . During the survey , 18 percent of respondents said that they watched TV 6 to 10 hours per week .
gold_template: This survey details a distribution of the templateTitle[0] number of templateXValue[0] templateTitle[2] on templateTitle[3] templateXValue[last] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . During the survey , templateYValue[1] templateScale of templateYLabel[1] said that they watched templateXValue[last] templateXValue[1] to templateXValue[1] templateXValue[0] per week .

generated_template: This statistic presents the templateTitle[2] of templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] as of 2018 , broken down templateTitle[7] templateTitle[8] . During the survey , it was found that templateYValue[max] templateScale of the templateYLabel[1] stated that they had templateTitleSubject[0] templateTitle[2] templateXValue[last] .
generated: This statistic presents the spent of United Kingdom hours in the UK as of 2018 , broken down Kingdom UK .  During the survey , it was found that 39 % of the respondents stated that they had United Kingdom spent I_don't_watch_TV .

Example 30:
titleEntities: {'Subject': ['Americans'], 'Date': ['2013']}
title: Sexual orientation of Americans in 2013
Data:
                                0         1         2
Sexual Orientation  Lesbian_/_Gay  Bisexual  Straight
Women                         1.5       0.9      97.7
Men                           1.8       0.4      97.8 

gold: This statistic shows the share of adults in the United States who identify as lesbian , gay , bisexual , or straight in 2013 . In 2013 , 1.5 percent of American adult women identify as lesbian , while 1.8 of American adult men identify as gay .
gold_template: This statistic shows the share of adults in the country who identify as templateValue[0][0] , templateValue[0][0] , templateValue[0][1] , or templateValue[0][2] in templateTitleDate[0] . In templateTitleDate[0] , templateValue[1][0] templateScale of American adult templateLabel[1][0] identify as templateValue[0][0] , while templateValue[2][0] of American adult templateLabel[2][0] identify as templateValue[0][0] .

generated_template: This statistic depicts the distribution of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] , templateTitle[10] templateTitle[11] . It was found that templateValue[1][0] templateScale of the templateValue[0][0] and templateValue[2][0] templateScale of all templateValue[0][0] .
generated: This statistic depicts the distribution of Sexual orientation Americans in the Americans ( ) in 2013 , .  It was found that 1.5 % of the Lesbian_/_Gay and 1.8 % of all Lesbian_/_Gay .

Example 31:
titleEntities: {'Subject': ['Scotland'], 'Date': ['2000', '2019']}
title: Unemployment rate in Scotland 2000 to 2019
X_Axis['Year']: ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['Unemployment', 'rate']: ['6.8', '6.4', '6.3', '5.7', '5.7', '5.4', '5.3', '4.8', '4.7', '7', '8.2', '8.1', '8', '7.3', '6', '5.8', '5.2', '4.2', '3.9', '3.6']

gold: This statistic shows the unemployment rate in Scotland between 2000 and 2019 . The unemployment rate was at its lowest in the most recent period , in 2019 , having fallen to 3.6 percentage points since the peak of 8.2 percent in 2010 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . The templateYLabel[0] templateYLabel[1] was at its lowest in the most recent period , in templateXValue[max] , having fallen to templateYValue[idxmax(X)] templateScale points since the peak of templateYValue[max] templateScale in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Scotland from 2000 to 2019 .  In 2019 , the Unemployment rate in Scotland was at approximately 3.6 % .

Example 32:
titleEntities: {'Subject': ['Canada'], 'Date': ['2002', '2012', '2016']}
title: Liver cirrhosis deaths : Canada 2002 , 2012 , and 2016
Data:
           0     1     2
Year    2002  2012  2016
Male    10.4  10.6  10.9
Female   4.7   5.1   5.2 

gold: This statistic shows the death rate from liver cirrhosis in Canada in 2002 , 2012 , and 2016 , by gender . In 2016 , there were 10.9 male deaths per 100,000 population , aged 15 years and over , resulting from cirrhosis of the liver in Canada .
gold_template: This statistic shows the death rate from templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateValue[0][0] , templateValue[0][1] , and templateValue[0][last] , by gender . In templateValue[0][last] , there were templateValue[1][last] templateLabel[1][0] templateTitle[2] per 100,000 population , aged 15 years and over , resulting from templateTitle[1] of the templateTitle[0] in templateTitleSubject[0] .

generated_template: The templateTitle[0] templateTitle[1] at templateTitle[2] was templateValue[1][0] years old for men in the United Kingdom ( templateTitleSubject[0] ) in templateValue[0][0] , and templateValue[2][0] years for women . The templateTitle[1] of the templateTitle[3] has remained relatively stable over the past few years . The recent years it was found that the lowest point for men .
generated: The Liver cirrhosis at deaths was 10.4 years old for men in the United Kingdom ( Canada ) in 2002 , and 4.7 years for women .  The cirrhosis of the Canada has remained relatively stable over the past few years .  The recent years it was found that the lowest point for men .

Example 33:
titleEntities: {'Subject': ['Number'], 'Date': ['2019']}
title: Number of operable nuclear reactor plants by country 2019
X_Axis['Country']: ['United_States', 'France', 'China', 'Japan', 'Russia', 'South_Korea', 'India', 'Canada', 'Ukraine', 'United_Kingdom', 'Sweden', 'Spain', 'Belgium', 'Germany', 'Czech_Republic', 'Switzerland', 'Pakistan', 'Slovakia', 'Hungary', 'Finland', 'Argentina', 'Mexico', 'Romania', 'South_Africa', 'Brazil', 'Bulgaria', 'Iran', 'Netherlands', 'Armenia', 'Slovenia']
Y_Axis['Number', 'of', 'reactors']: ['97', '58', '45', '37', '36', '24', '22', '19', '15', '15', '8', '7', '7', '7', '6', '5', '5', '4', '4', '4', '3', '2', '2', '2', '2', '2', '1', '1', '1', '1']

gold: The statistic shows the number of operable nuclear reactors as of June 2019 , by country . In the United Kingdom , 15 nuclear power reactors were in operation as of this time . Operable nuclear reactors are those connected to the grid .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] as of 2019 , templateTitle[5] templateXLabel[0] . In the templateXValue[0] templateXValue[9] , templateYValue[8] templateTitle[2] power templateYLabel[1] were in operation as of this time . templateTitle[1] templateTitle[2] templateYLabel[1] are those connected to the grid .

generated_template: As of templateTitleDate[0] , there were 52 templateTitle[2] templateYLabel[1] templateTitle[4] templateTitle[5] worldwide , with the majority being built in templateXValue[0] . Comparatively , there were 186 templateTitle[2] templateYLabel[1] shut down permanently as of that year . The templateXValue[3] templateXValue[9] had permanently shut down 37 templateTitle[2] facilities as of 2020 .
generated: As of 2019 , there were 52 nuclear reactors plants by worldwide , with the majority being built in United_States .  Comparatively , there were 186 nuclear reactors shut down permanently as of that year .  The Japan United_Kingdom had permanently shut down 37 nuclear facilities as of 2020 .

Example 34:
titleEntities: {'Subject': ['Global'], 'Date': ['2005', '2018']}
title: Global bauxite import and export 2005 to 2018
Data:
               0        1        2        3        4         5        6        7        8        9       10       11       12       13
Year        2018     2017     2016     2015     2014      2013     2012     2011     2010     2009     2008     2007     2006     2005
Export   55968.7  45622.2  48752.7  71404.6  43256.4   91886.1  55652.9  65749.5  48278.3  29452.8  42525.4  38762.0  28678.0  24045.7
Import  108775.3  94101.9  78531.7  88653.6  69174.6  103475.2  72623.0  75092.9  57152.3  44580.8  62685.1  59961.2  42992.7  37427.3 

gold: This statistic shows the sum of the imports and exports of bauxite worldwide from 2005 to 2018 . Total global bauxite exports increased from 24 million in 2005 to 55.97 million metric tons at the end of 2018 . Total global imports of bauxite reached around 108.8 million metric tons at end of 2018 .
gold_template: This statistic shows the sum of the imports and exports of templateTitle[1] worldwide from templateValue[0][last] to templateValue[0][0] . Total templateTitleSubject[0] templateTitle[1] exports templatePositiveTrend from 24 templateScale in templateValue[0][last] to 55.97 templateScale metric tons at the end of templateValue[0][0] . Total templateTitleSubject[0] imports of templateTitle[1] reached around 108.8 templateScale metric tons at end of templateValue[0][0] .

generated_template: This statistic shows the templateTitle[0] of the templateYLabel[2] templateYLabel[3] of templateTitle[2] employees in the United Kingdom ( templateTitleSubject[0] ) from templateValue[0][last] to templateValue[0][0] . According to the survey , there were templateValue[1][0] templateScale of the employees in templateValue[0][0] , compared to the country .
generated: This statistic shows the Global of the import employees in the United Kingdom ( Global ) from 2005 to 2018 .  According to the survey , there were 55968.7 % of the employees in 2018 , compared to the country .

Example 35:
titleEntities: {'Subject': ['CropScience'], 'Date': ['2005', '2018']}
title: Bayer CropScience 's revenue 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Revenues', 'in', 'million', 'euros']: ['14266', '9577', '9915', '10367', '9494', '8819', '8383', '7255', '6830', '6510', '6382', '5826', '5700', '5896']

gold: This statistic shows the revenue of Bayer CropScience from 2005 to 2018 . Bayer CropScience is a subgroup of the Bayer AG and produces products for crop protection and nonagricultural pest control . It is also active in seeds and plant traits .
gold_template: This statistic shows the templateTitle[3] of templateTitle[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] templateTitleSubject[0] is a subgroup of the templateTitle[0] AG and produces products for crop protection and nonagricultural pest control . It is also active in seeds and plant traits .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the total templateYLabel[1] of templateTitleSubject[1] templateTitle[4] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Revenues of million euros from 2005 to 2018 .  In 2018 , the total million of CropScience 2005 amounted to 14266 million euros .

Example 36:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2024']}
title: Inflation rate in South Korea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2', '1.9', '1.8', '1.4', '0.89', '0.46', '1.48', '1.94', '0.97', '0.71', '1.28', '1.3', '2.19', '4.03', '2.94', '2.76', '4.67', '2.54', '2.24', '2.75', '3.59', '3.52', '2.76', '4.07', '2.26', '0.81', '7.51', '4.44', '4.93', '4.48', '6.27', '4.8', '6.21', '9.33', '8.57', '5.7', '7.15', '3.05', '2.75', '2.46', '2.27']

gold: This statistic shows the average inflation rate in South Korea from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in South Korea amounted to about 1.48 percent compared to the previous year . For further info , see the South Korean GDP .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . For further info , see the templateTitleSubject[0] Korean GDP .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in the South Korea from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in South Korea amounted to about 1.48 % compared to the previous Year .

Example 37:
titleEntities: {'Subject': ['Ownership'], 'Date': ['2012']}
title: Ownership and personal use of tablet computers 2012 , by country
X_Axis['Country']: ['Spain', 'Australia', 'Italy', 'United_States', 'United_Kingdom', 'France', 'Japan', 'Germany']
Y_Axis['Percentage', 'of', 'population']: ['24', '24', '23', '20', '19', '15', '12', '10']

gold: This statistic shows the percentage of tablet computer ownership and usage among the populations of selected countries as of September 2012 . In the U.S. tablet ownership and usage was found to be at 20 percent of the population .
gold_template: This statistic shows the templateScale of templateTitle[3] computer templateTitleSubject[0] and usage among the populations of selected countries as of 2012 . In the U.S. templateTitle[3] templateTitleSubject[0] and usage was found to be at templateYValue[3] templateScale of the templateYLabel[1] .

generated_template: This statistic gives information on the templateTitle[1] templateYLabel[0] of templateTitleSubject[0] 's templateYLabel[1] as of templateTitleDate[0] . During the survey period , templateYValue[max] templateScale of templateTitleSubject[0] templateYLabel[1] lived in the templateXValue[0] .
generated: This statistic gives information on the personal Percentage of Ownership 's population as of 2012 .  During the survey period , 24 percentage of Ownership population lived in the Spain .

Example 38:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Murder in the U.S. : number of victims by race/ethnicity and gender 2018
Data:
             0      1      2           3             4                   5                       6        7
Race     Total  White  Black  Other_race  Unknown_race  Hispanic_or_Latino  Not_Hispanic_or_Latino  Unknown
Total    14123   6088   7407         395           233                2173                    9066     1840
Male     10914   4255   6237         273           149                1752                    7005     1371
Female    3180   1832   1168         121            59                 421                    2058      450
Unknown     29      1      2           1            25                   0                       3       19 

gold: This statistic displays the number of murder victims in the United States by race/ethnicity and gender in 2018 . In 2018 , 3,180 murder victims were female . Additional information on black/African American murder victims in the United StatesAccording to male deaths by firearm-related injuries by ethnicity , the amount of black victims has fallen by over half since 1970 in proportion to the population .
gold_template: This statistic displays the templateTitle[2] of templateTitle[0] templateTitle[3] in the templateTitle[1] templateTitle[4] templateTitle[5] and templateTitle[6] in templateTitleDate[0] . In templateTitleDate[0] , templateValue[3][0] templateTitle[0] templateTitle[3] were templateLabel[3][0] . Additional information on black/African American templateTitle[0] templateTitle[3] in the United StatesAccording to templateLabel[2][0] deaths templateTitle[4] firearm-related injuries templateTitle[4] ethnicity , the amount of templateValue[0][2] templateTitle[3] has fallen templateTitle[4] over half since 1970 in proportion to the population .

generated_template: This statistic shows the templateScale of templateTitleSubject[0] templateTitle[1] in templateTitle[4] in templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . According to the survey findings , templateValue[1][0] templateScale of templateLabel[2][0] templateTitle[1] were templateLabel[2][0] templateTitle[1] in templateTitleSubject[0] .
generated: This statistic shows the percentage of U.S. in by 2018 , sorted race/ethnicity gender .  According to the survey findings , 14123 % of Male U.S. were Male U.S. in .

Example 39:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2013']}
title: Most popular cheese brands in the United Kingdom ( UK ) 2013
X_Axis['Cheese', 'Brands']: ['Cathedral_City', 'Supermarket_own_label_cheese', 'Philadelphia', 'Pilgrims_Choice', 'Dairylea', 'Philadelphia_Light', 'Seriously_Strong_Cheddar', 'Babybel']
Y_Axis['Share', 'of', 'respondents', 'currently', 'purchasing']: ['51', '42', '27', '26', '25', '23', '20', '17']

gold: This statistic displays the leading cheese brands in the United Kingdom ( UK ) , according to a survey conducted in November 2013 . Cathedral city and supermarket own label cheeses ranked highest , with the greatest percentage of respondents purchasing their cheese products at the time of survey . Retail sales volumes of cheese have increased year on year , with cheddar cheese the most popular type of hard cheese in the United Kingdom , mature cheddar in particular .
gold_template: This statistic displays the leading templateXValue[1] templateXLabel[1] in the templateTitleSubject[0] ( templateTitleSubject[1] ) , according to a survey conducted in 2013 . templateXValue[0] and templateXValue[1] label cheeses ranked highest , with the greatest templateScale of templateYLabel[1] templateYLabel[3] their templateXValue[1] products at the time of survey . Retail sales volumes of templateXValue[1] have templatePositiveTrend year on year , with templateXValue[6] templateXValue[1] the templateTitle[0] templateTitle[1] type of hard templateXValue[1] in the templateTitleSubject[0] , mature templateXValue[6] in particular .

generated_template: This statistic provides information on the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[4] as of 2017 . According to the survey , it was found that templateYValue[max] templateScale of the people who were living in the templateTitleSubject[0] users . The templateTitle[0] templateXLabel[0] in templateXValue[0] was ranked first with templateYValue[1] templateScale of templateYLabel[1] .
generated: This statistic provides information on the Most popular cheese in the United as of 2017 .  According to the survey , it was found that 51 % of the people who were living in the United Kingdom users .  The Most Cheese in Cathedral_City was ranked first with 42 % of respondents .

Example 40:
titleEntities: {'Subject': ['Los Angeles'], 'Date': ['1937', '2020']}
title: Career rushing yards leaders - Los Angeles Rams 1937 to 2020
X_Axis['Month']: ['Steven_Jackson', 'Eric_Dickerson', 'Marshall_Faulk', 'Lawrence_McCutcheon', 'Dick_Bass', 'Todd_Gurley', 'Dan_Towler', 'Les_Josephson', 'Tank_Younger', 'Wendell_Tyler', 'Cullen_Bryant', 'Jerome_Bettis', 'Willie_Ellison', 'Jon_Arnett', 'Cleveland_Gary', 'Jim_Bertelsen', 'Greg_Bell', 'John_Cappelletti', 'Charles_White', 'Tommy_Wilson']
Y_Axis['Rushing', 'yards']: ['10138', '7245', '6959', '6186', '5417', '5404', '3493', '3407', '3296', '3266', '3119', '3091', '2901', '2892', '2634', '2466', '2375', '2246', '2133', '2130']

gold: The statistic shows Los Angeles Rams players with the most rushing yards in franchise history . Steven Jackson is the career rushing leader of the Los Angeles Rams with 10,138 yards .
gold_template: The statistic shows templateTitleSubject[0] Rams players with the most templateYLabel[0] templateYLabel[1] in franchise history . templateXValue[0] is the templateTitle[0] templateYLabel[0] leader of the templateTitleSubject[0] Rams with templateYValue[max] templateYLabel[1] .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[5] players with the most templateYLabel[0] templateYLabel[1] in franchise history . templateXValue[0] is the templateTitle[0] templateYLabel[0] leader with the templateTitleSubject[0] Saints with templateYValue[max] templateYLabel[1] .
generated: The statistic shows Los Angeles Angeles players with the most Rushing yards in franchise history .  Steven_Jackson is the Career Rushing leader with the Los Angeles Saints with 10138 yards .

Example 41:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Consumers with a newspaper or magazine subscriptions in the U.S. 2017 , by age group
X_Axis['Response']: ['Total', 'Millennials', 'Gen_X', 'Boomers', 'Retirees']
Y_Axis['Share', 'of', 'respondents']: ['41', '33', '35', '45', '54']

gold: This statistic provides information on the share of consumers with an active newspaper or magazine subscription in the United States as of January 2017 , sorted by age . According to the source , 54 percent of Retirees who subscribe to service subscriptions had a newspaper or magazine subscription as of January 2017 .
gold_template: This statistic provides information on the templateYLabel[0] of templateTitle[0] templateTitle[1] an active templateTitle[2] or templateTitle[3] subscription in the templateTitle[5] as of 2017 , sorted templateTitle[7] templateTitle[8] . According to the source , templateYValue[max] templateScale of templateXValue[last] who subscribe to service templateTitle[4] had a templateTitle[2] or templateTitle[3] subscription as of 2017 .

generated_template: This statistic provides information on the templateYLabel[0] of templateTitle[0] templateTitle[1] an active templateTitleSubject[0] or templateTitleSubject[0] subscription in the templateTitle[6] as of 2017 , sorted templateTitle[8] templateTitle[9] . According to the source , templateYValue[max] templateScale of templateXValue[1] who subscribe to online video or music subscriptions had a templateTitleSubject[0] or templateTitleSubject[0] subscription as of 2017 .
generated: This statistic provides information on the Share of Consumers newspaper an active U.S. or subscription in the by as of 2017 , sorted group .  According to the source , 54 % of Millennials who subscribe to online video or music subscriptions had a U.S. or subscription as of 2017 .

Example 42:
titleEntities: {'Subject': ['Czech Republic'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in the Czech Republic 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['30070.3', '28569.08', '27205.52', '25881.83', '24569.06', '23213.95', '23112.59', '20409.98', '18458.24', '17728.7', '19768.84', '19912.51', '19739.9', '21736.84', '19831.4', '19787.77', '22819.46', '18434.54', '15206.4', '13362.34', '11687.92', '9777.81', '8028.36', '6599.31', '5997.69', '6304.16', '6453.43', '5993.0', '6490.09', '5784.1']

gold: The statistic shows gross domestic product ( GDP ) per capita in the Czech Republic from 1995 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateYLabel[2] templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country .
generated: The statistic shows the capita Gross domestic product ( GDP ) per capita in the Czech Republic from 1995 to 2017 , with projections up until 2024 .  GDP is the total value of all goods and services produced in a Year .  It is considered to be a very important indicator of the economic strength of a country .

Example 43:
titleEntities: {'Subject': ['New York Unemployment'], 'Date': ['1992', '2018']}
title: New York - Unemployment rate 1992 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Unemployment', 'rate']: ['4.1', '4.7', '4.9', '5.3', '6.3', '7.7', '8.5', '8.3', '8.6', '8.3', '5.4', '4.6', '4.5', '5', '5.8', '6.4', '6.1', '4.8', '4.5', '5.2', '5.6', '6.4', '6.2', '6.3', '6.9', '7.9', '8.6']

gold: This statistic displays the unemployment rate in the state of New York from 1992 to 2018 . In 2018 , unemployment in New York was 4.1 percent .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] in the state of templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[min] templateScale .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[0] templateScale .
generated: This statistic displays the Unemployment rate in New York Unemployment from 1992 to 2018 .  In 2018 , Unemployment in New York Unemployment was 4.1 % .

Example 44:
titleEntities: {'Subject': ['United States'], 'Date': ['2018']}
title: Facebook usage frequency in the United States 2018 , by ethnicity
Data:
                             0                   1                     2                    3                      4      5                      6
Frequency  At_least_once_a_day  A_few_times_a_week  At_least_once_a_week  A_few_times_a_month  At_least_once_a_month  Never  Don't_know/No_opinion
White                       64                  12                     4                    3                      4     11                      2
Hispanic                    56                  15                     4                    6                      5     13                      1
Black                       53                  15                     7                    3                      4     13                      5
Other                       49                  14                     3                    7                      6     19                      2 

gold: This statistic presents the frequency of internet users in the United States accessing Facebook , sorted by ethnicity . As of February 2018 , it was found that 56 percent of Hispanic U.S. online users accessed Facebook at least once a day .
gold_template: This statistic presents the templateLabel[0][0] of internet users in the templateTitleSubject[0] accessing templateTitle[0] , sorted templateTitle[6] templateTitle[7] . As of 2018 , it was found that templateValue[2][0] templateScale of templateLabel[2][0] templateTitle[4] online users accessed templateTitle[0] at templateValue[0][0] a templateValue[0][0] .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] adult Americans on the templateTitle[0] of templateTitle[1] . The survey respondents , templateValue[1][0] templateScale of respondents stated that they think that they used the templateValue[0][0] templateTitle[1] templateValue[0][0] .
generated: This statistic shows the results of a survey among United States adult Americans on the Facebook of usage .  The survey respondents , 64 % of respondents stated that they think that they used the At_least_once_a_day usage .

Example 45:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of fresh fruit by fruit type 2018
X_Axis['Fruits']: ['Bananas', 'Apples', 'Oranges', 'Grapes', 'Avocados', 'Pineapples', 'Strawberries', 'Lemons', 'Pears', 'Peaches', 'Cherries']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['28.2', '16.9', '8.2', '8.1', '8.0', '7.8', '7.1', '4.2', '2.9', '2.2', '1.3']

gold: This statistic highlights the per capita consumption of fresh fruit in the United States in 2018 , by single fruit type . Bananas and apples were the leading fruit types in the U.S. in 2018 , based on per capita consumption . The per capita consumption of fresh apples amounted to 16.9 pounds that year .
gold_template: This statistic highlights the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] in templateTitleDate[0] , templateTitle[6] single templateTitle[5] templateTitle[8] . templateXValue[0] and templateXValue[1] were the leading templateTitle[5] types in the templateTitleSubject[0] in templateTitleDate[0] , based on templateYLabel[0] templateYLabel[1] templateYLabel[2] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateXValue[1] amounted to templateYValue[1] templateYLabel[3] that year .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[1] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateXValue[0] amounted to about templateYValue[max] templateYLabel[3] that year .
generated: The statistic presents the Per capita consumption of fresh fruit in the U.S. in 2018 , fruit Fruits .  According to the report , the U.S. Per capita consumption of fresh Bananas amounted to about 28.2 pounds that year .

Example 46:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Leading life insurance companies in the U.S. 2018 , by assets
X_Axis['Insurers']: ['Prudential_Financial', 'MetLife_Inc.', 'New_York_Life', 'TIAA-CREF', 'American_International_Group', 'Northwestern_Mutual', 'Lincoln_Financial', 'Massachusetts_Mutual', 'Manulife_Financial', 'Jackson_National', 'Transamerica_Corporation', 'AXA_Financial', 'Voya_Financial', 'Principal_Financial', 'Brighthouse_Holdings_Group', 'Nationwide', 'Allianz', 'Pacific_Life', 'Hopmeadow_Holdings_Group', 'Ameriprise_Financial', 'Thrivent_Financial_For_Lutherans', 'Sammons_Financial', 'Athene_Group', 'State_Farm', 'Guardian']
Y_Axis['Total', 'assets', 'in', 'billion', 'U.S.', 'dollars']: ['577.91', '409.62', '324.78', '316.04', '285.0', '272.27', '255.64', '254.87', '247.57', '236.99', '201.2', '194.99', '189.37', '185.01', '179.3', '170.47', '145.15', '135.58', '117.91', '104.11', '94.23', '85.25', '83.78', '78.03', '75.59']

gold: In 2018 , Prudential Financial was the largest life insurance company in the U.S. , with assets amounting to approximately 578 billion U.S. dollars . Life insurance - additional information Life insurance is an important component of basic financial planning and acts as a form of protection against the loss of income resulting from the death of an insured individual . The person named as the beneficiary of the deceased receives the proceeds and is therefore protected against any financial impact that may result from the death .
gold_template: In templateTitleDate[0] , templateXValue[0] was the largest templateXValue[2] templateTitle[2] company in the templateYLabel[3] , with templateYLabel[1] amounting to approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . templateXValue[2] templateTitle[2] - additional information templateXValue[2] templateTitle[2] is an important component of basic templateXValue[0] planning and acts as a form of protection against the loss of income resulting from the death of an insured individual . The person named as the beneficiary of the deceased receives the proceeds and is therefore protected against any templateXValue[0] impact that may result from the death .

generated_template: This statistic gives information on the average templateYLabel[0] of the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . The templateXValue[0] accounted for over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in the templateYLabel[3] templateYLabel[4] .
generated: This statistic gives information on the average Total of the U.S. in 2018 , U.S. Insurers .  The Prudential_Financial accounted for over 577.91 billion U.S. dollars in the U.S. dollars .

Example 47:
titleEntities: {'Subject': ['Zambia'], 'Date': ['2019']}
title: Youth unemployment rate in Zambia in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['15.94', '16.09', '16.12', '16.39', '16.49', '16.92', '17.05', '17.03', '21.45', '26.83', '21.44', '16.02', '19.3', '23.17', '28.26', '26.75', '25.55', '24.6', '23.08', '21.34', '20.88']

gold: The statistic shows the youth unemployment rate in Zambia from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Zambia was at 15.94 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Youth unemployment rate in Zambia from 1999 and 2019 .  According to the source , the data are ILO estimates .  In 2019 , the estimated Youth unemployment rate in Zambia was at 15.94 % .

Example 48:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: U.S. per capita consumption of dried fruit 2000 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02', '2000/01']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['1.0', '2.34', '2.41', '2.42', '2.27', '2.44', '2.3', '2.31', '2.26', '2.14', '2.21', '2.25', '2.34', '2.29', '2.34', '2.32', '2.59', '2.43', '2.46']

gold: The timeline depicts the per capita consumption of dried fruit in the United States from 2000/2001 to 2018/2019 . According to the report , the U.S. per capita consumption of dried fruit amounted to about 2.34 pounds in 2017/18 . In contrast , Americans consumed 116.75 pounds of fresh fruit per capita in the same year .
gold_template: The timeline depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from 2000/2001 to 2018/2019 . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to about templateYValue[1] templateYLabel[3] in templateXValue[1] . In contrast , Americans consumed 116.75 templateYLabel[3] of fresh templateTitle[5] templateYLabel[0] templateYLabel[1] in the same templateXLabel[0] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] in the templateTitle[0] from templateXValue[last] to templateXValue[0] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] amounted to about templateYValue[min] templateYLabel[3] in templateXValue[idxmin(Y)] .
generated: The timeline shows the Per capita consumption of dried in the U.S. from 2000/01 to 2018/19 .  According to the report , the U.S. Per capita consumption of dried amounted to about 1.0 pounds in 2018/19 .

Example 49:
titleEntities: {'Subject': ['LVMH Group'], 'Date': ['2008', '2019']}
title: Total number of stores of the LVMH Group worldwide 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Total', 'number', 'of', 'stores']: ['4915', '4592', '4374', '3948', '3860', '3708', '3384', '3204', '3040', '2545', '2423', '2314']

gold: LVMH had a total of 4,915 stores in operation around the world as of 2019 , up from 2,314 in 2008 . LVMH , an amalgamation of Louis Vuitton , Moët , and Hennessy , is the top selling luxury personal goods companies in the world . The company operates globally selling a diverse range of products .
gold_template: templateTitleSubject[0] had a templateYLabel[0] of templateYValue[max] templateYLabel[2] in operation around the world as of templateXValue[idxmax(Y)] , up from templateYValue[min] in templateXValue[idxmin(Y)] . templateTitleSubject[0] , an amalgamation of Louis Vuitton , Moët , and Hennessy , is the top selling luxury personal goods companies in the world . The company operates globally selling a diverse range of products .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] employees was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Total number of the stores Group of LVMH Group from 2008 to 2019 .  In 2019 , the Total of LVMH Group employees was 4915 stores .

Example 50:
titleEntities: {'Subject': ['Daimler AG'], 'Date': ['2006', '2019']}
title: Dividend per share of Daimler AG 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Dividend', 'per', 'share', 'in', 'euros']: ['0.9', '3.25', '3.65', '3.25', '3.25', '2.45', '2.25', '2.2', '2.2', '1.85', '0.0', '0.6', '2.0', '1.5']

gold: The statistic shows the dividend per share of Daimler AG from the fiscal year of 2006 to the fiscal year of 2019 . In its 2019 fiscal year , Daimler AG paid out a dividend of approximately 0.9 euros per share .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , templateTitleSubject[0] paid out a templateYLabel[0] of approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Dividend per of the Daimler AG worldwide from 2006 to 2019 .  In 2019 , the average Dividend per of Daimler AG amounted to approximately 0.9 share euros .

Example 51:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2018']}
title: Suicides in Sweden in 2018 , by gender and age group
Data:
                        0            1            2            3           4
Year   75_years_and_older  65-74_years  45-64_years  15-44_years  1-14_years
Men                   110          122          291          361           5
Women                  49           42          137          151           4 

gold: The highest number of suicides in Sweden in 2018 was committed among men from 14 to 44 years , amounting to 361 suicides . The corresponding figure among women in this age group was 151 , a significantly lower number . For all age groups , more suicides were committed among men than among women .
gold_template: The highest number of templateTitle[0] in templateTitleSubject[0] in templateTitleDate[0] was committed among templateLabel[1][0] from 14 to 44 templateValue[0][0] , amounting to templateValue[1][3] templateTitle[0] . The corresponding figure among templateLabel[2][0] in this templateTitle[5] templateTitle[6] was templateValue[2][3] , a significantly lower number . For all templateTitle[5] groups , more templateTitle[0] were committed among templateLabel[1][0] than among templateLabel[2][0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] , templateTitle[6] templateTitle[7] . During the survey , templateValue[1][1] templateScale of the templateTitle[0] were female inhabitants in the templateTitleSubject[0] .
generated: This statistic shows the Suicides Sweden of 2018 by gender in the age in 2018 , group .  During the survey , 122 % of the Suicides were female inhabitants in the Sweden .

Example 52:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2024']}
title: Ratio of government expenditure to gross domestic product ( GDP ) in Malaysia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'government', 'expenditure', 'to', 'GDP']: ['20.35', '20.37', '20.46', '20.41', '20.54', '22.97', '22.7', '21.58', '22.7', '24.71', '25.96']

gold: The statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in Malaysia from 2014 to 2018 , with projections up to 2024 . In 2018 , government expenditure in Malaysia amounted to about 22.7 percent of the country 's gross domestic product .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up to templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to the templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale of the templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to the gross domestic product ( GDP ) in Malaysia from 2014 to 2018 , with projections up until 2024 .  In 2018 , government expenditure in Malaysia amounted to about 22.7 % of the gross domestic product .

Example 53:
titleEntities: {'Subject': ['World'], 'Date': ['2018']}
title: World nuclear power consumption by country 2018
X_Axis['Country']: ['United_States', 'France', 'China', 'Russian_Federation', 'South_Korea', 'Canada', 'Ukraine', 'Germany', 'UK', 'Sweden', 'Spain', 'Japan']
Y_Axis['Share', 'of', 'nuclear', 'power', 'consumption']: ['31.4', '15.3', '10.9', '7.6', '4.9', '3.7', '3.1', '2.8', '2.4', '2.5', '2.1', '1.8']

gold: This statistic represents the percentage of world nuclear power consumption in 2018 , by country . That year , the United States accounted for more than one third of the total nuclear power consumption worldwide .
gold_template: This statistic represents the templateScale of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . That year , the templateXValue[0] accounted for more than one third of the total templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , the templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the World of the nuclear power consumption by in 2018 .  In that year , the United_States had the highest Share nuclear of 31.4 power consumption .

Example 54:
titleEntities: {'Subject': ['Boeing'], 'Date': ['2019']}
title: Number of Boeing aircraft orders by airline 2019
X_Axis['Airline']: ['Emirates', 'Korean_Air', 'Lufthansa', 'British_Airways', 'BDS_USAF_Tanker_Program', 'Bamboo_Airways', 'BDS_U.S._Navy_(P-8A_Poseidon)', 'SunExpress_Airlines', 'Air_New_Zealand', 'FedEx_Express', 'Unidentified']
Y_Axis['Number', 'of', 'aircraft', 'orders']: ['30', '20', '20', '18', '15', '10', '10', '10', '8', '7', '49']

gold: At 30 aircraft orders , Emirates was Boeing 's largest customer in 2019 . In November 2019 , the airline updated its order book , made some contractual changes , and placed an order for 30 Dreamliners . Boeing reported 246 gross orders in 2019 .
gold_template: At templateYValue[0] templateYLabel[1] templateYLabel[2] , templateXValue[0] was templateTitleSubject[0] 's largest customer in templateTitleDate[0] . In 2019 , the templateXLabel[0] updated its order book , made some contractual changes , and placed an order for templateYValue[0] Dreamliners . templateTitleSubject[0] reported 246 gross templateYLabel[2] in templateTitleDate[0] .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateXValue[3] templateXLabel[1] templateTitle[7] templateTitle[8] in templateTitleDate[0] . According to the survey , the average templateYLabel[0] templateYLabel[1] of the banking templateTitle[7] templateTitle[8] was templateXValue[0] , with a total of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic presents the Number aircraft of the Number Boeing British_Airways Airline 2019 in .  According to the survey , the average Number aircraft of the banking 2019 was Emirates , with a total of 49 orders .

Example 55:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2018']}
title: Number of cars on the road in the United Kingdom ( UK ) 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Number', 'of', 'cars', 'in', 'millions']: ['32.5', '32.2', '31.8', '31.2', '30.5', '30.9', '30.5', '30.4', '30.3', '30.1', '30.3', '30.2', '29.9', '29.8', '29.4', '29.0', '28.5', '27.8', '27.2']

gold: The United Kingdom is home to some 32.5 million passenger cars . After slowly increasing over the past years , figures reached their peak in 2018 , with UK roads busier than ever before . The only noticeable decline – apart from 2014 when the source changed – was seen in 2009 during the recession .
gold_template: The templateTitleSubject[0] is home to some templateYValue[max] templateScale passenger templateYLabel[1] . After slowly templatePositiveTrend over the past years , figures reached their peak in templateXValue[idxmax(Y)] , with templateTitleSubject[1] roads busier than ever before . The only noticeable decline – apart from templateXValue[4] when the source changed – was seen in templateXValue[9] during the recession .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateYLabel[3] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number cars of the United Kingdom of United Kingdom from 2000 to 2018 .  In 2018 , the average Number cars of United Kingdom United millions was 32.5 millions .

Example 56:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Mexico 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['1579.38', '1508.49', '1442.3', '1380.72', '1322.49', '1274.18', '1222.05', '1156.95', '1077.91', '1170.57', '1314.57', '1274.44', '1201.09', '1180.49', '1057.8', '900.05', '1109.99', '1052.7', '975.38', '877.48', '782.24', '729.34', '772.11', '756.69', '707.91', '600.23', '526.52', '500.42', '410.97', '360.1', '527.81', '500.8', '403.73', '348.14', '290.4', '246.08', '201.93', '165.06', '150.51', '217.39', '204.86']

gold: The statistic shows Mexico 's GDP from 1984 to 2018 , with projections up until 2024 . In 2018 , Mexico 's GDP amounted to approximately 1.22 trillion US dollars.Economy of Mexico GDP is an indicator primarily used to gauge the state and health of a national economy . GDP is the total market value of all final goods and services that have been produced within national borders in a given period of time , usually a year .
gold_template: The statistic shows templateTitleSubject[0] 's templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateTitle[3] amounted to approximately templateYValue[6] templateScale US dollars.Economy of templateTitleSubject[0] templateTitle[3] is an indicator primarily used to gauge the state and health of a national economy . templateTitle[3] is the total market value of all final goods and services that have been produced within national borders in a given period of time , usually a templateXLabel[0] .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Mexico from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 57:
titleEntities: {'Subject': ['Christian Dior December'], 'Date': ['2018', '2018']}
title: Revenue share by geographic region of delivery for Christian Dior December 2018
X_Axis['Country']: ['Other_markets', 'Asia_(excluding_Japan)', 'Japan', 'United_States', 'Europe_(excluding_France)', 'France']
Y_Axis['Revenue', 'share']: ['11', '29', '7', '24', '19', '10']

gold: Christian Dior SE , or simply known as Dior , is one of France 's most emblematic luxury goods company . As of December 2016 , 26 percent of its revenue came from Asian countries ( excluding Japan ) , seven percent of its revenue came from Japan , 26 percent from the United States , 19 percent from Europe ( excluding France ) . France was responsible for 10 percent of its revenue and other markets such as South Africa , Brazil and countries from the Arabian Gulf accounted for 12 percent of its revenue .
gold_template: templateTitleSubject[0] SE , or simply known as templateTitleSubject[0] , is one of templateXValue[last] 's most emblematic luxury goods company . As of templateTitleSubject[0] 2016 , 26 templateScale of its templateYLabel[0] came from Asian countries ( excluding templateXValue[2] ) , templateYValue[min] templateScale of its templateYLabel[0] came from templateXValue[2] , 26 templateScale from the templateXValue[3] , templateYValue[4] templateScale from templateXValue[4] ( excluding templateXValue[last] ) . templateXValue[last] was responsible templateTitle[6] templateYValue[last] templateScale of its templateYLabel[0] and templateXValue[0] such as South Africa , Brazil and countries from the Arabian Gulf accounted templateTitle[6] 12 templateScale of its templateYLabel[0] .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[2] of templateTitleSubject[0] templateYLabel[1] as of templateTitleDate[0] , templateTitle[4] templateYLabel[0] from templateTitleSubject[0] . During that year , it was found that templateXValue[0] accounted for templateYValue[max] templateScale of all templateYLabel[2] .
generated: This statistic gives information on the share by of Christian Dior December share as of 2018 , region Revenue from Christian Dior December .  During that year , it was found that Other_markets accounted for 29 % of all share .

Example 58:
titleEntities: {'Subject': ['Rovio Entertainment'], 'Date': ['2018']}
title: Revenue generated by Rovio Entertainment 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Revenue', 'in', 'million', 'euros']: ['281.2', '297.2', '192.0', '142.1', '158.3', '173.5', '152.2', '75.6', '6.5']

gold: The statistic presents the annual revenue generated by Rovio Entertainment worldwide from 2010 to 2018 . In 2018 , the developer of a popular mobile game - Angry Birds - generated 281 million euros in revenue .
gold_template: The statistic presents the annual templateYLabel[0] templateTitle[1] templateTitle[2] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the developer of a popular mobile game - Angry Birds - templateTitle[1] templateYValue[0] templateScale templateYLabel[2] in templateYLabel[0] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company generated a templateYLabel[0] of approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] .
generated: The statistic shows the Revenue of the Rovio Entertainment from 2010 to 2018 .  In 2018 , the company generated a Revenue of approximately 281.2 million euros .

Example 59:
titleEntities: {'Subject': ['Gannett'], 'Date': ['2013', '2018']}
title: Gannett 's revenue 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['2.92', '3.15', '3.05', '2.89', '3.17', '3.32']

gold: This statistic presents Gannett Company 's annual revenue from 2013 to 2018 . In 2018 , the publisher of USA Today generated a total revenue of 2.92 billion U.S. dollars .
gold_template: This statistic presents templateTitleSubject[0] Company templateTitle[1] annual templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the publisher of USA Today generated a total templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: templateTitleSubject[0] generated some templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] figures in templateXValue[max] , down from templateYValue[1] templateScale a templateXLabel[0] earlier . The company generated templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .
generated: Gannett generated some 2.92 billion U.S. dollars in Revenue figures in 2018 , down from 3.15 billion a Year earlier .  The company generated Revenue of 2.92 billion U.S. dollars in 2013 .

Example 60:
titleEntities: {'Subject': ['GDP'], 'Date': ['2016']}
title: Higher education spending as a share of GDP worldwide 2016 , by country
Data:
              0        1        2       3       4            5       6        7        8            9      10         11       12     13      14       15      16      17           18         19      20        21        22     23      24     25               26              27         28        29                  30       31      32       33            34     35              36     37          38
Country  Norway  Austria  Finland  Turkey  Sweden  Switzerland  Canada  Belgium  Estonia  Netherlands  France  Argentina  Iceland  Chile  Brazil  Germany  Mexico  Poland  New_Zealand  Australia  Israel  Slovenia  Colombia  Spain  Latvia  Korea  Slovak_Republic  Czech_Republic  Lithuania  Portugal  Russian_Federation  Hungary  Greece  Ireland  South_Africa  Italy  United_Kingdom  Japan  Luxembourg
Public      1.8      1.6      1.5     1.4     1.4          1.3     1.2      1.2      1.2          1.1     1.1        1.1      1.1      1       1        1     0.9     0.9          0.9        0.8     0.8       0.8       0.8    0.8     0.7    0.7              0.7             0.7        0.7       0.7                 0.7      0.7     0.6      0.6           0.6    0.5             0.5    0.4         0.4
Private     0.1      0.1      0.1     0.5     0.2            0     1.1      0.2      0.2          0.5     0.3          0      0.1    1.7       0      0.2     0.4     0.2          0.9        1.2     0.6       0.1       0.8    0.4     0.3    1.1              0.3             0.2        0.3       0.4                 0.4      0.4       0      0.2             0    0.3             1.2      1           0 

gold: The statistic shows the expenditure on higher education as a share of GDP in selected countries worldwide in 2016 . The United States invested 2.5 percent of its GDP in higher education in 2013 ; Public investment amounted to 0.9 of GDP and private to 1.6 percent . To find out more about the expenditure on higher education per student in selected countries worldwide in 2016 , please click here .
gold_template: The statistic shows the expenditure on templateTitle[0] templateTitle[1] as a templateTitle[3] of templateTitleSubject[0] in selected countries templateTitle[5] in templateTitleDate[0] . The templateValue[0][36] States invested 2.5 templateScale of its templateTitleSubject[0] in templateTitle[0] templateTitle[1] in 2013 ; templateLabel[1][0] investment amounted to templateValue[1][16] of templateTitleSubject[0] and templateLabel[2][0] to templateValue[1][1] templateScale . To find out more about the expenditure on templateTitle[0] templateTitle[1] per student in selected countries templateTitle[5] in templateTitleDate[0] , please click here .

generated_template: This statistic shows the templateTitle[1] of templateTitle[2] and templateTitle[3] templateTitle[4] in the templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] in the templateValue[0][0] amounted to approximately templateValue[2][last] templateScale U.S. dollars .
generated: This statistic shows the education of spending and share GDP in the worldwide from 2016 to .  In 2016 , the education spending and share GDP in the Norway amounted to approximately 0 billion U.S. dollars .

Example 61:
titleEntities: {'Subject': ['European'], 'Date': []}
title: Broadcasting rights revenue of European soccer leagues 2017/2018
X_Axis['Country']: ['England', 'Spain', 'Italy', 'Germany', 'France', 'Turkey', 'Portugal', 'Russia', 'Netherlands', 'Scotland', 'Denmark', 'Sweden', 'Poland', 'Norway', 'Austria']
Y_Axis['Revenue', 'in', 'million', 'euros']: ['3210', '1609', '1294', '1248', '791', '376', '225', '112', '89', '64', '50', '42', '39', '35', '22']

gold: This statistic shows the revenue from broadcasting rights of soccer leagues in Europe in the 2017/18 season . In that season , the broadcasting revenues of the English Premier League amounted to roughly 3.21 billion euros .
gold_template: This statistic shows the templateYLabel[0] from templateTitle[0] templateTitle[1] of templateTitle[4] templateTitle[5] in Europe in the 2017/18 season . In that season , the templateTitle[0] revenues of the English Premier League amounted to roughly templateYValue[max] templateScale templateYLabel[2] .

generated_template: This statistic displays the templateYLabel[0] of the leading templateTitle[0] templateTitle[1] worldwide in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In that year , the templateXValue[0] generated approximately templateYValue[max] templateScale templateYLabel[2] , a templateYLabel[0] of templateYValue[1] templateScale templateYLabel[2] , while in templateXValue[1] templateXValue[2] had the second at templateYValue[2] templateScale templateYLabel[2] .
generated: This statistic displays the Revenue of the leading Broadcasting rights worldwide in , soccer Country .  In that year , the England generated approximately 3210 million euros , a Revenue of 1609 million euros , while in Spain Italy had the second at 1294 million euros .

Example 62:
titleEntities: {'Subject': ['United States'], 'Date': ['2012', '2019']}
title: Automatic washers unit shipments in the United States 2012 - 2019 , by quarter
X_Axis['Quarter']: ["4Q_'19", "3Q_'19'", "2Q_'19'", "1Q_'19", "4Q_'18", "3Q_'18'", "2Q_'18'", "1Q_'18", "4Q_'17", "3Q_'17", "2Q_'17", "1Q_'17", "4Q_'16", "3Q_'16", "2Q_'16", "1Q_'16", "4Q_'15", "3Q_'15", "2Q_'15", "1Q_'15", "4Q_'14", "3Q_'14", "2Q_'14", "1Q_'14", "4Q_'13", "3Q_'13", "2Q_'13", "1Q_'13", "4Q_'12", "3Q_'12", "2Q_'12", "1Q_'12"]
Y_Axis['Unit', 'shipments', 'in', 'thousands']: ['2905.3', '2748.4', '2811.7', '2505.2', '2774.9', '2612.6', '2690.6', '2406.6', '2655.4', '2512.1', '2594.6', '2329.7', '2626.5', '2359.8', '2480.9', '2199.2', '2193.0', '2446.4', '2436.5', '2042.9', '2272.1', '2168.7', '2287.8', '1902.0', '2081.9', '2039.5', '2194.7', '1859.1', '1883.9', '1791.5', '1901.6', '1732.2']

gold: This forecast illustrates automatic washer shipments in the United States from the first quarter of 2012 to the fourth quarter of 2019 . In the fourth quarter of 2018 , automatic washer shipments are projected to amount to 2.77 million units in the United States .
gold_template: This forecast illustrates templateTitle[0] washer templateYLabel[1] in the templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the fourth templateXLabel[0] of 2018 , templateTitle[0] washer templateYLabel[1] are projected to amount to templateYValue[4] templateScale units in the templateTitleSubject[0] .

generated_template: The statistic shows the distribution of the templateTitle[1] templateTitle[2] templateYLabel[2] templateYLabel[1] in the templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . According to the fourth templateXLabel[0] of that templateXLabel[0] , templateTitle[2] around templateYValue[last] templateScale in the templateTitle[4] .
generated: The statistic shows the distribution of the washers unit thousands shipments in the United States from the first Quarter of 2012 to the fourth Quarter of 2019 .  According to the fourth Quarter of that Quarter , unit around 1732.2 thousands in the United .

Example 63:
titleEntities: {'Subject': ['Malaysia', 'Facebook'], 'Date': ['2017', '2023']}
title: Malaysia : number of Facebook users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['24.2', '23.8', '23.5', '23.1', '22.7', '22.4', '22.0']

gold: This statistic shows the number of Facebook users in Malaysia from 2017 to 2023 . In 2023 , the number of Facebook users in Malaysia is expected to reach 24.2 million , up from 22 million in 2017 . Further information With more than 2 billion monthly active users , Facebook is the most popular social network worldwide .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[idxmax(X)] templateScale , up from templateYValue[5] templateScale in templateXValue[min] . Further information With more than 2 templateScale monthly active templateYLabel[1] , templateTitleSubject[1] is the most popular social network worldwide .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[idxmax(X)] templateScale , up from templateYValue[5] templateScale in templateXValue[5] .
generated: This statistic shows the Number of Facebook users in Malaysia from 2017 to 2023 .  In 2023 , the Number of Facebook users in Malaysia is expected to reach 24.2 millions , up from 22.4 millions in 2018 .

Example 64:
titleEntities: {'Subject': ['NFL', 'Facebook'], 'Date': ['2019']}
title: NFL teams - number of Facebook fans 2019
X_Axis['Team']: ['Dallas_Cowboys', 'New_England_Patriots', 'Pittsburgh_Steelers', 'Green_Bay_Packers', 'Denver_Broncos', 'San_Francisco_49ers', 'New_Orleans_Saints', 'Chicago_Bears', 'Seattle_Seahawks', 'New_York_Giants', 'Oakland_Raiders', 'Philadelphia_Eagles', 'Carolina_Panthers', 'Baltimore_Ravens', 'Miami_Dolphins', 'Indianapolis_Colts', 'Houston_Texans', 'Minnesota_Vikings', 'Atlanta_Falcons', 'Washington_Redskins', 'New_York_Jets', 'Detroit_Lions', 'Kansas_City_Chiefs', 'Los_Angeles_Chargers', 'Arizona_Cardinals', 'Cleveland_Browns', 'Cincinnati_Bengals', 'Tennessee_Titans', 'Tampa_Bay_Buccaneers', 'Buffalo_Bills', 'Los_Angeles_Rams', 'Jacksonville_Jaguars']
Y_Axis['Facebook', 'fans', 'in', 'millions']: ['8.58', '7.07', '6.38', '5.3', '4.25', '4.05', '4.02', '4.0', '3.93', '3.86', '3.37', '3.25', '2.33', '2.27', '2.26', '2.19', '2.18', '2.16', '2.0', '1.92', '1.88', '1.87', '1.62', '1.54', '1.45', '1.31', '1.15', '0.95', '0.94', '0.89', '0.86', '0.63']

gold: With 8.58 million fans , the Dallas Cowboys have the most followed National Football League team account on Facebook . Second on the list are the New England Patriots , six-time winners of the Super Bowl . NFL on social media While the Cowboys lead the way on Facebook , over on Twitter , the New England Patriots have the largest following , while The Jacksonville Jaguars are bringing up the rear with the lowest amount of followers on the social media site .
gold_template: With templateYValue[max] templateScale templateYLabel[1] , the templateXValue[0] have the most followed National Football League templateXLabel[0] account on templateYLabel[0] . Second on the list are the templateXValue[1] Patriots , six-time winners of the Super Bowl . templateTitleSubject[0] on social media While the templateXValue[0] lead the way on templateYLabel[0] , over on Twitter , the templateXValue[1] Patriots have the largest following , while The templateXValue[last] are bringing up the rear with the lowest amount of followers on the social media site .

generated_template: The graph shows the teams with the most templateYLabel[1] in the templateTitleSubject[0] League as of 2019 . The templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] with templateYValue[max] templateScale templateYLabel[4] .
generated: The graph shows the teams with the most fans in the NFL League as of 2019 .  The Dallas_Cowboys had the highest Facebook fans with 8.58 millions .

Example 65:
titleEntities: {'Subject': ['H-1B U.S.'], 'Date': ['2011']}
title: Number of H-1B visas issued in U.S. , by countries with the most recipients 2011
X_Axis['Country']: ['India', 'China-mainland', 'U.K.', 'South_Korea', 'Mexico', 'Philippines', 'France', 'Japan', 'China-Taiwan', 'Germany', 'Other']
Y_Axis['Number', 'of', 'visas', 'issued']: ['72438', '10849', '3660', '3501', '2647', '2369', '2069', '2054', '1705', '1627', '26215']

gold: This statistic shows the number of H-1B visas issued in the United States as of 2011 , in order of the countries with the most recipients . 72,438 H-1B visas had been issued to Indian workers for the United States as of 2011 .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] in the templateTitle[4] as of templateTitleDate[0] , in order of the templateTitle[6] templateTitle[7] the templateTitle[8] templateTitle[9] . templateYValue[max] templateTitleSubject[0] templateYLabel[1] had been templateYLabel[2] to Indian workers for the templateTitle[4] as of templateTitleDate[0] .

generated_template: This statistic shows the 20 templateTitle[0] of the templateTitle[2] in templateTitleDate[0] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateXValue[0] , accounting for about templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Number of the visas in 2011 .  In 2011 , the Number visas of H-1B U.S. was India , accounting for about 72438 issued .

Example 66:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Number of homicides by firearm in the U.S. 2006 to 2018
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'homicides']: ['11006', '10372', '9103', '7803', '8454', '8897', '8653', '8874', '9199', '9528', '10129', '10225']

gold: The statistic shows the number of homicides by firearm in the United States from 2006 to 2018 . In 2018 , about 11,006 homicides in the United States were committed by firearm .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateTitleDate[max] . In templateTitleDate[max] , about templateYValue[max] templateYLabel[1] in the templateTitle[4] were committed templateTitle[2] templateTitle[3] .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitle[4] at templateYLabel[2] in templateTitleSubject[0] templatePositiveTrend by about templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the total Number of homicides in U.S. from 2006 to 2017 .  In 2017 , the Number of U.S. at homicides in U.S. increased by about 11006 % .

Example 67:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2019']}
title: Unemployment rate in Belgium 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['6.44', '6.32', '7.09', '7.83', '8.48', '8.52', '8.43', '7.54', '7.14', '8.29', '7.91', '6.98', '7.46', '8.25', '8.44', '7.36', '7.68', '6.91', '6.18', '6.59', '8.65']

gold: This statistic shows the unemployment rate in Belgium from 1999 to 2019 . In 2019 , the unemployment rate in Belgium was at approximately 6.44 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Belgium from 1999 to 2019 .  In 2019 , the Unemployment rate in Belgium was at approximately 6.44 % .

Example 68:
titleEntities: {'Subject': ['Arsenal FC'], 'Date': ['2009']}
title: Value of Arsenal FC 's jersey sponsorship 2009 to 2020
X_Axis['Year']: ['2019/20_(Fly_Emirates)', '2018/19_(Fly_Emirates)', '2017/18_(Fly_Emirates)', '2016/17_(Fly_Emirates)', '2015/16_(Fly_Emirates)', '2014/15_(Fly_Emirates)', '2013/14_(Fly_Emirates)', '2012/13_(Fly_Emirates)', '2011/12_(Fly_Emirates)', '2010/11_(Fly_Emirates)', '2009/10_(Fly_Emirates)']
Y_Axis['Jersey', 'sponsorship', 'revenue', 'in', 'million', 'GBP']: ['40.0', '40.0', '30.0', '30.0', '30.0', '30.0', '30.0', '5.5', '5.5', '5.5', '5.5']

gold: The statistic shows the revenue Arsenal FC generated from its jersey sponsorship deal from the 2009/10 season to the 2019/20 season . In the 2019/20 season , Arsenal FC received 40 million GBP from its jersey sponsor Fly Emirates .
gold_template: The statistic shows the templateYLabel[2] templateTitleSubject[0] generated from its templateYLabel[0] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , templateTitleSubject[0] received templateYValue[max] templateScale templateYLabel[4] from its templateYLabel[0] sponsor Fly Emirates .

generated_template: The statistic shows the templateYLabel[2] templateTitleSubject[0] generated from its templateYLabel[0] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , templateTitleSubject[0] received templateYValue[max] templateScale templateYLabel[4] from its templateYLabel[0] sponsor Etihad .
generated: The statistic shows the revenue Arsenal FC generated from its Jersey sponsorship deal from the 2009/10_(Fly_Emirates) season to the 2019/20_(Fly_Emirates) season .  In the 2019/20_(Fly_Emirates) season , Arsenal FC received 40.0 million GBP from its Jersey sponsor Etihad .

Example 69:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010']}
title: Casino visitors in the U.S. ordered by age group 2010
X_Axis['Age', 'of', 'casino', 'visitors']: ['Age_21-34', 'Age_35-49', 'Age_50-64', 'Age_65_and_over', "Don't_know_/_refused"]
Y_Axis['Percentage', 'of', 'respondents']: ['18', '21', '33', '25', '3']

gold: The graph depicts the age group distribution of casino vistors in the U.S. in 2010 . 25 percent of casino visitors were aged 65 years or older .
gold_template: The graph depicts the templateXValue[0] templateTitle[6] distribution of templateXLabel[1] vistors in the templateTitleSubject[0] in templateTitleDate[0] . templateYValue[3] templateScale of templateXLabel[1] templateXLabel[2] were aged templateXValue[3] years or older .

generated_template: Among templateXValue[0] templateTitle[11] groups templateTitle[2] on the United Kingdom ( templateTitleSubject[0] ) , with over templateYValue[max] templateScale of templateYLabel[1] that year . The most common result of a templateTitle[2] survey conducted in templateTitleDate[0] . Of respondents said that they had an average of more than 500 templateScale of six templateScale of templateTitle[8] tracking apps has templatePositiveTrend significantly within recent templateXValue[1] .
generated: Among Age_21-34 2010 groups U.S. on the United Kingdom ( U.S. ) , with over 33 percentage of respondents that year .  The most common result of a U.S. survey conducted in 2010 .  Of respondents said that they had an average of more than 500 percentage of six percentage of 2010 tracking apps has increased significantly within recent Age_35-49 .

Example 70:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Poverty rate of black families in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Percentage', 'of', 'poor', 'black', 'families']: ['17.7', '18.2', '19', '21.1', '22.9', '22.8', '23.7', '24.2', '24.1', '22.7', '22', '22.1', '21.6', '22.1', '22.8', '22.3', '21.5', '20.7', '19.3', '21.8', '23.4', '23.6', '26.1', '26.4', '27.3', '31.3', '31.1', '30.4', '29.3']

gold: This statistic shows the percentage of black families in the U.S. who live below the poverty level from 1990 to 2018 . In 2018 , 17.7 percent of black families were living below the poverty line in the United States . Poverty is the state of one who lacks a certain amount of material possessions or money .
gold_template: This statistic shows the templateScale of templateYLabel[2] templateYLabel[3] in the templateTitleSubject[0] who live below the templateTitle[0] level from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateYLabel[2] templateYLabel[3] were living below the templateTitle[0] line in the templateTitle[4] . templateTitle[0] is the state of one who lacks a certain amount of material possessions or money .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the carmaker templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] in templateXValue[max] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Percentage poor of the carmaker U.S. from 1990 to 2018 .  The Percentage poor of the U.S. black in 2018 was 17.7 black families .

Example 71:
titleEntities: {'Subject': ['Unemployed'], 'Date': ['2020']}
title: Unemployed persons worldwide until 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Unemployed', 'persons', 'in', 'millions']: ['174.3', '173.6', '172.5', '174.1', '197.7', '197.1', '201.3', '200.13', '197.36', '195.45', '196.63']

gold: The statistic shows the number of unemployed persons worldwide from 2010 to 2017 and projections until 2020 . In 2017 , there were about 174.1 million unemployed persons worldwide .
gold_template: The statistic shows the number of templateYLabel[0] templateYLabel[1] templateTitle[2] from templateXValue[min] to templateXValue[3] and projections templateTitle[3] templateXValue[max] . In templateXValue[3] , there were about templateYValue[3] templateScale templateYLabel[0] templateYLabel[1] templateTitle[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of the templateTitle[7] distribution of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateYLabel[0] of the templateTitleSubject[0] of templateTitleSubject[0] employees amounted to approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: This statistic shows the Unemployed of millions the 2020 distribution of Unemployed from 2010 to 2020 .  The Unemployed Unemployed of the Unemployed of employees amounted to approximately 174.3 millions in 2020 .

Example 72:
titleEntities: {'Subject': ['Italy'], 'Date': ['2019']}
title: Leading banks by market capitalization in Italy 2019
X_Axis['Company', 'Name']: ['Intesa_Sanpaolo', 'UniCredit', 'Mediobanca', 'Fineco_Bank', 'UBI_Banca', 'Banco_BPM', 'BPER_Banca', 'Banca_Monte_dei_Paschi_di_Siena', 'Credito_Emiliano', 'Banca_Popolare_di_Sondrio']
Y_Axis['Market', 'capitalization', 'in', 'million', 'euros']: ['38043.87', '24228.42', '8863.88', '5902.19', '2947.23', '2842.07', '1935.76', '1737.76', '1640.93', '761.8']

gold: With a market capitalization of over 38 billion euros , Intesa Sanpaolo was the leading bank in Italy listed on the Milan Stock Exchange as of September 2019 . Intesa Sanpaolo , together with its fierce competitor UniCredit , dominates the banking sector in the country . The two banking groups are not only the leaders in the domestic market , but also ranked among the 20 leading banks in Europe in terms of market capitalization in 2019 .
gold_template: With a templateYLabel[0] templateYLabel[1] of over templateYValue[max] templateScale templateYLabel[3] , templateXValue[0] was the templateTitle[0] templateXValue[3] in templateTitleSubject[0] listed on the Milan Stock Exchange as of 2019 . templateXValue[0] , together with its fierce competitor templateXValue[1] , dominates the banking sector in the country . The two banking groups are not only the leaders in the domestic templateYLabel[0] , but also ranked among the 20 templateTitle[0] templateTitle[1] in Europe in terms of templateYLabel[0] templateYLabel[1] in templateTitleDate[0] .

generated_template: As of 2019 , it was found that goods accounted for templateYValue[3] templateScale of all employees making it the templateTitle[0] templateYLabel[2] templateYLabel[3] in the country . The site 's it goods templateXValue[2] has also templateYValue[2] templateScale of any templateXValue[2] and templateYValue[2] templateScale of all templateYLabel[2] templateYLabel[3] .
generated: As of 2019 , it was found that goods accounted for 5902.19 million of all employees making it the Leading million euros in the country .  The site 's it goods Mediobanca has also 8863.88 million of any Mediobanca and 8863.88 million of all million euros .

Example 73:
titleEntities: {'Subject': ['Top'], 'Date': ['2018']}
title: Top female table tennis players worldwide 2018
X_Axis['Table', 'Tennis', 'Players']: ['Yuling_Zhu_(China)', 'Shiwen_Liu_(China)', 'Meng_Chen_(China)', 'Kasumi_Ishikawa_(Japan)', 'Manyu_Wang_(China)', 'Ning_Ding_(China)', 'Mima_Ito_(Japan)', 'I-Ching_Cheng_(Chinese_Taipei)', 'Miu_Hirano_(Japan)', 'Xingtong_Chen_(China)', 'Tianwei_Feng_(Singapore)', 'Hyowon_Suh_(Korea)', 'Hoi_Kem_Doo_(Hong_Kong)', 'Hitomi_Sato_(Japan)', 'Yuting_Gu_(China)', 'Saki_Shibata_(Japan)', 'Sofia_Polcanova_(Austria)', 'Jie_Li_(Netherlands)', 'Ho_Ching_Lee_(Hong_Hong)', 'Elizabeta_Samara_(Romania)']
Y_Axis['Number', 'of', 'ranking', 'points']: ['16524', '15669', '15294', '15135', '15105', '14784', '13910', '13231', '13193', '12708', '12565', '11954', '11940', '11898', '11607', '10851', '10618', '10406', '10395', '10063']

gold: This graph depicts the top female professional table tennis players worldwide as of September 2018 , by number of ranking points . Top of the list was the Chinese table tennis player , Yuling Zhu , who had 16,524 ranking points worldwide as of September 2018 .
gold_template: This graph depicts the templateTitleSubject[0] templateTitle[1] professional templateXLabel[0] templateXLabel[1] templateXLabel[2] templateTitle[5] as of 2018 , by templateYLabel[0] of templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] of the list was the Chinese templateXLabel[0] templateXLabel[1] player , templateXValue[0] , who had templateYValue[max] templateYLabel[1] templateYLabel[2] templateTitle[5] as of 2018 .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[4] in the templateTitle[5] as of 2019 , ranked by templateXValue[0] . According to the source , templateXValue[0] was ranked first , with over templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic gives information on the Top female table of players in the worldwide as of 2019 , ranked by Yuling_Zhu_(China) .  According to the source , Yuling_Zhu_(China) was ranked first , with over 16524 % ranking points .

Example 74:
titleEntities: {'Subject': ['Bangladesh'], 'Date': ['2024']}
title: Total population of Bangladesh 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['175.4', '173.6', '171.82', '170.06', '168.31', '166.59', '164.88', '163.19', '161.51', '159.86', '158.22']

gold: This statistic shows the total population of Bangladesh from 2014 to 2024  . In 2018 , the estimated total population of Bangladesh amounted to approximately 164.88 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] amounted to around templateYValue[6] templateScale templateYLabel[0] .
generated: The statistic shows the Total population of the Bangladesh from 2014 to 2018 , with projections up until 2024 .  In 2018 , the Total population of the Bangladesh amounted to around 164.88 millions Inhabitants .

Example 75:
titleEntities: {'Subject': ['Argentina', 'Facebook'], 'Date': ['2017', '2023']}
title: Argentina : number of Facebook users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['28.0', '27.4', '26.8', '26.2', '25.6', '25.0', '24.5']

gold: This statistic shows the number of Facebook users in Argentina in 2017 and 2018 with projections until 2023 . In 2023 , the number of Facebook users in Argentina is expected to reach 28 million , up from 24.5 million in 2017 .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] in templateXValue[min] and templateXValue[5] with projections until templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[max] templateScale , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there were approximately templateYValue[5] templateScale templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] , and this figure is projected to grow to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .
generated: This statistic shows the Number of Facebook users in Argentina from 2017 to 2023 .  In 2018 , there were approximately 25.0 millions Facebook users in Argentina , and this figure is projected to grow 28.0 millions in 2023 .

Example 76:
titleEntities: {'Subject': ['Global'], 'Date': []}
title: Global barley producers by country 2018/19
X_Axis['Country']: ['European_Union', 'Russia', 'Others', 'Canada', 'Ukraine', 'Turkey', 'Australia', 'Argentina', 'Kazakhstan', 'United_States', 'Iran', 'Morocco', 'Ethiopia', 'Algeria', 'China', 'India', 'Belarus']
Y_Axis['Amount', 'in', 'million', 'metric', 'tons']: ['56.34', '16.6', '9.68', '8.4', '7.4', '7.4', '7.3', '4.5', '4.2', '3.33', '3.1', '2.91', '2.17', '1.95', '1.85', '1.77', '1.7']

gold: This statistic provides a forecast of barley production volume worldwide in 2018/19 , by country . In that year , Argentina produced about 4.5 million metric tons of barley .
gold_template: This statistic provides a forecast of templateTitle[1] production volume worldwide in templateTitle[5] , templateTitle[3] templateXLabel[0] . In that year , templateXValue[7] produced about templateYValue[7] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[1] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] worldwide from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateXValue[0] had an templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Global barley producers by Amount worldwide from to .  In , the European_Union had an Amount of 56.34 metric tons .

Example 77:
titleEntities: {'Subject': ["L'Oréal"], 'Date': ['2009', '2018']}
title: Consolidated sales of L'Oréal worldwide 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Sales', 'in', 'million', 'euros']: ['26937', '26024', '25837', '25257', '22532', '22124', '21638', '20343', '19496', '17473']

gold: This statistic depicts the consolidated sales of L'Oreal worldwide from 2009 to 2018 . In 2018 , L'Oreal 's consolidated sales worldwide amounted to about 27 billion euros . L'Oreal is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .
gold_template: This statistic depicts the templateTitle[0] templateYLabel[0] of L'Oreal templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , L'Oreal 's templateTitle[0] templateYLabel[0] templateTitle[3] amounted to about templateYValue[max] templateScale templateYLabel[2] . L'Oreal is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .

generated_template: The statistic presents the templateYLabel[2] templateTitle[0] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateYLabel[2] in the templateTitleSubject[0] amounted to templateYValue[3] templateScale templateYLabel[2] .
generated: The statistic presents the euros Consolidated of the L'Oréal worldwide from 2009 to 2018 .  In 2018 , the Consolidated of euros in the L'Oréal amounted to 25257 million euros .

Example 78:
titleEntities: {'Subject': ['United Kingdom HMRC'], 'Date': ['2000', '2019']}
title: Betting and gaming : United Kingdom HMRC tax receipts 2000 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02', '2000/01']
Y_Axis['Betting', 'and', 'gaming', 'tax', 'receipts', 'in', 'billion', 'GBP']: ['2.99', '2.86', '2.74', '2.67', '2.12', '2.1', '1.68', '1.63', '1.53', '1.44', '1.47', '1.48', '1.39', '1.42', '1.42', '1.34', '1.29', '1.44', '1.51']

gold: This statistic shows the total United Kingdom ( UK ) HMRC betting and gaming tax receipts from fiscal year 2000/01 to fiscal year 2018/19 . Over the period there were a number of fluctuations but overall there was an increase . In the most recent recorded period , betting and gaming tax receipts totaled almost 3 billion British pounds , which was also the peak .
gold_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitleSubject[0] templateYLabel[0] and templateYLabel[1] templateYLabel[2] templateYLabel[3] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . Over the period there were a number of fluctuations but overall there was an templatePositiveTrend . In the most recent recorded period , templateYLabel[0] and templateYLabel[1] templateYLabel[2] templateYLabel[3] totaled almost templateYValue[max] templateScale British pounds , which was also the peak .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] of templateTitleSubject[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] was templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Betting gaming of the receipts of United Kingdom HMRC from 2000/01 to 2018/19 .  In 2018/19 , the Betting gaming in United Kingdom HMRC was 2.99 tax receipts .

Example 79:
titleEntities: {'Subject': ['Leading'], 'Date': ['2019']}
title: Leading cause of ransomware infection 2019
X_Axis['Cause', 'of', 'ransomware', 'infection']: ['Spam/phishing_emails', 'Lack_of_cyber_security_training', 'Weak_passwords/access_management', 'Poor_user_practices/gullibility', 'Malicious_websites/web_ads', 'Report_clickbait']
Y_Axis['Percentage', 'of', 'respondents']: ['67', '36', '30', '25', '16', '16']

gold: This statistic depicts the leading causes of ransomware infections according to MSPs worldwide in 2019 . According to the survey , 67 percent of responding MSPs indicated that phishing scams were the most common cause of ransomware infection .
gold_template: This statistic depicts the templateTitleSubject[0] causes of templateXLabel[1] infections according to MSPs worldwide in templateTitleDate[0] . According to the survey , templateYValue[max] templateScale of responding MSPs indicated that phishing scams were the most common templateXLabel[0] of templateXLabel[1] templateXLabel[2] .

generated_template: It was found that templateYValue[max] templateScale of the people using using templateXValue[0] in the United Kingdom ( templateTitleSubject[0] ) . The next highest amount of adult templateXValue[1] during the survey period , with templateYValue[1] templateScale of respondents said that they intended to their templateXValue[0] .
generated: It was found that 67 percentage of the people using Spam/phishing_emails in the United Kingdom ( Leading ) .  The next highest amount of adult Lack_of_cyber_security_training during the survey period , with 36 percentage of respondents said that they intended to their Spam/phishing_emails .

Example 80:
titleEntities: {'Subject': ['San Francisco Giants'], 'Date': ['2002', '2019']}
title: Franchise value of the San Francisco Giants 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['3000', '2850', '2650', '2250', '2000', '1000', '786', '643', '563', '483', '471', '494', '459', '410', '381', '368', '382', '355']

gold: This graph depicts the value of the San Francisco Giants franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of three billion U.S. dollars . The San Francisco Giants are owned by Charles Johnson , who bought the franchise for 100 million U.S. dollars in 1993 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Giants templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Giants are owned by Charles Johnson , who bought the templateYLabel[0] for 100 templateScale templateYLabel[3] templateYLabel[4] in 1993 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the value of the San Francisco Giants Francisco Franchise of the National Basketball Association from 2002 to 2019 .  In 2019 , the Franchise had an estimated value of 3000 million U.S. dollars .

Example 81:
titleEntities: {'Subject': ['China'], 'Date': ['1985', '2019']}
title: U.S. imports of trade goods from China 1985 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985']
Y_Axis['Import', 'volume', 'in', 'billion', 'U.S.', 'dollars']: ['452.24', '539.67', '505.22', '462.42', '483.2', '468.47', '440.43', '425.62', '399.37', '364.95', '296.37', '337.77', '321.44', '287.77', '243.47', '196.68', '152.44', '125.19', '102.28', '100.02', '81.79', '71.17', '62.56', '51.51', '45.54', '38.79', '31.54', '25.73', '18.97', '15.24', '11.99', '8.51', '6.29', '4.77', '3.86']

gold: In 2019 , Chinese exports of trade goods to the United States amounted to about 452.24 billion U.S. dollars ; a significant increase from 1985 levels , when imports from China amounted to about 3.86 billion U.S. dollars . U.S. exports to China Compared to U.S. imports from China , the value of U.S. exports to China in 2019 amounted to 106.63 billion U.S. dollars . China is the United States ' largest trading partner , while China was the United States third largest goods export market .
gold_template: In templateXValue[max] , Chinese exports of templateTitle[2] templateTitle[3] to the templateTitle[0] amounted to about templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] ; a significant templatePositiveTrend templateTitle[4] templateXValue[min] levels , when templateTitle[1] templateTitle[4] templateTitleSubject[0] amounted to about templateYValue[idxmin(X)] templateScale templateYLabel[3] templateYLabel[4] . templateYLabel[3] exports to templateTitleSubject[0] Compared to templateYLabel[3] templateTitle[1] templateTitle[4] templateTitleSubject[0] , the value of templateYLabel[3] exports to templateTitleSubject[0] in templateXValue[max] amounted to 106.63 templateScale templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] is the templateTitle[0] ' largest trading partner , while templateTitleSubject[0] was the templateTitle[0] third largest templateTitle[3] export market .

generated_template: This statistic shows the growth in the templateYLabel[3] templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] to templateTitleSubject[0] templateTitle[4] templateXValue[min] , with a forecast for templateXValue[max] . In templateXValue[max] , the templateYLabel[2] of templateTitle[2] in the country stood at approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the growth in the U.S. Import volume of trade goods to China from 1985 , with a forecast for 2019 .  In 2019 , the billion of trade in the country stood at approximately 452.24 billion U.S. dollars .

Example 82:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2017', '2023']}
title: Vietnam : number of social network users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['52.8', '51.9', '50.9', '49.6', '48.0', '46.2', '43.8']

gold: This statistic shows the number of social network users in Vietnam from 2017 to 2023 . In 2021 , it is estimated that there will be around 50.9 million social network users in the country , up from 52.8 million in 2017 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , it is estimated that there will be around templateYValue[2] templateScale templateTitle[2] templateTitle[3] templateYLabel[1] in the country , up from templateYValue[idxmax(X)] templateScale in templateXValue[min] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , it is estimated that there will be around templateYValue[5] templateScale templateTitle[2] templateTitle[3] templateYLabel[1] in the country , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] .
generated: This statistic shows the Number of Vietnam users in the Vietnam from 2017 to 2023 .  In 2018 , it is estimated that there will be around 46.2 millions social network users in the country , up from 43.8 millions in 2017 .

Example 83:
titleEntities: {'Subject': ['Sub-Saharan Africa'], 'Date': ['2017']}
title: Fertility rate in Sub-Saharan Africa 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['4.77', '4.84', '4.91', '4.99', '5.06', '5.13', '5.2', '5.26', '5.32', '5.37', '5.42']

gold: This statistic shows the fertility rate in Sub-Saharan Africa from 2007 to 2017 . The fertility rate is the average number of children born to one woman while being of child-bearing age . Sub-Saharan Africa includes almost all countries south of the Sahara desert .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] to one templateYLabel[4] while being of child-bearing age . templateTitleSubject[0] includes almost all countries south of the Sahara desert .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] a templateYLabel[4] will have during her child-bearing years . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] 's population was templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate in Sub-Saharan Africa from 2007 to 2017 .  The Fertility rate is the average Number of children a woman will have during her child-bearing years .  In 2017 , the Fertility rate of Sub-Saharan Africa 's population was 4.77 children per woman .

Example 84:
titleEntities: {'Subject': ['Thailand'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Thailand 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['3.65', '3.65', '3.65', '3.49', '3.01', '2.87', '4.13', '4.02', '3.36', '3.13', '0.98']

gold: Thailand 's gross domestic product ( GDP ) grew at a rate of 4 percent in 2017 . It is expected to grow slightly faster in the coming year before converging to a long-run growth rate around 3.6 percent . This steady growth can be attributed to Thailand 's relatively developed economy .
gold_template: templateTitleSubject[0] 's templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templatePositiveTrend at a templateTitle[5] of templateYValue[0] templateScale in templateXValue[7] . It is expected to grow slightly faster in the coming templateXLabel[0] before converging to a long-run templateYLabel[1] templateTitle[5] around 3.6 templateScale . This steady templateYLabel[1] can be attributed to templateTitleSubject[0] 's relatively developed economy .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Thailand from 2014 to 2018 , with projections up until 2024 .  In 2018 , Thailand 's real Gross domestic product increased by around 4.13 % compared to the previous Year .

Example 85:
titleEntities: {'Subject': ['Number'], 'Date': []}
title: U.S. investment banking : Number of establishments and employees
Data:
                   0     1     2     3
Year            2006  2007  2008  2009
Establishments   7.6   9.3   4.5   4.9
Employees        156   184   137   115 

gold: The statistic represents the number of establishments and employees of investment banking businesses from 2006 to 2009 . In 2006 , U.S. investment banking businesses had 7,600 establishments and 156,000 employees .
gold_template: The statistic represents the templateTitleSubject[0] of templateTitle[4] and templateTitle[5] of templateTitle[1] templateTitle[2] businesses from templateValue[0][0] to templateValue[0][last] . In templateValue[0][0] , templateTitle[0] templateTitle[1] templateTitle[2] businesses had 7,600 templateTitle[4] and 156,000 templateTitle[5] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . In templateValue[0][0] , the templateTitle[0] of the templateTitleSubject[0] in the templateTitleSubject[0] was templateValue[1][0] U.S. dollars .
generated: This statistic shows the U.S. of investment in the Number from 2009 to 2006 , employees .  In 2006 , the U.S. of the Number in the Number was 7.6 U.S. dollars .

Example 86:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2000', '2018']}
title: Health spending as percent of GDP in South Korea 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'GDP']: ['8.1', '7.6', '7.33', '7.05', '6.81', '6.58', '6.44', '6.29', '6.22', '6.09', '5.68', '5.38', '5.18', '4.84', '4.58', '4.6', '4.32', '4.58', '4']

gold: The total health expenditure in South Korea in 2018 accounted for approximately 8.1 percent of Korea 's Gross Domestic Product ( GDP ) that year . This was the result of the steepest increase in the past ten years and was the first time health spending as a share of the GDP exceeded eight percent . This share saw a continuous increase over the past decade , indicating that as the GDP grew , health spending grew at an even faster rate .
gold_template: The total templateTitle[0] expenditure in templateTitleSubject[0] in templateXValue[max] accounted for approximately templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's Gross Domestic Product ( templateYLabel[1] ) that templateXLabel[0] . This was the result of the steepest templatePositiveTrend in the past ten years and was the first time templateTitle[0] templateTitle[1] as a share of the templateYLabel[1] exceeded templateYValue[max] templateScale . This share saw a continuous templatePositiveTrend over the past decade , indicating that as the templateYLabel[1] templatePositiveTrend , templateTitle[0] templateTitle[1] templatePositiveTrend at an even faster rate .

generated_template: Recent figures tell us that the templateTitle[0] spent templateYValue[2] templateScale of its gross domestic product on templateTitle[1] care in templateXValue[2] . Projections show us that in the subsequent two years the templateTitle[1] spending in the templateTitleSubject[0] .
generated: Recent figures tell us that the Health spent 7.33 percentage of its gross domestic product on spending care in 2016 .  Projections show us that in the subsequent two years the spending in the South Korea .

Example 87:
titleEntities: {'Subject': ['Italian'], 'Date': ['2019']}
title: Most valuable Italian brands in 2019
X_Axis['Brand']: ['Eni', 'Enel', 'Gucci', 'TIM', 'Ferrari', 'Poste_Italiane_Spa', 'Generali', 'Intesa_Sanpaolo_Group', 'Prada', 'Ray-Ban']
Y_Axis['Brand', 'value', 'in', 'million', 'U.S.', 'dollars']: ['10711', '10319', '10192', '8707', '8327', '3120', '7099', '5466', '4489', '3897']

gold: The multinational company Eni S.p.A. heads the ranking of the most valuable Italian brands , reaching a brand value of 10.7 billion U.S. dollars as of 2019 . The energy company outranks Enel and Gucci with brand values of roughly 10.3 and 10.1 billion dollars , respectively . TIM followed directly with an estimated brand equity of about 8.7 billion dollars .
gold_template: The multinational company templateXValue[0] S.p.A. heads the ranking of the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] , reaching a templateXLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] as of templateTitleDate[0] . The energy company outranks templateXValue[1] and templateXValue[2] with templateXLabel[0] values of roughly 10.3 and 10.1 templateScale templateYLabel[4] , respectively . templateXValue[3] followed directly with an estimated templateXLabel[0] equity of about templateYValue[3] templateScale templateYLabel[4] .

generated_template: With a templateXLabel[0] templateYLabel[1] of around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , templateXValue[0] was the leading templateXValue[0] with a templateYLabel[1] of templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] . This was followed by templateXValue[1] , templateXValue[2] with templateYValue[2] templateScale templateYLabel[3] templateYLabel[4] , and templateXValue[3] templateXValue[0] .
generated: With a Brand value of around 10711 million U.S. dollars , Eni was the leading Eni with a value of 10319 million U.S. dollars .  This was followed by Enel , Gucci with 10192 million U.S. dollars , and TIM Eni .

Example 88:
titleEntities: {'Subject': ['Celgene'], 'Date': ['2013', '2018']}
title: Celgene 's top product revenues 2013 to 2018
Data:
                              0                          1                   2                  3      4
Product Name  Revlimid_(cancer)  Pomalyst/Imnovid_(cancer)  Otezla_(psoriasis)  Abraxane_(cancer)  Other
2013                     4280.3                      305.4                   0              648.9      0
2014                       4980                      679.7                69.8              848.2      0
2015                     5801.1                      983.3               471.7              967.5      0
2016                     6973.6                     1310.7              1017.2              973.4      0
2017                       8187                       1614                1279                992    901
2018                       9685                       2040                1608               1062    870 

gold: This statistic reveals the revenues generated by biotech company Celgene 's top products between 2013 and 2018 . In 2014 , one of the company 's top products , Otezla , generated revenues of around 70 million U.S. dollars . Otezla 's annual revenue increased up to 1.6 billion dollars until 2018 .
gold_template: This statistic reveals the templateTitle[4] generated by biotech company templateTitleSubject[0] templateTitle[1] templateTitle[2] products between templateTitleDate[min] and templateTitleDate[max] . In templateLabel[2][0] , one of the company templateTitle[1] templateTitle[2] products , templateValue[0][2] , generated templateTitle[4] of around templateValue[2][2] templateScale U.S. dollars . templateValue[0][2] templateTitle[1] annual revenue templatePositiveTrend up to 1.6 templateScale dollars until templateTitleDate[max] .

generated_template: The statistic shows the templateTitle[0] of the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , sorted templateTitle[4] templateTitle[5] . According to the source , the templateTitle[0] of templateTitleSubject[0] in templateValue[0][0] in 2013 was templateValue[3][last] templateScale .
generated: The statistic shows the Celgene of the Celgene from 2013 to 2018 , sorted revenues 2013 .  According to the source , the Celgene of in Revlimid_(cancer) 2013 was 0 % .

Example 89:
titleEntities: {'Subject': ['Russia'], 'Date': ['2024']}
title: Inflation rate in Russia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['4', '4', '4', '3.9', '3.52', '4.68', '2.88', '3.68', '7.04', '15.53', '7.82', '6.76', '5.07', '8.44', '6.85', '11.65', '14.11', '9.01', '9.68', '12.68', '10.89', '13.67', '15.78', '21.46', '20.78', '85.74', '27.68', '14.77', '47.74', '197.47', '307.63']

gold: The statistic shows the inflation rate in Russia from 1994 to 2018 , with projections up until 2024 . The inflation rate is calculated using the price increase of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the year .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price templatePositiveTrend of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Russia from 1994 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Russia amounted to about 2.88 % compared to the previous Year .

Example 90:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Peak online shopping hours of U.S. consumers 2015
X_Axis['Year']: ['7-11_AM', '11_AM_-_4_PM', '4-9_PM', '9_PM_-_2_AM']
Y_Axis['Share', 'of', 'respondents']: ['10', '28', '48', '14']

gold: This statistic presents the preferred time of the day for consumers in the United States to shop online . During the April 2015 survey , 48 percent of survey respondents stated that their peak online shopping hours were between 4 and 9 PM .
gold_template: This statistic presents the preferred time of the day for templateTitle[5] in the templateTitle[4] to shop templateTitle[1] . During the 2015 survey , templateYValue[max] templateScale of survey templateYLabel[1] stated that their templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] were between templateXValue[1] and templateXValue[last] templateXValue[1] .

generated_template: This statistic presents the templateScale of online consumers in the templateTitle[0] who are templateTitleSubject[0] templateTitle[2] members . During the 2017 survey period , it was found that templateYValue[max] templateScale of templateYLabel[1] had between templateYValue[min] and templateYValue[min] templateScale of templateYLabel[1] stated that they used the social network .
generated: This statistic presents the percentage of online consumers in the Peak who are U.S. shopping members .  During the 2017 survey period , it was found that 48 % of respondents had between 10 and thousands of respondents stated that they used the social network .

Example 91:
titleEntities: {'Subject': ['Atlanta Falcons'], 'Date': ['2019']}
title: Regular season home attendance of the Atlanta Falcons 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['572811', '583184', '575681', '559998', '562845', '493515', '561795', '560773', '551892', '542800', '545384', '512520', '547610', '563456']

gold: This graph depicts the total regular season home attendance of the Atlanta Falcons franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 572,811 .
gold_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[idxmax(X)] .

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] in the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] franchise was templateYValue[idxmax(X)] .
generated: The statistic depicts the Home attendance of the Regular season home in the National Football League from 2006 to 2019 .  In 2019 , the Regular season Home attendance of the Atlanta Falcons franchise was 572811 .

Example 92:
titleEntities: {'Subject': ['United States'], 'Date': ['1992', '2017']}
title: Clothing and clothing accessories store sales in the United States 1992 to 2017
X_Axis['Year']: ['17', '16', '15', '14', '13', '12', '11', '10', '9', '8', '7', '6', '5', '4', '3', '2', '1', '0', '99', '98', '97', '96', '95', '94', '93', '92']
Y_Axis['Billion', 'U.S.', 'dollars']: ['258.74', '259.84', '255.8', '250.41', '244.72', '239.49', '228.61', '213.29', '204.48', '215.58', '221.21', '213.19', '200.97', '190.25', '178.69', '172.3', '167.29', '167.67', '159.75', '149.15', '140.29', '136.58', '131.33', '129.08', '124.75', '120.1']

gold: This timeline shows clothing and clothing accessories store sales in the United States from 1992 to 2017 . In 2017 , U.S. clothing and clothing accessories store sales amounted to about 258.74 billion U.S. dollars . According to the NAICS definition , companies operating in this sector are retailing clothing and clothing accessories from fixed point-of-sale locations.The clothing Industry in the United StatesIn 2012 , the global apparel market was valued at 1.7 trillion U.S. dollars and was estimated to employ approximately 75 million people , making it one of the largest industries in the world .
gold_template: This timeline shows templateTitle[0] and templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[1] templateTitle[0] and templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] . According to the NAICS definition , companies operating in this sector are retailing templateTitle[0] and templateTitle[0] templateTitle[2] from fixed point-of-sale locations.The templateTitle[0] Industry in the templateTitleSubject[0] StatesIn 2012 , the global apparel market was valued at 1.7 templateScale templateYLabel[1] templateYLabel[2] and was estimated to employ approximately 75 templateScale people , making it templateXValue[16] of the largest industries in the world .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] on templateTitle[4] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[0] templateTitle[1] templateTitle[2] on templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Clothing accessories store on sales in the United States from 1992 to 2017 .  In 2017 , the Clothing accessories on store sales amounted to about 258.74 billion U.S. dollars .

Example 93:
titleEntities: {'Subject': ['Nigeria'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Nigeria 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['762.02', '683.21', '613.39', '550.07', '494.83', '446.54', '398.19', '376.36', '405.44', '493.84', '568.5', '514.97', '460.95', '414.1', '369.06', '297.46', '330.26', '262.22', '222.79', '169.65', '130.35', '102.94', '93.98', '73.13', '67.82', '57.48', '209.68', '187.87', '172.69', '132.23', '81.13']

gold: The statistic shows gross domestic product ( GDP ) in Nigeria from 1994 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Nigeria from 1994 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 94:
titleEntities: {'Subject': ['Value'], 'Date': ['2018']}
title: Value of the global eyewear market from 2018 to 2025
X_Axis['Year']: ['2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018']
Y_Axis['Market', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['210.87', '197.08', '184.18', '172.13', '160.87', '150.35', '140.51', '131.32']

gold: The global eyewear market , which is made up of spectacles , contact lenses , sunglasses , and other eyewear products , was estimated to be worth around 131 billion U.S. dollars in 2018 and was forecast to reach a value of 210.8 billion U.S. dollars by 2025 . What type of products make up the eyewear market ? The eyewear market is typically broken down into four main categories : prescription ( Rx ) eyeglasses , non-prescription ( plano ) sunglasses , over-the-counter ( OTC ) readers , and contact lenses . In the North American eyewear market , sunglasses are the leading product category in terms of unit sales.Eyewear in the United States The United States was the leading eyewear market in the world as of 2018 .
gold_template: The templateTitle[1] templateTitle[2] templateYLabel[0] , which is made up of spectacles , contact lenses , sunglasses , and other templateTitle[2] products , was estimated to be worth around templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmin(Y)] and was forecast to reach a templateYLabel[1] of 210.8 templateScale templateYLabel[3] templateYLabel[4] by templateXValue[max] . What type of products make up the templateTitle[2] templateYLabel[0] ? The templateTitle[2] templateYLabel[0] is typically broken down into four main categories : prescription ( Rx ) eyeglasses , non-prescription ( plano ) sunglasses , over-the-counter ( OTC ) readers , and contact lenses . In the North American templateTitle[2] templateYLabel[0] , sunglasses are the leading product category in terms of unit sales.Eyewear in the country The country was the leading templateTitle[2] templateYLabel[0] in the world as of templateXValue[min] .

generated_template: The statistic depicts the templateYLabel[2] of the templateTitle[1] templateTitle[2] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[1] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] was at templateYValue[idxmin(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic depicts the billion of the global eyewear Market worldwide from 2018 to 2025 .  In 2018 , the value of the Value global eyewear Market was at 131.32 billion U.S. dollars .

Example 95:
titleEntities: {'Subject': ['Nintendo Wii'], 'Date': ['2019']}
title: Top selling Nintendo Wii games worldwide in 2019
X_Axis['Month']: ['Wii_Sports', 'Mario_Kart_Wii', 'Wii_Sports_Resort', 'New_Super_Mario_Bros._Wii', 'Wii_Play', 'Wii_Fit', 'Wii_Fit_Plus', 'Super_Smash_Bros._Brawl', 'Super_Mario_Galaxy', 'Wii_Party']
Y_Axis['Sales', 'in', 'million', 'units']: ['82.88', '37.24', '33.11', '30.28', '28.02', '22.67', '21.13', '13.3', '12.79', '9.34']

gold: This statistic shows the top selling Nintendo Wii titles worldwide . With approximately 82.88 million units sold , Wii Sports was the top selling Wii game as of September 2019 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateXValue[0] titles templateTitle[5] . With approximately templateYValue[max] templateScale templateYLabel[2] sold , templateXValue[0] was the templateTitle[0] templateTitle[1] templateXValue[0] game as of 2019 .

generated_template: The statistic gives information on the most popular templateTitle[1] templateTitle[2] in the templateTitle[0] in templateTitleDate[0] , sorted templateTitle[6] templateXLabel[0] . During the survey period , it was found that templateXValue[0] accounted for templateYValue[max] templateScale of all time .
generated: The statistic gives information on the most popular selling Nintendo in the Top in 2019 , sorted 2019 Month .  During the survey period , it was found that Wii_Sports accounted for 82.88 million of all time .

Example 96:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2019']}
title: Employment by economic sector in Sweden 2019
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2019   2018   2017   2016   2015   2014   2013   2012   2011   2010   2009
Agriculture   1.79   1.81   1.83   1.89   2.04   1.97   2.03   2.06   1.99    2.1   2.18
Industry     17.81  17.98  18.17   18.2  18.29   18.6  19.16  19.61  19.93  19.88  20.16
Services     80.39  80.21  80.01  79.91  79.68  79.42  78.81  78.33  78.08  78.02  77.67 

gold: The statistic shows the distribution of employment in Sweden by economic sector from 2009 to 2019 . In 2019 , 1.79 percent of the employees in Sweden were active in the agricultural sector , 17.81 percent in industry and 80.39 percent in the service sector .
gold_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .

generated_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .
generated: The statistic shows the distribution of Employment in Sweden by economic sector from 2009 to 2019 .  In 2019 , 1.79 % of the employees in Sweden were active in the agricultural sector , 17.81 % in Industry and 80.39 % in the service sector .

Example 97:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2007', '2019']}
title: Driving theory test pass rates in the United Kingdom 2007 - 2019 by gender
Data:
              0        1        2        3        4        5        6        7        8        9       10       11
Year    2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09  2007/08
Male       45.7     46.8     46.8     47.7     48.7     48.8     56.2     58.1     60.4     61.1     62.6     62.3
Female     49.2     50.7     50.7       51     53.6     54.7     62.3     63.7       66     66.9     68.7     68.9 

gold: This statistic compares the pass rates for men and women in driving theory tests in the United Kingdom ( UK ) between 2007/08 and 2018/19 . Women consistently performed better than men at the theory tests by around six percent . Although the pass rates have been falling , the most recent drop can possibly be attributed , in part , to new , previously unpublished questions being used in the new tests .
gold_template: This statistic compares the templateTitle[3] templateTitle[4] for men and women in templateTitle[0] templateTitle[1] tests in the templateTitleSubject[0] ( UK ) between templateValue[0][last] and templateValue[0][0] . Women consistently performed better than men at the templateTitle[1] tests templateTitle[9] around six templateScale . Although the templateTitle[3] templateTitle[4] have been templateNegativeTrend , the most recent drop can possibly be attributed , in part , to new , previously unpublished questions being used in the new tests .

generated_template: This statistic provides a comparison of the average amount of time templateTitle[4] on templateTitle[5] housework by gender in templateTitleSubject[0] member templateTitle[8] as well as templateValue[0][21] , templateValue[0][2] and templateValue[0][12] . As of templateTitleDate[0] , Portuguese men templateTitle[4] templateValue[1][3] templateTitle[1] templateTitle[2] templateTitle[3] on unpaid templateTitle[6] on templateTitle[0] while for women the templateTitle[0] was templateValue[2][3] templateTitle[1] .
generated: This statistic provides a comparison of the average amount of time rates on United housework by gender in United Kingdom member 2019 as well 2007/08 , 2016/17 and 2007/08 .  As of 2007 , Portuguese men rates 47.7 theory test pass on unpaid Kingdom on Driving while for women the Driving was 51 theory .

Example 98:
titleEntities: {'Subject': ['French'], 'Date': ['2017', '2017']}
title: French channels with the largest number of subscribers on YouTube 2017
X_Axis['Name']: ['Cyprien', 'Norman_fait_des_vidéos', 'Squeezie', 'Remi_Gaillard', 'Warner_Bros._Pictures', 'CyprienGaming', 'TheSoundYouNeed', 'Mister_V', 'The_Yellow_Laugh', 'Tibo_InShape']
Y_Axis['Number', 'of', 'subscribers', 'in', 'millions']: ['11.39', '9.88', '8.98', '6.42', '4.58', '4.51', '4.25', '4.04', '3.97', '3.87']

gold: The comedy YouTube channels of Cyprien and Norman fait des vidéos were the two leading Youtube channels in France as of October 2017 . At that time , Cyprien recorded 11.39 million subscribers , while Norman fait des videos ranked in second with 9.88 million followers . YouTube 's success storys Beside music channels , most of the YouTube channels in this ranking are comedy channels moderated by young adults around topics regarding everyday life situations and problems of younger generations , as well as joking about the adult life from a Millennial perspective .
gold_template: The comedy templateTitle[6] templateTitle[1] of templateXValue[0] and templateXValue[1] des templateXValue[1] were the two leading templateTitle[6] templateTitle[1] in France as of October templateTitleDate[0] . At that time , templateXValue[0] recorded templateYValue[max] templateScale templateYLabel[1] , while templateXValue[1] des videos ranked in second templateTitle[2] templateYValue[1] templateScale followers . templateTitle[6] 's success storys Beside music templateTitle[1] , most of the templateTitle[6] templateTitle[1] in this ranking are comedy templateTitle[1] moderated by young adults around topics regarding everyday life situations and problems of younger generations , as well as joking about the adult life from a Millennial perspective .

generated_template: This statistic shows the ranking of templateXLabel[0] templateXLabel[1] templateTitle[2] in the templateTitleSubject[0] as of 2019 . During that period of time , templateXValue[0] had the largest templateYLabel[0] of templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the ranking of Name largest in the French as of 2019 .  During that period of time , Cyprien had the largest Number of 11.39 millions subscribers .

Example 99:
titleEntities: {'Subject': ['Europe'], 'Date': ['2017']}
title: Dentists : number practicing in Europe in 2017 , by country
X_Axis['Country']: ['Germany', 'Italy', 'France', 'United_Kingdom', 'Poland', 'Belgium', 'Sweden', 'Czechia', 'Hungary', 'Austria', 'Norway', 'Denmark', 'Switzerland', 'Lithuania', 'Slovenia', 'Latvia', 'Estonia', 'Luxembourg']
Y_Axis['Number', 'of', 'employees']: ['70445', '49552', '43167', '34760', '13331', '8516', '8077', '7921', '6589', '5009', '4590', '4247', '4217', '2839', '1455', '1380', '1265', '581']

gold: In 2017 , there were over 70 thousand practicing dentists in Germany , the highest number recorded in Europe . Followed by Italy with almost 50 thousand dentists and then France with around 43.1 thousand . As these countries have some of the highest populations in the European Union , it is follows that they have the highest number of practicing dentists .
gold_template: In templateTitleDate[0] , there were over templateYValue[max] thousand templateTitle[2] templateTitle[0] in templateXValue[0] , the highest templateYLabel[0] recorded in templateTitleSubject[0] . Followed templateTitle[5] templateXValue[1] with almost templateYValue[1] thousand templateTitle[0] and then templateXValue[2] with around 43.1 thousand . As these countries have some of the highest populations in the European Union , it is follows that they have the highest templateYLabel[0] of templateTitle[2] templateTitle[0] .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] working at the templateTitle[0] templateTitleSubject[0] templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . According to the source , templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] , with templateYValue[max] thousand people .
generated: This statistic displays the Number of employees working at the Dentists Europe practicing in 2017 , Country .  According to the source , Germany had the highest Number of employees , with 70445 thousand people .

Example 100:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2011', '2018']}
title: Malaysia maternal mortality ratio 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Maternal', 'mortality', 'ratio', 'per', 'hundred', 'thousand', 'live', 'births']: ['23.5', '25.0', '29.1', '23.8', '22.3', '21.4', '23.2', '26.2']

gold: The statistic represents the maternal mortality ratio in Malaysia from 2011 to 2018 . In 2018 , the maternal mortality ratio in Malaysia was approximately 23.5 maternal deaths per one hundred thousand live births .
gold_template: The statistic represents the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was approximately templateYValue[idxmax(X)] templateYLabel[0] deaths templateYLabel[3] one hundred thousand templateYLabel[6] templateYLabel[7] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] was approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Maternal of mortality the Malaysia from 2011 to 2018 .  In 2018 , the Malaysia of was approximately 23.5 ratio per .

Example 101:
titleEntities: {'Subject': ['Crunchyroll'], 'Date': ['2012', '2018']}
title: Number of Crunchyroll subscribers 2012 to 2018
X_Axis['Month']: ['November_2018', 'February_2017', 'November_2015', 'November_2014', 'March_2013', 'September_2012']
Y_Axis['Number', 'of', 'users', 'in', 'thousands']: ['2000', '1000', '750', '400', '200', '100']

gold: As of November 2018 , the anime-focused subscription video-on-demand platform Crunchyroll had around two million paying subscribers . Subscription numbers have grown rapidly in recent years , with the company managing to double its number of paying subscribers between February 2017 and November 2018 . What is Crunchyroll ? Similar to services like Netflix and Hulu , Crunchyroll provides subscribers with on-demand access to shows and video media through either monthly subscriptions or cheaper , ad-supported programming .
gold_template: As of templateXValue[0] , the anime-focused subscription video-on-demand platform templateTitleSubject[0] had around templateYValue[max] templateScale paying templateTitle[2] . Subscription numbers have grown rapidly in recent years , with the company managing to double its templateYLabel[0] of paying templateTitle[2] between templateXValue[1] and templateXValue[0] . What is templateTitleSubject[0] ? Similar to services like Netflix and Hulu , templateTitleSubject[0] provides templateTitle[2] with on-demand access to shows and video media through either monthly subscriptions or cheaper , ad-supported programming .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in the templateTitle[2] from templateXValue[last] to templateXValue[0] . templateXValue[0] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateXValue[0] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Number Crunchyroll in the subscribers from September_2012 to November_2018 .  November_2018 , the Number Crunchyroll subscribers in November_2018 amounted to 2000 thousands .

Example 102:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2013']}
title: Most popular multi-purpose cleaning products in the United Kingdom ( UK ) 2013
X_Axis['Product', 'Type']: ['Flash', 'Mr_Muscle', 'Dettol', "Supermarket's_own_spray_cleaner", 'Cif', 'Cif_(cream)', 'Dettol_(wipes)', 'Cilit_bang']
Y_Axis['Share', 'of', 'respondents', 'currently', 'purchasing']: ['38', '31', '30', '28', '22', '22', '21', '20']

gold: This statistic displays the leading surface and multi-purpose cleaning product brands in the United Kingdom , according to a survey conducted November 2013 . Flash surface sprays ranked highest with 38 percent of respondents purchasing their products at the time of survey .
gold_template: This statistic displays the leading surface and templateTitle[2] templateTitle[3] templateXLabel[0] brands in the templateTitleSubject[0] , according to a survey conducted 2013 . templateXValue[0] surface sprays ranked highest with templateYValue[max] templateScale of templateYLabel[1] templateYLabel[3] their templateTitle[4] at the time of survey .

generated_template: The statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2013 . During the survey , it was found that the templateXValue[0] accounted for templateYValue[max] templateScale of the templateXValue[last] with templateYValue[min] templateScale of the templateXValue[last] respondents .
generated: The statistic presents the Most popular multi-purpose cleaning products United in the Kingdom as of 2013 .  During the survey , it was found that the Flash accounted for 38 % of the Cilit_bang with 20 % of the Cilit_bang respondents .

Example 103:
titleEntities: {'Subject': ['Countries'], 'Date': ['2018']}
title: Countries with the largest number of cosmetic procedures 2018
X_Axis['Country']: ['United_States', 'Brazil', 'Mexico', 'Germany', 'India', 'Italy', 'Argentina', 'Colombia', 'Australia', 'Thailand']
Y_Axis['Number', 'of', 'procedures']: ['4361867', '2267405', '1043247', '922056', '895896', '854208', '608960', '408789', '202642', '140123']

gold: Cosmetic procedures are popular among people that want to enhance select features of their body through surgical and nonsurgical methods . As of 2018 , the United States had the largest number of cosmetic procedures during that year with over 4.3 million procedures . Brazil had the second largest number of cosmetic procedures with almost 2.3 million procedures during that year .
gold_template: templateTitle[4] templateYLabel[1] are popular among people that want to enhance select features of their body through surgical and nonsurgical methods . As of templateTitleDate[0] , the templateXValue[0] had the templateTitle[2] templateYLabel[0] of templateTitle[4] templateYLabel[1] during that year templateTitle[1] over 4.3 templateScale templateYLabel[1] . templateXValue[1] had the second templateTitle[2] templateYLabel[0] of templateTitle[4] templateYLabel[1] templateTitle[1] almost templateYValue[1] templateScale templateYLabel[1] during that year .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[4] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . According to the source , there were over templateYValue[2] templateYLabel[2] templateYLabel[3] of people in templateXValue[2] .
generated: The statistic shows the Number of the largest number Countries procedures in 2018 , .  According to the source , there were over 1043247 procedures of people in Mexico .

Example 104:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Public opinion on U.S. use of drone strikes , by country 2014
Data:
                    0       1       2          3      4       5          6      7        8         9                10      11     12    13      14        15        16       17      18       19           20     21         22           23      24       25          26     27        28           29       30       31        32              33      34      35     36     37            38             39       40     41     42      43
Country     Venezuela  Jordan  Greece  Nicaragua  Egypt  Brazil  Argentina  Spain  Senegal  Colombia  Palestinian_ter.  Turkey  Japan  Peru  Mexico  Malaysia  Thailand  Vietnam  Russia  Tunisia  South_Korea  Italy  Indonesia  El_Salvador  France  Lebanon  Bangladesh  Chile  Tanzania  Philippines  Germany  Ukraine  Pakistan  United_Kingdom  Uganda  Poland  China  Ghana  South_Africa  United_States  Nigeria  Kenya  India  Israel
Disapprove         92      90      89         88     87      87         87     86       86        86                84      83     82    81      80        80        79       78      78       77           75     74         74           73      72       71          70     68        67           67       67       66        66              59      56      54     52     47            46             41       39     38     36      27
Approve             4       5       8          9      4       7          5     12       11         9                 7       7     12    10      14         6        12       12       7       17           23     18         10           11      27       23          22     15        27           24       30       11         3              33      36      32     35     29            27             52       42     53     28      65 

gold: This statistic shows the opinion of respondents from different countries around the world in 2014 , on the use of drone strikes by the United States to target extremists . In Greece , 92 percent of respondents opposed America 's use of drone strikes .
gold_template: This statistic shows the templateTitle[1] of respondents from different countries around the world in templateTitleDate[0] , on the templateTitle[3] of templateTitle[4] templateTitle[5] templateTitle[6] the templateValue[0][33] templateValue[0][39] to target extremists . In templateValue[0][2] , templateValue[1][0] templateScale of respondents opposed America 's templateTitle[3] of templateTitle[4] templateTitle[5] .

generated_template: The statistic shows the templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[0] in templateTitleDate[0] , templateTitle[2] templateTitle[3] templateTitle[4] . In templateTitleDate[0] , the templateLabel[0][0] of the European Union in templateValue[0][0] was the highest in the world with a templateTitle[4] of templateValue[2][0] templateScale .
generated: The statistic shows the strikes by country in the Public in 2014 , U.S. use drone .  In 2014 , the Country of the European Union in Venezuela was the highest in the world with a drone of 4 million .

Example 105:
titleEntities: {'Subject': ['Bulgaria'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Bulgaria 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['13316.36', '12445.46', '11650.9', '10887.5', '10133.07', '9518.44', '9313.83', '8275.44', '7496.08', '7017.41', '7888.56', '7667.59', '7399.39', '7836.58', '6743.74', '6859.68', '7152.79', '5812.68', '4444.51', '3839.45', '3344.62', '2689.77', '2074.58', '1783.72', '1613.94', '1673.66', '1631.94', '1228.13', '1153.87', '1511.49', '892.19', '501.48', '913.31', '222.71', '2253.9', '5079.81', '4965.9', '3030.63', '2611.0', '2949.18', '3446.96']

gold: The statistic shows gross domestic product ( GDP ) per capita in Bulgaria from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateYLabel[0] of the total templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . According to the total market value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows the GDP of the total capita in the Bulgaria from 1984 to 2017 , with projections up until 2024 .  According to the total market value of all goods and services produced in a country in a Year .  It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

Example 106:
titleEntities: {'Subject': ['South Africa'], 'Date': ['2017', '2023']}
title: South Africa : number of internet users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'internet', 'users', 'in', 'millions']: ['49.0', '46.8', '44.4', '41.9', '39.3', '36.6', '33.9']

gold: This statistic provides information on the number of internet users in South Africa from 2017 to 2023 . In 2018 , South Africa had 36.6 million internet users . This figure is projected to grow to 49 million internet users in 2022 .
gold_template: This statistic provides information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateTitleSubject[0] had templateYValue[5] templateScale templateYLabel[1] templateYLabel[2] . This figure is projected to grow to templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[1] .

generated_template: In templateXValue[5] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to templateYValue[5] templateScale . This figure is projected to grow to templateYValue[max] templateScale in templateXValue[idxmax(Y)] . The templateYLabel[1] penetration in templateTitleSubject[0] is projected to grow to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .
generated: In 2018 , the Number of internet users in South Africa amounted to 36.6 millions .  This figure is projected to grow 49.0 millions in 2023 .  The internet penetration in South Africa is projected to grow 49.0 millions in 2023 .

Example 107:
titleEntities: {'Subject': ['Cesarean'], 'Date': ['2017']}
title: Cesarean sections in selected countries 2017
X_Axis['Country']: ['Turkey', 'Korea', 'Poland', 'Hungary', 'Italy', 'United_States', 'Switzerland', 'Germany', 'Slovak_Republic', 'Luxembourg', 'Austria', 'Canada', 'United_Kingdom', 'Spain', 'Lithuania', 'Slovenia', 'Belgium', 'France', 'Estonia', 'Latvia', 'Sweden', 'Finland', 'Iceland', 'Norway', 'Israel']
Y_Axis['Cesarean', 'sections', 'per', '1,000', 'live', 'births']: ['531.4', '451.9', '393.0', '373.0', '338.2', '320.0', '318.6', '301.7', '296.8', '296.8', '292.5', '277.3', '273.8', '243.7', '222.6', '213.8', '209.7', '197.0', '194.4', '193.9', '166.4', '164.9', '161.7', '159.5', '148.4']

gold: Among all countries that are part of the Organization for Economic Co-operation and Development ( OECD ) , Turkey , followed by Korea , had the highest rate of Caesarean section births ( or C-sections ) . At that time , the rate of C-sections in Turkey was 531.4 per 1,000 live births . Among global regions , Latin America and the Caribbean had the highest percentage of births that were delivered by C-section , while West and Central Africa had the lowest rates of Cesarean section births .
gold_template: Among all templateTitle[3] that are part of the Organization for Economic Co-operation and Development ( OECD ) , templateXValue[0] , followed by templateXValue[1] , had the highest rate of Caesarean section templateYLabel[5] ( or C-sections ) . At that time , the rate of C-sections in templateXValue[0] was templateYValue[max] templateYLabel[2] 1,000 templateYLabel[4] templateYLabel[5] . Among global regions , Latin America and the Caribbean had the highest templateScale of templateYLabel[5] that were delivered by C-section , while West and Central Africa had the lowest rates of templateYLabel[0] section templateYLabel[5] .

generated_template: This statistic shows the 20 templateTitle[0] of the member states of the templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In templateTitleDate[0] , the templateXValue[0] had the largest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Cesarean of the member states of the selected countries 2017 in .  In 2017 , the Turkey had the largest Cesarean sections of 531.4 per 1,000 .

Example 108:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Sales of the leading 15 optical retailers in the U.S. 2018
X_Axis['Optical', 'retailer']: ['Vision_Source', 'Luxottica_retail', 'Walmart_stores', 'National_Vision', 'Costco_Wholesale', 'Visionworks_of_America', 'MyEyeDr/Capital_Vision_Services_LLC', 'Eyecare_Partners', 'Warby_Parker', 'Refac_Optical_Group', 'Eyemart_Express', 'Now_Optics_LLC', "Cohen's_Fashion_Optical", 'Texas_State_Optical', 'Emerging_Vision_Inc.']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['2840.0', '2470.0', '1762.0', '1537.0', '1129.8', '947.4', '602.0', '420.0', '315.0', '275.5', '263.0', '155.0', '152.0', '120.0', '110.4']

gold: This statistic depicts the sales of the leading 15 optical retailers in the United States in 2018 . In that year , Visionworks of America was the sixth ranked optical retailer in the United States , with sales that amounted to about 947.4 million U.S. dollars .
gold_template: This statistic depicts the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateXValue[9] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[5] of templateXValue[5] was the sixth ranked templateXValue[9] templateXLabel[1] in the templateTitle[5] , with templateYLabel[0] that amounted to about templateYValue[5] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: As of 2019 , templateXValue[0] was ranked first among the templateTitle[0] templateTitle[1] templateTitle[2] templateXValue[0] , with a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateXValue[1] , templateXValue[2] with templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] in the templateTitle[4] . .
generated: As of 2019 , Vision_Source was ranked first among the Sales leading 15 Vision_Source , with a Sales of 2840.0 million U.S. dollars .  Luxottica_retail , Walmart_stores with 1762.0 million U.S. dollars in the retailers .  .

Example 109:
titleEntities: {'Subject': ['United Kingdom'], 'Date': []}
title: Organ donation rate in the United Kingdom 2018/19 , by country
X_Axis['Country']: ['Scotland', 'Northern_Ireland', 'England', 'Wales']
Y_Axis['Rate', 'of', 'donation', 'per', 'million', 'population']: ['17.9', '23.0', '24.5', '30.7']

gold: In 2018/19 , the organ donation rate in Wales was 30.7 per million population , the highest rate in the United Kingdom . With 24.5 organ donations per million population , England had the second highest rate . Scotland had the lowest rate of the countries of the UK .
gold_template: In templateTitle[5] , the templateTitle[0] templateYLabel[1] templateYLabel[0] in templateXValue[last] was templateYValue[max] templateYLabel[2] templateScale templateYLabel[4] , the highest templateYLabel[0] in the templateTitleSubject[0] . With templateYValue[2] templateTitle[0] donations templateYLabel[2] templateScale templateYLabel[4] , templateXValue[2] had the second highest templateYLabel[0] . templateXValue[0] had the lowest templateYLabel[0] of the countries of the UK .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] in templateTitleDate[0] . In templateTitleDate[0] , the templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Rate donation of the Organ donation in .  In , the Scotland had the highest Rate donation of United Kingdom was 30.7 per million .

Example 110:
titleEntities: {'Subject': ['LINE'], 'Date': ['2012', '2015']}
title: LINE app : quarterly revenue 2012 to 2015
X_Axis['Quarter']: ["Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12"]
Y_Axis['Revenue', 'in', 'billion', 'yen']: ['30.0', '29.9', '25.3', '25.4', '23.6', '20.9', '17.7', '14.6', '12.2', '10.2', '7.4', '4.5', '2.2']

gold: This statistic gives information on the quarterly revenue of the chat-app LINE . In the fourth quarter of 2015 , the Japanese chat app had generated 30 billion yen in revenues . This figure translates to approximately 247 million US dollars .
gold_template: This statistic gives information on the templateTitle[2] templateYLabel[0] of the chat-app templateTitleSubject[0] . In the fourth templateXLabel[0] of templateTitleDate[max] , the Japanese chat templateTitle[1] had generated templateYValue[max] templateScale templateYLabel[2] in revenues . This figure translates to approximately 247 templateScale US dollars .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the United Kingdom ( templateTitleSubject[0] ) templateYLabel[0] templateYLabel[1] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the second templateXLabel[0] of templateTitleDate[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] was templateYValue[0] templateScale templateYLabel[3] .
generated: This statistic shows the LINE app quarterly revenue the United Kingdom ( LINE ) Revenue billion from the first Quarter of 2012 to the fourth Quarter of 2015 .  In the second Quarter of 2015 , the LINE app quarterly revenue of LINE was 30.0 billion yen .

Example 111:
titleEntities: {'Subject': ['Red Lobster', 'Canada'], 'Date': ['2013']}
title: Red Lobster restaurants in the U.S. and Canada 2013
X_Axis['Year']: ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013']
Y_Axis['Number', 'of', 'restaurants']: ['654', '661', '667', '673', '680', '679', '682', '680', '680', '690', '694', '698', '704', '705']

gold: The statistic shows the number of Red Lobster restaurants in the United States and Canada from 2000 to 2013 . In 2013 , there were 705 Red Lobster restaurants in North America .
gold_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] restaurants in the templateTitle[3] and templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitleSubject[0] restaurants in North America .

generated_template: This statistic shows the templateYLabel[0] of The templateTitleSubject[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , U.S.-based restaurant chain The templateTitleSubject[0] had templateYValue[max] templateYValue[idxmax(X)] , up from templateYValue[1] the previous templateXLabel[0] .
generated: This statistic shows the Number of The Red Lobster restaurants from 2000 to 2013 .  In 2013 , U.S.-based restaurant chain The Red Lobster had 705 , up from 661 the previous Year .

Example 112:
titleEntities: {'Subject': ['Alphabet'], 'Date': ['2016']}
title: Alphabet : number of full-time employees 2016 , by department
Data:
                                0      1      2      3      4      5     6     7     8
Year                         2016   2015   2014   2013   2012   2011  2010  2009  2008
Research and development    27169  23336  20832  18593  19746  11665  9508  7443  7254
Sales and marketing         20902  19082  17621  15348  15306  11933  8778  7338  8002
General and administrative   9695   8452   7510   6563   6214   4651  3346  2941  3109
Operations                  14287  10944   7637   7252  12595   4218  2768  2113  1857 

gold: This statistic shows the number of Alphabet employees between 2008 and 2016 . As of that year , out of Alphabet 's total number of employees , 27,169 were working in the research and development department . Up until Alphabet 's creation in 2015 , these figures were reported as Google employees .
gold_template: This statistic shows the templateTitle[1] of templateTitleSubject[0] templateTitle[3] between templateValue[0][last] and templateValue[0][0] . As of that templateLabel[0][0] , out of templateTitleSubject[0] 's total templateTitle[1] of templateTitle[3] , templateValue[1][0] were working in the templateLabel[1][0] and templateLabel[1][1] templateTitle[6] . Up until templateTitleSubject[0] 's creation in templateValue[0][1] , these figures were reported as Google templateTitle[3] .

generated_template: This statistic gives information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] in the templateTitle[0] from templateValue[0][last] to templateValue[0][0] , sorted templateTitle[5] templateTitle[6] . In templateValue[0][0] , the templateTitle[1] of templateTitle[2] in the templateTitle[0] amounted to templateValue[1][0] templateScale templateYLabel[2] templateYLabel[3] , up from templateValue[1][last] templateScale of the previous templateLabel[0][0] .
generated: This statistic gives information on the Alphabet number full-time in the Alphabet from 2008 to 2016 , sorted by department .  In 2016 , the number of full-time in the Alphabet amounted to 27169 % , up from 7254 % of the previous Year .

Example 113:
titleEntities: {'Subject': ['Global'], 'Date': ['2017']}
title: Global lemon and lime production 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Production', 'in', 'million', 'metric', 'tons']: ['17.22', '17.35', '16.99', '16.25', '15.23', '15.01', '15.07', '14.85', '17.23', '17.18', '16.25', '13.66', '12.33', '12.3', '12.62', '12.3', '12.16', '11.38']

gold: This statistic depicts the production of lemons and limes worldwide from 2000 to 2017 . According to the report , global lemon and lime production amounted to approximately 17.22 million metric tons in 2017 .
gold_template: This statistic depicts the templateYLabel[0] of lemons and limes worldwide from templateXValue[min] to templateXValue[max] . According to the report , templateTitleSubject[0] templateTitle[1] and templateTitle[2] templateYLabel[0] amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] .

generated_template: This statistic represents the total templateYLabel[0] of templateTitle[2] in the templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[2] produced approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[1] were produced worldwide .
generated: This statistic represents the total Production of lime in the Global between 2000 and 2017 .  In 2017 , lime produced approximately 17.22 million metric tons of lemon were produced worldwide .

Example 114:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. airports - public and private 1990 to 2018
Data:
                 0      1      2      3      4      5      6      7      8      9     10     11     12     13     14     15     16     17     18     19
Year            18     17     16     14     13     12     11     10      9      8      7      6      5      4      3      2      1      0     95     90
Public use    5087   5104   5136   5145   5155   5171   5172   5175   5178   5202   5221   5233   5270   5288   5286   5286   5294   5317   5415   5589
Private use  14549  14263  14112  13863  14009  14269  14339  14353  14298  14451  14839  14757  14584  14532  14295  14286  14062  13964  12809  11901 

gold: How many airports are in the U.S. ? While the number of public use airports in the United States has fallen since 1990 , the number of private use airports has increased in recent years . In 2018 , there were 5,087 public airports in the U.S. , a slight decrease from the 5,145 public airports operating in 2014 . Conversely , the number of private airports increased over this period from 13,863 to 14,549 .
gold_template: How many templateTitle[1] are in the templateTitleSubject[0] ? While the number of templateTitle[2] templateLabel[1][1] templateTitle[1] in the templateTitle[0] has fallen since templateTitleDate[min] , the number of templateTitle[3] templateLabel[1][1] templateTitle[1] has templatePositiveTrend in recent years . In templateTitleDate[max] , there were templateValue[1][0] templateTitle[2] templateTitle[1] in the templateTitleSubject[0] , a slight templateNegativeTrend from the templateValue[1][3] templateTitle[2] templateTitle[1] operating in 2014 . Conversely , the number of templateTitle[3] templateTitle[1] templatePositiveTrend over this period from templateValue[2][3] to templateValue[2][0] .

generated_template: The statistic shows the templateTitle[0] of the employees in the United Kingdom ( templateTitleSubject[0] ) from templateValue[0][last] to templateValue[0][0] , templateTitle[4] templateTitle[5] . In templateValue[0][0] , the templateTitle[0] of employees in the templateTitleSubject[0] was templateValue[1][0] templateScale .
generated: The statistic shows the U.S. of the employees in the United Kingdom ( U.S. ) from 90 to 18 , 1990 2018 .  In 18 , the U.S. of employees in the U.S. was 5087 million .

Example 115:
titleEntities: {'Subject': ['Europe'], 'Date': ['2018']}
title: Pet population in Europe 2018 , by animal type
X_Axis['Pet', 'Type']: ['Cat', 'Dog', 'Ornamental_bird', 'Small_mammal', 'Aquaria', 'Reptiles']
Y_Axis['Number', 'of', 'pets', 'in', 'thousands']: ['103828', '85184', '50212', '26794', '15493', '7848']

gold: Cats and dogs are European households ' favorite animals to keep , although more unconventional choices such as reptiles and small mammals are chosen as pets in high numbers too . In 2018 , the population of pet cats in Europe was over 103 million . Germany and France were the two countries where the number of cats was the highest .
gold_template: Cats and dogs are European households ' favorite animals to keep , although more unconventional choices such as templateXValue[last] and templateXValue[3] mammals are chosen as templateYLabel[1] in high numbers too . In templateTitleDate[0] , the templateTitle[1] of templateXLabel[0] cats in templateTitleSubject[0] was over 103 templateScale . Germany and France were the two countries where the templateYLabel[0] of cats was the highest .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[4] in templateTitleDate[0] . According to the source , the templateXValue[0] is the templateTitle[0] of people using templateTitle[0] with a total of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Pet of the population Europe by in 2018 .  According to the source , the Cat is the Pet of people using Pet with a total of 103828 thousands .

Example 116:
titleEntities: {'Subject': ['Germany'], 'Date': ['1992', '2019']}
title: Inflation rate in Germany 1992 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Year-on-year', 'change']: ['1.4', '1.8', '1.5', '0.5', '0.5', '1', '1.4', '2', '2.1', '1.1', '0.3', '2.6', '2.3', '1.6', '1.5', '1.7', '1.1', '1.3', '2', '1.4', '0.6', '1', '2', '1.4', '1.8', '2.6', '4.5', '5.1']

gold: This statistic shows the inflation rate in Germany from 1992 to 2019 . In 2019 , the inflation rate in Germany was at 1.4 percent . The inflation rate is calculated using the price increase of a product basket defined by the German Federal Statistical Office .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] was at templateYValue[0] templateScale . The templateTitle[0] templateTitle[1] is calculated using the price templatePositiveTrend of a product basket defined by the German Federal Statistical Office .

generated_template: In templateTitleDate[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to templateYValue[max] templateScale . In the templateTitle[0] templateTitle[1] in templateTitleSubject[0] had templateNegativeTrend to templateYValue[1] templateScale . The term of the Chinese government in the Chinese government in the Chinese , the Chinese government as well as changing family and work situations of the Chinese people , templateTitle[0] templateTitle[1] has subsided over the past decades .
generated: In 2019 , the Inflation rate in Germany amounted to 5.1 % .  In the Inflation rate in Germany had dropped to 1.8 % .  The term of the Chinese government in the Chinese government in the Chinese , the Chinese government as well changing family and work situations of the Chinese people , Inflation rate has subsided over the past decades .

Example 117:
titleEntities: {'Subject': ['Coal'], 'Date': ['2010', '2018']}
title: Coal - global proved reserves by type 2010 to 2018
Data:
                                 0       1       2       3       4
Year                          2010    2012    2014    2016    2018
Anthracite and bituminous   404762  404762  403199  816214  734903
Sub-bituminous and lignite  456176  456176  488332  323117  319879 

gold: This statistic represents proved coal reserves worldwide between 2010 to 2018 , by type . In 2018 , global proved reserves of anthracite and bituminous coal amounted to around 735 billion metric tons . As of 2018 , the life years of coal is predicted to be a about 132 years at the current rate of production .
gold_template: This statistic represents templateTitle[2] templateTitleSubject[0] templateTitle[3] worldwide between templateValue[0][0] to templateValue[0][last] , templateTitle[4] templateTitle[5] . In templateValue[0][last] , templateTitle[1] templateTitle[2] templateTitle[3] of templateLabel[1][0] and templateLabel[1][1] templateTitleSubject[0] amounted to around 735 templateScale metric tons . As of templateValue[0][last] , the life years of templateTitleSubject[0] is predicted to be a about 132 years at the current rate of production .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] of the United Kingdom ( templateTitleSubject[0] ) from templateValue[0][last] to templateValue[0][0] , templateTitle[4] templateTitle[5] . According to the source , templateValue[1][0] templateScale of people were employed in templateValue[0][0] .
generated: This statistic shows the Coal of global proved of the United Kingdom ( Coal ) from 2018 to 2010 , by type .  According to the source , 404762 % of people were employed in 2010 .

Example 118:
titleEntities: {'Subject': ['Canadian', 'Canada'], 'Date': ['2015']}
title: Most popular Canadian product categories purchased by consumers in Canada 2015
X_Axis['Response']: ['Food', 'Beer', 'Clothing', 'Furniture', 'Wine']
Y_Axis['Share', 'of', 'respondents']: ['87', '46', '45', '38', '34']

gold: In 2015 , some 87 percent of Canadians stated that , out of any product category , they are most likely to purchase food from Canadian producers or brands as opposed to imported products . Beer and clothing were the next two most popular product segments , with 46 and 45 percent of respondents stating they would likely buy Canadian goods respectively . In the same survey , some 25 percent of consumers said that they always buy Canadian products .
gold_template: In templateTitleDate[0] , some templateYValue[max] templateScale of Canadians stated that , out of any templateTitle[3] category , they are templateTitle[0] likely to purchase templateXValue[0] from templateTitleSubject[0] producers or brands as opposed to imported products . templateXValue[1] and templateXValue[2] were the next two templateTitle[0] templateTitle[1] templateTitle[3] segments , with templateYValue[1] and templateYValue[2] templateScale of templateYLabel[1] stating they would likely buy templateTitleSubject[0] goods respectively . In the same survey , some 25 templateScale of templateTitle[7] said that they always buy templateTitleSubject[0] products .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded templateXValue[3] by Harris Interactive in 2014 , templateYValue[max] templateScale of the templateYLabel[1] stated that they used templateTitle[2] the social network .
generated: This statistic shows the results of a survey among Canadian adult popular .  The survey was fielded Furniture by Harris Interactive in 2014 , 87 % of the respondents stated that they used Canadian the social network .

Example 119:
titleEntities: {'Subject': ['Countries'], 'Date': ['1900', '2016']}
title: Countries with the most affected people from earthquakes 1900 to 2016
X_Axis['Country']: ['China', 'India', 'Chile', 'Indonesia', 'Nepal', 'Peru', 'Philippines', 'Turkey', 'Guatemala', 'Haiti']
Y_Axis['Number', 'of', 'affected', 'people', '(in', 'millions)']: ['70.9', '26.18', '8.53', '7.4', '6.31', '5.8', '5.78', '5.63', '5.19', '3.4']

gold: This statistic shows the Number of affected people in countries with the greatest earthquake occurrence from 1900 to 2016 . During this period , about 70.9 million people in China were affected by earthquakes .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] templateTitle[1] the greatest earthquake occurrence templateTitle[5] templateTitleDate[min] to templateTitleDate[max] . During this period , about templateYValue[max] templateScale templateYLabel[2] in templateXValue[0] were templateYLabel[1] by templateTitle[6] .

generated_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] in templateTitleDate[0] , sorted templateTitle[5] templateXLabel[0] . It was that year , templateXValue[0] had the largest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Countries most the affected people in 1900 , sorted earthquakes Country .  It was that year , China had the largest Number affected of 70.9 people (in .

Example 120:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Leading hair conditioner brands in the U.S. 2018
X_Axis['Brand']: ['Organix', 'Shea_Moisture', 'Garnier_Whole_Blends', "It's_A_10_Miracle", 'Garnier_Fructis_Sleek_And_Shine', 'TRESemmé_Moisture_Rich', 'Dove_Nutritive_Solutions', 'Suave_Professionals', 'Herbal_Essences_Bio_Renew', "L'Oréal_EverPure"]
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['133.7', '77.1', '63.0', '59.8', '52.0', '37.0', '36.9', '35.6', '34.6', '29.0']

gold: This statistic shows the sales of the leading hair conditioner/creme rinse brands in the United States in 2018 . In that year , the leading hair conditioner brand of the United States was OGX with sales that amounted to approximately 133.7 million U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] conditioner/creme rinse templateTitle[3] in the templateTitle[4] in templateTitleDate[0] . In that year , the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] of the templateTitle[4] was OGX with templateYLabel[0] that amounted to approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic gives information on the templateYLabel[0] of the templateTitle[0] templateTitle[2] templateTitle[3] in the templateTitle[4] as of 2019 . During the survey period , it was found that templateXValue[0] accounted for templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of the templateTitle[0] templateTitle[4] templateYLabel[0] . templateXValue[1] was ranked second with templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] .
generated: This statistic gives information on the Sales of the Leading conditioner brands in the U.S. as of 2019 .  During the survey period , it was found that Organix accounted for 133.7 million U.S. dollars of the Leading U.S. Sales .  Shea_Moisture was ranked second with 77.1 million U.S. dollars in Sales .

Example 121:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2017', '2023']}
title: Indonesia : number of internet users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['149.9', '141.3', '131.0', '119.4', '107.2', '95.2', '84.0']

gold: In 2018 , 95.2 million people were accessing the internet in Indonesia . This figure is projected to grow to almost 150 million in 2023 . Internet usage in Indonesia With over 143.26 million internet users , Indonesia is one of the biggest online markets worldwide .
gold_template: In templateXValue[5] , templateYValue[5] templateScale people were accessing the templateTitle[2] in templateTitleSubject[0] . This figure is projected to grow to almost templateYValue[max] templateScale in templateXValue[idxmax(Y)] . templateTitle[2] usage in templateTitleSubject[0] With over 143.26 templateScale templateTitle[2] templateYLabel[1] , templateTitleSubject[0] is one of the biggest online markets worldwide .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there were around templateYValue[5] templateScale templateTitle[2] templateTitle[3] templateYLabel[1] in the templateTitle[2] templateTitle[3] templateYLabel[1] in the templateTitleSubject[0] , up from templateYValue[min] templateScale in templateXValue[idxmin(Y)] .
generated: This statistic shows the Number of internet users in Indonesia from 2017 to 2023 .  In 2018 , there were around 95.2 millions internet users in the internet users in the Indonesia , up from 84.0 millions in 2017 .

Example 122:
titleEntities: {'Subject': ['Global'], 'Date': ['2018']}
title: Global pineapple production by leading countries 2018
X_Axis['Country']: ['Costa_Rica', 'Philippines', 'Brazil', 'Thailand', 'Indonesia', 'India', 'Nigeria', 'China_mainland', 'Mexico', 'Colombia']
Y_Axis['Production', 'in', 'thousand', 'metric', 'tons']: ['3418.16', '2730.99', '2650.48', '2113.38', '1805.51', '1706.0', '1664.51', '1573.47', '999.59', '900.4']

gold: In 2018 , Costa Rica , Philippines , and Brazil were the top three pineapple producers worldwide . Costa Rica generated 3.42 million metric tons of pineapples in that year . Overall pineapple production in that year amounted to 27.92 million metric tons .
gold_template: In templateTitleDate[0] , templateXValue[0] , templateXValue[1] , and templateXValue[2] were the top three templateTitle[1] producers worldwide . templateXValue[0] generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of pineapples in that year . Overall templateTitle[1] templateYLabel[0] in that year amounted to 27.92 templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the ranking of the templateTitle[1] the leading templateTitle[1] worldwide in templateTitleDate[0] , templateTitle[4] . templateXValue[0] produced a reported templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] templateTitle[1] .
generated: The statistic shows the ranking of the pineapple leading worldwide in 2018 , leading .  Costa_Rica produced a reported 3418.16 thousand metric tons of Global pineapple .

Example 123:
titleEntities: {'Subject': ['Per'], 'Date': ['2009', '2012']}
title: Per capita consumption of chewing gum in selected countries worldwide 2009 to 2012
X_Axis['Country']: ['United_States', 'Austria', 'Belgium', 'Germany', 'France', 'Great_Britain', 'Spain', 'Russia', 'Poland', 'China', 'India']
Y_Axis['Index', 'value']: ['152', '114', '112', '100', '96', '89', '85', '84', '70', '31', '10']

gold: The table shows the per capita consumption of chewing gum in selected countries worldwide from 2009 to 2012 . In 2010 , the United States ' index value of per capita consumption of chewing gum amounted to 152.Chewing gum marketChewing gum belongs to the snack and confectionery segment and is the smallest category besides chocolate and sugary products . Chewing gum has a chewy , easily formable texture and usually has – besides some exceptions – a sweet taste .
gold_template: The table shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] in templateTitle[5] templateTitle[6] templateTitle[7] from templateTitleDate[min] to templateTitleDate[max] . In 2010 , the templateXValue[0] ' templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] amounted to 152.Chewing templateTitle[4] marketChewing templateTitle[4] belongs to the snack and confectionery segment and is the smallest category besides chocolate and sugary products . templateTitle[3] templateTitle[4] has a chewy , easily formable texture and usually has – besides some exceptions – a sweet taste .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the member states templateTitle[4] templateTitleSubject[0] in templateTitleDate[0] . In that year , the templateYLabel[0] templateYLabel[1] of the templateXValue[0] amounted to templateYValue[max] .
generated: The statistic shows the Index value of the member states gum Per in 2009 .  In that year , the Index value of the United_States amounted to 152 .

Example 124:
titleEntities: {'Subject': ['Miele'], 'Date': ['2006/07', '2017/18']}
title: Miele : global revenue 2006/07 to 2017/18
X_Axis['Year']: ['2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['4.1', '3.93', '3.71', '3.49', '3.22', '3.15', '3.04', '2.95', '2.83', '2.77', '2.81', '2.74']

gold: The statistic illustrates the revenue of Miele & Cie. from 2006/07 to 2017/18 . In its fiscal year 2016/17 , Miele made total revenue of 4.1 billion euros worldwide , a record high . Miele 's annual sales have witnessed continuous growth during the measured period .
gold_template: The statistic illustrates the templateYLabel[0] of templateTitleSubject[0] & Cie. from templateXValue[last] to templateXValue[0] . In its fiscal templateXLabel[0] templateXValue[1] , templateTitleSubject[0] made total templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] worldwide , a record high . templateTitleSubject[0] 's annual sales have witnessed continuous growth during the measured period .

generated_template: This statistic shows the total templateYLabel[0] of the templateTitle[1] templateTitleSubject[0] professional templateTitle[3] templateTitle[4] ( templateTitle[5] Five ) from templateXValue[last] to templateXValue[0] . In the templateXValue[2] season , the total templateYLabel[0] of the `` templateTitle[5] Five '' templateTitle[4] was estimated at templateYValue[2] templateScale templateYLabel[2] .
generated: This statistic shows the total Revenue of the global Miele professional 2006/07 2017/18 ( Five ) from 2006/07 to 2017/18 .  In the 2015/16 season , the total Revenue of the `` 2017/18 Five '' 2017/18 was estimated at 3.71 billion euros .

Example 125:
titleEntities: {'Subject': ['Germany'], 'Date': ['2006', '2018']}
title: Number of road deaths in Germany 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['3275', '3180', '3206', '3459', '3377', '3339', '3600', '4009', '3648', '4152', '4477', '4949', '5091']

gold: This line graph shows the number of road traffic fatalities per year in Germany between 2006 and 2018 . In 2009 , 4,152 individuals were killed in motor vehicle accidents . An overall decrease in the number of road deaths was observed within the time period shown .
gold_template: This line graph shows the templateYLabel[0] of templateTitle[1] traffic templateYLabel[1] per templateXLabel[0] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[9] , templateYValue[9] individuals were killed in motor vehicle accidents . An overall templateNegativeTrend in the templateYLabel[0] of templateTitle[1] templateTitle[2] was observed within the time period shown .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] due to templateTitle[0] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[idxmax(X)] templateYLabel[1] reported due to the templateTitle[1] .
generated: The statistic shows the Number of fatalities due to Number in the road from 2006 to 2018 .  In 2018 , there were a total of 3275 fatalities reported due to the road .

Example 126:
titleEntities: {'Subject': ['Gross Domestic Product'], 'Date': ['2019', '2030']}
title: U.S . Gross Domestic Product - forecast 2019 to 2030
X_Axis['Year']: ['2030', '2029', '2028', '2027', '2026', '2025', '2024', '2023', '2022', '2021', '2020', '2019']
Y_Axis['GDP', 'in', 'billion', 'U.S.', 'dollars']: ['32003', '30850', '29737', '28653', '27615', '26653', '25724', '24809', '23916', '23029', '22111', '21220']

gold: This graph shows a forecast of the Gross Domestic Product of the United States of America for fiscal years 2019 to 2030 . The Gross Domestic Product ( GDP ) refers to the market value of all final goods and services produced within a country in a given period . According to the CBO , the United States GDP will increase steadily over the next decade from 21.22 trillion U.S. dollars in 2019 to 32 trillion U.S. dollars in 2030 .
gold_template: This graph shows a templateTitle[4] of the templateTitleSubject[0] Product of the country of America for fiscal years templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] Product ( templateYLabel[0] ) refers to the market value of all final goods and services produced within a country in a given period . According to the CBO , the country templateYLabel[0] will templatePositiveTrend steadily over the next decade from templateYValue[min] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . The templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: This statistic shows the U.S Gross Domestic ( GDP ) in the Gross Domestic Product from 2019 to 2023 , with projections up until 2030 .  The GDP refers to the total market value of all goods and services that are produced within a country per Year .  It is an important indicator of the economic strength of a country .

Example 127:
titleEntities: {'Subject': ['BP'], 'Date': ['2010', '2018']}
title: R & D expenditure of BP 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Expenditure', 'in', 'million', 'U.S.', 'dollars']: ['429', '391', '400', '418', '663', '707', '674', '636', '780']

gold: This statistic depicts BP 's expenditure on research and development from 2010 to 2018 . In 2011 , the company 's R & D expenditure stood at approximately 636 million U.S. dollars . BP is a globally operating oil and gas company , and the eighth largest company generally worldwide as of 2017 , measured by revenue .
gold_template: This statistic depicts templateTitleSubject[0] 's templateYLabel[0] on research and development from templateXValue[min] to templateXValue[max] . In templateXValue[7] , the company 's templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] stood at approximately templateYValue[7] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] is a globally operating oil and gas company , and the eighth largest company generally worldwide as of templateXValue[1] , measured by revenue .

generated_template: This statistic shows the templateYLabel[0] of pharmaceutical company templateTitleSubject[0] for templateTitle[2] and templateTitle[3] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] Pharmaceutical Industries is one of the largest generic drug manufacturers in the world . In templateXValue[max] , the company invested about templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateTitle[2] templateTitle[3] .
generated: This statistic shows the Expenditure of pharmaceutical company BP for D and expenditure from 2010 to 2018 .  BP Pharmaceutical Industries is one of the largest generic drug manufacturers in the world .  In 2018 , the company invested about 429 million U.S. dollars in D expenditure .

Example 128:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2012', '2016']}
title: Factors influencing U.S. consumers ' foods and beverages purchases 2012 to 2016
Data:
                   0     1     2     3     4
Year            2012  2013  2014  2015  2016
Taste             87    89    90    83    84
Price             73    71    73    68    71
Healthfulness     61    64    71    60    64
Convenience       53    56    51    52    52
Sustainability    35    36    38    35    41 

gold: The graph shows which factors are important to consumers in the U.S. when purchasing foods and beverages from 2012 to 2016 . In 2012 , 73 % of respondents say that the price has an impact on their decision to buy foods and beverages .
gold_template: The graph shows which templateTitle[0] are important to templateTitle[3] in the templateTitleSubject[0] when purchasing templateTitle[5] and templateTitle[6] from templateValue[0][0] to templateValue[0][last] . In templateValue[0][0] , templateValue[2][0] templateScale of respondents say that the templateLabel[2][0] has an impact on their decision to buy templateTitle[5] and templateTitle[6] .

generated_template: This statistic shows the templateScale of templateTitle[1] in the United Kingdom ( templateTitleSubject[0] ) templateTitle[3] from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . In templateValue[0][0] , the templateTitle[7] of templateLabel[1][0] templateLabel[1][1] templateLabel[1][1] templateLabel[1][2] are predicted to reach templateValue[1][0] templateScale , while in comparison , half of the other three templateScale of the templateTitle[1] .
generated: This statistic shows the percentage of influencing in the United Kingdom ( U.S. ) consumers from 2016 to 2012 , purchases 2012 .  In 2012 , the purchases of Taste are predicted to reach 87 % , while in comparison , half of the other three percent of the influencing .

Example 129:
titleEntities: {'Subject': ['Venezuela'], 'Date': ['2018']}
title: Venezuela 's budget balance in relation to GDP 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Budget', 'balance', 'in', 'relation', 'to', 'GDP']: ['-30.56', '-16.61', '-10.83', '-10.65', '-15.55', '-11.27', '-10.45', '-8.24', '-4.74', '-8.69', '-3.46']

gold: The statistic shows Venezuela 's budget balance in relation to GDP between 2008 and 2017 , with projections up until 2018 . A positive value indicates a budget surplus , a negative value indicates a deficit . In 2017 , the budget deficit of Venezuela amounted to around 16.61 percent of GDP .
gold_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[1] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[1] , the templateYLabel[0] deficit of templateTitleSubject[0] amounted to around 16.61 templateScale of templateYLabel[3] .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[max] , templateTitleSubject[0] templateTitle[1] surplus amounted to around templateYValue[idxmax(X)] templateScale of templateYLabel[3] .
generated: The statistic shows Venezuela 's Budget balance in relation to GDP between 2008 and 2012 , with projections up until 2018 .  A positive value indicates a Budget surplus , a negative value indicates a deficit .  In 2018 , Venezuela 's surplus amounted to around -30.56 % of GDP .

Example 130:
titleEntities: {'Subject': ['Most'], 'Date': ['2018']}
title: Most miserable countries in the world 2018
X_Axis['Country']: ['Venezuela', 'Argentina', 'Iran', 'Brazil', 'Turkey', 'Nigeria', 'South_Africa', 'Bosnia_and_Herzegovina', 'Egypt', 'Ukraine', 'Nicaragua', 'Jordan', 'Uruguay', 'Honduras', 'Macedonia', 'Armenia', 'Jamaica', 'Saudi_Arabia', 'Colombia', 'Paraguay']
Y_Axis['Misery', 'index', 'score']: ['1746439.1', '105.6', '75.7', '53.6', '53.3', '43.0', '42.0', '38.2', '36.8', '34.3', '31.2', '30.9', '27.1', '26.8', '26.4', '25.1', '24.9', '23.5', '23.2', '22.9']

gold: This statistic shows the most miserable countries in the world as rated according to the misery index for 2018 . In 2018 , the most miserable country in the world was considered to be Venezuela with a misery index score of 1746439.1 . Quality of life around the world The economic indicator , misery index was created by Arthur Okun .
gold_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] in the templateTitle[3] as rated according to the templateYLabel[0] templateYLabel[1] for templateTitleDate[0] . In templateTitleDate[0] , the templateTitleSubject[0] templateTitle[1] templateXLabel[0] in the templateTitle[3] was considered to be templateXValue[0] with a templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[max] . Quality of life around the templateTitle[3] The economic indicator , templateYLabel[0] templateYLabel[1] was created by Arthur Okun .

generated_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] in templateTitleDate[0] , based on templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Most miserable the countries world in 2018 , based on 2018 Country .  In 2018 , the Venezuela had the highest Misery index of 1746439.1 score .

Example 131:
titleEntities: {'Subject': ['Spain'], 'Date': ['2010', '2018']}
title: Electricity prices for households in Spain 2010 - 2018 , semi-annually
X_Axis['Year']: ['2018_S1', '2017_S2', '2017_S1', '2016_S2', '2016_S1', '2015_S2', '2015_S1', '2014_S2', '2014_S1', '2013_S2', '2013_S1', '2012_S2', '2012_S1', '2011_S2', '2011_S1', '2010_S2', '2010_S1']
Y_Axis['Euro', 'cents', 'per', 'kilowatt-hour']: ['23.83', '21.77', '22.96', '22.84', '21.85', '23.7', '23.09', '23.67', '21.65', '22.73', '22.28', '22.75', '21.9', '20.88', '19.81', '18.51', '17.28']

gold: This statistic presents the electricity prices for household end users in Spain semi-annually from 2010 to 2018 . In the first half of 2018 , the average electricity price for Spanish households was 23.83 euro cents per kWh .
gold_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[15] to templateXValue[0] . In the first half of templateXValue[0] , the average templateTitle[0] price templateTitle[2] Spanish templateTitle[3] was templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

generated_template: Since the first half of templateXValue[17] , household templateTitle[0] templateTitle[1] in templateTitleSubject[0] have seen an overall templatePositiveTrend , templatePositiveTrend to templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( kWh ) in the first half of templateXValue[0] . This was the highest price during the reporting period . templateTitle[1] templateNegativeTrend noticeably between templateXValue[9] and the first half of templateXValue[7] , with templateTitle[3] paying less than templateYValue[min] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: Since the first half of 2010_S1 , household Electricity prices in Spain have seen an overall increase , to 23.83 Euro cents per kilowatt-hour ( kWh ) in the first half of 2018_S1 .  This was the highest price during the reporting period .  prices fell noticeably between 2013_S2 and the first half of 2014_S2 , with households paying less than 17.28 Euro cents per kWh .

Example 132:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Brazil 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.26', '2.41', '2.36', '2.37', '2.04', '0.88', '1.11', '1.06', '-3.31', '-3.55', '0.51']

gold: The statistic shows the growth in real GDP in Brazil from between 2014 and 2018 , with projections up until 2024 . In 2018 , Brazil 's real gross domestic product increased by 1.11 percent compared to the previous year.Brazilian growth and civic unrest GDP is a reliable tool used to indicate the shape of a national economy . It is one of the most well-known and well-understood measurements of the state of a country .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] year.Brazilian templateYLabel[1] and civic unrest templateYLabel[0] is a reliable tool used to indicate the shape of a national economy . It is templateYValue[5] of the most well-known and well-understood measurements of the state of a country .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Brazil from 2014 to 2018 , with projections up until 2024 .  In 2018 , Brazil 's real Gross domestic product increased by around 1.11 % compared to the previous Year .

Example 133:
titleEntities: {'Subject': ['Game'], 'Date': ['2014', '2017']}
title: Game developer distribution worldwide 2014 - 2017 , by gender
Data:
                                  0     1     2     3
Year                           2017  2016  2015  2014
Men                              74    72    75    76
Women                            21    23    22    22
Transgender/Androgynous/Other     5   4.7     3     2 

gold: The statistic shows the distribution of game developers worldwide from 2014 to 2017 , broken down by gender . In 2017 , 74 percent of responding game developers were men , while 21 percent were women .
gold_template: The statistic shows the templateTitle[2] of templateTitleSubject[0] developers templateTitle[3] from templateValue[0][last] to templateValue[0][0] , broken down templateTitle[6] templateTitle[7] . In templateValue[0][0] , templateValue[1][0] templateScale of responding templateTitleSubject[0] developers were templateLabel[1][0] , while templateValue[2][0] templateScale were templateLabel[2][0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in the templateTitleSubject[0] were templateLabel[2][0] .
generated: This statistic shows the Game developer of Game worldwide 2014 2017 by from 2014 to 2017 .  In 2017 , 74 % of the employees in the Game were Women .

Example 134:
titleEntities: {'Subject': ['Worldwide'], 'Date': ['2007', '2018']}
title: Rolls-Royce - Worldwide sales volume of automobiles 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'sold', 'automobiles']: ['4107', '3362', '4011', '3785', '4063', '3630', '3575', '3538', '2711', '1002', '1212', '1010']

gold: Rolls-Royce Motor Cars delivered approximately 4,100 automobiles in 2018 , representing a 22.2 percent increase from the previous year . The luxury auto brand is a wholly owned subsidiary of BMW . Record sales took place in 2018 thanks to the successful release of the Phantom VIII in July 2017 .
gold_template: templateTitle[0] Motor Cars delivered approximately 4,100 templateYLabel[2] in templateXValue[max] , representing a 22.2 templateScale templatePositiveTrend from the previous templateXLabel[0] . The luxury auto brand is a wholly owned subsidiary of BMW . Record templateTitle[2] took place in templateXValue[max] thanks to the successful release of the Phantom VIII in 2017 .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[7] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] .
generated: This statistic shows the Number sold of the 2018 of the Worldwide from 2007 to 2018 .  In 2018 , the average Number sold of Worldwide amounted to 4107 automobiles .

Example 135:
titleEntities: {'Subject': ['Latin America Caribbean'], 'Date': ['2017']}
title: Latin America & the Caribbean : homicide rates 2017 , by country
X_Axis['Country']: ['El_Salvador', 'Jamaica', 'Honduras', 'Belize', 'Bahamas', 'Brazil', 'Guatemala', 'Colombia', 'Mexico', 'Puerto_Rico', 'Guyana', 'Costa_Rica', 'Dominican_Republic', 'Grenada', 'Panama', 'Uruguay', 'Peru', 'Nicaragua', 'Ecuador', 'Suriname', 'Argentina', 'Chile']
Y_Axis['Homicides', 'per', '100,000', 'inhabitants']: ['61.8', '57.0', '41.7', '37.9', '30.9', '30.5', '26.1', '24.9', '24.8', '18.5', '14.8', '12.3', '11.3', '11.1', '9.7', '8.2', '7.7', '7.4', '5.8', '5.5', '5.2', '4.3']

gold: Countries in Central America and the Caribbean registered some of the highest homicide rates in the Latin American region in 2017 . El Salvador ranked first , with nearly 62 homicides committed per 100,000 inhabitants . Jamaica came in second , with 57 homicides per 100,000 people .
gold_template: Countries in Central templateTitleSubject[0] and the templateTitleSubject[0] registered some of the highest templateTitle[4] templateTitle[5] in the templateTitleSubject[0] American region in templateTitleDate[0] . templateXValue[0] ranked first , with nearly templateYValue[max] templateYLabel[0] committed templateYLabel[1] 100,000 templateYLabel[3] . templateXValue[1] came in second , with templateYValue[1] templateYLabel[0] templateYLabel[1] 100,000 people .

generated_template: The statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . templateXValue[0] had the highest rate of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the 20 Latin America Caribbean America the & Caribbean homicide rates in 2017 .  El_Salvador had the highest rate of 61.8 100,000 inhabitants .

Example 136:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2024']}
title: Inflation rate in Malaysia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2.29', '2.28', '2.25', '2.22', '2.08', '1.02', '0.97', '3.8', '2.08', '2.1', '3.14', '2.11', '1.66', '3.17', '1.72', '0.6', '5.43', '2.03', '3.62', '3.04', '1.42', '1.07', '1.79', '1.43', '1.55', '2.73', '5.29', '2.66', '3.48', '3.47', '3.69', '3.55', '4.78', '4.33', '3.04', '2.56', '0.29', '0.74', '0.35', '2.59', '3.9']

gold: This statistic shows the average inflation rate in Malaysia from 1984 to 2018 , with projections up to 2024 . In 2018 , the average inflation rate in Malaysia amounted to about 0.97 percent compared to the previous year . Malaysia 's economy is slowly recovering The inflation rate is the annual rate of increase of a price index , normally the consumer price index over time .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up to templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . templateTitleSubject[0] 's economy is slowly recovering The templateYLabel[0] templateYLabel[1] is the annual templateYLabel[1] of templatePositiveTrend of a price index , normally the consumer price index over time .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Malaysia from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Malaysia amounted to about 0.97 % compared to the previous Year .

Example 137:
titleEntities: {'Subject': ['Boston Red Sox'], 'Date': ['2001', '2018']}
title: Boston Red Sox revenue 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['516', '453', '434', '398', '370', '357', '336', '310', '272', '266', '269', '263', '234', '206', '201', '190', '171', '152']

gold: The statistic depicts the revenue of the Boston Red Sox from 2001 to 2018 . In 2018 , the revenue of the Major League Baseball franchise amounted to 516 million U.S. dollars.The Boston Red Sox are owned by John Henry and Thomas Werner , who bought the franchise for 380 million U.S. dollars in 2002 .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Sox from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] Sox are owned by John Henry and Thomas Werner , who bought the franchise for 380 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[16] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Boston Red Sox , a franchise of the National Football League , from 2001 to 2018 .  In 2018 , the Revenue of the Boston Red Sox was 516 U.S. dollars .

Example 138:
titleEntities: {'Subject': ['MSC'], 'Date': ['2020', '2020']}
title: MSC - number of ships 2020
X_Axis['Ship', 'Type']: ['Chartered', 'Owned', 'Orderbook']
Y_Axis['Number', 'of', 'ships']: ['397', '177', '13']

gold: As of March 2 , 2020 , Mediterranean Shipping Company , had a total fleet size of 574 ships , of which roughly seven out of ten were chartered from lessors . Mediterranean Shipping Company is the world 's second largest container shipping operator and one of the leading cruise lines through its subsidiary MSC Cruises . New container ship orders Mediterranean Shipping Company had 17 additional haulers for containerized trade in its order book at the beginning of 2020 .
gold_template: As of 2 , templateTitleDate[0] , Mediterranean Shipping Company , had a total fleet size of 574 templateYLabel[1] , of which roughly seven out of ten were templateXValue[0] from lessors . Mediterranean Shipping Company is the world 's second largest container shipping operator and one of the leading cruise lines through its subsidiary templateTitleSubject[0] Cruises . New container templateXLabel[0] orders Mediterranean Shipping Company had 17 additional haulers for containerized trade in its order book at the beginning of templateTitleDate[0] .

generated_template: This statistic displays the templateYLabel[0] of ONE templateYLabel[1] as of 2 , templateTitleDate[0] . The Japanese transportation company had templateYLabel[1] with a capacity of around 1.6 templateScale twenty-foot-equivalent units in its fleet , which could be broken down to some 530,000 twenty-foot-equivalent units of templateXValue[1] capacity and around one templateScale twenty-foot equivalent units of templateXValue[0] capacity .
generated: This statistic displays the Number of ONE ships as of 2 , 2020 .  The Japanese transportation company had ships with a capacity of around 1.6 million twenty-foot-equivalent units in its fleet , which could be broken down to some 530,000 twenty-foot-equivalent units of Owned capacity and around one million twenty-foot equivalent units of Chartered capacity .

Example 139:
titleEntities: {'Subject': ['KPMG'], 'Date': ['2010', '2019']}
title: Revenue of KPMG worldwide by region 2010 to 2019
Data:
                  0      1      2      3      4      5      6      7      8      9
Year           2019   2018   2017   2016   2015   2014   2013   2012   2011   2010
EMA           12.89  12.98   11.5  11.34  11.31  12.45  11.64  11.51  11.66  10.83
Americas      11.72   11.1  10.48  10.02   9.34   8.51   7.88   7.45   7.05   6.37
Asia Pacific   5.14   4.88   4.42   4.06   3.79   3.86    3.9   4.07    4.0   3.43 

gold: KPMG generated almost 12.9 billion U.S. dollars in revenue from Europe , the Middle East , Africa and India ( EMA ) in 2019 . The revenue from this region contributed to the accounting firm achieving a record annual revenue of 29.75 billion U.S. dollars worldwide in 2019 . Europe a key market for KPMG KPMG reported growth across all three geographic regions in 2019 , with income from the EMA and Americas accounting for more than 80 percent of the firm 's worldwide revenue .
gold_template: templateTitleSubject[0] generated almost templateValue[1][0] templateScale U.S. dollars in templateTitle[0] from Europe , the Middle East , Africa and India ( templateLabel[1][0] ) in templateValue[0][0] . The templateTitle[0] from this templateTitle[4] contributed to the accounting firm achieving a record annual templateTitle[0] of 29.75 templateScale U.S. dollars templateTitle[2] in templateValue[0][0] . Europe a key market for templateTitleSubject[0] reported growth across all templateValue[3][last] geographic regions in templateValue[0][0] , with income from the templateLabel[1][0] and templateLabel[2][0] accounting for more than 80 templateScale of the firm 's templateTitle[2] templateTitle[0] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] of templateTitleSubject[0] templateTitle[3] from templateValue[0][0] to templateValue[0][last] , templateTitle[4] templateTitle[5] . In the fiscal templateLabel[0][0] of templateValue[0][last] , templateTitleSubject[0] employed templateValue[2][last] templateScale U.S. dollars in the templateLabel[2][0] .
generated: This statistic shows the Revenue of KPMG by from 2019 to 2010 , region 2010 .  In the fiscal Year of 2010 , KPMG employed 6.37 billion U.S. dollars in the Americas .

Example 140:
titleEntities: {'Subject': ['Spain'], 'Date': ['2019']}
title: Population of Spain in 2019 by gender and autonomous community
Data:
                   0          1                    2                   3        4                 5               6               7                  8       9      10                11           12        13       14         15        16     17       18
Community  Andalusia  Catalonia  Community_of_Madrid  Region_of_Valencia  Galicia  Castile_and_Leon  Basque_Country  Canary_Islands  Castile-La_Mancha  Murcia  Aragon  Balearic_Islands  Extremadura  Asturias  Navarre  Cantabria  La_Rioja  Ceuta  Melilla
Male         4168901    3721783              3205259             2462321  1302611           1186138         1055722         1100985            1023105  747648  653825            599693       527492    487643   322403     283599    154979  42785    42498
Female       4277660    3887716              3480212             2536390  1396153           1216739         1126196         1119285            1015335  746793  670571            598883       535306    532350   330124     298350    159508  41649    41788 

gold: Andalusia , with a total number of 8.44 million inhabitants , ranked first on the list of most populous autonomous communities in Spain as of 1st July 2019 . The least populated regions of Spain were the two autonomous cities of Ceuta and Melilla , both with a population of approximately 84 thousand inhabitants that year . The population of Spain has been in decline for many years and was projected to decrease by nearly one million by 2023 .
gold_template: templateValue[0][0] , with a total number of 8.44 templateScale inhabitants , ranked first on the list of most populous templateTitle[5] communities in templateTitleSubject[0] as of 1st 2019 . The least populated regions of templateTitleSubject[0] were the two templateTitle[5] cities of templateValue[0][17] and templateValue[0][18] , both with a templateTitle[0] of approximately 84 thousand inhabitants that year . The templateTitle[0] of templateTitleSubject[0] has been in decline for many years and was projected to templateNegativeTrend templateTitle[3] nearly templateValue[1][4] templateScale templateTitle[3] 2023 .

generated_template: This statistic provides information on the average amount of time templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , sorted templateTitle[6] templateTitle[7] . As of that time , it was found that templateValue[1][0] templateScale of that templateValue[2][0] templateScale of templateLabel[2][0] templateTitle[4] had reported .
generated: This statistic provides information on the average amount of time gender in Spain 2019 , sorted community .  As of that time , it was found that 4168901 % of that 4277660 % of Female gender had reported .

Example 141:
titleEntities: {'Subject': ['Belgium', 'Instagram'], 'Date': ['2019', '2019']}
title: Most popular Belgium-based influencers on Instagram 2019
X_Axis['Instagram', 'User']: ['angele_vl_(Angèle_stage_name_of_Angèle_Van_Laeken)', 'jimmylabeeu__(Jimmy_Labeeu)', 'driesmertens__(Dries_Mertens)', 'elvis.romeo_(Roméo_Elvis)', 'amelie_lens_(Amelie_Lens)', 'gaellegd_(Gaelle_Garcia_Diaz)', 'aurelievandaelen__(Aurélie_Van_Daelen)', 'pommelinetilliere_(Pommeline_Tillière)', 'chloekitembo_(Chloe_Kitembo)', 'matthiasgeerts_(Matthias_Geerts)']
Y_Axis['Number', 'of', 'followers']: ['2488193', '1885413', '1547373', '1442010', '1139768', '1135845', '720274', '519828', '408670', '308271']

gold: In December 2019 , singer-songwriter Angèle had the most followers of all Belgian influencers on Instagram . This according to data provided by InfluencerDB . The musician had several hit songs in 2018 , including number one hit Tout Oublier in Belgium and France with her brother Romeo Elvis ( fourth in this ranking ) .
gold_template: In 2019 , singer-songwriter templateXValue[0] had the templateTitle[0] templateYLabel[1] of all Belgian templateTitle[3] on templateXLabel[0] . This according to data provided by InfluencerDB . The musician had several hit songs in 2018 , including templateYLabel[0] one hit Tout Oublier in Belgium and France with her brother Romeo Elvis ( fourth in this ranking ) .

generated_template: This statistic gives information on the templateTitle[0] templateYLabel[0] of templateTitle[2] on templateTitle[4] templateXValue[0] . As of the survey period , it was found that templateValue[2][0] templateScale of 2019 , it templateXValue[last] templateXValue[2] .
generated: This statistic gives information on the Most Number of Belgium-based on Instagram angele_vl_(Angèle_stage_name_of_Angèle_Van_Laeken) .  As of the survey period , it was found that % of 2019 , it matthiasgeerts_(Matthias_Geerts) driesmertens__(Dries_Mertens) .

Example 142:
titleEntities: {'Subject': ['South Africa'], 'Date': ['2015']}
title: Literacy rate in South Africa 2015
Data:
                  0      1      2      3      4      5      6
Year           2015   2014   2012   2011   2010   2009   2007
Adult total   94.37  94.14  93.73   93.1  92.88  92.89  88.72
Adult male     95.4  95.29  94.96  94.25  94.14  94.12  90.72
Adult female  93.41  93.07  92.59  92.05  91.71  91.77  87.04 

gold: As of 2015 , South Africa 's total literacy rate was around 94.37 percent , which means almost 95 percent of all South Africans could read and write . There is literally no definition The literacy rate measures the percentage of people aged 15 and above who can read and write . There is no common definition for what counts as being literate ; individual countries use individual assessments and standards to define it .
gold_template: As of templateValue[0][0] , templateTitleSubject[0] 's templateLabel[1][1] templateTitle[0] templateTitle[1] was around templateValue[1][0] templateScale , which means almost templateValue[2][0] templateScale of all templateTitleSubject[0] Africans could read and write . There is literally no definition The templateTitle[0] templateTitle[1] measures the templateScale of people aged 15 and above who can read and write . There is no common definition for what counts as being literate ; individual countries use individual assessments and standards to define it .

generated_template: The statistic depicts the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . The templateTitle[0] templateTitle[1] measures the templateScale of people aged 15 and above who can read and write . In templateValue[0][0] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] was around templateValue[1][0] templateScale .
generated: The statistic depicts the Literacy rate in South Africa from 2007 to 2015 .  The Literacy rate measures the percentage of people aged 15 and above who can read and write .  In 2015 , South Africa 's Literacy rate was around 94.37 % .

Example 143:
titleEntities: {'Subject': ['Annual'], 'Date': ['2019']}
title: Annual rent in the most expensive retail locations globally 2019
X_Axis['Retail', 'Locations']: ['Causeway_Bay_main_street_shops_(Hong_Kong)', 'Upper_5th_Avenue_49th-60th_Sts_(New_York)', 'New_Bond_Street_(London)', 'Avenue_des_Champs-Elysees_(Paris)', 'Via_Montenapoleone_(Milan)', 'Ginza_(Tokyo)', 'Pitt_Street_Mall_(Sydney)', 'Bahnhofstrasse_(Zurich)', 'Myeongdong_(Seoul)', 'Kohlmarkt_(Vienna)', 'Wangfujing_(Beijing)', 'Kaufinger/Neuhauser_(Munich)', 'Grafton_Street_(Dublin)', "Portal_de_L'Angel_(Barcelona)", 'Ermou_(Athens)', 'Orchard_Road_(Singapore)', 'Kalverstraat_(Amsterdam)', 'Na_Prikope_street_(Prague)', 'Stoleshnikov_(Moscow)', 'Khan_Market_(New_Delhi)']
Y_Axis['Annual', 'rent', 'in', 'U.S.', 'dollars', 'per', 'square', 'foot']: ['2745', '2250', '1714', '1478', '1447', '1251', '1076', '866', '862', '513', '471', '469', '401', '361', '361', '312', '301', '298', '288', '243']

gold: As of June 2019 , the annual rent of a retail space in the Avenue des Champs-Elysees in Paris amounted to 1,478 U.S. dollars per square foot . However , Causeway Bay in Hong Kong was the most expensive retail location worldwide . Luxury retail locations – additional information Where renters complain about high rents , top retailers see it differently .
gold_template: As of 2019 , the templateYLabel[0] templateYLabel[1] of a templateXLabel[0] space in the templateXValue[1] templateXValue[3] in Paris amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] . However , templateXValue[0] in Hong Kong was the templateTitle[2] templateTitle[3] templateXLabel[0] location worldwide . Luxury templateXLabel[0] templateXLabel[1] – additional information Where renters complain about high rents , top retailers see it differently .

generated_template: The statistic shows the distribution of templateTitleSubject[0] employees templateTitle[4] as of 2019 , templateTitleDate[0] , broken down templateTitle[8] templateXLabel[0] . The templateXValue[0] , with over templateYValue[max] templateScale of the templateYLabel[3] templateYLabel[4] of people , followed by templateXValue[1] with templateYValue[1] templateScale templateYLabel[3] .
generated: The statistic shows the distribution of Annual employees retail as of 2019 , broken down 2019 Retail .  The Causeway_Bay_main_street_shops_(Hong_Kong) , with over 2745 % of the dollars per of people , followed by Upper_5th_Avenue_49th-60th_Sts_(New_York) with 2250 million dollars .

Example 144:
titleEntities: {'Subject': ['Ireland'], 'Date': ['2024']}
title: National debt of Ireland 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'billion', 'U.S.', 'dollars']: ['224.31', '228.22', '230.52', '231.85', '227.68', '229.83', '226.92', '221.59', '220.87', '221.87', '223.78']

gold: This statistic shows the national debt of Ireland from 2014 to 2018 , with projections up until 2024 . In 2018 , the national debt of Ireland was around 226.92 billion U.S. dollars . For comparison , the Greek debt amounted to approximately 303 billion euros that same year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was around templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] . For comparison , the Greek templateYLabel[1] amounted to approximately 303 templateScale euros that same templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The amount of the templateYLabel[1] of the templateTitleSubject[0] amounted to around templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[6] .
generated: The statistic shows the National debt of the Ireland from 2014 to 2018 , with projections up until 2024 .  The amount of the debt of the Ireland amounted to around 226.92 billion U.S. dollars in 2018 .

Example 145:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2019']}
title: Population of Luxembourg 2019 , by age
X_Axis['Year']: ['0-4_years', '5-9_years', '10-14_years', '15-19_years', '20-24_years', '25-29_years', '30-34_years', '35-39_years', '40-44_years', '45-49_years', '50-54_years', '55-59_years', '60-64_years', '65-69_years', '70-74_years', '75-79_years', '80-84_years', '85-89_years', '90-94_years', '95_years_and_older']
Y_Axis['Number', 'of', 'inhabitants']: ['32592', '33355', '32660', '33206', '38038', '46125', '47863', '48111', '46177', '46183', '46695', '41130', '33431', '26712', '21074', '16260', '12270', '8069', '3177', '766']

gold: In 2019 , the population of Luxembourg amounted to nearly 614,000 . The largest age groups were formed by those between 30 and 34 years old ( 47,863 people ) and between 35 and 39 years old ( 48,111 people ) . By comparison : only 766 people were 95 years or older .
gold_template: In templateTitleDate[0] , the templateTitle[0] of templateTitleSubject[0] amounted to nearly 614,000 . The largest templateTitle[4] groups were formed templateTitle[3] those between 30 and 34 templateXValue[0] old ( templateYValue[6] people ) and between 35 and 39 templateXValue[0] old ( templateYValue[max] people ) . templateTitle[3] comparison : only templateYValue[min] people were templateXValue[last] templateXValue[0] or templateXValue[last] .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateXValue[last] , there were just over templateYValue[max] templateScale of the templateTitle[0] templateYLabel[0] during the templateTitleSubject[1] .
generated: This statistic shows the Population of the Luxembourg 2019 in the Luxembourg from 2019 to .  In 95_years_and_older , there were just over 48111 % of the Population Number during the Luxembourg .

Example 146:
titleEntities: {'Subject': ['Washington Redskins', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Washington Redskins ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['3400', '3100', '3100', '2950', '2850', '2400', '1700', '1600', '1555', '1550', '1550', '1538', '1467', '1423', '1264', '1104', '952', '845']

gold: This graph depicts the franchise value of the Washington Redskins of the National Football League from 2002 to 2019 . In 2019 , the franchise value came to around 3.4 billion U.S. dollars . The Washington Redskins are owned by Daniel Snyder who bought the franchise for 750 million U.S. dollars in 1999 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Daniel Snyder who bought the templateYLabel[0] for 750 templateScale templateYLabel[3] templateYLabel[4] in 1999 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Terry and Kim Pegula .
generated: This graph depicts the Franchise value of the Washington Redskins of the National Football League from 2002 to 2019 .  In 2019 , the Franchise value came to 3400 million U.S. dollars .  The Washington Redskins are owned by Terry and Kim Pegula .

Example 147:
titleEntities: {'Subject': ['Ralph Lauren'], 'Date': ['2009', '2019']}
title: Polo Ralph Lauren 's workforce worldwide 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'employees']: ['24300', '23500', '23300', '26000', '25000', '23000', '23000', '25000', '24000', '19000', '17000']

gold: This statistic shows the number of employees of Polo Ralph Lauren worldwide from 2009 to 2019 . The New York-based fashion company Polo Ralph Lauren had approximately 24,300 employees worldwide as of 2019.Ralph Lauren CorporationThe company began in 1967 and is an internationally renowned brand . The company got its start by selling creatively designed neckties .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitle[0] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . The New York-based fashion company templateTitle[0] templateTitleSubject[0] had approximately templateYValue[0] templateYValue[idxmax(X)] templateTitle[5] as of 2019.Ralph templateTitleSubject[0] CorporationThe company began in 1967 and is an internationally renowned brand . The company got its start by selling creatively designed neckties .

generated_template: This statistic shows the templateYLabel[0] of people employed by agricultural company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had a total of templateYValue[idxmax(X)] templateYLabel[1] .
generated: This statistic shows the Number of people employed by agricultural company Ralph Lauren from 2009 to 2019 .  In 2019 , Ralph Lauren had a total of 24300 employees .

Example 148:
titleEntities: {'Subject': ['Iran'], 'Date': ['2018']}
title: Urbanization in Iran 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['74.9', '74.39', '74.88', '73.36', '72.83', '72.3', '71.76', '71.2', '70.63', '70.05', '69.46']

gold: This statistic shows the degree of urbanization in Iran from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 74.9 percent of Iran 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Iran from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 74.9 % of Iran 's total population lived in urban areas and cities .

Example 149:
titleEntities: {'Subject': ['Greece'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Greece 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'rate', 'compared', 'to', 'previous', 'year']: ['0.93', '0.9', '1.35', '1.71', '2.22', '1.98', '1.93', '1.51', '-0.19', '-0.44', '0.74']

gold: The statistic depicts Greece 's gross domestic product ( GDP ) growth rate from 2014 to 2018 , with projections up until 2024 . GDP refers to the total market value of all goods and services that are produced within a country per year . It is an important indicator of the economic strength of a country .
gold_template: The statistic depicts templateTitleSubject[0] 's templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .

generated_template: The statistic shows the templateYLabel[1] templateYLabel[2] of the real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the growth rate of the real Gross domestic product ( GDP ) in the Greece from 2014 to 2018 , with projections up until 2024 .  GDP refers to the total market value of all goods and services that are produced within a country per Year .  It is an important indicator of the economic strength of a country .

Example 150:
titleEntities: {'Subject': ['Global'], 'Date': ['2017', '2022']}
title: Global mobile video traffic 2017 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Traffic', 'in', 'PB', 'per', 'month']: ['60889', '42734', '29149', '19279', '12051', '6821']

gold: This statistic provides information on the level of global mobile video traffic from 2017 to 2022 . In 2018 , global mobile video traffic amounted to 12,051 petabytes per month and is expected to multiply to 60,889 PB per month in 2022 .
gold_template: This statistic provides information on the level of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[4] petabytes templateYLabel[2] templateYLabel[3] and is expected to multiply to templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . According to the source , the templateTitle[0] of the templateTitleSubject[0] was templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Traffic of per month of the mobile video traffic 2017 from to 2022 .  According to the source , the Global of the Global was 6821 million per month .

Example 151:
titleEntities: {'Subject': ['Grand Teton National Park'], 'Date': ['2008', '2019']}
title: Number of visitors to Grand Teton National Park in the U.S. 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'visitors', 'in', 'millions']: ['3.41', '3.49', '3.32', '3.27', '3.15', '2.79', '2.69', '2.71', '2.59', '2.67', '2.58', '2.49']

gold: This statistic shows the number of recreational visitors to Grand Teton National Park in the United States from 2008 to 2019 . The number of visitors to Grand Teton National Park amounted to approximately 3.41 million in 2019 .
gold_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[1] to templateTitleSubject[0] National templateTitleSubject[0] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] to templateTitleSubject[0] National templateTitleSubject[0] amounted to approximately templateYValue[0] templateYValue[idxmax(X)] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[1] to the templateTitleSubject[0] Memorial in the templateTitle[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] to the templateTitleSubject[0] Memorial amounted to approximately templateYValue[0] templateYValue[idxmax(X)] in templateXValue[max] .
generated: This statistic shows the Number of recreational visitors to the Grand Teton National Park Memorial in the Park from 2008 to 2019 .  The Number of visitors to the Grand Teton National Park Memorial amounted to approximately 3.41 in 2019 .

Example 152:
titleEntities: {'Subject': ['Regional'], 'Date': ['2017']}
title: Regional distribution of social network users worldwide 2017
X_Axis['Country']: ['East_Asia', 'Southeast_Asia', 'South_America', 'South_Asia', 'North_America', 'Western_Europe', 'Eastern_Europe', 'Africa', 'Central_America', 'Middle_East', 'Central_Asia', 'Oceania']
Y_Axis['Share', 'of', 'social', 'media', 'users']: ['33', '11', '9', '9', '8', '8', '7', '6', '4', '3', '1', '1']

gold: This statistic gives information on the distribution of worldwide social media users as of January 2017 , by global region . During that period of time , 33 percent of worldwide social media users were located in East Asia , followed by 11 percent of users in Southeast Asia . Social media worldwide – additional information Social media is a global phenomenon .
gold_template: This statistic gives information on the templateTitle[1] of templateTitle[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] as of 2017 , by global region . During that period of time , templateYValue[max] templateScale of templateTitle[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] were located in templateXValue[0] , followed by templateYValue[1] templateScale of templateYLabel[3] in templateXValue[1] templateXValue[0] . templateYLabel[1] templateYLabel[2] templateTitle[5] – additional information templateYLabel[1] templateYLabel[2] is a global phenomenon .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . According to the source , the people , the templateXValue[0] was the templateTitle[2] , with a total of templateYValue[max] templateYLabel[1] , followed by templateXValue[1] with templateYValue[1] templateScale .
generated: This statistic shows the Share of the distribution social network users in 2017 , Country .  According to the source , the people , the East_Asia was the social , with a total of 33 social , followed by Southeast_Asia with 11 million .

Example 153:
titleEntities: {'Subject': ['Greece'], 'Date': ['2018']}
title: Infant mortality rate in Greece 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['3.6', '3.7', '3.8', '3.8', '3.6', '3.5', '3.4', '3.3', '3.3', '3.3', '3.3']

gold: The statistic shows the infant mortality rate in Greece from 2008 to 2018 . In 2018 , the infant mortality rate in Greece was at about 3.6 deaths per 1,000 live births .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality rate in Greece from 2008 to 2018 .  In 2018 , the Infant mortality rate in Greece was at about 3.6 Deaths per 1,000 live births .

Example 154:
titleEntities: {'Subject': ['NBA', 'Facebook'], 'Date': ['2019']}
title: NBA teams - number of Facebook fans 2019
X_Axis['Nba', 'Team']: ['NBA', 'Los_Angeles_Lakers', 'Chicago_Bulls', 'Miami_Heat', 'Houston_Rockets', 'Golden_State_Warriors', 'Boston_Celtics', 'Cleveland_Cavaliers', 'Sacramento_Kings', 'Oklahoma_City_Thunder', 'San_Antonio_Spurs', 'New_York_Knicks', 'Dallas_Mavericks', 'Los_Angeles_Clippers', 'Indiana_Pacers', 'Orlando_Magic', 'Brooklyn_Nets', 'Toronto_Raptors', 'Portland_Trailblazers', 'Denver_Nuggets', 'Minnesota_Timberwolves', 'Phoenix_Suns', 'Milwaukee_Bucks', 'Utah_Jazz', 'Memphis_Grizzlies', 'Detroit_Pistons', 'New_Orleans_Pelicans', 'Philadelphia_76ers', 'Charlotte_Hornets', 'Atlanta_Hawks', 'Washington_Wizards']
Y_Axis['Facebook', 'fans', 'in', 'millions']: ['38.05', '21.57', '17.74', '15.27', '12.56', '11.72', '9.01', '8.59', '7.51', '7.07', '7.05', '5.83', '4.37', '3.78', '3.25', '2.75', '2.7', '2.68', '2.46', '2.01', '1.89', '1.88', '1.82', '1.81', '1.8', '1.79', '1.75', '1.75', '1.74', '1.61', '1.55']

gold: With 21.57 million fans , the Los Angeles Lakers have the most followed National Basketball Association team account on Facebook . Bringing up the rear is the official account of the Washington Wizards , which has 1.55 million fans . The Facebook presence of the NBA itself has 38.05 million fans .
gold_template: With templateYValue[1] templateScale templateYLabel[1] , the templateXValue[1] Lakers have the most followed National Basketball Association templateXLabel[1] account on templateYLabel[0] . Bringing up the rear is the official account of the templateXValue[last] , which has templateYValue[min] templateScale templateYLabel[1] . The templateYLabel[0] presence of the templateXValue[0] itself has templateYValue[max] templateScale templateYLabel[1] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] . The templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] at templateYValue[max] templateScale templateYLabel[4] dollars .
generated: The statistic shows the NBA teams number Facebook in the fans in 2019 .  The NBA had the highest Facebook of fans at 38.05 millions dollars .

Example 155:
titleEntities: {'Subject': ['Chained'], 'Date': ['2000', '2019']}
title: Chained consumer price index of all urban consumers 2000 to 2019
X_Axis['December', 'value']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Chained', 'Consumer', 'Price', 'Index', '(1999=100)']: ['144.73', '141.7', '139.55', '137.22', '134.79', '134.21', '133.51', '131.77', '129.84', '126.14', '124.54', '121.56', '121.3', '117.0', '114.4', '111.2', '107.8', '106.0', '103.9', '102.6']

gold: This statistic shows the unadjusted chained Consumer Price Index in the United States from 2000 to 2019 , at year-end . In December 2019 , the chained consumer price index stood at 144.73 , reflecting a 44.73 percent increase from the base year of 1999 . The average wages garnered in select countries around the world based on purchasing power can be accessed here .
gold_template: This statistic shows the unadjusted templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the country from templateXValue[last] to templateXValue[0] , at year-end . In templateXLabel[0] templateXValue[0] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] stood at templateYValue[max] , reflecting a 44.73 templateScale templatePositiveTrend from the base year of 1999 . The average wages garnered in select countries around the world based on purchasing power can be accessed here .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] templateYLabel[1] in the country was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Chained of Price Index of the Chained consumer price index all Chained from 2000 to 2019 .  In 2019 , the Chained of Consumer in the country was 144.73 Price Index .

Example 156:
titleEntities: {'Subject': ['UK'], 'Date': ['2012', '2015']}
title: Health/Fitness club companies - Fitness First UK revenue 2012 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['676.52', '773.36', '842.63', '919.98']

gold: The statistic depicts the revenue of the Fitness First UK company from 2012 to 2015 . In 2013 , the health club chain generated a revenue of 842.63 million U.S. dollars . In the same year , the UK based company operated 378 clubs .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitle[3] templateTitle[4] templateTitleSubject[0] company from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the health templateTitle[1] chain generated a templateYLabel[0] of templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] . In the same templateXLabel[0] , the templateTitleSubject[0] based company operated 378 clubs .

generated_template: The statistic presents the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the source of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic presents the Revenue of the UK from 2012 to 2015 .  In 2015 , the source of the Major League Baseball franchise amounted to 676.52 million U.S. dollars .

Example 157:
titleEntities: {'Subject': ['Michigan'], 'Date': ['2018']}
title: Population of Michigan , by race and ethnicity 2018
X_Axis['Race']: ['White_alone', 'Black_or_African_American_alone', 'Hispanic_or_Latino_(of_any_race)', 'Asian_alone', 'Two_or_more_races', 'American_Indian_and_Alaska_Native_alone', 'Some_other_race_alone', 'Native_Hawaiian_and_Other_Pacific_Islander_alone']
Y_Axis['Number', 'of', 'residents']: ['7476047', '1363183', '517381', '324358', '250796', '46417', '14443', '3290']

gold: This statistic shows the population of Michigan , distinguished by race and Hispanic origin in 2018 . In 2018 , about 1.36 million people in Michigan were Black or African American .
gold_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] , distinguished templateTitle[2] templateXValue[6] and templateXValue[2] origin in templateTitleDate[0] . In templateTitleDate[0] , about templateYValue[1] templateScale people in templateTitleSubject[0] were templateXValue[1] or templateXValue[1] .

generated_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] , distinguished templateTitle[2] templateXValue[5] and templateXValue[2] origin in templateTitleDate[0] . In templateTitleDate[0] , about templateYValue[1] templateScale people in templateTitleSubject[0] were templateXValue[1] or templateXValue[1] .
generated: This statistic shows the Population of Michigan , distinguished by American_Indian_and_Alaska_Native_alone and Hispanic_or_Latino_(of_any_race) origin in 2018 .  In 2018 , about 1363183 million people in Michigan were Black_or_African_American_alone or .

Example 158:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Number of married people in Canada , by age group 2018
X_Axis['Age', 'group']: ['15_to_19_years', '20_to_24_years', '25_to_29_years', '30_to_34_years', '35_to_39_years', '40_to_44_years', '45_to_49_years', '50_to_54_years', '55_to_59_years', '60_to_64_years', '65_to_69_years', '70_to_74_years', '75_to_79_years', '80_to_84_years', '85_to_89_years', '90_to_94_years', '95_to_99_years', '100_years_and_over']
Y_Axis['Number', 'of', 'people']: ['2793', '84864', '510931', '1076088', '1319123', '1347563', '1379171', '1475369', '1623642', '1526827', '1292505', '1011113', '639487', '373961', '182464', '53439', '8642', '636']

gold: This statistic shows the number of married people in Canada in 2018 , distinguished by age group . In 2018 , 2,793 married Canadians were between 15 and 19 years of age .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] in templateTitleDate[0] , distinguished templateTitle[4] templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , templateYValue[0] templateTitle[1] Canadians were between templateXValue[0] and templateXValue[0] of templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] ranking of templateXLabel[0] templateXLabel[1] in templateTitleDate[0] , sorted templateTitle[5] templateXLabel[1] . In that year , templateYValue[max] templateScale of the templateYLabel[1] in templateYLabel[2] was living in templateXValue[0] .
generated: This statistic shows the Number ranking of Age group in 2018 , sorted age group .  In that year , 1623642 % of the people in was living in 15_to_19_years .

Example 159:
titleEntities: {'Subject': ['England'], 'Date': ['2013', '2013']}
title: Consumer preference : fiction versus non-fiction books in England 2013
X_Axis['Response']: ['Mixture_of_both', 'Fiction', 'Non-fiction', 'Neither']
Y_Axis['Share', 'of', 'respondents']: ['44', '26', '22', '8']

gold: This statistic displays the preferred book types ( fiction versus non-fiction ) among respondents in England in 2013 . Of respondents , 26 percent said they preferred fiction , while 44 percent reported liking a mixture of fiction and non-fiction .
gold_template: This statistic displays the preferred book types ( templateXValue[1] templateTitle[3] templateXValue[2] ) among templateYLabel[1] in templateTitleSubject[0] in templateTitleDate[0] . Of templateYLabel[1] , templateYValue[1] templateScale said they preferred templateXValue[1] , while templateYValue[max] templateScale reported liking a templateXValue[0] of templateXValue[1] and templateXValue[2] .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] adult Americans on the templateTitle[0] of templateTitle[1] in the templateTitleSubject[1] as of 2019 . During the survey , it was found templateXValue[1] templateYValue[min] templateScale of templateYLabel[1] stated that they were templateXValue[last] .
generated: This statistic shows the results of a survey among England adult Americans on the Consumer of preference in the England as of 2019 .  During the survey , it was found Fiction 8 % of respondents stated that they were Neither .

Example 160:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Argentina 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['579.62', '557.18', '518.66', '481.84', '443.25', '445.47', '519.49', '642.93', '556.77', '642.46', '563.61', '611.47', '579.67', '527.64', '424.73', '334.63', '363.55', '287.92', '232.89', '199.27', '164.92', '138.15', '108.73', '291.74', '308.49', '307.67', '324.24', '317.55', '295.12', '280.08', '279.15', '256.37', '247.99', '205.52', '153.21', '88.57', '138.04', '117.85', '114.95', '95.59', '126.56']

gold: The statistic shows gross domestic product ( GDP ) in Argentina from 1984 to 2018 , with projections up until 2024 . The gross domestic product denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Argentina from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 161:
titleEntities: {'Subject': ['India'], 'Date': ['2011']}
title: Old-age dependency ratio by state and union territory in India 2011
X_Axis['State', 'And', 'Union', 'Territory']: ['Kerala', 'Punjab', 'Tamil_Nadu', 'Maharashtra', 'Andaman_&_Nicobar_Islands', 'Odisha', 'Uttarakhand', 'Karnataka', 'Bihar', 'Haryana', 'Uttar_Pradesh', 'Madhya_Pradesh', 'West_Bengal', 'Chattisgarh', 'Rajasthan', 'Jharkhand', 'Gujarat', 'Jammu_and_Kashmir', 'Assam', 'Delhi']
Y_Axis['Old-age', 'dependency', 'ratio']: ['19.6', '16.1', '15.8', '15.7', '15.4', '15.4', '14.9', '14.8', '14.2', '14.1', '13.9', '13.4', '13.2', '13.1', '13', '12.7', '12.6', '12.5', '11', '10.4']

gold: The statistic illustrates the leading states and union territories in India with the highest old-age dependency ratio in 2011 . In that year , the state of Punjab had an old-age dependency ratio of just over 16 percent .
gold_template: The statistic illustrates the leading states and templateXLabel[1] territories in templateTitleSubject[0] with the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] . In that year , the templateXLabel[0] of templateXValue[1] had an templateYLabel[0] templateYLabel[1] templateYLabel[2] of just over templateYValue[1] templateScale .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] . The templateTitle[0] templateYLabel[0] of the templateTitleSubject[0] in templateTitleDate[0] was templateXValue[0] with over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Old-age dependency ratio by the United Kingdom ( India ) in 2011 .  The Old-age Old-age of the India in 2011 was Kerala with over 19.6 % ratio .

Example 162:
titleEntities: {'Subject': ['TV U.S.'], 'Date': ['2017']}
title: Reasons for watching reality TV in the U.S. 2017 , by age
Data:
                      0              1                           2                                                             3             4                                                5                                   6      7              8           9
Race   I_like_the_drama  It's_mindless  It's_good_background_noise  It_helps_me_forget_about_real_issues_in_my_life/in_the_world  I'm_invested  It_helps_me_stay_informed_about_popular_culture  To_connect_with_likeminded_viewers  Other  None_of_these  Don't_know
18-24                28             17                          17                                                            13            12                                               11                                   7      6             24           8
25-34                26             19                          13                                                            17            10                                               14                                   5      6             34           5
35-44                18             14                          12                                                            14             6                                               11                                   7      4             42           5
45-54                17             12                          10                                                            12             4                                                8                                   3      8             48           7
55+                  11              8                           8                                                            11             2                                                9                                   2      7             59           4 

gold: The main reason given for watching reality TV by U.S. adults was that respondents liked the drama , with 28 percent of 18 to 29-year-olds citing this as the primary appeal of the genre . Other reasons for viewing reality television included it being ‘ mindless ' or thought of as ‘ good background noise ' _ , and some survey participants agreed that reality TV helps them forget about real issues in their lives . Attitudes to reality TV in the U.S .
gold_template: The main reason given templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] templateTitle[7] templateTitleSubject[0] adults was that respondents liked the templateValue[0][0] , templateValue[0][6] templateValue[1][max] templateScale of templateValue[2][idxmax(1)] to 29-year-olds citing this as the primary appeal of the genre . templateValue[0][7] templateTitle[0] templateTitle[1] viewing templateTitle[3] television included it being ‘ templateValue[0][1] ' or thought of as ‘ templateValue[0][2] noise ' _ , and some survey participants agreed that templateTitle[3] templateTitleSubject[0] templateValue[0][3] them templateValue[0][3] real templateValue[0][3] in their lives . Attitudes to templateTitle[3] templateTitleSubject[0] in the templateTitle[5] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitle[4] as of 2019 , sorted templateTitle[6] templateTitle[7] . During the survey , it was found that templateValue[1][max] templateScale of respondents aged templateLabel[1][0] templateLabel[1][1] stated that they had a templateTitle[0] templateTitle[1] of templateValue[1][last] templateScale .
generated: This statistic gives information on the Reasons for watching of the TV as of 2019 , sorted 2017 by .  During the survey , it was found that 28 % of respondents aged 18-24 stated that they had a Reasons for of 8 % .

Example 163:
titleEntities: {'Subject': ['UK'], 'Date': ['2019']}
title: Garden , plants and flowers : quarterly purchase spend in the UK 2013 to 2019
X_Axis['Quarter']: ['Q2_2019', 'Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018', 'Q4_2017', 'Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015', 'Q3_2015', 'Q2_2015', 'Q1_2015', 'Q4_2014', 'Q3_2014', 'Q2_2014', 'Q1_2014', 'Q4_2013', 'Q3_2013', 'Q2_2013', 'Q1_2013']
Y_Axis['Expenditure', 'in', 'million', 'GBP']: ['1471', '1480', '1483', '1484', '1468', '1399', '1453', '1431', '1466', '1319', '1266', '1274', '1203', '1186', '1125', '1111', '1054', '1030', '1091', '1087', '1092', '1039', '947', '903', '911', '925']

gold: This statistic shows consumer expenditure on garden , plants and flowers in the United Kingdom ( UK ) from 1st quarter 2013 to 2nd quarter 2019 , based on volume . In the 2nd quarter of 2019 , UK households purchased 1.47 billion British pounds worth of plants , flowers and other garden goods .
gold_template: This statistic shows consumer templateYLabel[0] on templateTitle[0] , templateTitle[1] and templateTitle[2] in the United Kingdom ( templateTitleSubject[0] ) from 1st templateXLabel[0] templateXValue[22] to 2nd templateXLabel[0] templateXValue[0] , based on volume . In the 2nd templateXLabel[0] of templateXValue[0] , templateTitleSubject[0] households purchased templateYValue[0] templateScale British pounds worth of templateTitle[1] , templateTitle[2] and other templateTitle[0] goods .

generated_template: This statistic shows consumers ' templateTitle[0] templateTitle[1] templateYLabel[0] on the templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateXValue[0] . In the most recently reported templateXLabel[0] , templateYLabel[0] on templateTitle[1] sector amounted to over templateYValue[max] templateScale British pounds .
generated: This statistic shows consumers ' Garden plants Expenditure on the UK from the first Quarter of 2019 to the fourth Quarter of Q2_2019 .  In the most recently reported Quarter , Expenditure on plants sector amounted to over 1484 million British pounds .

Example 164:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2000', '2017']}
title: Great Britain : Divorce rate 2000 to 2017
Data:
                      0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17
Year               2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  2000
England and Wales   8.4   8.9   8.5   9.3   9.8  10.0  10.1  10.2   9.8  10.5  11.1  11.5  12.2  13.3  13.3  12.8  12.9  12.7
Scotland              0     0     0     0     0     0     0     0     0  11.1  12.2  12.4  10.3  10.5  10.1    10   9.7  10.3 

gold: This statistic shows the divorce rate in Great Britain from 2000 to 2017 . The divorce rate is defined as 'people divorcing per 1,000 married male/female population aged 16 and over ' _ . After increasing year on year from 2000 to 2004 ( a rise of 1.3 ) , the divorce rate dropped consistently until 2009 .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . The templateTitle[2] templateTitle[3] is defined as 'people divorcing per 1,000 married male/female population aged 16 and over ' _ . After templatePositiveTrend templateLabel[0][0] on templateLabel[0][0] from templateValue[0][last] to templateValue[0][13] ( a rise of 1.3 ) , the templateTitle[2] templateTitle[3] templateNegativeTrend consistently until templateValue[0][8] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitleSubject[0] amounted to approximately templateValue[1][0] templateScale .
generated: This statistic shows the Great Britain Divorce of the rate in the Great Britain from 2000 to 2017 .  In 2017 , the Great Britain Divorce of the Great Britain amounted to approximately 8.4 million .

Example 165:
titleEntities: {'Subject': ['Omnicom Group'], 'Date': ['2006', '2018']}
title: Omnicom Group 's revenue 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['15.29', '15.27', '15.42', '15.13', '15.32', '14.58', '14.22', '13.87', '12.54', '11.72', '13.36', '12.69', '11.38']

gold: This statistic presents the global revenue of U.S. advertising network Omnicom from 2006 to 2018 . In 2018 , the Omnicom Group generated a global revenue of 15.29 billion U.S. dollars , slight increase from 15.27 billion a year earlier . Here you can find out which of Omnicom 's operating sectors generates the most revenue .
gold_template: This statistic presents the global templateYLabel[0] of templateYLabel[2] advertising network templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] generated a global templateYLabel[0] of templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] , slight templatePositiveTrend from templateYValue[1] templateScale a templateXLabel[0] earlier . Here you can find out which of templateTitleSubject[0] templateTitle[2] operating sectors generates the most templateYLabel[0] .

generated_template: The statistic presents the global templateYLabel[0] generated by the templateYLabel[2] advertising giant templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[2] global templateYLabel[0] was templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateScale a templateXLabel[0] earlier .
generated: The statistic presents the global Revenue generated by the U.S. advertising giant Omnicom Group from 2006 to 2018 .  In 2018 , Omnicom Group 's global Revenue was 15.42 billion U.S. dollars , up from 15.27 billion a Year earlier .

Example 166:
titleEntities: {'Subject': ['National Basketball Association'], 'Date': ['2019']}
title: National Basketball Association highest-paid players ( salaries & endorsements ) 2019
Data:
                                               0                                      1                                     2                                          3                               4                                  5                                        6                                        7                                8                                    9
Player        LeBron_James_(Cleveland_Cavaliers)  Stephen_Curry_(Golden_State_Warriors)  Kevin_Durant_(Golden_State_Warriors)  Russell_Westbrook_(Oklahoma_City_Thunder)  James_Harden_(Houston_Rockets)  Chris_Paul_(Los_Angeles_Clippers)  Giannis_Antetokounmpo_(Milwaukee_Bucks)  Damian_Lillard_(Portland_Trail_Blazers)  Blake_Griffin_(Detroit_Pistons)  Paul_George_(Oklahoma_City_Thunder)
Salary                                      35.7                                   37.5                                  30.0                                       35.7                            30.4                               35.7                                     24.2                                     28.0                             32.1                                 30.6
Endorsements                                  53                                     42                                    35                                         18                              17                                  8                                       19                                       13                                7                                    8 

gold: The graph shows the top-ten NBA players in terms of earnings ( salary & endorsement deals ) in 2019 . LeBron James was the top-earning player in the National Basketball Association in 2019 , with a salary of 35.7 million U.S. dollars and around 53 million U.S. dollars in off-court income . Highest paid NBA players – additional information NBA players have two main sources of revenues : salary from the team and endorsements .
gold_template: The graph shows the top-ten NBA templateTitle[4] in terms of earnings ( templateLabel[1][0] templateTitle[6] endorsement deals ) in templateTitleDate[0] . templateValue[0][0] was the top-earning templateLabel[0][0] in the templateTitleSubject[0] Association in templateTitleDate[0] , with a templateLabel[1][0] of templateValue[1][0] templateScale U.S. dollars and around templateValue[2][0] templateScale U.S. dollars in off-court income . Highest paid NBA templateTitle[4] – additional information NBA templateTitle[4] have two main sources of revenues : templateLabel[1][0] from the team and templateTitle[7] .

generated_template: The statistic shows the results of a survey concerning the leading highest-paid templateTitleSubject[0] in templateTitleDate[0] . templateValue[0][0] earned templateValue[1][0] templateScale U.S. dollars in the templateTitle[4] and templateValue[2][0] templateScale U.S. dollars in templateTitleDate[0] .
generated: The statistic shows the results of a survey concerning the leading highest-paid National Basketball Association in 2019 .  LeBron_James_(Cleveland_Cavaliers) earned 35.7 million U.S. dollars in the players and 53 million U.S. dollars in 2019 .

Example 167:
titleEntities: {'Subject': ['Jordan'], 'Date': ['2019']}
title: Youth unemployment rate in Jordan in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['36.68', '37.24', '34.86', '35.63', '30.9', '28.43', '29.87', '28.84', '30.03', '28.88', '29.12', '28.51', '29.04', '30.45', '31.75', '31.37', '31.06', '32.62', '31.08', '29.4', '29.65']

gold: The statistic shows the youth unemployment rate in Jordan from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Jordan was at 36.68 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Youth unemployment rate in Jordan from 1999 and 2019 .  According to the source , the data are ILO estimates .  In 2019 , the estimated Youth unemployment rate in Jordan was at 36.68 % .

Example 168:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Gender distribution of newspaper journalists in the U.S. 2017 , by publication
Data:
                   0                  1                        2                  3                      4                   5                6              7                  8        9                   10                       11                    12                         13
Newspaper  USA_Today  Chicago_Sun_Times  The_Wall_Street_Journal  Houston_Chronicle  San_Jose_Mercury_News  The_New_York_Times  Chicago_Tribune  New_York_Post  Los_Angeles_Times  Newsday  The_Washington_Post  The_Dallas_Morning_News  The_Arizona_Republic  The_Philadelphia_Inquirer
Female            31                 32                       36                 38                     38                  41               42             42                 43       43                   44                       44                    46                         48
Male              69                 68                       64                 62                     62                  59               58             58                 57       57                   56                       56                    54                         52 

gold: The statistic shows the gender breakdown of journalists employed at selected newspapers in the Unites States as of November 2017 . According to the report , 41 percent of the journalists reporting for the New York Times were female as of the date of survey , and 48 percent of journalists at The Philadelphia Inquirer were women . The publication with the lowest share of female journalists was USA Today with 31 percent of women working for the newspaper in that role .
gold_template: The statistic shows the templateTitle[0] breakdown of templateTitle[3] employed at selected newspapers in the Unites States as of 2017 . According to the report , templateValue[1][5] templateScale of the templateTitle[3] reporting for the templateValue[0][5] Times were templateLabel[1][0] as of the date of survey , and templateValue[1][last] templateScale of templateTitle[3] at The templateValue[0][13] were women . The templateTitle[7] with the lowest share of templateLabel[1][0] templateTitle[3] was templateValue[0][0] with templateValue[1][0] templateScale of women working for the templateLabel[0][0] in that role .

generated_template: The statistic shows the templateTitle[5] of adults in the templateTitleSubject[0] who have used templateTitle[1] templateTitle[2] templateTitleDate[0] , templateTitle[4] templateLabel[0][0] . According to the survey period , it was found that templateValue[1][0] templateScale of templateLabel[2][0] respondents said that they had a templateLabel[2][0] .
generated: The statistic shows the 2017 of adults in the U.S. who have used distribution newspaper 2017 , U.S. Newspaper .  According to the survey period , it was found that 31 % of Male respondents said that they had a Male .

Example 169:
titleEntities: {'Subject': ['Nevada'], 'Date': ['1965', '2018']}
title: Number of slot machines and other mobile gaming devices in casinos Nevada 1965 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965']
Y_Axis['Number', 'of', 'slots', 'in', 'thousands']: ['164.1', '165.0', '167.69', '171.1', '175.46', '179.84', '182.57', '187.37', '191.45', '206.01', '213.8', '182.18', '148.52', '104.0', '80.96', '48.76', '35.17', '25.45']

gold: In the U.S. state of Nevada , there were approximately 164.1 thousand slot machines and other mobile gaming devices in casinos in 2018 . Since 1965 , this figure had been steadily increasing up until the year 2000 in which it also reached its peak with around 213 thousand slot machines and other mobile gaming devices in casinos . From 2000 on , the number of casino gaming devices in Nevada has been going down continuously .
gold_template: In the U.S. state of templateTitleSubject[0] , there were approximately templateYValue[0] thousand templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] in templateXValue[max] . Since templateXValue[min] , this figure had been steadily templatePositiveTrend up until the templateXLabel[0] templateXValue[10] in which it also reached its peak with around 213 thousand templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . From templateXValue[10] on , the templateYLabel[0] of casino templateTitle[5] templateTitle[6] in templateTitleSubject[0] has been going down continuously .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[2] templateYLabel[3] templateTitle[7] of the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of the templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number slots of the thousands casinos of the Number from 1965 to 2018 .  In 2018 , the Number of the Nevada was 164.1 thousands .

Example 170:
titleEntities: {'Subject': ['BRIC'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in the BRIC countries 2024
Data:
               0         1         2         3         4         5         6         7        8        9        10
Year        2024      2023      2022      2021      2020      2019      2018      2017     2016     2015      2014
Brazil  10606.46  10167.44    9738.0   9344.11   8955.65   8796.91   8958.58   9926.29  8751.18  8845.67  12175.96
China   14811.79  13723.67  12718.79   11773.1   10872.5  10098.87   9580.24    8677.4  8115.83  8166.76   7701.69
India    3209.67   2965.51   2737.15   2529.34   2338.12   2171.64   2037.69   2014.01  1761.63  1639.69   1610.36
Russia  13314.54  12728.11  12183.61  11704.55  11305.12  11162.65  11289.44  10750.81  8722.89  9261.68   14069.1 

gold: The statistic shows gross domestic product ( GDP ) per capita in the BRIC countries from 2014 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateTitle[3] ) templateTitle[4] templateTitle[5] in the templateTitleSubject[0] templateTitle[7] from templateValue[0][last] to templateValue[0][6] , with projections up until templateValue[0][0] . templateTitle[3] is the total value of all goods and services produced in a country in a templateLabel[0][0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][6] , with projections up until templateValue[0][0] . In templateValue[0][6] , the average templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateValue[1][6] templateScale compared to the previous templateLabel[0][0] .
generated: The statistic shows the Gross domestic product GDP in the BRIC from 2014 to 2018 , with projections up until 2024 .  In 2018 , the average Gross domestic in BRIC amounted to about 8958.58 million compared to the previous Year .

Example 171:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global internet usage rate 2019 , by gender and region
Data:
              0             1     2            3             4       5
Country  Europe  The_Americas   CIS  Arab_States  Asia_Pacific  Africa
Male       84.9          77.6  73.6         58.5          54.6    33.8
Female     80.3          76.8    71         44.2          41.3    22.6 

gold: There are still vast disparities between the internet access rates of male and female online users in global regions worldwide . In 2019 , 22.6 percent of Africa 's female population had online access , compared to 33.8 percent of men . In the Americas , the share of female online access was 76.8 percent .
gold_template: There are still vast disparities between the templateTitle[1] access rates of templateLabel[1][0] and templateLabel[2][0] online users in templateTitleSubject[0] regions worldwide . In templateTitleDate[0] , templateValue[2][last] templateScale of templateValue[0][5] 's templateLabel[2][0] population had online access , compared to templateValue[1][last] templateScale of men . In the templateValue[0][1] , the share of templateLabel[2][0] online access was templateValue[2][1] templateScale .

generated_template: This statistic shows the number of templateTitle[1] in the templateTitle[0] as of templateTitleDate[0] , sorted templateTitle[6] templateTitle[7] . During the survey period , it was found that templateValue[2][0] templateLabel[2][0] templateTitle[1] in the templateTitle[0] .
generated: This statistic shows the number of internet in the Global as of 2019 , sorted gender region .  During the survey period , it was found that 80.3 Female internet in the Global .

Example 172:
titleEntities: {'Subject': ['Global'], 'Date': ['2017']}
title: Global distribution systems : company revenues 2017
X_Axis['Company']: ['Amadeus', 'Sabre', 'Travelport']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['5.81', '3.6', '2.45']

gold: This statistic shows the revenue of selected global distribution systems in 2017 . The global distribution system Amadeus generated a revenue of approximately 5.81 billion U.S. dollars ( 4.85 billion euros ) in 2017 . The revenue of Amadeus has been consistent over the last four years , remaining above 4.1 billion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] of selected templateTitleSubject[0] templateTitle[1] templateTitle[2] in templateTitleDate[0] . The templateTitleSubject[0] templateTitle[1] system templateXValue[0] generated a templateYLabel[0] of approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] ( 4.85 templateScale euros ) in templateTitleDate[0] . The templateYLabel[0] of templateXValue[0] has been consistent over the last templateYValue[1] years , remaining above 4.1 templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] in the templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[1] templateXLabel[0] templateXLabel[1] with a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Global distribution systems company revenues Revenue in the 2017 in .  In that year , Amadeus was the Global distribution Company with a Revenue of 5.81 billion U.S. dollars .

Example 173:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2019']}
title: Year-to-year percentage of change in holiday retail sales in the U.S. 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'change', 'in', 'retail', 'sales']: ['4', '2.1', '5.2', '3.2', '3.1', '5.1', '2.9', '2.6', '4.6', '5.2', '0.2', '-4.7', '2.7', '3.2', '6.2', '6.8', '5.1', '2.1', '2.07', '3.4']

gold: This statistic shows the year-over-year growth of holiday retail sales in the United States from 2000 to 2019 . In 2019 , holiday retail sales were forecast to grow by approximately four percent compared to the previous year . Holiday Season in the United States The holiday season is just around the corner and it 's truly the best time of the year for both consumers and retailers of all shapes and sizes .
gold_template: This statistic shows the year-over-year growth of templateTitle[3] templateYLabel[2] templateYLabel[3] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] templateYLabel[2] templateYLabel[3] were forecast to grow by approximately templateYValue[0] templateScale compared to the previous templateXLabel[0] . templateTitle[3] Season in the templateTitle[6] The templateTitle[3] season is just around the corner and it 's truly the best time of the templateXLabel[0] for both consumers and retailers of all shapes and sizes .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[2] templateYLabel[3] of the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of a templateYLabel[4] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Percentage change of the retail sales of the Year-to-year from 2000 to 2019 .  In 2019 , the average Percentage change of a sales of 6.8 retail sales .

Example 174:
titleEntities: {'Subject': ['Middle East', 'North Africa'], 'Date': ['2010']}
title: Religious diversity in Middle East/North Africa in 2010 , by religion
X_Axis['Religion']: ['Muslims', 'Christians', 'Jews', 'Unaffiliated', 'Hindus']
Y_Axis['Percentage', 'of', 'population']: ['93', '4', '2', '1', '1']

gold: This statistic shows religious diversity in Middle East and North Africa in 2010 , by share of religious population . In 2010 , about 93 percent of population in the Middle East and North Africa were Muslims .
gold_template: This statistic shows templateTitle[0] templateTitle[1] in templateTitleSubject[0] East and North templateTitleSubject[1] in templateTitleDate[0] , templateTitle[6] share of templateTitle[0] templateYLabel[1] . In templateTitleDate[0] , about templateYValue[max] templateScale of templateYLabel[1] in the templateTitleSubject[0] East and North templateTitleSubject[1] were templateXValue[0] .

generated_template: This statistic shows templateTitle[0] templateTitle[1] in the templateTitle[2] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] share of templateTitle[0] templateYLabel[1] . In templateTitleDate[0] , around templateYValue[max] templateScale of templateYLabel[1] , in templateTitle[2] countries , identified as Hindu .
generated: This statistic shows Religious diversity in the Middle Middle East in 2010 , share of Religious population .  In 2010 , around 93 percentage of population , in Middle countries , identified as Hindu .

Example 175:
titleEntities: {'Subject': ['Europe'], 'Date': ['2018']}
title: Leading automotive manufacturing countries in Europe 2018
Data:
                           0        1        2               3               4        5         6       7        8        9
Country              Germany    Spain   France  United_Kingdom  Czech_Republic    Italy  Slovakia  Poland  Hungary  Romania
Passenger Cars       5645581  2291492  1674717         1671166         1413881   742642    949365  514700   472107   364654
Commercial Vehicles   505258   635574   601623           73486            6112   342801         0  175029        0        0
Total                6150839  2927066  2276340         1744652         1419993  1085443    949365  689729   472107   364654 

gold: This statistic illustrates the ten leading countries in terms of automotive manufacturers in Europe in 2018 , categorized per vehicle type . That year , Germany was the leading automobile manufacturer , with approximately 6.1 million vehicles produced . Of these , the majority were passenger cars .
gold_template: This statistic illustrates the ten templateTitle[0] templateTitle[3] in terms of templateTitle[1] manufacturers in templateTitleSubject[0] in templateTitleDate[0] , categorized per vehicle type . That year , templateValue[0][0] was the templateTitle[0] automobile manufacturer , with approximately 6.1 templateScale templateLabel[2][1] produced . Of these , the majority were templateLabel[1][0] templateLabel[1][1] .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . During that time , templateValue[0][0] had the highest templateTitle[0] templateTitle[1] of templateValue[0][0] , with templateValue[1][0] templateScale of respondents reached templateValue[2][0] templateScale U.S. dollars in templateValue[0][0] .
generated: This statistic shows the Leading of the automotive manufacturing of countries Europe 2018 in .  During that time , Germany had the highest Leading automotive of Germany , with 5645581 % of respondents reached 505258 million U.S. dollars in Germany .

Example 176:
titleEntities: {'Subject': ['South Africa'], 'Date': ['2019']}
title: Employment by economic sector in South Africa 2019
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2019   2018   2017   2016   2015   2014   2013   2012   2011   2010   2009
Agriculture   5.09   5.16   5.22   5.57   5.61   4.65   4.98   4.84    4.6   4.86   5.07
Industry     23.18  23.24   23.3  23.33  23.83  23.42  23.52  23.45  24.18   24.4  25.26
Services     71.73   71.6  71.48   71.1  70.56  71.93   71.5  71.72  71.22  70.74  69.68 

gold: The statistic shows the distribution of employment in South Africa by economic sector from 2009 to 2019 . In 2019 , 5.09 percent of the employees in South Africa were active in the agricultural sector , 23.18 percent in industry and 71.73 percent in the service sector .
gold_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] by templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .

generated_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .
generated: The statistic shows the distribution of Employment in South Africa by economic sector from 2009 to 2019 .  In 2019 , 5.09 % of the employees in South Africa were active in the agricultural sector , 23.18 % in Industry and 71.73 % in the service sector .

Example 177:
titleEntities: {'Subject': ['IC3'], 'Date': ['2001', '2018']}
title: IC3 : total damage caused by reported cyber crime 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Total', 'damage', 'in', 'million', 'U.S.', 'dollars']: ['2710.0', '1418.7', '1450.7', '1070.71', '800.49', '781.84', '581.44', '485.25', '559.7', '264.6', '239.1', '198.4', '183.1', '68.1', '125.6', '54.0', '17.8']

gold: The statistic shows the amount of damages caused by cyber crime reported to the IC3 from 2001 to 2018 . In the last reported period , the annual loss of complaints referred to the IC3 amounted to 2.71 billion U.S. dollars , up from 1 billion U.S. dollars in 2015 .
gold_template: The statistic shows the amount of damages templateTitle[3] templateTitle[4] templateTitle[6] templateTitle[7] templateTitle[5] to the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the last templateTitle[5] period , the annual loss of complaints referred to the templateTitleSubject[0] amounted to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[3] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of the templateTitle[0] templateYLabel[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] templateTitle[3] templateYLabel[3] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Total of million U.S. of the IC3 Total damage caused by 2001 to 2018 .  In 2018 , the IC3 of caused U.S. was 2710.0 million U.S. .

Example 178:
titleEntities: {'Subject': ['India'], 'Date': ['2015', '2023']}
title: India : number of internet users 2015 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Number', 'of', 'internet', 'users', 'in', 'millions']: ['666.4', '634.9', '601.0', '564.5', '525.3', '483.0', '437.4', '295.39', '259.88']

gold: In 2018 , India had 483 million internet users . This figure is projected to grow to 666.4 million internet users in 2023 . Despite the untapped potential , India already is the second-largest online market worldwide .
gold_template: In templateXValue[5] , templateTitleSubject[0] had templateYValue[5] templateScale templateYLabel[1] templateYLabel[2] . This figure is projected to grow to templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[idxmax(Y)] . Despite the untapped potential , templateTitleSubject[0] already is the second-largest online market worldwide .

generated_template: This statistic provides information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateTitleSubject[0] had templateYValue[5] templateScale templateYLabel[1] templateYLabel[2] . This figure is projected to grow to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .
generated: This statistic provides information on the Number of internet users in India from 2015 to 2023 .  In 2018 , India had 483.0 millions internet users .  This figure is projected to grow 666.4 millions in 2023 .

Example 179:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Cities with the most parkland per 1,000 residents in the U.S. 2018
X_Axis['City']: ['Anchorage', 'Chesapeake', 'Scottsdale', 'Fremont', 'Jacksonville', 'New_Orleans', 'North_Las_Vegas', 'Honolulu', 'Virginia_Beach', 'Nashville/Davidson', 'Albuquerque', 'El_Paso', 'Irvine', 'Kansas_City', 'Phoenix']
Y_Axis['Acres', 'of', 'parkland', 'per', '1,000', 'residents']: ['3012.8', '228.1', '126.4', '106.0', '74.9', '70.8', '68.7', '65.8', '54.0', '49.6', '47.2', '44.0', '42.8', '35.7', '30.7']

gold: This graph depicts the acres of parkland per 1,000 residents in the most populous cities in the United States in 2018 . In Scottsdale in 2018 , there were 126.4 acres of parkland per 1,000 residents .
gold_template: This graph depicts the templateYLabel[0] of templateYLabel[1] templateYLabel[2] 1,000 templateYLabel[4] in the templateTitle[2] populous templateTitle[0] in the templateTitle[7] in templateTitleDate[0] . In templateXValue[2] in templateTitleDate[0] , there were templateYValue[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] 1,000 templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateXLabel[0] templateTitle[3] of templateXValue[0] templateYLabel[3] templateTitle[6] templateTitle[7] in the United Kingdom ( UK ) in templateTitleDate[0] . templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] , with templateYValue[max] templateYLabel[2] templateYLabel[3] of templateYLabel[1] , followed by templateXValue[1] with templateYValue[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Cities most City per of Anchorage 1,000 U.S. 2018 in the United Kingdom ( UK ) in 2018 .  Anchorage had the highest Acres parkland , with 3012.8 per 1,000 of parkland , followed by Chesapeake with 228.1 per 1,000 .

Example 180:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2008', '2019']}
title: Desktop/laptop ownership among U.S. adults 2008 to 2019
X_Axis['Month']: ['February_2019', 'January_2018', 'November_2016', 'April_2016', 'April_2015', 'January_2012', 'December_2011', 'June_2010', 'January_2008']
Y_Axis['Share', 'of', 'U.S.', 'adults']: ['74', '73', '78', '74', '73', '78', '75', '78', '74']

gold: The statistic shows the penetration rate of desktop and laptop computers among adults in the United States between 2008 and 2019 . As of February 2019 , 74 percent of Americans owned a desktop or laptop computer .
gold_template: The statistic shows the penetration rate of desktop and laptop computers templateTitle[2] templateYLabel[2] in the templateTitle[3] between templateXValue[last] and templateXValue[0] . As of templateXValue[0] , templateYValue[last] templateScale of Americans owned a desktop or laptop computer .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] in the templateTitle[1] templateTitle[3] were using templateTitle[5] networks as of 2019 , sorted templateTitle[8] templateXLabel[0] . During that period of time , templateYValue[max] templateScale of adult templateXValue[last] and templateYValue[min] templateScale of adult templateYLabel[1] templateYLabel[2] respectively .
generated: This statistic shows the Share of adults in the ownership U.S. were using 2008 networks as of 2019 , sorted 2019 Month .  During that period of time , 78 percent of adult January_2008 and 73 percent of adult U.S. adults respectively .

Example 181:
titleEntities: {'Subject': ['French'], 'Date': ['2010', '2017']}
title: French households savings rate 2010 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Savings', 'rate']: ['14.6', '14', '14.5', '14.4', '14.3', '15.1', '15.6', '15.8']

gold: The statistic presents the personal savings rate in France from 2010 to 2017 . The personal savings rate amounted to 14.6 percent in the first quarter of 2017 in France . The savings rate refers to the amount of money , expressed as a percentage or ratio , that a person deducts from his disposable personal income .
gold_template: The statistic presents the personal templateYLabel[0] templateYLabel[1] in France from templateXValue[min] to templateXValue[max] . The personal templateYLabel[0] templateYLabel[1] amounted to templateYValue[idxmax(X)] templateScale in the first quarter of templateXValue[max] in France . The templateYLabel[0] templateYLabel[1] refers to the amount of money , expressed as a templateScale or ratio , that a person deducts from his disposable personal income .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] of templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] , up from templateYValue[1] the previous templateXLabel[0] .
generated: This statistic shows the French Savings rate of French from 2010 to 2017 .  The French of was 15.8 14.6 rate in 2010 , up from 14 the previous Year .

Example 182:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010', '2018']}
title: Volume of bottled water in the U.S. 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Sales', 'volume', 'in', 'billion', 'dollars']: ['13.85', '13.2', '12.43', '11.52', '10.9', '10.2', '9.7', '9.11', '8.76', '8.45', '8.67', '8.76', '8.25']

gold: This timeline shows the sales volume of bottled water in the United States from 2006 to 2018 . According to the report , the volume of bottled water in the U.S. amounted to about 13.85 billion gallons in 2018 .
gold_template: This timeline shows the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . According to the report , the templateYLabel[1] of templateTitle[1] templateTitle[2] in the templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] templateScale gallons in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[1] templateTitle[2] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Sales volume of the dollars bottled water U.S. from 2006 to 2018 .  In 2018 , the average Sales volume of U.S. stood at 13.85 dollars .

Example 183:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1955', '2028']}
title: U.S. elementary and secondary schools : pupil-teacher ratio 1955 to 2028
Data:
            0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19    20    21    22    23    24    25    26    27    28    29    30    31    32    33    34    35    36    37    38    39    40    41    42    43    44    45    46    47    48    49    50    51    52    53    54    55    56    57    58    59    60    61
Year     2028  2027  2026  2025  2024  2023  2022  2021  2020  2019  2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  2000  1999  1998  1997  1996  1995  1994  1993  1992  1991  1990  1989  1988  1987  1986  1985  1984  1983  1982  1981  1980  1979  1978  1977  1976  1975  1974  1973  1972  1971  1970  1965  1960  1955
Public   15.2  15.3  15.3  15.4  15.5  15.6  15.7  15.8  15.8  15.9  16.0  16.1  16.0  16.0  16.1  16.1  16.0  16.0  16.0  15.4  15.3  15.4  15.6  15.6  15.8  15.9  15.9  15.9  16.0  16.1  16.4  16.8  17.1  17.3  17.3  17.4  17.4  17.3  17.2  17.2  17.3  17.6  17.7  17.9  18.1  18.4  18.6  18.8  18.7  19.1  19.3  19.7  20.2  20.4  20.8  21.3  21.7  22.3  22.3  24.7  25.8  26.9
Private  11.4  11.5  11.6  11.6  11.7  11.7  11.7  11.8  11.8  11.8  11.9  11.9  11.9  11.9  12.1  12.2  13.1  13.0  13.0  12.5  12.8  13.0  13.2  13.5  13.7  13.8  14.1  14.3  14.5  14.7  15.0  15.2  15.5  15.7  16.2  16.7  16.1  15.6  15.6  15.7  15.2  15.6  15.7  16.2  16.8  17.0  17.2  17.6  17.7  18.1  18.7  18.4  19.3  19.6  20.4  21.2  21.6  22.6  23.0  28.3  30.7  31.7 

gold: This graph shows the pupil-teacher ratio in public and private elementary and secondary schools in the United States from 1955 to 2016 with additional projection until 2028 . The pupil-teacher ratio for private schools in 2016 was 11.9 .
gold_template: This graph shows the templateTitle[4] templateTitle[5] in templateLabel[1][0] and templateLabel[2][0] templateTitle[1] and templateTitle[2] templateTitle[3] in the templateTitle[0] from templateValue[0][last] to templateValue[0][12] with additional projection until templateValue[0][0] . The templateTitle[4] templateTitle[5] for templateLabel[2][0] templateTitle[3] in templateValue[0][12] was templateValue[2][10] .

generated_template: How many templateTitle[0] students are there in the templateTitleSubject[0] ? In templateValue[0][11] , there were templateValue[1][11] templateScale templateTitle[0] students in the templateTitleSubject[0] enrolled in templateTitle[2] colleges and templateValue[2][11] templateScale students enrolled in templateTitle[3] colleges . These figures are expected to templatePositiveTrend to templateValue[1][0] templateScale and templateValue[2][0] templateScale respectively by the templateLabel[0][0] templateValue[0][0] . The difference between templateTitle[2] and templateTitle[3] colleges There are lots of different colleges and universities in the templateTitle[5] , and one key aspect when students choose which templateTitle[0] to attend is whether it is a templateTitle[2] or templateTitle[3] institution .
generated: How many U.S. students are there in the U.S. ? In 2017 , there were 16.1 million U.S. students in the U.S. enrolled in secondary colleges and 11.9 million students enrolled in schools colleges .  These figures are expected to increase 15.2 % and 11.4 % respectively by the Year 2028 .  The difference between secondary and schools colleges There are lots of different colleges and universities in the ratio , and one key aspect when students choose which U.S. to attend is whether it is a secondary or schools institution .

Example 184:
titleEntities: {'Subject': ['United Arab Emirates'], 'Date': ['2018']}
title: Urbanization in the United Arab Emirates 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['86.52', '86.25', '85.97', '85.67', '85.38', '85.07', '84.75', '84.42', '84.09', '83.74', '83.38']

gold: This statistic shows the degree of urbanization in the United Arab Emirates from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 86.52 percent of United Arab Emirates ' total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in the templateTitleSubject[0] Emirates from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] Emirates ' templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in United Arab Emirates from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 86.52 % of United Arab Emirates 's total population lived in urban areas and cities .

Example 185:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Number of oncology practices in the U.S. by region and size 2013
Data:
                            0          1      2     3
Country               Midwest  Northeast  South  West
1-2 physicians             33         31     59    24
3-6 physicians             41         41     29    46
7 or more physicians       55         40     75    52 

gold: This survey displays the number of oncology practices in the United States based on the number of physicians , by region , in 2013 . In that year , there were 24 oncology practices in the western part of the United States with only one or two physicians . The size and demographic of medical professionals in oncology plays a significant role in the accessibility and quality of cancer care for patients in the United States .
gold_template: This survey displays the templateTitle[0] of templateTitle[1] templateTitle[2] in the templateTitle[3] based on the templateTitle[0] of templateLabel[1][1] , templateTitle[4] templateTitle[5] , in templateTitleDate[0] . In that year , there were templateValue[1][min] templateTitle[1] templateTitle[2] in the western part of the templateTitle[3] with only one or two templateLabel[1][1] . The templateTitle[6] and demographic of medical professionals in templateTitle[1] plays a significant role in the accessibility and quality of cancer care for patients in the templateTitle[3] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[8] templateTitle[9] . In templateTitleDate[0] , the average templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateValue[0][1] stood at templateValue[1][1] templateScale in templateValue[0][1] .
generated: This statistic shows the Number of oncology practices U.S. in the United Kingdom ( U.S. ) in 2013 , .  In 2013 , the average Number oncology practices U.S. in the Northeast stood at 31 % in Northeast .

Example 186:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016']}
title: Leading cigarette brands in the United Kingdom ( UK ) 2016 , by sales value
X_Axis['Month']: ['Sterling', 'Mayfair', 'Lambert_and_Butler', 'Marlboro', 'Richmond', 'Players', 'John_Player_Special', 'Carlton', 'Silk_Cut', 'Windsor']
Y_Axis['Sales', 'value', 'in', 'million', 'GBP']: ['1511.3', '1046.1', '787.7', '782.2', '724.3', '718.9', '677.2', '540.6', '503.5', '461.9']

gold: This statistic shows the leading cigarette brands in the United Kingdom ( UK ) in 2016 , by total value of sales . The Sterling cigarette brand was the top selling cigarette brand in 2016 , generating sales of approximately 1.51 billion British pounds . The most expensive country in which to purchase cigarettes in Europe is the United Kingdom ( UK ) .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] , templateTitle[7] total templateYLabel[1] of templateYLabel[0] . The templateXValue[0] templateTitle[1] brand was the top selling templateTitle[1] brand in templateTitleDate[0] , generating templateYLabel[0] of approximately templateYValue[max] templateScale British pounds . The most expensive country in which to purchase cigarettes in Europe is the templateTitleSubject[0] ( templateTitleSubject[1] ) .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitleSubject[0] templateTitle[4] templateTitle[5] in templateTitleDate[0] . According to the source , templateXValue[0] had the largest templateTitle[0] templateTitle[1] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Leading cigarette Sales of the United Kingdom Kingdom UK in 2016 .  According to the source , Sterling had the largest Leading cigarette of 1511.3 million GBP .

Example 187:
titleEntities: {'Subject': ['Food Stamps'], 'Date': ['1995', '2020']}
title: Government spending for SNAP benefits ( Food Stamps ) in the U.S. 1995 to 2020
X_Axis['Year']: ['2020', '2015', '2010', '2005', '2000', '1995']
Y_Axis['Spending', 'in', 'billion', 'U.S.', 'dollars']: ['74', '80', '70', '33', '18', '26']

gold: The statistic shows total U.S. government spending for the Supplemental Nutrition Assistance Program ( SNAP , formerly called Food Stamps ) from 1995 to 2020 . In 2010 , about 70 billion U.S. dollars were spent for the Supplemental Nutrition Assistance Program .
gold_template: The statistic shows total templateYLabel[2] templateTitle[0] templateYLabel[0] templateTitle[2] the Supplemental Nutrition Assistance Program ( templateTitle[3] , formerly called templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . In templateXValue[2] , about templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] were spent templateTitle[2] the Supplemental Nutrition Assistance Program .

generated_template: This statistic depicts the worldwide templateYLabel[0] of templateTitle[2] sponsorship from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateTitleSubject[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] amounted to templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic depicts the worldwide Spending of for sponsorship from 1995 to 2020 .  In 2015 , the Food Stamps Spending of for SNAP benefits amounted to 80 billion U.S. dollars .

Example 188:
titleEntities: {'Subject': ['Finland'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Finland 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['1.35', '1.34', '1.39', '1.47', '1.47', '1.24', '1.66', '3.04', '2.77', '0.5', '-0.63']

gold: The statistic shows the growth in real GDP in Finland from 2014 to 2018 , with projections up until 2024 . In 2018 , Finland 's real gross domestic product increased by around 1.66 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Finland from 2014 to 2018 , with projections up until 2024 .  In 2018 , Finland 's real Gross domestic product increased by around 1.66 % compared to the previous Year .

Example 189:
titleEntities: {'Subject': ['CVS Health'], 'Date': ['2010', '2017']}
title: CVS Health 's revenue 2010 - 2017 by segment
Data:
                       0       1       2      3      4      5      6      7
Year                2017    2016    2015   2014   2013   2012   2011   2010
Pharmacy services  130.6  119.96  100.36  88.44  76.21  73.44  56.87  47.15
Retail pharmacy     79.4    81.1   72.01   67.8  65.62  63.65   59.6  57.35 

gold: This statistic depicts CVS Health 's revenue from 2010 to 2017 , by segment . The CVS Health Corporation is a U.S. drug retailing company , with three major segments : Pharmacy Services , Retail/LTC and Corporate . CVS Health is headquartered in Woonsocket , Rhode Island .
gold_template: This statistic depicts templateTitleSubject[0] 's templateTitle[3] from templateValue[0][last] to templateValue[0][0] , templateTitle[6] templateTitle[7] . The templateTitleSubject[0] Corporation is a U.S. drug retailing company , with three major segments : templateLabel[1][0] templateLabel[1][1] , Retail/LTC and Corporate . templateTitleSubject[0] is headquartered in Woonsocket , Rhode Island .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] of the templateTitleSubject[0] templateTitle[3] templateTitle[4] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees were employed in the templateTitleSubject[0] , templateValue[2][0] templateScale U.S. dollars .
generated: This statistic shows the CVS of Health the CVS Health revenue 2010 from to 2017 .  In 2017 , 130.6 % of the employees were employed in the CVS Health , 79.4 million U.S. dollars .

Example 190:
titleEntities: {'Subject': ['U.S.'], 'Date': ['19', '20']}
title: U.S. disposable personal income - monthly percentage of change 2019/20
X_Axis['Month']: ["Jan_'20", "Dec_'19", "Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19"]
Y_Axis['Monthly', 'percentage', 'of', 'change']: ['0.6', '0.1', '0.4', '0', '0.3', '0.6', '0.3', '0.4', '0.3', '0.4', '0.4', '0.5', '-0.2']

gold: This statistic shows the monthly percentage of change in disposable personal income in the United States from January 2019 to January 2020 . Disposable personal income increased by 0.6 percent in January 2020 from the previous month . The data are in current U.S. dollars , seasonally adjusted at annual rates .
gold_template: This statistic shows the templateYLabel[0] templateScale of templateYLabel[2] in templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[0] from 2019 to 2020 . templateTitle[1] templateTitle[2] templateTitle[3] templatePositiveTrend by templateYValue[max] templateScale in 2020 from the previous templateXLabel[0] . The data are in current templateTitleSubject[0] dollars , seasonally adjusted at annual rates .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of the templateTitle[3] templateTitleSubject[0] from 2019 to 2020 . In templateTitle[4] templateTitleDate[0] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[0] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the U.S. Monthly of the income U.S. from 2019 to 2020 .  In monthly 19 , the U.S. Monthly percentage of U.S. amounted to approximately 0.6 change .

Example 191:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2017', '2023']}
title: Indonesia : mobile phone internet users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'mobile', 'phone', 'internet', 'users', 'in', 'millions']: ['100.4', '97.6', '94.3', '90.3', '85.2', '79.7', '72.5']

gold: The statistic shows the number of mobile internet users in Indonesia from 2017 to 2027 . In 2017 , 72.5 million people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 100.4 million mobile phone internet users .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to 2027 . In templateXValue[min] , templateYValue[idxmin(X)] templateScale people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number of mobile internet users in Indonesia from 2017 to 2023 .  In 2017 , 72.5 millions people accessed the internet through their mobile phone .  In 2023 , this figure is projected to amount 100.4 millions mobile phone internet users .

Example 192:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': ['1993', '2018']}
title: Death from amphetamine drug use in England and Wales 1993 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993']
Y_Axis['Number', 'of', 'deaths']: ['100', '91', '96', '90', '85', '56', '49', '46', '48', '50', '59', '50', '49', '50', '43', '33', '51', '28', '40', '67', '48', '41', '33', '35', '19', '37']

gold: In 2018 , there were 100 amphetamine-related deaths in England and Wales . Although this is a slight decrease from the preceding year , the number of deaths as a result of amphetamine use has sharply increased in recent years . The number of deaths remained around 50 a year before jumping to 85 mortalities in 2014 .
gold_template: In templateXValue[max] , there were templateYValue[idxmax(X)] amphetamine-related templateYLabel[1] in templateTitleSubject[0] and templateTitleSubject[1] . Although this is a slight templateNegativeTrend templateTitle[1] the preceding templateXLabel[0] , the templateYLabel[0] of templateYLabel[1] as a result of templateTitle[2] templateTitle[4] has sharply templatePositiveTrend in recent years . The templateYLabel[0] of templateYLabel[1] remained around templateYValue[9] a templateXLabel[0] before jumping to templateYValue[4] mortalities in templateXValue[4] .

generated_template: In templateXValue[max] , templateYValue[idxmax(X)] people died as a result of templateTitle[3] templateTitle[2] in templateTitleSubject[0] and templateTitleSubject[1] , this was by far the highest amount of recorded templateYLabel[1] in the 25-year period shown . Since the turn of the century , the templateYLabel[0] of templateYLabel[1] has remained relatively high apart templateTitle[1] a drastic fall in templateXValue[8] and templateXValue[7] before subsequently templatePositiveTrend again . Seriousness of MDMA offences In the United Kingdom ( UK ) MDMA is categorized as a class A drug which means it carries the most severe penalties for both possession and supply .
generated: In 2018 , 100 people died as a result of drug amphetamine in England and Wales , this was by far the highest amount of recorded deaths in the 25-year period shown .  Since the turn of the century , the Number of deaths has remained relatively high apart from a drastic fall in 2010 and 2011 before subsequently rising again .  Seriousness of MDMA offences In the United Kingdom ( UK ) MDMA is categorized as a class drug which means it carries the most severe penalties for both possession and supply .

Example 193:
titleEntities: {'Subject': ['Nuclear'], 'Date': ['2005', '2019']}
title: Nuclear power plants : permanent shutdowns 2005 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Number', 'of', 'shutdowns']: ['3', '7', '5', '4', '7', '1', '6', '3', '13', '1', '3', '2', '1', '8', '2']

gold: This statistic represents the global number of permanent nuclear reactor shutdowns between 2005 and August 2019 . Following the nuclear accident in Japan in March 2011 , four reactors at Fukushima Daiichi nuclear power plant were shut down permanently . In total , there were seven permanent shutdowns in 2018 .
gold_template: This statistic represents the global templateYLabel[0] of templateTitle[3] templateTitleSubject[0] reactor templateYLabel[1] between templateXValue[min] and 2019 . Following the templateTitleSubject[0] accident in Japan in 2011 , templateYValue[3] reactors at Fukushima Daiichi templateTitleSubject[0] templateTitle[1] plant were shut down permanently . In total , there were templateYValue[1] templateTitle[3] templateYLabel[1] in templateXValue[1] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Number of shutdowns the Nuclear permanent shutdowns 2005 to 2019 .  In 2019 , the average Number shutdowns of Nuclear permanent shutdowns was 3 shutdowns .

Example 194:
titleEntities: {'Subject': ['Mavericks'], 'Date': ['2001', '2019']}
title: Dallas Mavericks ' revenue 2001 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['307', '287', '233', '194', '177', '168', '162', '137', '166', '146', '154', '153', '140', '140', '124', '117', '117', '105']

gold: The statistic shows the revenue of the Dallas Mavericks franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 307 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitleSubject[0] franchise from the templateXValue[last] season to the templateXValue[0] season . In templateXValue[0] , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the templateXValue[last] season to the templateXValue[0] season . In templateXValue[0] , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Mavericks franchise from the 2001/02 season to the 2018/19 season .  In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 307 million U.S. dollars .

Example 195:
titleEntities: {'Subject': ['Smithsonian'], 'Date': ['2018']}
title: Most visited Smithsonian museums and institutions in the U.S. 2018
X_Axis['Smithsonian', 'Museums']: ["National_Air_and_Space_Museum's_Steven_F._Udvar-Hazy_Center", 'National_Air_and_Space_Museum', 'National_Museum_of_Natural_History', 'National_Museum_of_American_History', 'Smithsonian_American_Art_Museum_(With_the_Renwick_Gallery)', 'National_Portrait_Gallery', 'National_Museum_of_African_American_History_and_Culture', 'National_Zoo', 'National_Museum_of_the_American_Indian_(Washington_D.C.)', 'Smithsonian_Institution_Building_"The_Castle"', 'Hirshhorn_Museum_and_Sculpture_Garden', 'Renwick_Gallery_of_the_Smithsonian_American_Art_Museum', 'National_Postal_Museum', 'National_Museum_of_the_American_Indian_Heye_Center_(New_York_City)', 'Cooper_Hewitt_Smithsonian_Design_Museum', 'Freer_Gallery_of_Art', 'National_Museum_of_African_Art', 'S._Dillon_Ripley_Center', 'Arthur_M._Sackler_Gallery']
Y_Axis['Number', 'of', 'visits', 'in', 'millions']: ['6.2', '6.2', '4.8', '4.1', '3.15', '2.3', '1.9', '1.8', '1.1', '1.0', '0.88', '0.71', '0.5', '0.41', '0.32', '0.24', '0.14', '0.13', '0.13']

gold: This statistic shows the most visited Smithsonian museums and institutions in the United States in 2018 . The National Air and Space Museum was the most visited Smithsonian museum in 2018 , with approximately 6.2 million visits . Smithsonian museums –additional information The Smithsonian museums are part of the Smithsonian Institution which belongs to the United States government .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateXValue[4] templateXLabel[1] and templateTitle[4] in the templateTitle[5] in templateTitleDate[0] . The templateXValue[0] and templateXValue[0] templateXValue[1] was the templateTitle[0] templateTitle[1] templateXValue[4] templateXValue[1] in templateTitleDate[0] , with approximately templateYValue[max] templateScale templateYLabel[1] . templateXValue[4] templateXLabel[1] –additional information The templateXValue[4] templateXLabel[1] are part of the templateXValue[4] templateXValue[9] which belongs to the templateTitle[5] government .

generated_template: Total templateYLabel[1] , templateXValue[0] and templateXValue[2] were the most templateXValue[7] and with the highest templateYLabel[0] of people , with templateYValue[max] templateScale templateYLabel[4] . The templateYLabel[0] of templateYLabel[1] who have experienced templateTitle[0] since templateXValue[2] with templateYValue[2] templateScale templateYLabel[1] , respectively .
generated: Total visits , National_Air_and_Space_Museum's_Steven_F._Udvar-Hazy_Center and National_Museum_of_Natural_History were the most National_Zoo and with the highest Number of people , with 6.2 millions .  The Number of visits who have experienced Most since National_Museum_of_Natural_History with 4.8 millions visits , respectively .

Example 196:
titleEntities: {'Subject': ['MINI'], 'Date': ['2006', '2018']}
title: MINI - worldwide sales volume 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Sales', 'in', 'units']: ['361531', '371881', '360233', '338466', '302183', '305030', '301526', '285060', '234175', '216538', '232425', '222875', '188077']

gold: Deliveries of BMW MINI-badged cars stood at about 361,500 units in 2018 . BMW has operated the MINI brand since 2000 and adapted the trademark compact style originally designed by Birmingham-based British Motor Corporation ( BMC ) to meet modern preferences . In the UK , the MINI brand was launched with great success .
gold_template: Deliveries of BMW MINI-badged cars stood at about 361,500 templateYLabel[1] in templateXValue[max] . BMW has operated the templateTitleSubject[0] brand since 2000 and adapted the trademark compact style originally designed by Birmingham-based British Motor Corporation ( BMC ) to meet modern preferences . In the UK , the templateTitleSubject[0] brand was launched with great success .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] amounted to templateYValue[3] templateScale .
generated: This statistic shows the Sales of worldwide sales units in the MINI from 2006 to 2018 .  In 2016 , the Sales of units in the MINI amounted to 338466 million .

Example 197:
titleEntities: {'Subject': ['Netherlands'], 'Date': []}
title: Distribution of gross domestic product ( GDP ) across economic sectors in the Netherlands
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
Agriculture   1.63   1.86   1.77   1.72   1.74   1.79   1.66   1.58   1.78   1.49   1.58
Industry     17.93   17.7  17.64  18.19  18.41  19.33  20.02  20.07  19.69  20.44  21.61
Services     69.94  70.07  70.22  70.06  69.91  69.07  68.73  68.45  68.37  67.73  66.13 

gold: This statistic shows the distribution of the gross domestic product ( GDP ) across economic sectors in the Netherlands from 2008 to 2018 . In 2018 , agriculture contributed around 1.63 percent to the GDP of the Netherlands , 17.93 percent came from the industry and 69.94 percent from the service sector .
gold_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of the templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the service sector .

generated_template: The statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the service sector .
generated: The statistic shows the Distribution of the gross domestic product ( GDP ) across economic sectors in Netherlands from 2008 to 2018 .  In 2018 , Agriculture contributed around 1.63 % to the GDP of Netherlands , 17.93 % came from the Industry and 69.94 % from the service sector .

Example 198:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Participants in tackle football in the U.S. from 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'participants', 'in', 'millions']: ['5.16', '5.22', '5.48', '6.22', '5.98', '6.17', '6.45', '5.99', '6.91', '6.79', '7.69', '7.94', '8.4']

gold: The number of people over the age of six participating in tackle football in the United States amounted to approximately 5.16 million in 2018 . Tackle football vs flag football Football is a popular recreational sport in the United States - for adults , adolescents and children alike . Played with almost the same rules as professional football , children can start playing tackle football around the age of five .
gold_template: The templateYLabel[0] of people over the age of templateYValue[3] participating in templateTitle[1] templateTitle[2] in the templateTitle[3] amounted to approximately templateYValue[min] templateScale in templateXValue[idxmin(Y)] . templateTitle[1] templateTitle[2] vs flag templateTitle[2] is a popular recreational sport in the templateTitle[3] - for adults , adolescents and children alike . Played with almost the same rules as professional templateTitle[2] , children can start playing templateTitle[1] templateTitle[2] around the age of templateYValue[min] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Number of participants in tackle the football U.S. 2006 to 2018 .  In 2018 , the Number of participants ( aged six years and older ) in tackle amounted to approximately 5.16 millions .

Example 199:
titleEntities: {'Subject': ['United States'], 'Date': ['2012']}
title: Total number of gang-related homicides in the United States 2012
X_Axis['Year']: ['2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Total', 'number', 'of', 'homicides']: ['2363', '1824', '2020', '2083', '1659', '1975']

gold: Over 2,300 homicides were gang-related in the United States in 2012 . This was the highest amount since 2007 and a reversal of a negative trend from the previous two years . Violent crime Violent crime in the United States is not limited to gang violence , but the murders in this statistic compose a significant portion of the number of reported murders in those years .
gold_template: Over 2,300 templateYLabel[2] were templateTitle[2] in the templateTitleSubject[0] in templateXValue[max] . This was the highest amount since templateXValue[min] and a reversal of a negative trend from the previous two years . Violent crime Violent crime in the templateTitleSubject[0] is not limited to gang violence , but the murders in this statistic compose a significant portion of the templateYLabel[1] of reported murders in those years .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[2] templateYLabel[3] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[2] templateTitle[3] amounted to templateYValue[idxmin(X)] templateScale templateYLabel[3] .
generated: This statistic shows the Total number of the homicides worldwide from 2007 to 2012 .  In 2007 , the Total number of the United States gang-related homicides amounted to 1975 million homicides .

Example 200:
titleEntities: {'Subject': ['Seattle Sounders FC'], 'Date': ['2019']}
title: Player expenses ( payroll ) of Seattle Sounders FC 2019
X_Axis['Seattle', 'Sounders', 'Players']: ['Nicolas_Lodeiro', 'Raul_Ruidiaz', 'Victor_Rodriguez', 'Kim_Kee-Hee', 'Roman_Torres', 'Jordan_Morris', 'Gustav_Svensson', 'Kelvin_Leerdam', 'Cristian_Roldan', 'Brad_Smith', 'Joevin_Jones', 'Xavier_Arreaga', 'Will_Bruin', 'Stefan_Frei', 'Harry_Shipp', 'Daniel_Leyva', 'Alfonso_Ocampo-Chavez', 'Bryan_Meredith', 'Jordy_Delem', 'Nouhou_Tolo', 'Henry_Wingo', 'Jonathan_Campbell', 'Saad_Abdul-Salaam', 'Trey_Muse', 'Alex_Roldan', 'Handwalla_Bwana']
Y_Axis['Payroll', 'in', 'thousand', 'U.S.', 'dollars']: ['2502.5', '1800.0', '1087.5', '772.0', '740.0', '619.6', '600.0', '600.0', '596.54', '564.37', '528.67', '500.85', '358.33', '337.5', '235.0', '89.87', '85.75', '70.88', '70.88', '70.73', '70.25', '70.25', '70.25', '65.0', '57.23', '57.23']

gold: The statistic shows the player expenses ( payroll ) of Seattle Sounders FC of Major League Soccer by player in 2019 . Nicolas Lodeiro received a salary of 2.5 million U.S. dollars .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of templateXLabel[0] templateXLabel[1] templateTitleSubject[0] of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] received a salary of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] received a salary of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Player expenses ( Payroll ) of the Seattle Sounders FC of Major League Soccer by Player in 2019 .  Nicolas_Lodeiro received a salary of 2502.5 thousand U.S. dollars .

Example 201:
titleEntities: {'Subject': ['Turkey'], 'Date': ['2019']}
title: Unemployment rate in Turkey 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['11.9', '10.9', '10.82', '10.84', '10.24', '9.88', '8.73', '8.15', '8.8', '10.66', '12.55', '9.71', '8.87', '8.72', '10.64', '10.84', '10.54', '10.36', '8.38', '6.5', '7.69']

gold: The statistic shows the unemployment rate in Turkey from 1999 to 2019 . In 2019 , the unemployment rate in Turkey was at about 11.9 percent . Trade in Turkey Turkey is a growing market and recognized as one of the world 's newest industrialized countries .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateScale . Trade in templateTitleSubject[0] is a templatePositiveTrend market and recognized as one of the world 's newest industrialized countries .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Turkey from 1999 to 2019 .  In 2019 , the Unemployment rate in Turkey was at approximately 11.9 % .

Example 202:
titleEntities: {'Subject': ['Switzerland'], 'Date': ['2006', '2018']}
title: Number of road deaths in Switzerland 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['233', '230', '216', '253', '243', '269', '339', '320', '327', '349', '357', '384', '370']

gold: In 2018 , 233 fatalities were recorded on Swiss roads . Between 2006 and 2018 , traffic related deaths declined by over one third , with the lowest number seen in 2016 at 216 such incidences . Switzerland was one of the safest countries in Europe for road users .
gold_template: In templateXValue[max] , templateYValue[idxmax(X)] templateYLabel[1] were recorded on Swiss roads . Between templateXValue[min] and templateXValue[max] , traffic related templateTitle[2] declined by over one third , with the lowest templateYLabel[0] seen in templateXValue[2] at templateYValue[min] such incidences . templateTitleSubject[0] was one of the safest countries in Europe for templateTitle[1] users .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] due to templateTitle[0] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[idxmax(X)] templateYLabel[1] reported due to templateTitle[0] in the templateTitle[1] .
generated: The statistic shows the Number of fatalities due to Number in the road from 2006 to 2018 .  In 2018 , there were a total of 233 fatalities reported due to Number in the road .

Example 203:
titleEntities: {'Subject': ['Botswana'], 'Date': ['2019']}
title: Youth unemployment rate in Botswana in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['37.52', '37.14', '36.57', '36.8', '36.83', '37.02', '36.61', '36.04', '35.6', '35.24', '32.5', '30.77', '31.01', '32.03', '36.67', '41.31', '46.62', '43.13', '39.45', '33.05', '37.05']

gold: The statistic shows the youth unemployment rate in Botswana from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Botswana was at 37.52 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Youth unemployment rate in Botswana from 1999 and 2019 .  According to the source , the data are ILO estimates .  In 2019 , the estimated Youth unemployment rate in Botswana was at 37.52 % .

Example 204:
titleEntities: {'Subject': ['Toys'], 'Date': ['2009']}
title: Toys and games market : market segmentation by value 2009
X_Axis['Name']: ['Other', 'Infant/Pre-school_toys', 'Activity_toys', 'Dolls', 'Ride-ons', 'Games_and_puzzles', 'Plush_toys']
Y_Axis['Market', 'share']: ['30.5', '16.2', '15.1', '12.2', '11.4', '9.5', '5.1']

gold: This graph depicts the share of toy categories of the total market value in 2009 . Activity toys accounted for 15.1 percent of total toys and games market revenue .
gold_template: This graph depicts the templateYLabel[1] of toy categories of the total templateYLabel[0] templateTitle[6] in templateTitleDate[0] . templateXValue[2] templateXValue[1] accounted for templateYValue[2] templateScale of total templateXValue[1] and templateXValue[5] templateYLabel[0] revenue .

generated_template: This statistic illustrates the global templateYLabel[0] templateYLabel[1] of templateXValue[0] held by the largest templateXLabel[0] templateTitle[2] in the templateTitleSubject[0] in templateTitleDate[0] . The company had a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateTitle[2] , followed templateTitle[5] templateXValue[1] , with a templateYLabel[0] templateYLabel[1] of templateYValue[1] templateScale .
generated: This statistic illustrates the global Market share of Other held by the largest Name market in the Toys in 2009 .  The company had a Market share of 30.5 % market , followed by Infant/Pre-school_toys , with a Market share of 16.2 % .

Example 205:
titleEntities: {'Subject': ['Iceland'], 'Date': ['2019']}
title: Employment by economic sector in Iceland 2019
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2019   2018   2017   2016   2015   2014   2013   2012   2011   2010   2009
Agriculture   3.69   3.72   3.76    3.9   4.24   4.54   4.45   5.55   5.49   5.59   4.83
Industry     17.13  17.31  17.48  17.46  17.84  18.43  18.64  18.22  18.55  18.44   19.5
Services     79.18  78.97  78.76  78.65  77.92  77.03  76.92  76.23  75.96  75.97  75.67 

gold: The statistic shows the distribution of employment in Iceland by economic sector from 2009 to 2019 . In 2019 , 3.69 percent of the employees in Iceland were active in the agricultural sector , 17.13 percent in industry and 79.18 percent in the service sector .
gold_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .

generated_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .
generated: The statistic shows the distribution of Employment in Iceland by economic sector from 2009 to 2019 .  In 2019 , 3.69 % of the employees in Iceland were active in the agricultural sector , 17.13 % in Industry and 79.18 % in the service sector .

Example 206:
titleEntities: {'Subject': ['Canadian'], 'Date': ['2018']}
title: Canadian snowmobile registrations by province 2018
X_Axis['Province']: ['Quebec', 'Ontario', 'Alberta', 'New_Foundland', 'British_Columbia', 'Manitoba', 'New_Brunswick', 'Saskatchewan', 'Nunavut', 'North_West_Territory', 'Nova_Scotia', 'Prince_Edward_Island', 'Yukon']
Y_Axis['Number', 'of', 'registrations']: ['201732', '155000', '73000', '58700', '44500', '35354', '19550', '16175', '14200', '9800', '7200', '2180', '1805']

gold: In the winter of 2019 , about 14,200 snowmobiles were registered in Nunavut . At some 200,000 , most snowmobile registrations took place in Quebec . In total , the International Snowmobile Manufacturers Association ( ISMA ) reported over 600,000 snowmobile registrations throughout Canada .
gold_template: In the winter of 2019 , about templateYValue[8] snowmobiles were registered in templateXValue[8] . At some 200,000 , most templateTitle[1] templateYLabel[1] took place in templateXValue[0] . In total , the International templateTitle[1] Manufacturers Association ( ISMA ) reported over 600,000 templateTitle[1] templateYLabel[1] throughout Canada .

generated_template: In templateTitleDate[0] , the templateXLabel[0] templateXLabel[1] templateXValue[0] , with the highest templateYLabel[0] templateYLabel[1] in templateXValue[7] . That year , templateXValue[0] had a total of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: In 2018 , the Province Quebec , with the highest Number registrations in Saskatchewan .  That year , Quebec had a total of 201732 million registrations .

Example 207:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009', '2016']}
title: Sales of state lotteries in the U.S. 2009 to 2016
X_Axis['Year']: ['2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016']
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['58.25', '58.82', '63.08', '68.78', '68.88', '70.15', '73.87', '80.55']

gold: This statistic shows the annual sales of state lotteries in the United States from 2009 to 2016 . In 2016 , sales of state lotteries reached approximately 80.55 billion U.S. dollars , up from 73.87 billion the previous year . In 2016 , the state with the highest lottery sales was New York , with around 9.7 billion U.S. dollars in sales .
gold_template: This statistic shows the annual templateYLabel[0] of templateTitle[1] templateTitle[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] of templateTitle[1] templateTitle[2] reached approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] , up from templateYValue[6] templateScale the previous templateXLabel[0] . In templateXValue[idxmax(Y)] , the templateTitle[1] with the highest lottery templateYLabel[0] was New York , with around 9.7 templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateYLabel[0] of the templateYLabel[2] templateTitle[5] amounted to templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Sales state in the U.S. from 2009 to 2016 .  In 2012 , the Sales of the U.S. 2016 amounted to 68.78 billion U.S. dollars .

Example 208:
titleEntities: {'Subject': ['Global'], 'Date': ['2016', '2021']}
title: Global photo printing and merchandising market value 2016 and 2021
X_Axis['Year']: ['2021', '2016']
Y_Axis['Market', 'revenue', 'in', 'billion', 'U.S.', 'dollars']: ['19.09', '14.5']

gold: This graphic displays the market revenue of the global photo printing and merchandising market in 2016 and provides a forecast for 2021 . In 2016 , the global photo printing and merchandising market was worth 14.5 billion U.S. dollars .
gold_template: This graphic displays the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] and templateTitle[3] templateYLabel[0] in templateXValue[min] and provides a forecast for templateXValue[max] . In templateXValue[min] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] and templateTitle[3] templateYLabel[0] was worth templateYValue[idxmin(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] was estimated at templateYValue[idxmin(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Global photo in Global from 2016 to 2021 .  In 2016 , the Global photo in Global was estimated at 14.5 billion U.S. dollars .

Example 209:
titleEntities: {'Subject': ['Rocky Mountain National Park'], 'Date': ['2008', '2019']}
title: Number of visitors to the Rocky Mountain National Park in the U.S. 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'visitors', 'in', 'millions']: ['4.67', '4.59', '4.44', '4.52', '4.16', '3.43', '2.99', '3.23', '3.18', '2.96', '2.82', '2.76']

gold: This statistic shows the number of recreational visitors to the Rocky Mountain National Park in the United States from 2008 to 2019 . The number of visitors to the Rocky Mountain National Park amounted to approximately 4.67 million in 2019 .
gold_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[1] to the templateTitleSubject[0] National templateTitleSubject[0] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] to the templateTitleSubject[0] National templateTitleSubject[0] amounted to approximately templateYValue[max] templateYValue[idxmax(X)] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[1] to the templateTitleSubject[0] Memorial in the templateTitle[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] to the templateTitleSubject[0] Memorial amounted to approximately templateYValue[0] templateYValue[idxmax(X)] in templateXValue[max] .
generated: This statistic shows the Number of recreational visitors to the Rocky Mountain National Park Memorial in the Park from 2008 to 2019 .  The Number of visitors to the Rocky Mountain National Park Memorial amounted to approximately 4.67 in 2019 .

Example 210:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Average spend of eating out for lunch in the U.S. 2015
X_Axis['Days']: ['Per_day', 'Per_week', 'Per_year']
Y_Axis['Average', 'cost', 'in', 'U.S.', 'dollars']: ['11.14', '20.05', '1043.0']

gold: The statistic shows the average cost of eating out for lunch in the United States in 2015 . The average cost of eating out for lunch in the U.S. was 11.14 U.S. dollars in 2015 . Eating out for lunch two times per week costs an average of 1,043 U.S. dollars per year .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] in templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateYLabel[2] was templateYValue[min] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] . templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] two times templateXValue[0] templateXValue[1] costs an templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateXValue[0] templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] as of 2019 , sorted templateTitle[7] templateTitle[8] . According to the source , the templateXValue[0] accounted for over templateYValue[max] templateScale of all templateYLabel[2] templateYLabel[3] .
generated: This statistic gives information on the Average spend eating out for in the lunch as of 2019 , sorted 2015 .  According to the source , the Per_day accounted for over 1043.0 % of all U.S. dollars .

Example 211:
titleEntities: {'Subject': ['Tesla'], 'Date': ['2013', '2019']}
title: Tesla 's net loss attributable to common stockholders 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Loss', 'in', 'million', 'U.S.', 'dollars']: ['-862', '-976', '-1962', '-675', '-889', '-294', '-74']

gold: Net loss attributable to Tesla 's common stockholders was 862 million U.S. dollars in 2019 . In the same year , the California-based company burnt through a total of about 775 million U.S. dollars of cash . Focus on innovation drives costs Net losses began to accelerate in 2014 and peaked in 2017 .
gold_template: templateTitle[2] templateYLabel[0] templateTitle[4] to templateTitleSubject[0] templateTitle[1] templateTitle[5] templateTitle[6] was 862 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] . In the same templateXLabel[0] , the California-based company burnt through a total of about 775 templateScale templateYLabel[2] templateYLabel[3] of cash . Focus on innovation drives costs templateTitle[2] losses began to accelerate in templateXValue[5] and peaked in templateXValue[2] .

generated_template: The statistic shows the templateYLabel[0] of the employees of the templateTitle[7] templateTitle[2] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] of templateTitleSubject[0] employees amounted to over templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[2] .
generated: The statistic shows the Loss of the employees of the 2013 net Tesla from 2013 to 2019 .  According to the report , the Tesla of employees amounted to over -1962 million U.S. dollars in 2017 .

Example 212:
titleEntities: {'Subject': ['Great Britain', 'UK'], 'Date': ['2014']}
title: Best car manufacturers based on build quality in Great Britain ( UK ) 2014
X_Axis['Automobile', 'Manufacturers']: ['Lexus', 'Porsche', 'Audi', 'Skoda', 'Volvo', 'Jaguar', 'Mercedes', 'BMW', 'Honda', 'Mazda', 'Subaru', 'Kia', 'VW', 'Land_Rover', 'Toyota']
Y_Axis['Percentage', 'score']: ['94.27', '94.02', '93.42', '93.32', '92.47', '92.45', '92.4', '91.92', '91.33', '91.13', '90.73', '90.37', '89.7', '89.23', '88.85']

gold: This statistic shows the leading 15 car manufacturer brands in Great Britain according to the Auto Express Driver Power 2014 survey responses based on car build score . The survey was carried out by the British automotive magazine online between 2013 and 2014 . Unsurprisingly the top three spots are held by luxury car manufacturers , Lexus , Porsche and Audi respectively .
gold_template: This statistic shows the leading 15 templateTitle[1] manufacturer brands in templateTitleSubject[0] according to the Auto Express Driver Power templateTitleDate[0] survey responses templateTitle[3] on templateTitle[1] templateTitle[4] templateYLabel[1] . The survey was carried out by the British automotive magazine online between 2013 and templateTitleDate[0] . Unsurprisingly the top three spots are held by luxury templateTitle[1] templateXLabel[1] , templateXValue[0] , templateXValue[1] and templateXValue[2] respectively .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[7] in templateTitleDate[0] . The templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitleSubject[0] was templateXValue[0] , with a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[3] .
generated: The statistic shows the Best car manufacturers based build in the Britain in 2014 .  The Best car manufacturers of the Great Britain was Lexus , with a Percentage of 94.27 percentage score .

Example 213:
titleEntities: {'Subject': ['Orlando Magic', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Orlando Magic ( NBA ) games 2015/16
X_Axis['Year']: ['2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['39.4', '39.4', '39.4', '41.18', '43.65', '43.0', '40.3', '40.3', '38.46', '35.29']

gold: This graph depicts the average ticket price for Orlando Magic games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 35.29 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Orlando Magic games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Average ticket price was 35.29 U.S. dollars .

Example 214:
titleEntities: {'Subject': ['eBay Enterprise'], 'Date': ['1999', '2014']}
title: eBay Enterprise : annual net revenue 1999 to 2014
X_Axis['Year']: ['1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['5.5', '42.8', '102.6', '172.6', '241.9', '335.3', '440.0', '609.6', '750.0', '966.9', '1004.0', '1358.0', '509.0', '1083.0', '1166.0', '1238.0']

gold: This timeline represents eBay Enterprise 's , formerly known as GSI Commerce , annual net revenue from 1999 to 2014 , in million U.S. dollars . In 2010 , GSI Commerce reported a net revenue of 1.36 billion U.S. dollars before being acquired by eBay in June 2011 .
gold_template: This timeline represents templateTitleSubject[0] 's , formerly known as GSI Commerce , templateTitle[2] templateTitle[3] templateYLabel[0] from templateXValue[min] to templateXValue[max] , in templateScale templateYLabel[2] templateYLabel[3] . In templateXValue[11] , GSI Commerce reported a templateTitle[3] templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] before being acquired by templateTitleSubject[0] in 2011 .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the eBay Enterprise , a franchise of the National Football League , from 1999 to 2014 .  In 2014 , the Revenue of the eBay Enterprise was 1358.0 1238.0 U.S. dollars .

Example 215:
titleEntities: {'Subject': ['Digital'], 'Date': ['2014', '2018']}
title: Digital video ad impressions in the U.S. 2014 - 2018 , by ad length
Data:
                  0        1        2        3        4        5        6        7        8        9       10       11       12       13       14
Quarter     Q1_2018  Q4_2017  Q3_2017  Q2_2017  Q1_2017  Q4_2016  Q3_2016  Q2_2016  Q1_2016  Q4_2015  Q3_2015  Q2_2015  Q4_2014  Q3_2014  Q2_2014
15 seconds       46       35       37       37       47       55       46       36       35       36       53       50       40       35       54
30 seconds       52       59       59       53       45       36       50       60       61       60       44       48       59       63       46
60 seconds        0        0        0        0        0        0        0        0        0        0        1        1        0        1        0
Other             2        6        4       10        8        9        4        4        4        4        0        0        0        0        0 

gold: This statistic presents a distribution of digital video ad impression in the United States from the second quarter of 2014 to the first quarter of 2018 , by total ad length . It was found that 46 percent of the digital video ads viewed within the first quarter of 2018 were in the length of 15 seconds .
gold_template: This statistic presents a distribution of templateTitleSubject[0] templateTitle[1] templateTitle[2] impression in the templateTitle[4] from the second templateLabel[0][0] of templateValue[0][12] to the first templateLabel[0][0] of templateValue[0][0] , templateTitle[7] total templateTitle[2] templateTitle[9] . It was found that templateValue[1][0] templateScale of the templateTitleSubject[0] templateTitle[1] ads viewed within the first templateLabel[0][0] of templateValue[0][0] were in the templateTitle[9] of templateLabel[1][0] templateLabel[1][1] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] of the templateTitleSubject[0] templateTitle[4] from the first templateLabel[0][0] of templateTitleDate[min] to the first templateLabel[0][0] of templateTitleDate[max] . In the fourth templateLabel[0][0] of templateValue[0][0] , templateLabel[1][0] templateLabel[1][1] generated templateValue[2][0] templateScale of the templateLabel[2][0] .
generated: This statistic shows the Digital of video the Digital U.S. from the first Quarter of 2014 to the first Quarter of 2018 .  In the fourth Quarter of Q1_2018 , 15 seconds generated 52 % of the 30 .

Example 216:
titleEntities: {'Subject': ['Shades Grey U.S.'], 'Date': ['2012']}
title: Fifty Shades of Grey : U.S. sales figures in 2012 , by platform
X_Axis['Copy', 'Type']: ['Print', 'Digital']
Y_Axis['Number', 'of', 'copies', 'in', 'millions']: ['29', '15']

gold: The statistic presents U.S. sales figures of the 'Fifty Shades of Grey ' trilogy in 2012 , broken down by platform . The book sold 29 million print and 15 million digital copies in 2012 . It topped the 2012 best-seller lists in the categories adult fiction and romance .
gold_template: The statistic presents templateTitleSubject[0] templateTitle[4] templateTitle[5] of the 'Fifty templateTitleSubject[0] of templateTitleSubject[0] ' trilogy in templateTitleDate[0] , broken down templateTitle[7] templateTitle[8] . The book sold templateYValue[max] templateScale templateXValue[0] and templateYValue[min] templateScale templateXValue[last] templateYLabel[1] in templateTitleDate[0] . It topped the templateTitleDate[0] best-seller lists in the categories adult fiction and romance .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] of templateYLabel[1] in the templateTitle[4] as of 2017 . During the survey period , it was found that templateValue[2][0] templateScale of templateYLabel[1] stated that they had between templateXValue[1] and templateXValue[1] templateXValue[0] .
generated: This statistic gives information on the Fifty Shades Grey of copies in the sales as of 2017 .  During the survey period , it was found that millions of copies stated that they had between Digital and Print .

Example 217:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. students who have used hallucinogenic drugs 2017
Data:
                              0     1      2
Sex                      Female  Male  Total
All races / ethnicities     5.5   7.6    6.6
White (non-Hispanic)        6.4   7.9    7.2
Black (non-Hispanic)        1.4   4.8    3.3
Hispanic                    5.8   8.2    7.1 

gold: This statistic shows the share of American high school students in grades 9 to 12 who stated they have used hallucinogenic drugs at least once in their life as of 2017 , by gender and ethnicity . According to the survey , 7.9 percent of male student respondents with a white , non-Hispanic background stated they have used hallucinogenic drugs ( e.g . LSD , acid , PCP , angel dust , mescaline , or mushrooms ) one or more times during their life .
gold_template: This statistic shows the share of American high school templateTitle[1] in grades 9 to 12 templateTitle[2] stated they templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] at least once in their life as of templateTitleDate[0] , by gender and ethnicity . According to the survey , templateValue[2][1] templateScale of templateValue[0][1] student respondents with a templateLabel[2][0] , non-Hispanic background stated they templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] ( e.g . LSD , acid , PCP , angel dust , mescaline , or mushrooms ) templateValue[3][0] or more times during their life .

generated_template: This statistic shows the results of a survey among American templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . According to the survey , templateValue[1][0] templateScale of the templateTitle[1] templateTitle[2] templateTitle[4] were templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] 2015 .
generated: This statistic shows the results of a survey among American students who have in the U.S. in 2017 , hallucinogenic drugs .  According to the survey , 5.5 % of the students who used were hallucinogenic drugs 2017 2015 .

Example 218:
titleEntities: {'Subject': ['Countries'], 'Date': ['2013']}
title: Countries with the lowest prevalence of condom use among its male population 2013
X_Axis['Country']: ['Niger', 'Madagascar', 'Mali', 'Sierra_Leone', 'Burundi', 'Ethiopia', 'DC_Congo', 'East_Timor', 'Uganda', 'Nigeria', 'Senegal', 'Benin', 'Liberia']
Y_Axis['Percentage', 'of', 'males', 'aged', '15-49']: ['4', '7', '10', '13', '14', '16', '16', '19', '19', '20', '21', '22', '24']

gold: This statistic depicts the countries with lowest prevalence of condom use among its male population worldwide as of 2013 . As of that year , Niger had the lowest prevalence of condom use among men worldwide . Only four percent of its male population aged 15 - 49 years used condoms .
gold_template: This statistic depicts the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] templateTitle[9] worldwide as of templateTitleDate[0] . As of that year , templateXValue[0] had the templateTitle[2] templateTitle[3] of templateTitle[4] templateTitle[5] templateTitle[6] men worldwide . Only templateYValue[min] templateScale of templateTitle[7] templateTitle[8] templateTitle[9] templateYLabel[2] 15 - 49 years used condoms .

generated_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] in templateTitleDate[0] , based on templateXLabel[0] . In templateTitleDate[0] , the templateXValue[0] was ranked first , with templateYValue[max] templateYLabel[2] templateYLabel[3] of the templateTitle[2] templateTitle[3] rate , followed by templateXValue[1] with templateYValue[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Countries lowest the prevalence condom in 2013 , based on Country .  In 2013 , the Niger was ranked first , with 24 aged 15-49 of the prevalence condom rate , followed by Madagascar with 7 aged 15-49 .

Example 219:
titleEntities: {'Subject': ['Easton Bell Sports'], 'Date': ['2013', '2013']}
title: Sporting goods industry : Revenue of Easton Bell Sports 2013
X_Axis['Year']: ['2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Revenue', '(in', 'million', 'U.S.', 'dollars)']: ['780.4', '827.2', '834.9', '772.8', '716.3', '775.5', '724.7', '639.0']

gold: This graph depicts the revenue of the sporting goods company Easton-Bell Sports from 2006 to 2013 . In 2006 , Easton-Bell Sports generated revenues of 639 million U.S. dollars . Easton-Bell Sports makes sports equipment and clothing under the brands Bell Sports , Blackburn , Easton , Giro , and Riddell .
gold_template: This graph depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] company Easton-Bell templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , Easton-Bell templateTitleSubject[0] generated revenues of templateYValue[min] templateScale templateYLabel[3] dollars . Easton-Bell templateTitleSubject[0] makes templateTitleSubject[0] equipment and clothing under the brands templateTitleSubject[0] , Blackburn , templateTitleSubject[0] , Giro , and Riddell .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] templateTitle[4] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[3] dollars .
generated: This graph depicts the annual National Hockey League Revenue of the Easton Bell Sports goods from 2006 to 2013 .  In 2013 , the Revenue of the Easton Bell Sports Easton amounted to 780.4 million U.S. dollars .

Example 220:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2018']}
title: Share of economic sectors in the GDP in Belgium 2018
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
Agriculture    0.5   0.64   0.63   0.69   0.64   0.68   0.78   0.65   0.77   0.65   0.71
Industry     19.09  19.31  19.32  19.67  19.67  19.88  20.08  20.78  20.89   20.6   21.6
Services     69.54  69.25  69.32  69.23  69.16  68.77   68.5  67.97  67.66  68.09  66.96 

gold: This statistic shows the share of economic sectors in the gross domestic product ( GDP ) in Belgium from 2008 to 2018 . In 2018 , the share of agriculture in Belgium 's gross domestic product was 0.5 percent , industry contributed approximately 19.09 percent and the services sector contributed about 69.54 percent .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] in the gross domestic product ( templateTitle[3] ) in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitle[0] of templateLabel[1][0] in templateTitleSubject[0] 's gross domestic product was templateValue[1][0] templateScale , templateLabel[2][0] contributed approximately templateValue[2][0] templateScale and the templateLabel[3][0] sector contributed about templateValue[3][0] templateScale .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the service sector .
generated: This statistic shows the Share of the economic sectors GDP ( Belgium ) 2018 in Belgium from 2008 to 2018 .  In 2018 , Agriculture contributed around 0.5 % to the Belgium of , 19.09 % came from the Industry and 69.54 % from the service sector .

Example 221:
titleEntities: {'Subject': ['World Cup'], 'Date': ['2018']}
title: Domestic teams with the most players at the 2018 World Cup
X_Axis['Team']: ['Manchester_City', 'Real_Madrid', 'FC_Barcelona', 'Tottenham_Hotspur', 'FC_Paris_Saint-Germain', 'Chelsea_FC', 'FC_Bayern_München', 'Manchester_United', 'Juventus', 'Atlético_Madrid', 'Al-Hilal_Riad', 'Al-Ahli_Dschidda']
Y_Axis['Number', 'of', 'players']: ['16', '15', '14', '12', '12', '12', '11', '11', '11', '9', '9', '9']

gold: Manchester City was the most represented team at the World Cup in 2018 , with 16 of its players participating . One of those players , Benjamin Mendy , came back with a World Cup winners ' medal as France took home the trophy after beating Croatia 4 - 2 in the final . Premier League players at the World CupThe Premier League sent the largest contingent of players to the 2018 World Cup , with 108 of the footballers at the tournament plying their trade in the English top division .
gold_template: templateXValue[0] was the templateTitle[3] represented templateXLabel[0] at the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[2] templateYValue[max] of its templateYLabel[1] participating . One of those templateYLabel[1] , Benjamin Mendy , came back templateTitle[2] a templateTitleSubject[0] winners ' medal as France took home the trophy after beating Croatia 4 - 2 in the final . Premier League templateYLabel[1] at the templateTitleSubject[0] CupThe Premier League sent the largest contingent of templateYLabel[1] to the templateTitleDate[0] templateTitleSubject[0] , templateTitle[2] 108 of the footballers at the tournament plying their trade in the English top division .

generated_template: The statistics ranks the teams of the templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[5] in templateTitleDate[0] , by templateYLabel[0] of templateYLabel[1] . templateXValue[0] had the highest templateYLabel[0] of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in that year .
generated: The statistics ranks the teams of the Domestic teams players in the World in 2018 , by Number of players .  Manchester_City had the highest Number of 16 registered Domestic teams players in that year .

Example 222:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2019']}
title: Total U.S. hay production 2000 to 2019
X_Axis['Year']: ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['Production', 'in', 'thousand', 'tons']: ['153603', '156416', '149467', '157390', '158122', '150461', '140783', '146901', '146270', '147700', '145000', '129880', '117072', '135002', '139923', '134502', '134995', '131455', '123600', '128864']

gold: This statistic shows the total hay production in the United States from 2000 to 2019 . According to the report , approximately 123.6 million tons of hay were produced in the country in 2018 .
gold_template: This statistic shows the templateTitle[0] templateTitle[2] templateYLabel[0] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . According to the report , approximately templateYValue[18] templateScale templateYLabel[2] of templateTitle[2] were produced in the country in templateXValue[18] .

generated_template: The statistic shows the templateYLabel[2] of goods and services produced in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateYLabel[2] of goods and services produced in the templateTitle[0] . It is forecasted that templateXLabel[0] .
generated: The statistic shows the tons of goods and services produced in the Total from 2000 to 2019 .  According to the report , the tons of goods and services produced in the Total .  It is forecasted that Year .

Example 223:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2006', '2017']}
title: Daily computer usage penetration in Great Britain 2006 - 2017 , by age
Data:
        0      1      2      3      4      5
Year  65+  55-64  45-54  35-44  25-34  16-24
2006    9     36     56     63     61     63
2013   37     67     76     80     84     88
2014   42     74     83     86     86     79
2015   45     72     76     84     78     82
2017   51     76     83     82     81     79 

gold: This statistic displays daily computer usage penetration in Great Britain , by age , in 2006 and in the years from 2013 to 2017 excluding 2016 . In 2017 , 79 percent of 16 to 24 year olds used the computer daily , up from 63 percent in 2006 .
gold_template: This statistic displays templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] , templateTitle[8] templateTitle[9] , in templateTitleDate[min] and in the years from templateLabel[2][0] to templateTitleDate[max] excluding 2016 . In templateTitleDate[max] , templateValue[3][last] templateScale of 16 to 24 templateLabel[0][0] olds used the templateTitle[1] templateTitle[0] , up from templateValue[1][max] templateScale in templateTitleDate[min] .

generated_template: This statistic displays the templateScale of individuals in selected templateValue[0][0] who were using templateTitle[0] templateTitle[1] in the templateTitle[6] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[7] templateTitle[8] . In templateTitleDate[max] , templateValue[1][last] templateScale of respondents stated that they had their most common in templateValue[0][0] using a templateTitle[1] .
generated: This statistic displays the percentage of individuals in selected 65+ who were using Daily computer in the 2006 from to 2017 , by .  In 2017 , 63 % of respondents stated that they had their most common in 65+ using a computer .

Example 224:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global youth unemployment rate until 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Youth', 'unemployment', 'rate']: ['13.2', '13.2', '13.2', '13.2', '13.1', '13', '13', '12.9', '12.8', '12.9', '12.9']

gold: The statistic shows the global youth unemployment rate from 2009 to 2014 with a forecast up to 2019 . The global youth unemployment rate was at 12.9 percent in 2012 . Youth unemployment Just like the general unemployment rate , youth unemployment is recorded and monitored to gauge the job market situation in a country and worldwide .
gold_template: The statistic shows the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] from templateXValue[min] to templateXValue[5] with a forecast up to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmin(X)] templateScale in templateXValue[7] . templateYLabel[0] templateYLabel[1] Just like the general templateYLabel[1] templateYLabel[2] , templateYLabel[0] templateYLabel[1] is recorded and monitored to gauge the job market situation in a country and worldwide .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Youth unemployment rate in Global from 2009 and 2019 .  According to the source , the data are ILO estimates .  In 2019 , the estimated Youth unemployment rate in Global was at 13.2 % .

Example 225:
titleEntities: {'Subject': ['OLED'], 'Date': ['2016']}
title: OLED panel fabrication : U.S. cost targets 2016 to 2025
Data:
                        0     1     2     3
Year                 2016  2018  2020  2025
Depreciation          600   140    70    35
Labor                 100    25    15     5
Organic materials     150   100    50    15
Inorganic materials   200   140   100    30
Other fixed costs      50    15    10     5 

gold: This statistic shows the cost targets for organic light-emitting diode ( OLED ) panel fabrication in the United States from 2016 to 2025 , by cost area . The target for labor costs for 2025 is expected to reach decrease to 5 U.S. dollars per square meter of OLED panel produced .
gold_template: This statistic shows the templateTitle[4] templateTitle[5] for templateLabel[3][0] light-emitting diode ( templateTitleSubject[0] ) templateTitle[1] templateTitle[2] in the templateTitle[3] from templateValue[0][0] to templateValue[0][last] , by templateTitle[4] area . The target for templateLabel[2][0] templateLabel[5][2] for templateValue[0][last] is expected to reach templateNegativeTrend to templateValue[2][last] templateTitle[3] dollars per square meter of templateTitleSubject[0] templateTitle[1] produced .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] templateTitle[4] of the United Kingdom ( templateTitleSubject[0] ) from templateValue[0][last] to templateValue[0][0] . The templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] is expected to reach templateValue[1][0] templateScale templateYLabel[3] templateYLabel[4] in templateValue[0][0] , up from templateValue[1][last] templateScale U.S. dollars in templateValue[0][last] .
generated: This statistic shows the fabrication U.S. cost of the United Kingdom ( OLED ) from 2025 to 2016 .  The OLED fabrication U.S. cost is expected to reach 600 billion in 2016 , up from 35 billion U.S. dollars in 2025 .

Example 226:
titleEntities: {'Subject': ['Sudan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Sudan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['35.48', '34.89', '34.34', '33.93', '33.69', '30.87', '34.28', '45.94', '55.56', '65.27', '60.73', '52.89', '48.95', '66.45', '65.72', '54.81', '64.83', '59.44', '45.26', '35.18', '26.65', '21.36', '18.14', '15.72', '13.13', '10.71', '10.92', '10.24', '8.38', '6.69', '5.96', '5.25', '3.1', '2.53', '2.25', '16.85', '9.55', '11.96', '7.4', '5.55', '7.99']

gold: The statistic shows gross domestic product ( GDP ) in Sudan from 1984 to 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Sudan from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 227:
titleEntities: {'Subject': ['Latin America', 'Caribbean'], 'Date': ['2019', '2019']}
title: Employment by economic sector in Latin America and the Caribbean 2019
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2019   2018   2017   2016   2015   2014   2013   2012   2011   2010   2009
Agriculture  13.94  14.02  14.07  14.16  14.17  14.36  14.66  15.01  16.54  16.85  17.18
Industry     20.95  20.99  21.04   21.2  21.64  21.82  21.65  21.75  21.36  21.44  21.48
Services     65.11  64.99  64.89  64.64  64.19  63.81  63.69  63.24   62.1  61.71  61.34 

gold: The statistic shows the distribution of employment in Latin America and the Caribbean by economic sector from 2009 to 2019 . In 2019 , approximately 13.94 percent of the employees in Latin America and the Caribbean were working in the agricultural sector , 20.95 percent in industry and about 65.11 percent in the service sector .
gold_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] and the templateTitleSubject[1] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , approximately templateValue[1][0] templateScale of the employees in templateTitleSubject[0] and the templateTitleSubject[1] were working in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and about templateValue[3][0] templateScale in the service templateTitle[3] .

generated_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .
generated: The statistic shows the distribution of Employment in Latin America by economic sector from 2009 to 2019 .  In 2019 , 13.94 % of the employees in Latin America were active in the agricultural sector , 20.95 % in Industry and 65.11 % in the service sector .

Example 228:
titleEntities: {'Subject': ['Atlanta'], 'Date': ['2016', '2017']}
title: Quarterly average daily rate of hotels in Atlanta 2016 to 2017
Data:
           0    1    2    3
Quarter   Q1   Q2   Q3   Q4
2016     167  163  165  161
2017     170    0    0    0 

gold: This statistic shows the quarterly average daily rate of hotels in Atlanta in 2016 and 2017 . In the first quarter of 2017 , the average daily rate of hotels in Atlanta was 170 U.S. dollars , up from 161 dollars the previous quarter .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . In the first templateLabel[0][0] of templateTitleDate[max] , the templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] was templateValue[2][max] U.S. dollars , up from templateValue[1][min] dollars the previous templateLabel[0][0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . In the first templateLabel[0][0] of templateTitleDate[max] , the templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in the country was templateValue[2][max] U.S. dollars .
generated: This statistic shows the Quarterly average daily rate of hotels in Atlanta 2016 and 2017 .  In the first Quarter of 2017 , the average daily rate of hotels in Atlanta the country was 170 U.S. dollars .

Example 229:
titleEntities: {'Subject': ['Germany'], 'Date': ['2019']}
title: Market share of selected TV stations in Germany 2019
Data:
                                       0     1     2      3    4          5           6       7
TV Channel                           ZDF   ARD   RTL  Sat.1  Vox  ProSieben  kabel_eins  RTL_II
Total audience (3 years and older)  12.7  11.2   8.5    6.1  4.9        4.2         3.6     2.9
14-49 years                          5.6   6.5  11.9      8  6.9        9.2           5     5.2 

gold: This statistic shows data on the audience market share of selected television channels in Germany in 2019 . Among viewers aged three years and older , the market share of TV station ZDF amounted to almost 12.7 percent . In comparison , among the age group of people between 14 to 49 years , the channel had a market share of only 5.6 percent .
gold_template: This statistic shows data on the templateLabel[1][1] templateTitle[0] templateTitle[1] of templateTitle[2] television channels in templateTitleSubject[0] in templateTitleDate[0] . Among viewers aged templateValue[1][last] templateLabel[1][3] and older , the templateTitle[0] templateTitle[1] of templateLabel[0][0] station templateValue[0][0] amounted to almost templateValue[1][0] templateScale . In comparison , among the age group of people between 14 to 49 templateLabel[1][3] , the templateLabel[0][1] had a templateTitle[0] templateTitle[1] of only templateValue[2][0] templateScale .

generated_template: The statistic shows the results of a survey concerning the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] . The survey revealed that templateValue[1][0] templateScale of the respondents of the average templateValue[0][0] in a templateValue[0][0] .
generated: The statistic shows the results of a survey concerning the Market share selected TV stations in the United Kingdom ( Germany ) in 2019 .  The survey revealed that 12.7 % of the respondents of the average ZDF in a ZDF .

Example 230:
titleEntities: {'Subject': ['Sears', 'United States'], 'Date': ['2011', '2017']}
title: Number of Sears stores in the United States 2011 - 2017 , by type
Data:
                     0     1     2     3     4     5     6
Year              2017  2016  2015  2014  2013  2012  2011
Full-line stores   547   670   705   709   778   798   867
Specialty stores    23    25    26    29    50    54  1338 

gold: Sears Domestic operated about 547 full-line stores throughout the Unites States in 2017 . Sears Holdings is an American retailer , which operates through two well-known retail brands , Kmart and Sears . Sears Holdings has a significant number of Kmart and Sears Domestic stores in operation around the world .
gold_template: templateTitleSubject[0] Domestic operated about templateValue[1][0] templateLabel[1][0] templateTitle[2] throughout the Unites templateTitleSubject[1] in templateValue[0][0] . templateTitleSubject[0] Holdings is an American retailer , which operates through two well-known retail brands , Kmart and templateTitleSubject[0] . templateTitleSubject[0] Holdings has a significant templateTitle[0] of Kmart and templateTitleSubject[0] Domestic templateTitle[2] in operation around the world .

generated_template: The statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . The templateTitle[0] of the templateTitle[2] templateTitle[3] in templateValue[0][0] was templateValue[1][0] U.S. dollars in templateValue[0][0] .
generated: The statistic shows the Number of the Sears stores United in Sears from 2011 to 2017 .  The Number of the stores United in 2017 was 547 U.S. dollars in 2017 .

Example 231:
titleEntities: {'Subject': ['Boston Red Sox'], 'Date': ['2019']}
title: Franchise value of the Boston Red Sox 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['3200', '2800', '2700', '2300', '2100', '1500', '1312', '1000', '912', '870', '833', '816', '724', '617', '563', '533', '488', '426']

gold: This graph depicts the value of the Boston Red Sox franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 3.2 billion U.S. dollars.The Boston Red Sox are owned by John Henry and Thomas Werner , who bought the franchise for 380 million U.S. dollars in 2002 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Sox templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] dollars.The templateTitleSubject[0] Sox are owned by John Henry and Thomas Werner , who bought the templateYLabel[0] for 380 templateScale templateYLabel[3] templateYLabel[4] in templateXValue[min] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Terry and Kim Pegula .
generated: This graph depicts the Franchise value of the Boston Red Sox of the National Football League from 2002 to 2019 .  In 2019 , the Franchise value came to 3200 million U.S. dollars .  The Boston Red Sox are owned by Terry and Kim Pegula .

Example 232:
titleEntities: {'Subject': ['Ukraine'], 'Date': ['1994', '2024']}
title: Inflation rate of Ukraine 1994 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994']
Y_Axis['Inflation', 'rate', 'change', 'compared', 'to', 'previous', 'year']: ['5', '5', '5.02', '5.29', '5.89', '8.74', '10.95', '14.44', '13.91', '48.68', '12.1', '-0.26', '0.57', '7.96', '9.37', '15.9', '25.2', '12.84', '9.08', '13.52', '9.04', '5.21', '0.76', '11.96', '28.2', '22.68', '10.58', '15.89', '80.23', '376.75', '891.19']

gold: This statistic shows the Ukrainian inflation rate from 1994 to 2018 compared to the previous year , with projections up until 2024 . In 2018 , Ukraine 's inflation rate increased by 10.95 percent compared to 2017 .
gold_template: This statistic shows the Ukrainian templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[6] templateYLabel[3] to the templateYLabel[4] templateXLabel[0] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templatePositiveTrend by templateYValue[6] templateScale templateYLabel[3] to templateXValue[7] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[7] , the templateTitle[0] of templateTitleSubject[0] was at approximately templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Inflation rate of the 2024 from 1994 to 2024 .  In 2017 , the Inflation of Ukraine was at approximately 14.44 rate change compared .

Example 233:
titleEntities: {'Subject': ['Kohl'], 'Date': ['2012', '2018']}
title: Total number of employees of Kohl 's 2012 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'employees', 'in', 'thousands']: ['129', '137', '138', '140', '137', '137', '135']

gold: This timeline depicts the total number of employees of the Kohl 's Corporation from 2012 to 2018 . In 2018 , the Kohl 's Corporation employed 129,000 associates .
gold_template: This timeline depicts the templateTitle[0] templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] templateTitle[4] Corporation from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateTitle[4] Corporation employed templateYValue[min] associates .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] employed approximately templateYValue[0] people around the world .
generated: This statistic shows the Number of employees the Kohl between 2012 and 2018 .  In 2018 , Kohl employed approximately 129 people around the world .

Example 234:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2015']}
title: Recovery from an eating disorder in the United Kingdom ( UK ) 2015 , by disorder
Data:
                              0            1                2
Category          Full_recovery  Improvement  Chronically_ill
Anorexia nervosa             46           33               20
Bulimia nervosa              45           27               23 

gold: This statistic displays the recovery rate of individuals suffering from an eating disorder in the United Kingdom in 2015 . Approximately 46 percent of sufferers of anorexia nervosa and 45 percent of sufferers of bulimia nervosa make a full recovery .
gold_template: This statistic displays the templateValue[0][0] rate of individuals suffering templateTitle[1] an templateTitle[2] templateTitle[3] in the templateTitleSubject[0] in templateTitleDate[0] . Approximately templateValue[1][0] templateScale of sufferers of templateLabel[1][0] templateLabel[1][1] and templateValue[2][0] templateScale of sufferers of templateLabel[2][0] templateLabel[1][1] make a templateValue[0][0] .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . The templateTitle[0] of the templateTitleSubject[0] templateValue[0][0] templateValue[0][0] amounted to templateValue[2][0] templateScale templateYLabel[3] templateYLabel[4] in that year .
generated: This statistic shows the Recovery of the from eating disorder United in the Kingdom from 2015 to .  The Recovery of the United Kingdom Full_recovery amounted to 45 % in that year .

Example 235:
titleEntities: {'Subject': ['South Sudan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in South Sudan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['265.58', '270.37', '278.36', '244.0', '243.27', '275.18', '353.17', '273.14', '281.51', '1225.19', '1309.95']

gold: The statistic shows gross domestic product ( GDP ) per capita in South Sudan from 2014 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in South Sudan from 2014 to 2018 , with projections up until 2024 .  GDP refers to the total market value of all goods and services that are produced within a country per Year .  It is an important indicator of the economic strength of a country .

Example 236:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Mobile devices used by U.S. students 2015 , by type and frequency
Data:
                                                         0           1       2                          3
Response                     Laptop_notebook_or_Chromebook  Smartphone  Tablet  Hybrid_or_2_in_1_computer
Never                                                   17          42      42                         84
A few times a month or less                             22          16      21                          5
2-3 times per week or more                              61          41      37                         10 

gold: This statistic presents information on the frequency of mobile device usage for school work according to students in the United States as of March 2015 , by type of the mobile device used . During the survey period , it was found that 41 percent of the students used smartphones 2 - 3 times per week or more for school work .
gold_template: This statistic presents information on the templateTitle[9] of templateTitle[0] device usage for school work according to templateTitle[5] in the templateTitle[4] as of 2015 , templateTitle[3] templateTitle[8] of the templateTitle[0] device templateTitle[2] . During the survey period , it was found that templateValue[3][1] templateScale of the templateTitle[5] templateTitle[2] smartphones templateValue[0][3] - 3 templateLabel[2][1] templateLabel[3][2] templateLabel[3][3] or templateLabel[3][4] for school work .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] adult templateValue[0][0] in the United Kingdom ( templateTitleSubject[1] ) as of templateTitleDate[0] . According to the survey , templateValue[1][0] templateScale of respondents reported that they had used templateValue[0][0] in a .
generated: This statistic shows the results of a survey among U.S. adult Laptop_notebook_or_Chromebook in the United Kingdom ( U.S. ) as of 2015 .  According to the survey , 17 % of respondents reported that they had used Laptop_notebook_or_Chromebook in a .

Example 237:
titleEntities: {'Subject': ['Industrial'], 'Date': ['2004', '2018']}
title: Industrial robots - worldwide sales 2004 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Sales', 'in', 'thousand', 'units']: ['384', '382', '294', '254', '221', '178', '159', '166', '121', '60', '113', '114', '112', '120', '97']

gold: Worldwide industrial robot shipments increased by one percent from about 382,000 in 2017 to around 384,000 in 2018 . Global spending on industrial robots is projected to exceed 24 billion U.S. dollars by 2025 . Leading markets Japan , China , the United States , South Korea , and Germany are counted among the five leading industrial robot markets worldwide .
gold_template: templateTitle[2] templateTitleSubject[0] robot shipments templatePositiveTrend by one templateScale from about templateYValue[1] in templateXValue[1] to around templateYValue[max] in templateXValue[idxmax(Y)] . Global spending on templateTitleSubject[0] templateTitle[1] is projected to exceed 24 templateScale U.S. dollars by 2025 . Leading markets Japan , China , the country , South Korea , and Germany are counted among the five leading templateTitleSubject[0] robot markets templateTitle[2] .

generated_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the National Football League ( templateTitleSubject[0] ) in the templateTitle[7] from templateXValue[min] to templateXValue[max] . The templateTitle[0] of templateTitleSubject[0] templatePositiveTrend to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .
generated: Between 2004 and 2018 , the Sales thousand of the National Football League ( Industrial ) in the 2018 from 2004 to 2018 .  The Industrial of increased to 384 units in 2018 .

Example 238:
titleEntities: {'Subject': ['France'], 'Date': ['2012', '2028']}
title: Domestic travel spending in France 2012 to 2028
X_Axis['Year']: ['2028', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Spending', 'in', 'billion', 'euros']: ['131.4', '114.9', '112.3', '110.9', '108.1', '108.3', '107.5', '107.2']

gold: Domestic tourism expenditure is expected to reach 131.4 billion euros in 2028 . The spending of French tourists in their country has increased since the beginning of the 2010s and has already reached nearly 115 billion euros in 2018 . Similarly , the spending of international tourists in France is estimated to grow in the coming years .
gold_template: templateTitle[0] tourism expenditure is expected to reach templateYValue[max] templateScale templateYLabel[2] in templateXValue[idxmax(Y)] . The templateYLabel[0] of French tourists in their country has templatePositiveTrend since the beginning of the 2010s and has already reached nearly templateYValue[1] templateScale templateYLabel[2] in templateXValue[1] . Similarly , the templateYLabel[0] of international tourists in templateTitleSubject[0] is estimated to grow in the coming years .

generated_template: This statistic shows the total annual spend of templateTitle[0] tourists ( 'visitor exports ' _ ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with a forecast for templateXValue[max] . Inbound templateTitle[1] expenditure in templateTitleSubject[0] reached around templateYValue[5] templateScale templateYLabel[2] in templateXValue[5] .
generated: This statistic shows the total annual spend of Domestic tourists ( 'visitor exports ' _ ) in France from 2012 to 2013 , with a forecast for 2028 .  Inbound travel expenditure in France reached around 108.3 billion euros in 2014 .

Example 239:
titleEntities: {'Subject': ['PyeongChang'], 'Date': ['2018']}
title: Winter Olympic Games in PyeongChang 2018 final medal tally
Data:
              0        1       2              3            4            5                             6            7       8       9       10     11     12     13              14       15             16       17        18         19      20        21     22           23       24       25       26          27      28             29
Country  Norway  Germany  Canada  United_States  Netherlands  South_Korea  Olympic_Athletes_from_Russia  Switzerland  France  Sweden  Austria  Japan  Italy  China  Czech_Republic  Finland  Great_Britain  Belarus  Slovakia  Australia  Poland  Slovenia  Spain  New_Zealand  Hungary  Ukraine  Belgium  Kazakhstan  Latvia  Liechtenstein
Gold         14       14      11              9            8            5                             2            5       5       7        5      4      3      1               2        1              1        2         1          0       1         0      0            0        1        1        0           0       0              0
Silver       14       10       8              8            6            8                             6            6       4       6        3      5      2      6               2        1              0        1         2          2       0         1      0            0        0        0        1           0       0              0
Bronze       11        7      10              6            6            4                             9            4       6       1        6      4      5      2               3        4              4        0         0          1       1         1      2            2        0        0        0           1       1              1 

gold: The statistic reflects the medal count of the Winter Olympic Games in PyeongChang in 2018 . Norway topped the table , finishing the Games with a total of 39 medals - 14 gold , 14 silver , and 11 bronze .
gold_template: The statistic reflects the templateTitle[6] count of the templateTitle[0] templateValue[0][6] templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] . templateValue[0][0] topped the table , finishing the templateTitle[2] with a total of 39 medals - templateValue[1][0] templateLabel[1][0] , templateValue[1][0] templateLabel[2][0] , and templateValue[1][2] templateLabel[3][0] .

generated_template: templateValue[0][0] is the most successful nation of all time at the templateTitleSubject[0] Games , having amassed a total of 368 medals since the first templateTitleSubject[0] Olympics in templateTitleDate[min] – this tally includes templateValue[1][0] templateLabel[1][0] medals , templateValue[2][0] templateLabel[2][0] , and templateValue[3][0] templateLabel[3][0] . This total is especially impressive considering the Scandinavian templateLabel[0][0] has a population of just over five templateScale inhabitants . templateValue[0][0] dominate the medals Given templateValue[0][0] 's dominance at the top of the overall templateTitle[1] templateTitle[2] , it comes as no surprise that the three most successful templateTitleSubject[0] Olympians of all time come from templateValue[0][0] .
generated: Norway is the most successful nation of all time at the PyeongChang Games , having amassed a total of 368 medals since the first PyeongChang Olympics in 2018 – this tally includes 14 Gold medals , 14 Silver , and 11 Bronze .  This total is especially impressive considering the Scandinavian Country has a population of just over five million inhabitants .  Norway dominate the medals Given Norway 's dominance at the top of the overall Olympic Games , it comes as no surprise that the three most successful PyeongChang Olympians of all time come from Norway .

Example 240:
titleEntities: {'Subject': ['North America'], 'Date': ['2011', '2018']}
title: Share of population that uses a smartphone in North America 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Share', 'of', 'population']: ['66.5', '64', '60.5', '56.7', '51.3', '45.1', '37.9', '29.3']

gold: The statistic depicts the smartphone user penetration rate in North America , as percentage of the total population , from 2011 to 2018 . The smartphone penetration rate is forecast to reach 64 percent by 2017 .
gold_template: The statistic depicts the templateTitle[4] user penetration rate in templateTitleSubject[0] , as templateScale of the total templateYLabel[1] , from templateXValue[min] to templateXValue[max] . The templateTitle[4] penetration rate is forecast to reach templateYValue[1] templateScale by templateXValue[1] .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the Mexican templateYLabel[1] were accessing the templateTitle[1] . This figure is projected to grow to templateYValue[idxmax(X)] templateScale by templateXValue[idxmax(Y)] .
generated: This statistic presents the Share population that uses in North America from 2011 to 2018 .  In 2011 , 29.3 % of the Mexican population were accessing the population .  This figure is projected to grow 66.5 % by 2018 .

Example 241:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Most popular gaming related apps in the U.S. 2019 , by audience
X_Axis['Month']: ['Google_Play_Games', 'Pokémon_GO', 'Candy_Crush_Saga', 'GameSpot_(main)', 'IGN', 'Wordscapes', 'Lucky_Money', 'Game_Launcher_(for_Samsung)', 'Words_With_Friends', 'Solitaire_by_MobilityWare', 'Candy_Crush_Soda_Saga', 'Steam_Community_Activity', 'Coin_Master', 'Trivia_Today', 'Nintendo', 'Homescapes', 'Xbox', 'Zynga_(main)', 'Gardenscapes_-_New_Acres', 'Mario_Kart_Tour', 'Kotaku', 'Gamepedia', 'Cookie_Jam', 'POP!_Slots', 'Toon_Blast', 'Game_Tools_(for_Samsung)', 'GamesRadar+', 'Lucky_Time', 'Sudoku', 'EA']
Y_Axis['Million', 'unique', 'visitors']: ['18.74', '10.66', '10.23', '10.05', '9.56', '8.91', '8.83', '6.97', '6.95', '6.85', '5.95', '5.31', '4.83', '4.37', '4.36', '4.35', '4.35', '4.3', '4.26', '4.21', '3.92', '3.72', '3.49', '3.46', '3.43', '3.43', '3.3', '3.28', '3.05', '3.04']

gold: In a world where people are constantly on the move and seeking new forms of entertainment to make their journeys go faster , mobile gaming is bigger than ever . In September 2019 , Google Play Games was the most popular mobile gaming related app in the United States with over 18.74 million monthly users . The highest individual game on the list was Pokémon GO , which averaged 10.66 million users in the same month .
gold_template: In a world where people are constantly on the move and seeking templateXValue[18] forms of entertainment to make their journeys templateXValue[1] faster , mobile templateTitle[2] is bigger than ever . In 2019 , templateXValue[0] Games was the templateTitle[0] templateTitle[1] mobile templateTitle[2] templateTitle[3] app in the templateTitle[5] templateXValue[8] over templateYValue[max] templateScale monthly users . The highest individual templateXValue[7] on the list was templateXValue[1] , which averaged templateYValue[1] templateScale users in the same templateXLabel[0] .

generated_template: This statistic shows the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] of templateTitleSubject[0] templateTitle[4] templateYLabel[1] from templateTitleDate[min] to templateTitleDate[max] . According to the source , the templateTitle[0] templateTitle[1] templateTitle[2] of people were templateXValue[0] , at templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Most popular the gaming Million of U.S. apps unique from 2019 to .  According to the source , the Most popular gaming of people were Google_Play_Games , at 18.74 visitors .

Example 242:
titleEntities: {'Subject': ['U.S. June'], 'Date': ['2014', '2014']}
title: Chains with the largest share of the U.S. burger restaurant market as of June 2014
X_Axis['Burger', 'Restaurant', 'Chains']: ["McDonald's", 'Burger_King', "Wendy's", 'Sonic_Drive-Ins']
Y_Axis['Share', 'of', 'the', 'burger', 'restaurant', 'market']: ['34.3', '15.4', '13.9', '8.2']

gold: This statistic shows the restaurant chains with the largest share of the burger restaurant market in the United States as of June 2014 . McDonald 's comprised the largest share of the burger restaurant market at 34.3 percent .
gold_template: This statistic shows the templateXLabel[1] templateXLabel[2] templateTitle[1] the templateTitle[2] templateYLabel[0] of the templateXValue[1] templateXLabel[1] templateYLabel[3] in the templateTitle[4] as of templateTitleSubject[0] templateTitleDate[0] . McDonald 's comprised the templateTitle[2] templateYLabel[0] of the templateXValue[1] templateXLabel[1] templateYLabel[3] at templateYValue[max] templateScale .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[8] templateTitle[9] . The templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was templateXValue[0] in templateTitleDate[0] , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Chains largest share U.S. in U.S. June 2014 , .  The Chains largest share in U.S. June was McDonald's in 2014 , with a Share burger of 34.3 restaurant market .

Example 243:
titleEntities: {'Subject': ['Peru'], 'Date': ['2019']}
title: Employment by economic sector in Peru 2019
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2019   2018   2017   2016   2015   2014   2013   2012   2011   2010   2009
Agriculture  27.21  27.46  27.69  28.38  28.26  28.03  27.41  27.44  28.28  27.73  28.52
Industry     15.64  15.64  15.65  16.22  16.57  16.37  17.02  17.03  16.48  16.86  16.44
Services     57.15   56.9  56.65  55.41  55.17   55.6  55.56  55.53  55.24  55.42  55.04 

gold: The statistic shows the distribution of employment in Peru by economic sector from 2009 to 2019 . In 2019 , 27.21 percent of the workforce in Peru were active in the agricultural sector , 15.64 percent in industry and 57.15 percent in the service sector .
gold_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the workforce in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the service sector .
generated: This statistic shows the Employment of the by economic sector ( Peru ) 2019 in Peru from 2009 to 2019 .  In 2019 , Agriculture contributed around 27.21 % to the Peru of , 15.64 % came from the Industry and 57.15 % from the service sector .

Example 244:
titleEntities: {'Subject': ['UK', 'UK'], 'Date': ['2013']}
title: UK survey : when most consumers will start their Christmas shopping in the UK in 2013
X_Axis['Month']: ['November', 'Alredy_bought_presents', 'October', 'December', 'September', 'The_week_before_Christmas']
Y_Axis['Share', 'of', 'respondents']: ['31', '25', '17', '17', '8', '2']

gold: When are consumers more likely to their Christmas shopping in the UK , September 2013 . The majority of the consumers in the UK plan to do their Christmas shopping in November . One in four had already bought presents while 2 percent of the consumers planed to go shopping in December or the week before Christmas .
gold_template: templateTitle[2] are templateTitle[4] more likely to templateTitle[7] templateXValue[last] templateTitle[9] in the templateTitleSubject[0] , templateXValue[4] templateTitleDate[0] . The majority of the templateTitle[4] in the templateTitleSubject[0] plan to do templateTitle[7] templateXValue[last] templateTitle[9] in templateXValue[0] . One in four had already templateXValue[1] while templateYValue[min] templateScale of the templateTitle[4] planed to go templateTitle[9] in templateXValue[3] or the templateXValue[last] Christmas .

generated_template: This statistic illustrates the results of a survey about the leading templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] , templateTitle[9] templateTitle[10] . During the survey , it was found that templateYValue[max] templateScale of the templateYLabel[1] who had a templateXValue[0] .
generated: This statistic illustrates the results of a survey about the leading UK survey when most in the UK ( ) in 2013 , shopping UK .  During the survey , it was found that 31 % of the respondents who had a November .

Example 245:
titleEntities: {'Subject': ['China'], 'Date': ['2018']}
title: Distribution of the workforce across economic sectors in China 2018
Data:
                0     1     2     3     4     5     6     7     8     9    10
Year         2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008
Agriculture  26.1    27  27.7  28.3  29.5  31.4  33.6  34.8  36.7  38.1  39.6
Industry     27.6  28.1  28.8  29.3  29.9  30.1  30.3  29.5  28.7  27.8  27.2
Services     46.3  44.9  43.5  42.4  40.6  38.5  36.1  35.7  34.6  34.1  33.2 

gold: The statistic shows the distribution of the workforce across economic sectors in China from 2008 to 2018 . In 2018 , 26.1 percent of the workforce were employed in agriculture , 27.6 percent in industry and 46.3 percent in services . Distribution of workforce in China – additional information In 2012 , China was the largest exporting country worldwide with an export value of about two trillion U.S. dollars .
gold_template: The statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the templateTitle[1] were employed in templateLabel[1][0] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in templateLabel[3][0] . templateTitle[0] of templateTitle[1] in templateTitleSubject[0] – additional information In templateValue[0][6] , templateTitleSubject[0] was the largest exporting country worldwide with an export value of about two templateScale U.S. dollars .

generated_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .
generated: The statistic shows the distribution of in China workforce across economic from 2008 to 2018 .  In 2018 , 26.1 % of the employees in China were active in the agricultural economic , 27.6 % in Industry and 46.3 % in the service economic .

Example 246:
titleEntities: {'Subject': ['India'], 'Date': ['2011']}
title: Most preferred sources of entertainment in India in 2011
X_Axis['Response']: ['Using_the_internet_for_social_or_personal_interests', 'Watching_television_(any_device)', 'Reading_newspapers_(either_printed_or_online)', 'Listening_to_music_(using_any_device)', 'Reading_books_(either_physical_books_or_via_an_e-book_reader/online)', 'Reading_magazines_(either_printed_or_online)', 'Going_to_the_movies', 'Playing_videogames_(handhelds_PC_console_mobile/cellular/smartphone_online)', 'Listening_to_the_radio_(any_format/device)']
Y_Axis['Share', 'of', 'respondents']: ['51', '51', '46', '37', '26', '26', '24', '14', '12']

gold: The statistic shows the most preferred sources of entertainment for respondents from India in 2011 . During the Deloitte survey , 51 percent of respondents stated that using the internet for social or personal interests was their favorite source of entertainment .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateXValue[0] templateYLabel[1] from templateTitleSubject[0] in templateTitleDate[0] . During the Deloitte survey , templateYValue[max] templateScale of templateYLabel[1] stated that templateXValue[0] the templateXValue[0] social or templateXValue[0] was their favorite source of templateTitle[3] .

generated_template: As of templateTitleDate[0] , it was found that templateYValue[max] templateScale of adults in the country accessed the templateTitle[0] with templateYValue[1] templateScale of the templateYLabel[1] citing templateXValue[2] templateXValue[0] a templateXValue[1] templateXValue[2] . templateXValue[1] and templateXValue[2] were templateXValue[3] with templateYValue[2] templateScale of templateYLabel[1] .
generated: As of 2011 , it was found that 51 % of adults in the country accessed the Most with 51 % of the respondents citing Reading_newspapers_(either_printed_or_online) Using_the_internet_for_social_or_personal_interests a Watching_television_(any_device) Reading_newspapers_(either_printed_or_online) .  Watching_television_(any_device) and Reading_newspapers_(either_printed_or_online) were Listening_to_music_(using_any_device) with 46 % of respondents .

Example 247:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2017', '2023']}
title: Argentina : internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['94', '93', '91', '88', '85', '82', '77']

gold: This statistic provides information on internet user penetration in Argentina from 2017 to 2023 . In 2017 , 77 percent of the population in Argentina were accessing the internet . This figure is projected to grow to 94 percent by 2023 .
gold_template: This statistic provides information on templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the templateYLabel[1] in templateTitleSubject[0] were accessing the templateTitle[1] . This figure is projected to grow to templateYValue[idxmax(X)] templateScale by templateXValue[idxmax(Y)] .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[3] rate in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the Singaporean templateYLabel[1] were using the templateTitle[1] . In templateXValue[max] , this figure is projected to grow to templateYValue[idxmax(X)] templateScale .
generated: This statistic gives information on the internet penetration rate in Argentina from 2017 to 2023 .  In 2017 , 77 % of the Singaporean population were using the internet .  In 2023 , this figure is projected to grow 94 % .

Example 248:
titleEntities: {'Subject': ['Rome', 'Italy'], 'Date': ['2019']}
title: Average price for properties for sale in Rome , Italy 2019 , by area
X_Axis['Area']: ['Centro_Storico', 'Aventino_San_Saba_Caracalla', 'Parioli_Flaminio', 'Testaccio_Trastevere', 'Prati_Borgo_Mazzini_Delle_Vittorie', 'Salario_Trieste', 'Bologna_Policlinico', 'Corso_Francia_Vigna_Clara_Fleming_Ponte_Milvio', 'Termini_Repubblica', "Camilluccia_Cortina_d'Ampezzo", "Balduina_Medaglie_d'Oro_Degli_Eroi", 'Gregorio_VII_Baldo_degli_Ubaldi']
Y_Axis['Price', 'in', 'euros', 'per', 'square', 'meter']: ['7817', '6910', '5622', '5435', '5137', '4884', '4531', '4289', '4107', '4099', '4000', '3972']

gold: The statistic shows the average price for houses , apartments and residential real estate for sale in selected areas of the Italian city of Rome as of December 2019 . According to the data , the sale price in the historic city center ( Centro Storico ) amounted to over 7.8 thousand euros per square meter .
gold_template: The statistic shows the templateTitle[0] templateYLabel[0] templateTitle[2] houses , apartments and residential real estate templateTitle[2] templateTitle[5] in selected areas of the Italian city of templateTitleSubject[0] as of 2019 . According to the data , the templateTitle[5] templateYLabel[0] in the historic city center ( templateXValue[0] ) amounted to over templateYValue[max] thousand templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] , based on sales . In that year , the templateXValue[0] accounted for templateYValue[max] templateScale of the world 's templateTitle[0] templateTitle[1] templateTitle[2] .
generated: The statistic presents the Average price for properties in the for in 2019 , based on sales .  In that year , the Centro_Storico accounted for 7817 % of the world 's Average price for .

Example 249:
titleEntities: {'Subject': ['Pharmacy'], 'Date': ['1992', '2018']}
title: Pharmacy and drug store sales in the U.S. 1992 to 2018
X_Axis['Year']: ['18', '17', '16', '15', '14', '13', '12', '11', '10', '9', '8', '7', '6', '5', '4', '3', '2', '1', '0', '99', '98', '97', '96', '95', '94', '93', '92']
Y_Axis['Billion', 'U.S.', 'dollars']: ['285.87', '276.92', '271.88', '263.47', '250.55', '235.55', '229.95', '231.25', '222.23', '217.26', '210.86', '202.28', '191.02', '179.17', '169.23', '165.52', '153.94', '141.77', '130.86', '121.28', '108.42', '98.82', '91.81', '85.84', '81.99', '79.71', '77.79']

gold: This timeline depicts pharmacy and drug store sales in the United States from 1992 to 2018 . In 2018 , U.S. pharmacy and drug store sales amounted to about 285.87 billion U.S. dollars.U.S . Pharmacies and Drug Stores IndustryThe retail drug industry is comprised of pharmacies or drugstores , as they are known in North America .
gold_template: This timeline depicts templateTitleSubject[0] and templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[1] templateTitleSubject[0] and templateTitle[1] templateTitle[2] templateTitle[3] amounted to about templateYValue[max] templateScale templateYLabel[1] dollars.U.S . Pharmacies and templateTitle[1] Stores IndustryThe retail templateTitle[1] industry is comprised of pharmacies or drugstores , as they are known in North America .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[1] templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] amounted to about templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] .
generated: The statistic shows Pharmacy drug store in the Pharmacy from 1992 to 2018 .  In 2018 , U.S. Pharmacy drug store in the Pharmacy amounted to about 285.87 billion U.S. dollars .

Example 250:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2016', '2018']}
title: Participation in online gambling in the United Kingdom 2016 - 2018 , by age group
Data:
                  0                1                2                3                4                5
Year  65+_year_olds  55-64_year_olds  45-54_year_olds  35-44_year_olds  25-34_year_olds  16-24_year_olds
2016           11.3             16.5             19.6             21.5             20.5             15.7
2017           11.6             20.6             18.6             22.6             24.4             13.6
2018           10.6             22.7               22               22             23.1             12.2 

gold: The statistic displays the results of a survey on the proportion of people within different age groups who gamble online on a monthly basis in the United Kingdom ( UK ) from 2016 to 2018 . As of December 2018 , it was found that 23.1 percent of respondents between the age of 25 and 34 years stated that they took part in a form of online gambling in the past four weeks . This age group had the highest proportion of people who gamble online .
gold_template: The statistic displays the results of a survey on the proportion of people within different templateTitle[8] groups who gamble templateTitle[1] on a monthly basis in the templateTitleSubject[0] ( UK ) from templateTitleDate[min] to templateTitleDate[max] . As of 2018 , it was found that templateValue[3][max] templateScale of respondents between the templateTitle[8] of 25 and 34 years stated that they took part in a form of templateTitle[1] templateTitle[2] in the past four weeks . This templateTitle[8] templateTitle[9] had the highest proportion of people who gamble templateTitle[1] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] and templateTitle[2] templateTitle[3] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[7] templateTitle[8] . In templateTitleDate[max] , the templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in the templateTitleSubject[1] was templateValue[1][0] U.S. dollars .
generated: This statistic shows the Participation of online and gambling United in the United Kingdom from 2016 to 2018 , by age .  In 2018 , the online gambling United of Kingdom in United Kingdom the was 11.3 U.S. dollars .

Example 251:
titleEntities: {'Subject': ['Christmas'], 'Date': ['2011', '2013']}
title: U.S. consumer Christmas spending habits 2011 to 2013
Data:
                                       0                                 1                               2           3                           4
Response  Spend_more_than_last_Christmas  Spend_the_same_as_last_Christmas  Spend_less_than_last_Christmas  Don't_know  Do_not_celebrate_Christmas
2011                                  10                                42                              39           6                           3
2012                                   9                                47                              32           8                           4
2013                                  12                                49                              28           6                           4 

gold: The statistic depicts U.S. consumer Christmas spending habits from 2011 to 2013 . In 2011 , 42 percent of the respondents reported that they expect to spend the same as last Christmas .
gold_template: The statistic depicts templateTitle[0] templateTitle[1] templateValue[0][0] templateTitle[3] templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[min] , templateValue[1][max] templateScale of the respondents reported that they expect to templateValue[0][0] the templateValue[0][1] as templateValue[0][0] .

generated_template: This statistic presents the templateScale of online consumers in the templateTitle[0] who are templateValue[0][2] as of 2019 , sorted templateTitle[7] templateTitle[8] . During the survey period , templateValue[1][0] templateScale of respondents stated that they found here .
generated: This statistic presents the percentage of online consumers in the U.S. who are Spend_less_than_last_Christmas as of 2019 , sorted 2013 .  During the survey period , 10 % of respondents stated that they found here .

Example 252:
titleEntities: {'Subject': ['Tin'], 'Date': ['2017']}
title: Tin - leading companies worldwide based on production output 2017
X_Axis['Largest', 'Tin', 'Producers']: ['Yunnan_Tin_(China)', 'PT_Timah_(Indonesia)', 'Malaysia_Smelting_Corporation_(Malaysia)', 'Yunnan_Chengfeng_(China)', 'Minsur_(Peru)', 'EM_Vinto_(Bolivia)', 'Guangxi_China_Tin_(China)', 'Thaisarco_(Thailand)', 'Metallo_Chimique_(Belgium)', 'Gejiu_Zi-Li_(China)']
Y_Axis['Production', 'output', 'in', 'thousand', 'metric', 'tons']: ['74.5', '30.2', '27.2', '26.8', '18.0', '12.6', '11.5', '10.6', '9.7', '8.7']

gold: This statistic illustrates the leading tin producing companies worldwide in 2017 , ranked by production output . With a production output of around 12,600 metric tons , Bolivia 's state-owned Vinto smelter was ranked sixth . Tin producers Tin has replaced lead in a lot of soldering , and is now used in a variety of electronics products from televisions and tablets to smartphones .
gold_template: This statistic illustrates the templateTitle[1] templateXValue[0] producing templateTitle[2] templateTitle[3] in templateTitleDate[0] , ranked by templateYLabel[0] templateYLabel[1] . With a templateYLabel[0] templateYLabel[1] of around templateYValue[5] templateYLabel[3] templateYLabel[4] , Bolivia 's state-owned templateXValue[5] smelter was ranked sixth . templateXValue[0] templateXLabel[2] templateXValue[0] has replaced lead in a lot of soldering , and is now used in a variety of electronics products from televisions and tablets to smartphones .

generated_template: This statistic gives information on the average templateYLabel[0] of templateTitle[2] templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) , in templateTitleDate[0] . According to the source , the templateYLabel[3] templateYLabel[4] was templateXValue[0] with a templateYLabel[3] templateYLabel[4] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic gives information on the average Production of companies based in the United Kingdom ( Tin ) , in 2017 .  According to the source , the metric tons was Yunnan_Tin_(China) with a metric tons of 74.5 thousand metric tons .

Example 253:
titleEntities: {'Subject': ['TEUs'], 'Date': ['2020', '2020']}
title: MSC - number of TEUs 2020
X_Axis['Ownership', 'Type']: ['Chartered', 'Owned', 'Orderbook']
Y_Axis['Number', 'of', 'TEUs']: ['2771795', '1044800', '217500']

gold: This statistic represents MSC 's number of TEUs as of March 2 , 2020 . Switzerland-based Mediterranean Shipping Company chartered ships with a capacity of approximately 2.8 million twenty-foot equivalent units in March 2019 .
gold_template: This statistic represents templateTitle[0] 's templateYLabel[0] of templateYLabel[1] as of 2 , templateTitleDate[0] . Switzerland-based Mediterranean Shipping Company templateXValue[0] ships with a capacity of approximately templateYValue[max] templateScale twenty-foot equivalent units in 2019 .

generated_template: The statistic presents the templateYLabel[0] of templateYLabel[1] of the top templateXLabel[0] templateXLabel[1] worldwide in templateTitleDate[0] . The templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: The statistic presents the Number of TEUs the top Ownership Type worldwide in 2020 .  The Chartered had the highest Number TEUs of 2771795 million TEUs in 2020 .

Example 254:
titleEntities: {'Subject': ['France'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in France 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'in', 'billion', 'U.S.', 'dollars']: ['3214.61', '3093.66', '2983.8', '2876.23', '2771.62', '2707.07', '2780.15', '2591.78', '2472.28', '2439.44', '2856.7', '2811.96', '2685.31', '2864.03', '2647.54', '2697.04', '2932.22', '2660.9', '2320.53', '2199.68', '2117.96', '1843.65', '1498.91', '1377.41', '1365.97', '1494.57', '1505.18', '1454.56', '1606.04', '1602.13', '1396.65', '1324.24', '1404.39', '1273.59', '1272.43', '1026.18', '1020.88', '935.12', '772.84', '557.56', '532.34']

gold: This statistic shows the gross domestic product ( GDP ) in France from 1984 to 2018 , with a projection up until 2024 . GDP refers to the total market value of all goods and services that are produced within a country per year . It is an important indicator of the economic strength of a country .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with a projection up until templateXValue[max] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . templateYLabel[0] refers to the total market value of goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the Gross domestic product ( GDP ) in the France from 1984 to 2017 , with projections up until 2024 .  GDP refers to the total market value of goods and services that are produced within a country per Year .  It is an important indicator of the economic strength of a country .

Example 255:
titleEntities: {'Subject': ['China'], 'Date': ['2008', '2018']}
title: Population in China 2008 - 2018 , by gender
Data:
             0       1       2       3       4       5       6       7       8       9      10
Year      2018    2017    2016    2015    2014    2013    2012    2011    2010    2009    2008
Male    713.51  711.37  708.15  704.14  700.79  697.28  693.95  690.68  687.48  686.47  683.57
Female  681.87  678.71  674.56  670.48  667.03  663.44  660.09  656.67  653.43  648.03  644.45 

gold: In 2018 , there were around 713 million male inhabitants and 682 million female inhabitants living in China , amounting to nearly 1.4 billion people in total . The population of the world 's most populous country remains growing , but growth has been slowing down and the total population size is forecast to peak around the end of the 2020s . Birth control in China From the beginning of the 1970s on , having many children was no longer encouraged in mainland China .
gold_template: In templateValue[0][0] , there were around 713 templateScale templateLabel[1][0] inhabitants and templateValue[2][0] templateScale templateLabel[2][0] inhabitants living in templateTitleSubject[0] , amounting to nearly 1.4 templateScale people in total . The templateTitle[0] of the world 's most populous country remains templatePositiveTrend , but growth has been slowing down and the total templateTitle[0] size is forecast to peak around the end of the 2020s . Birth control in templateTitleSubject[0] From the beginning of the 1970s on , having many children was no longer encouraged in mainland templateTitleSubject[0] .

generated_template: This statistic provides information on the templateTitle[0] of the templateTitle[4] of templateValue[0][0] , from templateValue[0][last] to templateValue[0][0] , sorted templateTitle[6] templateTitle[7] . As of templateValue[0][0] , it was found that templateValue[1][0] templateScale of the templateTitle[4] of templateValue[0][0] . As of templateValue[0][0] , it is templateValue[2][0] templateScale of that templateValue[2][0] templateScale of templateTitle[4] that templateLabel[0][0] .
generated: This statistic provides information on the Population of the by of 2018 , from 2008 to 2018 , sorted gender .  As of 2018 , it was found that 713.51 % of the by of 2018 .  As of 2018 , it is 681.87 % of that 681.87 % of by that Year .

Example 256:
titleEntities: {'Subject': ['Market'], 'Date': ['2015']}
title: Market share of the global luggage market 2015 , by company
X_Axis['Company']: ['Samsonite_International_SA', 'LVMH_Moët_Hennessy_Louis_Vuitton_SA', 'Delsey_SA', 'Rimowa_GmbH', 'VF_Corp', 'Tumi_Holdings_Inc', 'VIP_Industries_Ltd', 'Kering_SA', 'Hermès_International_SCA', 'Shanghai_Taifeng_Cases_Co_Ltd']
Y_Axis['Market', 'share']: ['17.3', '5', '2', '1.8', '1.3', '1.3', '1.2', '1', '0.9', '0.6']

gold: The statistic shows the market share of the global luggage market in 2015 , by company . In that year , the global market share of Samsonite was approximately 17.3 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateYLabel[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In that year , the templateTitle[2] templateYLabel[0] templateYLabel[1] of templateXValue[0] was approximately templateYValue[max] templateScale .

generated_template: This statistic depicts the templateYLabel[0] templateYLabel[1] breakdown of templateTitle[0] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] , . templateXValue[0] held the largest templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitleSubject[0] templateXLabel[0] owners with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale . In total , templateXValue[0] was the templateTitle[0] templateTitle[1] templateYLabel[0] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale .
generated: This statistic depicts the Market share breakdown of Market in 2015 , Company .  Samsonite_International_SA held the largest Market share of Market Company owners with a Market share of 17.3 % .  In total , Samsonite_International_SA was the Market share with a Market share of 17.3 % .

Example 257:
titleEntities: {'Subject': ['Android'], 'Date': ['2018']}
title: Android app download ranges worldwide 2018
X_Axis['Number', 'of', 'downloads']: ['1-5', '5-10', '10-50', '50-100', '100-500', '500-1000', '1000-5000', '5000-10000', '10000-50000', '50000-100000', '100000-500000', '500000-1000000', '1000000-5000000', '5000000+']
Y_Axis['Share', 'of', 'apps']: ['7.59', '3.89', '18.48', '9.39', '21.12', '7.5', '14.11', '4.51', '7.41', '1.93', '2.66', '0.58', '0.63', '0.1']

gold: The chart shows the distribution of Android apps per download range as of the first quarter 2018 . As of the measured period , 21.12 percent of apps in the Google Play Store had generated 100 to 500 downloads . Only 0.1 percent of apps managed to accumulate more than five million downloads .
gold_template: The chart shows the distribution of templateTitleSubject[0] templateYLabel[1] per templateTitle[2] range as of the first quarter templateTitleDate[0] . As of the measured period , templateYValue[max] templateScale of templateYLabel[1] in the Google Play Store had generated 100 to 500 templateXLabel[1] . Only templateYValue[min] templateScale of templateYLabel[1] managed to accumulate more than templateYValue[7] templateScale templateXLabel[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] average templateYLabel[0] of people in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] . The templateXValue[0] , with over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of the people .
generated: This statistic shows the Android app average Share of people in the United Kingdom ( Android ) in 2018 .  The 1-5 , with over 21.12 million apps of the people .

Example 258:
titleEntities: {'Subject': ['Oakland Athletics'], 'Date': ['2006', '2019']}
title: Oakland Athletics average ticket price 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['24.3', '24.13', '24.46', '24.0', '22.84', '22.12', '21.64', '21.52', '22.04', '24.31', '29.2', '23.88', '22.1']

gold: This graph depicts the average ticket price for Oakland Athletics games in Major League Baseball from 2006 to 2019 . In 2019 , the average ticket price was at 24.30 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[0] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Oakland Athletics games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 24.3 U.S. dollars .

Example 259:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2014']}
title: Leading bar soap brands ranked by sales value in the United Kingdom ( UK ) 2014
X_Axis['Soap', 'Bar', 'Brand']: ['Dove', 'Imperial_Leather', 'Simple']
Y_Axis['Sales', 'in', 'million', 'GBP']: ['26.8', '11.1', '5.8']

gold: This statistic shows the leading 3 brands of bar soap sold in the United Kingdom ( UK ) ranked by their total sales in the year to December 2014 . The ranking reveals that Dove is the top bar soap brand sold in the United Kingdom , generating 26.8 million pounds in sales that year .
gold_template: This statistic shows the templateTitle[0] 3 templateTitle[3] of templateXLabel[1] templateXLabel[0] sold in the templateTitleSubject[0] ( templateTitleSubject[1] ) templateTitle[4] templateTitle[5] their total templateYLabel[0] in the year to 2014 . The ranking reveals that templateXValue[0] is the top templateXLabel[1] templateXLabel[0] templateXLabel[2] sold in the templateTitleSubject[0] , generating templateYValue[max] templateScale pounds in templateYLabel[0] that year .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] in templateTitleDate[0] , ranked templateTitle[5] templateTitle[6] . According to the source , the templateTitle[0] templateYLabel[0] for templateXValue[0] was approximately templateYValue[max] templateScale British pounds .
generated: This statistic shows the Leading bar Sales in the brands in 2014 , ranked by sales .  According to the source , the Leading Sales for Dove was approximately 26.8 million British pounds .

Example 260:
titleEntities: {'Subject': ['Minnesota Vikings', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Minnesota Vikings ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['427', '408', '392', '306', '281', '250', '234', '227', '227', '221', '209', '195', '182', '167', '164', '144', '135', '123']

gold: The statistic depicts the revenue of the Minnesota Vikings , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Minnesota Vikings was 427 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] are owned by John Stanton and Chris Larson , who bought the franchise for 1.2 templateScale templateYLabel[2] templateYLabel[3] and 100 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[2] and 1992 respectively .
generated: The statistic depicts the Revenue of the Minnesota Vikings from 2001 to 2018 .  In 2018 , the Revenue of the Major League Baseball franchise amounted to 427 million U.S. dollars.The Minnesota Vikings are owned by John Stanton and Chris Larson , who bought the franchise for 1.2 million U.S. dollars and 100 million U.S. dollars in 2016 and 1992 respectively .

Example 261:
titleEntities: {'Subject': ['Infant'], 'Date': ['1990', '2018']}
title: U.S. - Infant mortality rate 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Infant', 'mortality', 'rate', 'per', 'thousand', 'live', 'births']: ['5.6', '5.7', '5.7', '5.8', '5.9', '6.0', '6.1', '6.1', '6.2', '6.4', '6.5', '6.6', '6.7', '6.8', '6.9', '6.9', '6.9', '7.0', '7.1', '7.2', '7.3', '7.5', '7.7', '8.0', '8.2', '8.5', '8.8', '9.1', '9.4']

gold: In 2018 , the infant mortality rate in the United States was 5.6 out of every 1,000 births . This is a significant decrease from 1990 , when infant mortality was at 9.4 deaths out of every 1,000s births . What is Infant Mortality ? The infant mortality rate is the number of deaths of babies under the age of one per 1,000 live births .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[0] was templateYValue[min] templateYValue[idxmax(X)] of every 1,000 templateYLabel[6] . This is a significant templateNegativeTrend from templateXValue[min] , when templateYLabel[0] templateYLabel[1] was at templateYValue[idxmin(X)] deaths out of every 1,000s templateYLabel[6] . What is templateYLabel[0] templateYLabel[1] ? The templateYLabel[0] templateYLabel[1] templateYLabel[2] is the number of deaths of babies under the age of one templateYLabel[3] 1,000 templateYLabel[5] templateYLabel[6] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Infant mortality of the mortality rate 1990 2018 in the 2018 from 1990 to 2018 .  In 2018 , the Infant mortality of the Infant was 5.6 rate per .

Example 262:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009', '2019']}
title: Quarterly U.S. e-commerce retail sales 2009 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10", "Q2_'10", "Q1_'10", "Q4_'09", "Q3_'09", "Q2_'09", "Q1_'09"]
Y_Axis['E-commerce', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['158049', '153976', '147141', '140346', '134969', '132819', '129046', '122534', '118216', '114186', '110505', '106207', '101776', '99875', '96283', '92182', '88968', '86569', '83370', '80344', '77755', '75883', '73480', '70492', '68179', '65804', '63949', '62025', '60439', '58157', '56067', '54896', '52984', '49985', '48696', '46908', '45064', '43507', '41308', '39291', '38106', '37391', '35279', '34145']

gold: In the fourth quarter of 2019 , adjusted U.S. retail e-commerce sales amounted to over 158 billion U.S. dollars . Retail e-commerce sales in the United States are estimated from samples used for the Monthly Retail Trade Survey and exclude online travel services , ticket sales agencies and financial brokers . Online retail sales currently account for one tenth of total retail and approximately 5 percent of annual e-commerce revenue in the United States .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[max] , adjusted templateYLabel[3] templateTitle[3] templateYLabel[0] templateYLabel[1] amounted to over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . templateTitle[3] templateYLabel[0] templateYLabel[1] in the templateTitle[1] are estimated from samples used for the Monthly templateTitle[3] Trade Survey and exclude online travel services , ticket templateYLabel[1] agencies and financial brokers . Online templateTitle[3] templateYLabel[1] currently account for one tenth of total templateTitle[3] and approximately 5 templateScale of annual templateYLabel[0] revenue in the templateTitle[1] .

generated_template: This statistic shows the templateScale of the templateYLabel[1] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . As of the fourth templateXLabel[0] of templateTitleDate[0] , the Chinese e-commerce site accounted for templateYValue[0] templateScale of templateYLabel[1] .
generated: This statistic shows the million of the sales of U.S. from the first Quarter of 2009 to the fourth Quarter of 2019 .  As of the fourth Quarter of 2009 , the Chinese e-commerce site accounted for 158049 million of sales .

Example 263:
titleEntities: {'Subject': ['Texas'], 'Date': ['1960', '2018']}
title: Population density in Texas 1960 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2010', '2000', '1990', '1980', '1970', '1960']
Y_Axis['Number', 'of', 'residents', 'per', 'square', 'mile']: ['109.9', '108.4', '106.7', '105.2', '103.2', '101.2', '99.8', '96.3', '79.8', '65.0', '54.5', '42.9', '36.6']

gold: This graph shows the population density in the federal state of Texas from 1960 to 2018 . In 2018 , the population density of Texas stood at 109.9 residents per square mile of land area .
gold_template: This graph shows the templateTitle[0] templateTitle[1] in the federal state of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .

generated_template: This graph shows the templateTitle[0] templateTitle[1] in the federal state of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .
generated: This graph shows the Population density in the federal state of Texas from 1960 to 2018 .  In 2018 , the Population density of Texas stood at 109.9 residents per square mile of land area .

Example 264:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005', '2019']}
title: Microwaves ovens U.S. unit shipments 2005 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Unit', 'shipments', 'in', 'millions']: ['13.45', '13.05', '12.56', '11.33', '11.94', '9.75', '9.15', '9.58', '9.52', '9.33', '9.63', '11.34', '11.85', '13.69', '13.86']

gold: Forecasts suggest that nearly 13.5 million microwave ovens will be shipped in the United States in 2019 , marking the product 's highest sales figure in years . Although quite a mature market , microwave oven shipments have fluctuated significantly over the past few decades . Between 2005 and 2013 total shipments fell from around 13.9 million to 9.2 million per year ; but in recent years the market has recovered to previous levels .
gold_template: Forecasts suggest that nearly templateYValue[0] templateScale microwave templateTitle[1] will be shipped in the templateTitle[2] in templateXValue[max] , marking the product 's highest sales figure in years . Although quite a mature market , microwave oven templateYLabel[1] have fluctuated significantly over the past few decades . Between templateXValue[min] and templateXValue[6] total templateYLabel[1] templateNegativeTrend from around templateYValue[max] templateScale to templateYValue[min] templateScale per templateXLabel[0] ; but in recent years the market has recovered to previous levels .

generated_template: The statistic depicts the templateTitle[0] templateYLabel[0] templateYLabel[1] of e-Readers from templateXValue[min] to templateXValue[12] and forecasts up to and including templateXValue[max] . In templateXValue[13] , templateYLabel[0] templateYLabel[1] of templateTitle[0] in the templateTitle[3] are forecast to reach templateYValue[13] templateScale units .
generated: The statistic depicts the Microwaves Unit shipments of e-Readers from 2005 to 2007 and forecasts up to and including 2019 .  In 2006 , Unit shipments of Microwaves in the unit are forecast to reach 13.69 millions units .

Example 265:
titleEntities: {'Subject': ['Bahamas'], 'Date': ['2019']}
title: Unemployment rate in the Bahamas 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['11.5', '11.85', '12.38', '12.7', '12', '13.8', '16.18', '14.02', '14.52', '14.32', '14.25', '8.18', '7.88', '7.54', '10.2', '10.2', '10.84', '9.05', '6.87', '7.15', '7.45']

gold: This statistic shows the unemployment rate in the Bahamas from 1999 to 2019 . In 2019 , the unemployment rate in the Bahamas was at approximately 11.5 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Bahamas from 1999 to 2019 .  In 2019 , the Unemployment rate in Bahamas was at approximately 11.5 % .

Example 266:
titleEntities: {'Subject': ['Euro'], 'Date': ['1999', '2019']}
title: Euro to U.S. dollar annual average exchange rate 1999 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Exchange', 'rate']: ['1.12', '1.18', '1.13', '1.11', '1.11', '1.33', '1.33', '1.28', '1.39', '1.33', '1.39', '1.47', '1.37', '1.26', '1.24', '1.24', '1.13', '0.95', '0.9', '0.92', '1.07']

gold: This statistic displays the annual exchange rate ( average or standardized measure ) of the euro to the U.S. dollar ( EUR USD ) , according to the data from the European Central Bank , which cover the period from the introduction of the euro in 1999 up until 2019 . The average ( standardized ) measure is based on the calculation of many observations throughout the period in question . It is therefore different than an annual measure at point in time , which reflects concrete values as of end of the year .
gold_template: This statistic displays the templateTitle[3] templateYLabel[0] templateYLabel[1] ( templateTitle[4] or standardized measure ) of the templateTitleSubject[0] to the templateTitle[1] templateTitle[2] ( EUR USD ) , according to the data from the European Central Bank , which cover the period from the introduction of the templateTitleSubject[0] in templateXValue[min] up until templateXValue[max] . The templateTitle[4] ( standardized ) measure is based on the calculation of many observations throughout the period in question . It is therefore different than an templateTitle[3] measure at point in time , which reflects concrete values as of end of the templateXLabel[0] .

generated_template: This statistic displays the templateTitle[4] templateYLabel[0] templateYLabel[1] ( templateTitle[3] or standardized measure ) of the templateTitle[0] to the templateTitleSubject[0] templateTitle[2] ( EUR CHF ) , according to data from the European Central Bank , from the introduction of the templateTitle[0] in templateXValue[min] up until templateXValue[max] . The templateTitle[3] , or standardized , measure shows the calculation based on many observations throughout the period in question , which is different than an templateTitle[4] measure at point in time : this denotes concrete values as of specific day ( i.e . end of the templateXLabel[0] ) .
generated: This statistic displays the average Exchange rate ( annual or standardized measure ) of the Euro to the Euro dollar ( EUR CHF ) , according to data from the European Central Bank , from the introduction of the Euro in 1999 up until 2019 .  The annual , or standardized , measure shows the calculation based on many observations throughout the period in question , which is different than an average measure at point in time : this denotes concrete values as of specific day ( i.e end of the Year ) .

Example 267:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. sweet potato production 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Production', 'in', 'thousand', 'cwt']: ['27378', '35646', '31546', '31016', '29584', '24785', '26482', '26964', '23845', '19469', '18443', '18070', '16401', '15730', '16112', '15891', '12799', '14515', '13780']

gold: This statistic shows the production of sweet potatoes in the United States from 2000 to 2018 . According to the report , approximately 31.55 million cwt of potatoes were produced in the country in 2016 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] potatoes in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , approximately templateYValue[2] templateScale templateYLabel[2] of potatoes were produced in the country in templateXValue[2] .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[2] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . According to the report , there were over templateYValue[max] templateScale templateYLabel[2] in the templateTitle[1] templateYLabel[0] in templateXValue[idxmax(Y)] .
generated: This statistic shows the total Production of cwt in the sweet from 2000 to 2018 .  According to the report , there were over 35646 thousand cwt in the sweet Production in 2017 .

Example 268:
titleEntities: {'Subject': ['Canada'], 'Date': ['2015', '2019']}
title: Average retail price for ground beef in Canada 2015 to 2019
Data:
           0      1      2      3      4      5      6      7      8      9     10     11
Month    Dec    Nov    Oct    Sep    Aug    Jul    Jun    May    Apr    Mar    Feb    Jan
2015    12.8  12.86  13.23  12.96  12.66  12.57  12.75  12.64  12.59  12.43  12.29  11.99
2016   11.91  12.04  12.36   12.4  12.19  12.25  12.29  12.71  12.78  13.11  12.62  12.75
2017   12.21  12.35  12.11  12.45  12.41  12.19   12.2  12.38  12.36  12.21  12.02  11.93
2018   11.47  11.96  11.94  11.95  12.18  11.71  12.15  12.05  12.53   12.1  12.11  12.19
2019       0  11.69  11.54  11.67  11.23  10.86  11.41   11.3  11.19  11.29  11.41  11.55 

gold: In Canada , one kilogram of ground beef cost an average 11.69 Canadian dollars in November 2019 . This price has fluctuated throughout the year , with a high of 11.67 dollars in October 2019 . Ground , otherwise known as minced , beef is often considered a cheaper and more versatile alternative to other cuts of beef .
gold_template: In templateTitleSubject[0] , one kilogram of templateTitle[4] templateTitle[5] cost an templateTitle[0] templateValue[5][max] Canadian dollars in 2019 . This templateTitle[2] has fluctuated throughout the year , with a high of templateValue[5][3] dollars in October templateTitleDate[max] . templateTitle[4] , otherwise known as minced , templateTitle[5] is often considered a cheaper and more versatile alternative to other cuts of templateTitle[5] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In 2019 , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] was templateValue[5][0] Canadian dollars per 300 grams . templateTitle[5] in templateTitleSubject[0] - additional information templateTitle[5] , typically served hot , is a beverage prepared by brewing ground , templateTitle[4] templateTitle[5] beans with water .
generated: This statistic shows the Average retail price for ground beef in Canada from 2015 to 2019 .  In 2019 , the Average retail price for ground beef in Canada was 0 Canadian dollars per 300 grams .  beef in Canada - additional information beef , typically served hot , is a beverage prepared by brewing ground , beef beans with water .

Example 269:
titleEntities: {'Subject': ['Travel'], 'Date': ['2016', '2017']}
title: Travel and tourism 's direct contribution to employment worldwide 2016 - 2017 , by region
Data:
                      0           1                2               3              4                   5              6             7             8            9       10         11            12
Region  North_East_Asia  South_Asia  South_East_Asia  European_Union  North_America  Sub_Saharan_Africa  Latin_America  Other_Europe  North_Africa  Middle_East  Oceania  Caribbean  Central_Asia
2016              26.02       28.66            11.16           11.41          10.09                6.17           5.93          2.55          2.19         2.36     0.92       0.73          0.57
2017              30.49       29.76            14.46           11.89           9.94                6.79           5.71          2.54          2.51         2.34     0.89       0.76          0.38 

gold: This statistic shows the direct contribution of travel and tourism to employment worldwide from 2016 to 2017 , by region . Travel and tourism directly contributed approximately 14.46 million jobs to the South East Asian economy in 2017 .
gold_template: This statistic shows the templateTitle[3] templateTitle[4] of templateTitleSubject[0] and templateTitle[1] to templateTitle[5] templateTitle[6] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[9] templateLabel[0][0] . templateTitleSubject[0] and templateTitle[1] directly contributed approximately templateValue[2][2] templateScale jobs to the templateValue[0][1] templateValue[0][0] Asian economy in templateTitleDate[max] .

generated_template: This statistic shows the templateTitle[3] templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitleSubject[0] templateTitle[6] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[9] templateLabel[0][0] . templateTitle[0] and templateTitle[1] contributed approximately 1.79 templateScale U.S. dollars in templateTitle[3] to the templateValue[0][0] Asian economy in templateTitleDate[max] .
generated: This statistic shows the direct contribution of Travel and tourism to Travel worldwide from 2016 to 2017 , by Region .  Travel and tourism contributed approximately 1.79 trillion U.S. dollars in direct to the North_East_Asia Asian economy in 2017 .

Example 270:
titleEntities: {'Subject': ['Ghana'], 'Date': ['2018']}
title: Share of economic sectors in the GDP in Ghana 2018
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
Agriculture  18.27   19.7  20.98  20.25     20  20.45  22.13  23.66  28.04  30.99  29.41
Industry     31.53  30.78  28.23  31.68  34.59  34.86  27.14  23.86  18.01  18.51   19.4
Services     43.01  42.35  43.09  39.54  36.11  39.15  47.58  45.84  48.18  47.94  46.17 

gold: This statistic shows the share of economic sectors in the gross domestic product ( GDP ) in Ghana from 2008 to 2018 . In 2018 , the share of agriculture in Ghana 's gross domestic product was 18.27 percent , industry contributed approximately 31.53 percent and the services sector contributed about 43.01 percent .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] in the gross domestic product ( templateTitle[3] ) in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitle[0] of templateLabel[1][0] in templateTitleSubject[0] 's gross domestic product was templateValue[1][0] templateScale , templateLabel[2][0] contributed approximately templateValue[2][0] templateScale and the templateLabel[3][0] sector contributed about templateValue[3][0] templateScale .

generated_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .
generated: The statistic shows the distribution of Share in Ghana economic sectors GDP from 2008 to 2018 .  In 2018 , 18.27 % of the employees in Ghana were active in the agricultural GDP , 31.53 % in Industry and 43.01 % in the service GDP .

Example 271:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1960', '2018']}
title: Volume of homeowner equity in the U.S. 1960 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1990', '1980', '1970', '1960']
Y_Axis['Homeowner', 'equity', 'in', 'trillion', 'U.S.', 'dollars']: ['15.54', '14.51', '13.24', '12.03', '10.83', '9.74', '7.78', '6.27', '6.16', '6.24', '6.48', '9.99', '12.73', '13.27', '11.2', '9.59', '8.83', '8.22', '7.39', '4.32', '2.02', '0.59', '0.35']

gold: The statistic presents the value of homeowner equity in the United States from 1960 to 2018 . Home equity value is calculated by subtracting the value of remaining mortgage debt from the market value of the real estate property . That means that the value of home equity increases as the debtor pays off the mortgage .
gold_template: The statistic presents the value of templateYLabel[0] templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . Home templateYLabel[1] value is calculated by subtracting the value of remaining mortgage debt from the market value of the real estate property . That means that the value of home templateYLabel[1] increases as the debtor pays off the mortgage .

generated_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] has templatePositiveTrend to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] , up from templateYValue[1] templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . Between templateXValue[min] and templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: Between 1960 and 2018 , the Homeowner equity of U.S. has increased to 15.54 trillion U.S. dollars in 2018 , up from 14.51 trillion U.S. in the previous Year .  Between 1960 and 2018 , the Volume Homeowner equity of U.S. was 15.54 trillion U.S. .

Example 272:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: Per capita consumption of high fructose corn syrup in the U.S. 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['37.6', '40.5', '41.4', '42.5', '43.4', '43.7', '45.8', '46.7', '48.4', '49.7', '52.7', '55.9', '57.8', '58.9', '59.6', '60.6', '62.5', '62.2', '62.5']

gold: The timeline shows the per capita consumption of high fructose corn syrup ( HFCS ) in the United States from 2000 to 2018 . The U.S. per capita consumption of high fructose corn syrup amounted to 37.6 pounds in 2018 .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] ( HFCS ) in the templateTitle[7] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] amounted to templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[idxmin(Y)] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of fructose corn in the Per from 2000 to 2018 .  According to the report , the U.S. Per capita consumption of fructose corn amounted to approximately 37.6 pounds in 2018 .

Example 273:
titleEntities: {'Subject': ['Japan'], 'Date': ['2011']}
title: Estimated decrease of GDP in Japan due to earthquake and tsunami 2011
X_Axis['Category', 'Of', 'Gdp']: ['Total', 'Production', 'Private_consumption', 'Power_generation', 'Imports', 'Exports']
Y_Axis['Decrease', 'in', 'billion', 'euros']: ['70.0', '45.4', '18.2', '2.7', '2.0', '1.4']

gold: The statistic shows an estimate for the decrease of the gross domestic product ( GDP ) in Japan due to the effects of the earthquake and tsunami in 2011 by sector . Production in Japan is estimated to decrease by 45.4 billion euros due to the natural disaster .
gold_template: The statistic shows an estimate for the templateYLabel[0] of the gross domestic product ( templateXLabel[1] ) in templateTitleSubject[0] templateTitle[4] to the effects of the templateTitle[5] and templateTitle[6] in templateTitleDate[0] by sector . templateXValue[1] in templateTitleSubject[0] is templateTitle[0] to templateYLabel[0] by templateYValue[1] templateScale templateYLabel[2] templateTitle[4] to the natural disaster .

generated_template: The statistic shows the 20 templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateTitle[8] group . According to the source , templateXValue[0] generated approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] of templateYLabel[1] templateYLabel[3] templateYLabel[4] that year .
generated: The statistic shows the 20 Estimated decrease GDP Japan due in Japan 2011 , group .  According to the source , Total generated approximately 70.0 billion euros of billion euros that year .

Example 274:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2014', '2019']}
title: Monthly average of heating degree days in the United Kingdom ( UK ) 2014 to 2019
Data:
         0     1     2     3     4     5
Year  2019  2018  2017  2016  2015  2014
Jan   11.3  10.2  11.2   9.8  10.7   9.9
Feb    8.6  12.4   9.3  10.4  11.2   9.2
Mar    7.6  10.6   7.0   9.4   9.2   7.9
Apr    6.4   6.0   6.5   8.0   6.4   5.4
May      0   2.8   2.9   3.4   4.6   3.3
Jun      0   0.5   0.7   0.9   1.9   0.6
Jul      0     0   0.1   0.4   0.7   0.1
Aug      0   0.5   0.5   0.1   0.4   0.8
Sep      0   2.1   2.1   0.7   2.8   0.9
Oct      0   4.9   3.2   4.6   4.6   3.3
Nov      0   7.3   8.5   9.7     6   7.1
Dec      0   8.7  10.4     9     6    10 

gold: During this period , the greatest number of heating degree days ( HDD 's ) in the UK were recorded in January and February . In 2019 , there were 11.3 HDD 's in January , which was an increase on the previous year . The most HDD 's in the period of consideration was in February 2018 , at 12.4 days .
gold_template: During this period , the greatest number of templateTitle[2] templateTitle[3] templateTitle[4] ( HDD 's ) in the templateTitleSubject[1] were recorded in and . In templateValue[0][0] , there were templateValue[1][0] HDD 's in , which was an templatePositiveTrend on the previous templateLabel[0][0] . The most HDD 's in the period of consideration was in 2018 , at templateValue[2][1] templateTitle[4] .

generated_template: The statistic shows the estimated templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitle[5] templateTitle[6] in the United Kingdom ( templateTitleSubject[1] ) from templateValue[0][0] to templateValue[0][last] . In this period , the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitle[5] templateTitle[6] are expected to reach 8.42 templateScale dollars in templateValue[0][1] .
generated: The statistic shows the estimated Monthly average heating degree days of United Kingdom in the United Kingdom ( UK ) from 2019 to 2014 .  In this period , the average heating degree days of United Kingdom are expected to reach 8.42 billion dollars in 2018 .

Example 275:
titleEntities: {'Subject': ['PUBG'], 'Date': ['2018']}
title: PUBG unit sales worldwide 2018
X_Axis['Month']: ['June_2018', 'September_2017', 'August_2017', 'June_2017', 'May_2017']
Y_Axis['Unit', 'sales', 'in', 'millions']: ['50', '10', '8', '4', '2']

gold: Since it was first released by developer Bluehole in March 2017 , PlayerUnknown 's Battlegrounds ( PUBG ) has developed a loyal legion of fans . While the battle royale game only sold two million copies within the first few months of its Early Access release on Steam , this number had jumped to 50 million units just a year later in June 2018 . One of the biggest games on Steam PUBG is an online multiplayer game which pits up to 100 players against each other at one time .
gold_template: Since it was first released by developer Bluehole in 2017 , PlayerUnknown 's Battlegrounds ( templateTitleSubject[0] ) has developed a loyal legion of fans . While the battle royale game only sold templateYValue[min] templateScale copies within the first few months of its Early Access release on Steam , this number had jumped to templateYValue[max] templateScale units just a year later in templateXValue[0] . One of the biggest games on Steam templateTitleSubject[0] is an online multiplayer game which pits up to 100 players against each other at one time .

generated_template: How many people play templateTitleSubject[0] ? PlayerUnknown 's Battlegrounds ( templateTitleSubject[0] ) , which is available on templateTitle[1] and templateTitle[2] , crossed the templateYValue[max] templateScale templateTitle[3] templateTitle[4] mark in templateXValue[0] - an impressive figure considering it was released little over a year earlier . The Battle Royale game developed by Bluehole was made available to the public in 2017 . One of the biggest games on the best time period in terms of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitleSubject[0] 's current account for templateTitle[3] templateTitle[4] templateTitle[5] , a declining declining rate is still one of the leading the tournament .
generated: How many people play PUBG ? PlayerUnknown 's Battlegrounds ( PUBG ) , which is available on unit and sales , crossed the 50 millions worldwide 2018 mark in June_2018 - an impressive figure considering it was released little over a year earlier .  The Battle Royale game developed by Bluehole was made available to the public in 2017 .  One of the biggest games on the best time period in terms of worldwide 2018 PUBG 's current account for worldwide 2018 , a declining rate is still one of the leading tournament .

Example 276:
titleEntities: {'Subject': ['The'], 'Date': ['2019']}
title: The highest-paid female athletes 2019
Data:
                                     0                     1                          2                      3                         4                            5                         6                           7                         8                        9                         10                    11                       12                     13                       14
Month         Serena_Williams_(tennis)  Naomi_Osaka_(tennis)  Angelique_Kerber_(tennis)  Simona_Halep_(tennis)  Sloane_Stephens_(tennis)  Caroline_Wozniacki_(tennis)  Maria_Sharapova_(tennis)  Karolina_Pliskova_(tennis)  Elina_Svitolina_(tennis)  Venus_Williams_(tennis)  Garbine_Muguruza_(tennis)  Alex_Morgan_(soccer)  P.V._Sindhu_(badminton)  Madison_Keys_(tennis)  Ariya_Jutanugarn_(golf)
Prize money                        4.2                   8.3                        5.3                    6.2                       4.1                          3.5                       1.0                         4.6                       4.6                      0.9                        2.4                  0.25                      0.5                    2.5                      3.3
Endorsements                      25.0                  16.0                        6.5                    4.0                       5.5                          4.0                       6.0                         1.7                       1.5                      5.0                        3.5                   5.5                      5.0                    3.0                      2.0 

gold: The graph depicts the 10 highest-paid female athletes worldwide in 2019 . Serena Williams earned about 29.2 million U.S. dollars from salaries , prize money , endorsements , appearances and licensing .
gold_template: The graph depicts the 10 templateTitle[0] templateTitle[1] templateTitle[2] worldwide in templateTitleDate[0] . templateValue[0][0] earned about 29.2 templateScale U.S. dollars from salaries , templateLabel[1][0] templateLabel[1][1] , templateLabel[2][0] , appearances and licensing .

generated_template: This statistic shows the highest-paid templateTitleSubject[0] templateValue[0][0] . The templateValue[0][0] earned about templateValue[1][0] templateScale U.S. dollars from templateLabel[1][0] and templateValue[2][0] templateScale U.S. dollars in 2019 .
generated: This statistic shows the highest-paid Serena_Williams_(tennis) .  The Serena_Williams_(tennis) earned about 4.2 million U.S. dollars from Prize and 25.0 million U.S. dollars in 2019 .

Example 277:
titleEntities: {'Subject': ['Latvia'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Latvia 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['2.8', '2.58', '2.3', '2.14', '2.1', '1.84', '1.64', '1.58', '1.31', '1.11', '1.56', '1.49', '1.33']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Latvia from 2006 to 2018 . Since 2009 there has been an increasing trend in arrivals . In 2018 , the number of arrivals ( including both foreign and domestic ) at accommodation in Latvia amounted to approximately 2.8 million .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Since templateXValue[9] there has been an templatePositiveTrend trend in templateYLabel[1] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( including both foreign and domestic ) at templateTitle[3] in templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateScale .

generated_template: Between templateXValue[min] and templateXValue[max] templateYLabel[1] of both domestic and foreign guests at templateTitle[3] establishments in templateTitleSubject[0] have templatePositiveTrend . In templateXValue[max] there were over templateYValue[max] templateScale templateYLabel[1] at templateTitle[2] templateTitle[3] in templateTitleSubject[0] , compared to templateYValue[9] templateScale ten years earlier .
generated: Between 2006 and 2018 arrivals of both domestic and foreign guests at accommodation establishments in Latvia have increased .  In 2018 there were over 2.8 millions arrivals at tourist accommodation in Latvia , compared to 1.11 millions ten years earlier .

Example 278:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: U.S. per capita consumption of fresh mangoes 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['3.17', '3.22', '2.96', '2.6', '2.52', '2.87', '2.49', '2.53', '2.24', '2.02', '2.1', '2.1', '2.1', '1.88', '2.02', '2.06', '1.97', '1.79', '1.75']

gold: This statistic depicts the per capita consumption of fresh mangoes in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh mangoes amounted to approximately 3.17 pounds in 2018 . In the same year , the consumption of fresh fruit in the U.S. amounted to about 115.14 pounds .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] . In the same templateXLabel[0] , the templateYLabel[2] of templateTitle[4] fruit in the templateTitleSubject[0] amounted to about 115.14 templateYLabel[3] .

generated_template: This statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: This statistic depicts the Per capita consumption of fresh mangoes in the U.S. from 2000 to 2018 .  According to the report , the U.S. Per capita consumption of fresh mangoes amounted to approximately 3.17 pounds in 2018 .

Example 279:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2012', '2019']}
title: Home mortgage debt of households and nonprofit organizations in the U.S. 2012 to 2019
X_Axis['Quarter']: ['Q3_2019', 'Q2_2019', 'Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018', 'Q4_2017', 'Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015', 'Q3_2015', 'Q2_2015', 'Q1_2015', 'Q4_2014', 'Q3_2014', 'Q2_2014', 'Q1_2014', 'Q4_2013', 'Q3_2013', 'Q2_2013', 'Q1_2013', 'Q4_2012', 'Q3_2012', 'Q2_2012', 'Q1_2012']
Y_Axis['Debt', 'in', 'trillion', 'U.S.', 'dollars']: ['10.52', '10.46', '10.37', '10.32', '10.26', '10.19', '10.1', '10.04', '9.97', '9.9', '9.88', '9.79', '9.73', '9.67', '9.62', '9.59', '9.52', '9.49', '9.45', '9.46', '9.44', '9.45', '9.46', '9.47', '9.49', '9.49', '9.53', '9.56', '9.61', '9.66', '9.72']

gold: The home mortgage debt of households and nonprofit organizations amounted to approximately 10.52 trillion U.S. dollars in the third quarter of 2019 . Home mortgage sector in the United States Home mortgage sector debt in the United States has been steadily falling in recent years and is beginning to come out of a period of great difficulty and problems presented to it by the economic crisis of 2008 . For the previous generations in the United States the real estate market was quite stable .
gold_template: The templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] and templateTitle[4] templateTitle[5] amounted to approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the third templateXLabel[0] of templateXValue[0] . templateTitle[0] templateTitle[1] sector in the templateTitle[6] templateTitle[0] templateTitle[1] sector templateYLabel[0] in the templateTitle[6] has been steadily templateNegativeTrend in recent years and is beginning to come out of a period of great difficulty and problems presented to it by the economic crisis of 2008 . For the previous generations in the templateTitle[6] the real estate market was quite stable .

generated_template: Between the first templateXLabel[0] of templateXValue[0] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in the templateXValue[0] stood at templateYValue[max] templateYLabel[2] templateYLabel[3] . This figure has remained the highest point in the following templateXLabel[0] . In the first templateXLabel[0] of templateXValue[0] , the templateTitle[0] of templateXValue[last] stood at templateYValue[min] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: Between the first Quarter of Q3_2019 , the Home mortgage debt households U.S. in the Q3_2019 stood at 10.52 U.S. dollars .  This figure has remained the highest point in the following Quarter .  In the first Quarter of Q3_2019 , the Home of Q1_2012 stood at 9.44 Debt trillion U.S. dollars .

Example 280:
titleEntities: {'Subject': ['European'], 'Date': ['2017']}
title: European cities ranked worst for over-tourism 2017
X_Axis['Tourist', 'Places']: ['Barcelona_Spain', 'Amsterdam_Netherlands', 'Venice_Italy', 'Milan_Italy', 'Budapest_Hungary', 'Bucharest_Romania', 'Reykjavik_Iceland', 'Moscow_Russia', 'Athens_Greece', 'Nantes_France']
Y_Axis['Overtourism', 'score', '(1-10)']: ['2.05', '2.18', '2.19', '2.66', '2.89', '3.22', '3.26', '3.27', '3.45', '3.61']

gold: Barcelona in Spain ranked as the worst city for overtourism in Europe . It was followed by Amsterdam in the Netherlands and Venice in Italy . All three have faced issues associated with mass tourism and have been forced to implement measures to tackle problems with overtourism .
gold_template: templateXValue[0] in templateXValue[0] templateTitle[2] as the templateTitle[3] city templateTitle[4] templateYLabel[0] in Europe . It was followed by templateXValue[1] in the templateXValue[1] and templateXValue[2] in templateXValue[2] . All templateYValue[3] have faced issues associated with mass tourism and have been forced to implement measures to tackle problems with templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the United Kingdom ( templateTitleSubject[0] ) of templateTitleSubject[0] as of 2019 , based on the end of that month . In that year , templateXValue[0] had the templateTitle[0] other templateTitle[3] templateTitle[4] , with a score of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the European cities ranked worst the United Kingdom ( European ) of European as of 2019 , based on the end of that month .  In that year , Barcelona_Spain had the European other worst for , with a score of 3.61 (1-10) .

Example 281:
titleEntities: {'Subject': ['Hungary'], 'Date': ['2019']}
title: Unemployment rate in Hungary 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['3.46', '3.66', '4.16', '5.12', '6.81', '7.73', '10.18', '11', '11.03', '11.17', '10.03', '7.82', '7.41', '7.49', '7.19', '5.83', '5.79', '5.61', '5.67', '6.56', '6.93']

gold: This statistic shows the unemployment rate in Hungary from 1999 to 2019 . In 2019 , the unemployment rate in Hungary was at approximately 3.46 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Hungary from 1999 to 2019 .  In 2019 , the Unemployment rate in Hungary was at approximately 3.46 % .

Example 282:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': []}
title: Defense spending breakdown of the United Kingdom ( UK ) 2017/18 - 2018/19
Data:
                                 0              1                     2            3                                   4
Defense Category  Military_defense  Civil_defense  Foreign_military_aid  R&D_defense  Defense_(not_elsewhere_classified)
2017/18                      36409            140                   705         1231                                 169
2018/19                      37980             44                   696         1339                                 182 

gold: This statistic shows public sector expenditure on defense in the United Kingdom ( UK ) from 2017/18 to 2018/19 , by type . The majority of spending was for military defense . In 2018/19 , research and development accounted for 1.3 billion British pounds of the total of approximately 32.4 billion British pound expenditure .
gold_template: This statistic shows public sector expenditure on templateValue[0][0] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitle[6] to templateTitle[7] , by type . The majority of templateTitle[1] was for templateValue[0][0] . In templateTitle[7] , research and development accounted for 1.3 templateScale British pounds of the total of approximately 32.4 templateScale British pound expenditure .

generated_template: The statistic shows the templateScale of templateTitle[5] in the templateTitle[0] that templateValue[1][0] templateScale of templateLabel[1][0] templateLabel[1][1] templateTitle[4] in templateTitleDate[0] , templateTitle[6] templateTitle[7] . The survey revealed that templateValue[1][0] templateScale of templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] were templateValue[1][1] templateScale of the templateLabel[2][0] templateLabel[1][2] .
generated: The statistic shows the percentage of UK in the Defense that 36409 % of 2017/18 Kingdom in , 2017/18 2018/19 .  The survey revealed that 36409 % of 2017/18 were 140 % of the 2018/19 2017/18 .

Example 283:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1979', '2018']}
title: Wage and salary workers in the U.S. : hourly earnings 1979 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984', '1983', '1982', '1981', '1980', '1979']
Y_Axis['Wage', 'in', 'current', 'U.S.', 'dollars']: ['14.99', '14.63', '14.0', '13.44', '13.14', '12.93', '12.8', '12.71', '12.5', '12.44', '12.23', '11.95', '11.76', '11.19', '11.0', '10.85', '10.47', '10.19', '9.91', '9.53', '9.1', '8.75', '8.4', '8.17', '8.01', '7.87', '7.72', '7.5', '7.23', '6.99', '6.73', '6.47', '6.2', '6.03', '5.83', '5.59', '5.4', '5.15', '4.82', '4.44']

gold: In 2018 , the median hourly earnings of wage and salary workers in the United States was 14.99 U.S. dollars . This is an increase from 1979 , when median hourly earnings were at 4.44 U.S. dollars . Hourly Workers The United States national minimum wage is 7.25 U.S. dollars per hour , which has been the minimum wage since 2009 .
gold_template: In templateXValue[max] , the median templateTitle[4] templateTitle[5] of templateYLabel[0] and templateTitle[1] templateTitle[2] in the templateTitle[3] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[3] . This is an templatePositiveTrend from templateXValue[min] , when median templateTitle[4] templateTitle[5] were at templateYValue[idxmin(X)] templateYLabel[2] templateYLabel[3] . templateTitle[4] templateTitle[2] The templateTitle[3] national minimum templateYLabel[0] is 7.25 templateYLabel[2] templateYLabel[3] per hour , which has been the minimum templateYLabel[0] since templateXValue[9] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Wage current of the U.S. from 1979 to 2018 .  In 2018 , the Wage current of U.S. was 14.99 U.S. dollars .

Example 284:
titleEntities: {'Subject': ['CE'], 'Date': ['2013']}
title: Importance in decision-making process when purchasing CE in the U.S. 2013
Data:
              0        1                 2            3                4         5                6                               7      8                                              9                                     10                          11                      12               13
Response  Price  Quality  Product_features  Ease_of_use  Product_reviews  Warranty  Style_of_design  Previous_experience_with_brand  Brand  Friend/family_member/co-worker_recommendation  Environmental_friendliness_of_product  Salesperson_recommendation  Family_members_have_it  Friends_have_it
18-49        83       82                81           80               73        70               70                              67     65                                             54                                     41                          35                      32               32
50+          95       94                91           90               75        83               65                              78     69                                             51                                     35                          32                      28               21 

gold: This statistic shows the results of a 2013 survey regarding the decision-making process when purchasing CE in the U.S. in 2013 . The survey revealed that 83 percent of the respondents between the ages of 18 - 49 said the price is very or somewhat important to their decision-making process when buying consumer electronics .
gold_template: This statistic shows the results of a templateTitleDate[0] survey regarding the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] in the templateTitle[6] in templateTitleDate[0] . The survey revealed that templateValue[1][max] templateScale of the respondents between the ages of 18 - 49 said the templateValue[0][0] is very or somewhat important to their templateTitle[1] templateTitle[2] templateTitle[3] buying consumer electronics .

generated_template: This statistic shows the results of a survey conducted in the templateTitle[0] in templateTitleDate[0] , templateTitle[10] templateTitle[11] . The survey shows that templateValue[1][0] templateScale of the templateYLabel[1] were templateValue[0][0] on the survey .
generated: This statistic shows the results of a survey conducted in the Importance in 2013 , .  The survey shows that 83 % of the were Price on the survey .

Example 285:
titleEntities: {'Subject': ['Instagram'], 'Date': ['2017']}
title: Most hashtagged cities on Instagram worldwide 2017
X_Axis['State']: ['London', 'New_York', 'Paris', 'Dubai', 'Istanbul', 'Miami', 'Los_Angeles', 'Barcelona', 'Moscow', 'Chicago', 'Tokyo', 'Toronto', 'Berlin', 'Las_Vegas', 'Madrid', 'Hong_Kong', 'Amsterdam', 'Sydney', 'San_Fancisco', 'Bangkok']
Y_Axis['Number', 'of', 'hashtagged', 'posts', 'in', 'millions']: ['88.78', '88.51', '75.01', '59.64', '50.15', '44.27', '36.37', '34.11', '32.44', '31.37', '29.02', '26.31', '26.0', '24.63', '23.02', '21.74', '20.36', '20.27', '18.79', '17.66']

gold: This statistic presents the most popular cities worldwide on Instagram as of December 2017 , sorted by number of hashtagged posts . As of the measured period , London was the most popular city on Instagram with over 88.78 million mentions in hashtags .
gold_template: This statistic presents the templateTitle[0] popular templateTitle[2] templateTitle[4] on templateTitleSubject[0] as of 2017 , sorted by templateYLabel[0] of templateYLabel[1] templateYLabel[2] . As of the measured period , templateXValue[0] was the templateTitle[0] popular city on templateTitleSubject[0] with over templateYValue[max] templateScale mentions in hashtags .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitle[3] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In templateXValue[0] , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateXValue[last] with templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Most of the hashtagged cities Number in the Instagram in 2017 , State .  In London , had the highest Number hashtagged of Bangkok with 88.78 posts millions .

Example 286:
titleEntities: {'Subject': ['Philippines'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in the Philippines 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['548.05', '499.61', '455.37', '415.24', '383.48', '356.81', '330.91', '313.62', '304.9', '292.77', '284.59', '271.84', '250.09', '224.14', '199.59', '168.49', '173.6', '149.36', '122.21', '103.07', '91.37', '83.91', '81.36', '76.26', '81.02', '83.0', '72.21', '91.23', '91.79', '82.12', '71.0', '60.24', '58.7', '50.32', '49.1', '47.29', '42.08', '36.87', '33.17', '34.0', '34.86']

gold: The Philippines has a steadily growing economy , with a gross domestic product ( GDP ) that reached over 330 billion U.S. dollars in 2018 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The templateTitleSubject[0] has a steadily templatePositiveTrend economy , with a templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) that reached over 330 templateScale templateYLabel[4] templateYLabel[5] in templateXValue[6] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Philippines from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 287:
titleEntities: {'Subject': ['The'], 'Date': ['2020']}
title: The 25 most valuable brands worldwide 2020
X_Axis['Brand']: ['Amazon', 'Google', 'Apple', 'Microsoft', 'Samsung', 'ICBC', 'Facebook', 'Walmart', 'Ping_An', 'Huawei', 'Mercedes-Benz', 'Verizon', 'China_Construction_Bank', 'AT&T', 'Toyota', 'State_Grid', 'Disney', 'Agricultural_Bank_of_China', 'WeChat', 'Bank_of_China', 'Home_Depot', 'China_Mobile', 'Shell', 'Saudi_Aramco', 'Volkswagen']
Y_Axis['Brand', 'value', 'in', 'million', 'U.S.', 'dollars']: ['220791', '159722', '140524', '117072', '94494', '80791', '79804', '77520', '69041', '65084', '65041', '63692', '62602', '59103', '58076', '56965', '56123', '54658', '54146', '50630', '50508', '49023', '47529', '46768', '44897']

gold: As of 2020 , Amazon was the most valuable brand in the world with an estimated brand value of about 221 billion U.S. dollars . Google , which was the second leading brand in the world , had a brand value of about 160 billion U.S. dollars that year . Brand value Brand value , not to be mistaken with brand equity , is a phrase used in the marketing industry to describe the value of brands based on the implication that the owner of a well-known brand name can generate more money than from products with a less well known name , brand valuation is the estimation of a brands total value .
gold_template: As of templateTitleDate[0] , templateXValue[0] was the templateTitle[1] templateTitle[2] templateXLabel[0] in the world with an estimated templateXLabel[0] templateYLabel[1] of about templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . templateXValue[1] , which was the second leading templateXLabel[0] in the world , had a templateXLabel[0] templateYLabel[1] of about templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] that year . templateXLabel[0] templateYLabel[1] templateXLabel[0] templateYLabel[1] , not to be mistaken with templateXLabel[0] equity , is a phrase used in the marketing industry to describe the templateYLabel[1] of templateTitle[3] based on the implication that the owner of a well-known templateXLabel[0] name can generate more money than from products with a less well known name , templateXLabel[0] valuation is the estimation of a templateTitle[3] total templateYLabel[1] .

generated_template: This statistic presents a ranking of the templateTitle[0] templateTitle[1] templateXValue[0] templateTitle[3] templateTitle[4] in templateTitleDate[0] , sorted by templateYLabel[0] . According to the source , templateXValue[0] was the world 's templateXValue[0] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , followed by templateXValue[1] with templateYValue[1] templateScale templateYLabel[3] .
generated: This statistic presents a ranking of the 25 most Amazon brands worldwide in 2020 , sorted by Brand .  According to the source , Amazon was the world 's Amazon with a Brand value of 220791 million U.S. dollars , followed by Google with 159722 million U.S. .

Example 288:
titleEntities: {'Subject': ['Germany'], 'Date': ['2008', '2018']}
title: Revenue of the leading telecommunications companies in Germany 2008 to 2018
Data:
                        0      1      2      3      4      5      6     7     8     9    10
Year                 2018   2017   2016   2015   2014   2013   2012  2011  2010  2009  2008
Deutsche Telekom    24.36  24.58  24.63  25.06   25.0  25.38  25.77  26.4  27.3  28.0  28.9
Vodafone Germany    10.31  10.26  10.01  10.63  10.68   9.82   9.64  9.55  9.29  9.04  9.41
Telefónica Germany   7.32    7.3    7.5   7.89   5.52   4.91   5.21  5.04  4.83  3.75   3.6
E-Plus group            0      0      0      0      0    3.2    3.4  3.24  3.24  3.18  3.22 

gold: This statistic shows data on the revenues of the three ( four ) leading companies in the German telecommunications market from 2008 to 2018 . In 2018 , Vodafone generated a revenue of roughly 10.3 billion euros in Germany . In the same year , the revenue of Deutsche Telekom amounted to roughly 24.4 billion euros .
gold_template: This statistic shows data on the revenues of the templateValue[4][5] ( templateValue[3][9] ) templateTitle[1] templateTitle[3] in the German templateTitle[2] market from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[2][0] generated a templateTitle[0] of roughly templateValue[2][0] templateScale euros in templateTitleSubject[0] . In the same templateLabel[0][0] , the templateTitle[0] of templateLabel[1][0] templateLabel[1][1] amounted to roughly templateValue[1][0] templateScale euros .

generated_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . In templateValue[0][0] , the templateLabel[1][0] templateTitle[2] in templateTitleSubject[0] generated templateValue[2][0] templateScale U.S. dollars in templateLabel[2][0] .
generated: This statistic shows the Revenue of Germany from 2008 to 2018 , .  In 2018 , the Deutsche telecommunications in Germany generated 10.31 billion U.S. dollars in Vodafone .

Example 289:
titleEntities: {'Subject': ['Twitch July'], 'Date': ['2019', '2019']}
title: Leading games on Twitch as of July 2019 , by hours viewed
X_Axis['Video', 'Game']: ['Fortnite', 'League_of_Legends', 'GTA_V', 'Teamfight_Tactics', 'World_of_Warcraft', 'Overwatch', 'DOTA_2', 'Counter-Strike:_Global_Offensive', 'PUBG', 'Apex_Legends']
Y_Axis['Number', 'of', 'hours', 'in', 'millions']: ['24.8', '13.7', '13.1', '12.2', '10.8', '7.3', '6.7', '5.2', '5.1', '4.0']

gold: This statistic presents the leading gaming content from July 22 to July 28 , 2019 , ranked by number of hours viewed . Fortnite had the longest viewing time that week , with 24.8 million hours viewed across the Twitch network .
gold_template: This statistic presents the templateTitle[0] gaming content from templateTitleSubject[0] 22 to templateTitleSubject[0] 28 , templateTitleDate[0] , ranked templateTitle[5] templateYLabel[0] of templateYLabel[1] templateTitle[7] . templateXValue[0] had the longest viewing time that week , with templateYValue[max] templateScale templateYLabel[1] templateTitle[7] across the templateTitleSubject[0] network .

generated_template: According to a templateTitleDate[0] Statista survey , the ranking of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] , based on sales over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . It was followed by templateXValue[1] with templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] of templateXValue[2] .
generated: According to a 2019 Statista survey , the ranking of the Leading games Twitch July in Twitch July , based on sales over 24.8 millions .  It was followed by League_of_Legends with 13.7 millions of GTA_V .

Example 290:
titleEntities: {'Subject': ['London', 'UK'], 'Date': ['2011']}
title: Inner London ( UK ) : population 2011 , by ethnic group
X_Axis['Ethnic', 'Groups']: ['White', 'Black_/_African_/_Caribbean_/_Black_British', 'Asian_/_Asian_British', 'Mixed_/_Multiple_ethnic_groups', 'Other', 'Gypsy/Traveller/Irish_traveller']
Y_Axis['Share', 'of', 'population']: ['57.3', '16.8', '16', '5.9', '4.2', '0.1']

gold: This statistic shows the distribution of ethnic groups of Inner London , United Kingdom ( UK ) at the time of the 2011 National Census . 'White ' is the largest group , at 57.2 percent of the population , followed by 'Black / African / Caribbean / Black British , ' and Asian / Asian British . '
gold_template: This statistic shows the distribution of templateXValue[3] of templateTitle[0] templateTitleSubject[0] , United Kingdom ( templateTitleSubject[1] ) at the time of the templateTitleDate[0] National Census . 'White ' is the largest templateTitle[7] , at 57.2 templateScale of the templateYLabel[1] , followed templateTitle[5] 'Black templateXValue[1] / templateXValue[1] Black templateXValue[1] , ' and templateXValue[2] templateXValue[1] templateXValue[2] templateXValue[1] . '

generated_template: The statistic illustrates the distribution of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] in templateTitleDate[0] , broken down templateTitle[7] templateTitle[8] . In templateXValue[0] , there were around templateYValue[max] templateScale of the templateXValue[last] templateYLabel[1] in templateTitleSubject[0] .
generated: The statistic illustrates the distribution of Inner London UK in the London in 2011 , broken down group .  In White , there were around 57.3 % of the Gypsy/Traveller/Irish_traveller population in London .

Example 291:
titleEntities: {'Subject': ['BMW Group'], 'Date': ['2006', '2018']}
title: BMW Group - shipments 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Automobile', 'sales', 'in', 'units']: ['2490664', '2463526', '2367603', '2247485', '2117965', '1963798', '1845186', '1668982', '1461166', '1286310', '1435876', '1500678', '1373970']

gold: This statistic shows BMW Group 's global vehicle sales between 2006 and 2018 . In 2018 , the Munich-based company shipped about 2.49 million vehicles worldwide . BMW 's major brands include BMW ( automobiles and motorcycles ) , MINI and Rolls Royce .
gold_template: This statistic shows templateTitleSubject[0] 's global vehicle templateYLabel[1] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the Munich-based company shipped about templateYValue[max] templateScale vehicles worldwide . templateTitleSubject[0] 's major brands include templateTitleSubject[0] ( automobiles and motorcycles ) , MINI and Rolls Royce .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[2] templateYLabel[3] templateTitle[4] templateYLabel[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the data from the templateTitle[0] of the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Automobile of the units 2018 between 2006 and 2018 .  In 2018 , the data from the BMW of the BMW Group amounted to 2490664 units .

Example 292:
titleEntities: {'Subject': ['World Cup'], 'Date': ['1930', '2014']}
title: Runner-up at World Cup tournaments by country 1930 to 2014
X_Axis['Country']: ['Germany', 'Netherlands_the', 'Argentina', 'Czech_Republic', 'Brazil', 'Czechoslovakia', 'Italy', 'Hungary', 'France', 'Sweden']
Y_Axis['Number', 'of', 'runners-up']: ['4', '3', '3', '2', '2', '2', '2', '2', '1', '1']

gold: The statistic depicts the number of vice-championships at soccer World Cups since 1930 by country . Germany has been runners-up four times .
gold_template: The statistic depicts the templateYLabel[0] of vice-championships at soccer templateTitleSubject[0] Cups since templateTitleDate[min] templateTitle[4] templateXLabel[0] . templateXValue[0] has been templateYLabel[1] templateYValue[max] times .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] templateTitle[7] in templateTitleDate[0] . In that year , the templateXValue[0] had the largest templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number of the World Cup tournaments by World Cup 1930 2014 in 1930 .  In that year , the Germany had the largest Number of 4 runners-up .

Example 293:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013', '2025']}
title: Total number of retail prescriptions filled annually in the U.S. 2013 to 2025
X_Axis['Year']: ['2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Number', 'of', 'prescriptions', 'filled', 'in', 'billions']: ['4.98', '4.9', '4.83', '4.76', '4.69', '4.55', '4.38', '4.21', '4.1', '4.07', '4.4', '4.33', '4.24']

gold: This statistic depicts the total number of retail prescriptions filled annually in the United States from 2013 to 2025 . It is estimated that in 2019 , 4.38 billion retail prescriptions will be filled throughout the United States.U.S . Pharmacies and Drug Stores IndustryThe retail drug industry is comprised of pharmacies or drugstores , as they are known in North America .
gold_template: This statistic depicts the templateTitle[0] templateYLabel[0] of templateTitle[2] templateYLabel[1] templateYLabel[2] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . It is estimated that in templateXValue[6] , templateYValue[6] templateScale templateTitle[2] templateYLabel[1] will be templateYLabel[2] throughout the United States.U.S . Pharmacies and Drug Stores IndustryThe templateTitle[2] drug industry is comprised of pharmacies or drugstores , as they are known in North America .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .
generated: This statistic shows the Number prescriptions of the retail in U.S. from 2013 to 2025 .  The Number prescriptions is the prescriptions that divides a retail into two numerically equal groups ; that is , half the people are younger than this prescriptions and half are older .  It is a single index that summarizes the prescriptions distribution of a retail .

Example 294:
titleEntities: {'Subject': ['Oscars'], 'Date': ['2017', '2017']}
title: Twitter : top mentioned hashtags of the Oscars 2017
Data:
                0        1            2           3             4      5               6               7          8        9
Hashtags  #Oscars  #oscars  #Oscars2017  #Moonlight  #WinnerIsDJT  #Maga  #TrumpWins4USA  #HiddenFigures  #LaLaLand  #OSCARS
Tweets     793925    74804        86770       12728           855   1644             887            3550      10370     6404
Retweets  1987739    98341        85934       39728         42518  41202           38055           30263      31284    26829 

gold: The statistic presents the top mentioned hashtags on Twitter of the Academy Awards 2017 , broadcasted between February 26th and February 27th , 2017 . According to the source , the hashtag # Moonlight was tweeted around 12,700 times during the Oscar ceremony , while # LaLaLand appeared on Twitter 10,370 times during the same period .
gold_template: The statistic presents the templateTitle[1] templateTitle[2] templateLabel[0][0] on templateTitle[0] of the Academy Awards templateTitleDate[0] , broadcasted between 26th and 27th , templateTitleDate[0] . According to the source , the hashtag # Moonlight was tweeted around 12,700 times during the Oscar ceremony , while # LaLaLand appeared on templateTitle[0] templateValue[1][8] times during the same period .

generated_template: This statistic shows the distribution of employees employees in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[4] ranking . The templateTitle[0] of employees of templateValue[0][0] , compared to the statistic presents the templateValue[0][0] , with education .
generated: This statistic shows the distribution of employees in the United Kingdom ( Oscars ) in 2017 , Oscars ranking .  The Twitter of employees #Oscars , compared to the statistic presents the #Oscars , with education .

Example 295:
titleEntities: {'Subject': ['Asia Pacific', 'Christians'], 'Date': ['2010']}
title: 10 Countries in Asia Pacific with the largest number of Christians in 2010
X_Axis['Country']: ['Philippines', 'China', 'India', 'Indonesia', 'Australia', 'South_Korea', 'Vietnam', 'Papua_New_Guinea', 'Kazakhstan', 'Burma_(Myanmar)']
Y_Axis['Estimated', 'Christian', 'population', 'in', 'millions']: ['86.79', '67.07', '31.85', '21.16', '16.03', '14.1', '7.03', '6.8', '4.19', '3.79']

gold: This statistic shows the 10 countries in Asia Pacific with the largest number of Christians in 2010 . 67.07 million Christians lived in China .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] with the templateTitle[5] templateTitle[6] of templateTitleSubject[1] in templateTitleDate[0] . templateYValue[1] templateScale templateTitleSubject[1] lived in templateXValue[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateTitle[4] templateYLabel[0] templateYLabel[1] . The templateTitle[0] templateTitle[1] is the templateYLabel[1] that year , with a templateYLabel[0] templateYLabel[1] of approximately templateYValue[2] templateScale templateYLabel[3] .
generated: This statistic shows the 10 Countries the Asia Pacific in 2010 , largest Estimated Christian .  The 10 Countries is the Christian that year , with a Estimated Christian of approximately 31.85 millions .

Example 296:
titleEntities: {'Subject': ['UK'], 'Date': ['2010', '2017']}
title: UK cyber security : total market size 2010 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Market', 'size', 'in', 'million', 'GBP']: ['3489', '3330', '3173', '2996', '2796', '2621', '2492', '2407']

gold: This statistic show the market forecast of the total cyber security sector in the United Kingdom ( UK ) from 2010 to 2017 . The estimated market size of the total cyber security sector in 2017 is 3,489 million British pounds ( GBP ) . Cyber security exists because the threats and costs are great enough to warrant these measure .
gold_template: This statistic show the templateYLabel[0] forecast of the templateTitle[3] templateTitle[1] templateTitle[2] sector in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . The estimated templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[1] templateTitle[2] sector in templateXValue[max] is templateYValue[idxmax(X)] templateScale British pounds ( templateYLabel[3] ) . templateTitle[1] templateTitle[2] exists because the threats and costs are great enough to warrant these measure .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of the templateTitle[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of the templateYLabel[1] of the templateTitleSubject[0] amounted to templateYValue[0] templateYValue[idxmax(X)] templateYLabel[4] in templateXValue[max] .
generated: This statistic shows the Market of million GBP of the UK of from 2010 to 2017 .  The Market of the size of the UK amounted to 3489 GBP in 2017 .

Example 297:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Facebook user share in the United States 2019 , by gender
X_Axis['Sex']: ['Female', 'Male']
Y_Axis['Percentage', 'of', 'users']: ['53.7', '46.3']

gold: Facebook is the most popular social network in the United States and as of November 2019 , 53.7 percent of U.S. Facebook audiences were female , and 46.3 percent of users were male . The social network had a combined 247 million monthly active users in the United States and Canada as of the third quarter of 2019 . Facebook usage in the United States Thanks to its wide reach and vast range of products including Facebook Messenger , Instagram and WhatsApp , many internet users would find it hard to imagine an online experience without the company that arguably made social media mainstream .
gold_template: templateTitle[0] is the most popular social network in the templateTitleSubject[0] and as of 2019 , templateYValue[max] templateScale of templateTitle[4] templateTitle[0] audiences were templateXValue[0] , and templateYValue[min] templateScale of templateYLabel[1] were templateXValue[last] . The social network had a combined 247 templateScale monthly active templateYLabel[1] in the templateTitleSubject[0] and Canada as of the third quarter of templateTitleDate[0] . templateTitle[0] usage in the templateTitleSubject[0] Thanks to its wide reach and vast range of products including templateTitle[0] Messenger , Instagram and WhatsApp , many internet templateYLabel[1] would find it hard to imagine an online experience without the company that arguably made social media mainstream .

generated_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[1] worldwide as of 2020 , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[min] templateScale of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[max] templateScale were templateXValue[last] .
generated: This statistic gives information on the user of United States users worldwide as of 2020 , sorted 2019 by .  During the survey period , 46.3 percentage of United States United were Female and 53.7 percentage were Male .

Example 298:
titleEntities: {'Subject': ['Norway'], 'Date': ['2008', '2018']}
title: Number of births in Norway 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'live', 'births']: ['55120', '56633', '58890', '59058', '59084', '58995', '60255', '60220', '61442', '61807', '60497']

gold: This statistic shows the number of live births in Norway from 2008 to 2018 . The number of births has declined during the period , from the peak of roughly 61.8 thousand in 2009 to around 55.1 thousand in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[2] has declined during the period , from the peak of roughly templateYValue[max] thousand in templateXValue[idxmax(Y)] to around templateYValue[min] thousand in templateXValue[idxmin(Y)] .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[1] of the templateYLabel[2] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of the templateTitleSubject[0] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Number of the live of the births 2018 from 2008 to 2018 .  In 2018 , the Number of the Norway of amounted to 55120 births .

Example 299:
titleEntities: {'Subject': ['Oakland Raiders', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Oakland Raiders ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['357', '335', '321', '301', '285', '244', '229', '226', '217', '217', '215', '205', '189', '171', '169', '149', '144', '132']

gold: The statistic depicts the revenue of the Oakland Raiders , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Oakland Raiders was 357 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Giants , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Oakland Raiders Giants , a franchise of the National Football League , from 2001 to 2018 .  In 2018 , the Revenue of the Oakland Raiders was 357 U.S. dollars .

Example 300:
titleEntities: {'Subject': ['Yandex'], 'Date': ['2007', '2019']}
title: Yandex : annual revenue 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'billion', 'Russian', 'rubles']: ['175.39', '127.66', '94.1', '75.9', '59.8', '50.77', '39.5', '28.77', '20.03', '12.5', '8.73', '7.65', '4.25']

gold: In 2019 , Yandex 's growth continued , and the Russian internet services company accounted for over 175 billion Russian rubles in revenues , which was approximately 37 percent greater than the figures over the previous year . Yandex : a snapshot Like Google , Yandex 's search engine is the primary contact point with the company , but the company 's services go well beyond search . Incorporated in the Netherlands and with offices around the world , Yandex offers services primarily to the Russian language market covering ecommerce , navigation , advertising , mobile development as well as a taxi/ride-sharing service and a car sharing service .
gold_template: In templateXValue[max] , templateTitleSubject[0] 's growth continued , and the templateYLabel[2] internet services company accounted for over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in revenues , which was approximately 37 templateScale greater than the figures over the previous templateXLabel[0] . templateTitleSubject[0] : a snapshot Like Google , templateTitleSubject[0] 's search engine is the primary contact point with the company , but the company 's services go well beyond search . Incorporated in the Netherlands and with offices around the world , templateTitleSubject[0] offers services primarily to the templateYLabel[2] language market covering ecommerce , navigation , advertising , mobile development as well as a taxi/ride-sharing service and a car sharing service .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company employed approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Revenue of billion the Yandex worldwide from 2007 to 2019 .  In 2019 , the company employed approximately 175.39 billion rubles .

Example 301:
titleEntities: {'Subject': ['Boston Celtics'], 'Date': ['2010', '2016']}
title: Boston Celtics concession stand prices ( beer , soft drink , hot dog ) 2010 to 2016
Data:
                  0        1        2        3        4        5
Year        2015/16  2014/15  2013/14  2012/13  2011/12  2010/11
Beer            8.5      8.5     8.25      8.0     7.25     7.25
Soft drink      5.0      5.0      5.5      4.0     3.75     3.75
Hot dog         5.5      5.5      5.5     4.75      4.5      4.5 

gold: The statistic depicts the concession stand prices at games of the Boston Celtics ( NBA ) from 2010/11 to 2015/16 . In the 2013/14 season , a 16 ounce beer cost 8.25 U.S. dollars .
gold_template: The statistic depicts the templateTitle[2] templateTitle[3] templateTitle[4] at games of the templateTitleSubject[0] ( NBA ) from templateValue[0][last] to templateValue[0][0] . In the templateValue[0][2] season , a 16 ounce templateTitle[5] cost templateValue[1][2] U.S. dollars .

generated_template: The statistic depicts the templateTitle[2] templateTitle[3] templateTitle[4] at games of the templateTitleSubject[0] ( NBA ) from templateValue[0][last] to templateValue[0][0] . In the templateValue[0][3] season , a 16 ounce templateTitle[5] cost templateValue[1][0] U.S. dollars .
generated: The statistic depicts the concession stand prices at games of the Boston Celtics ( NBA ) from 2010/11 to 2015/16 .  In the 2012/13 season , a 16 ounce beer cost 8.5 U.S. dollars .

Example 302:
titleEntities: {'Subject': ['Greece'], 'Date': ['2015']}
title: Median age of the population in Greece 2015
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Median', 'age', 'in', 'years']: ['53.4', '53.2', '52.7', '51.5', '49.7', '47.7', '45.6', '43.4', '41.1', '39.4', '38.0', '36.5', '35.1', '33.9', '33.0', '32.4', '32.3', '29.9', '28.3', '26.9', '25.5']

gold: The statistic depicts the median age in Greece from 1950 to 2050 . The median age of a population is an index that divides the population into two equal groups : half of the population is older than the median age and the other half younger . In 2015 , the median age of Greece 's population was 43.4 years .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of a templateTitle[2] is an index that divides the templateTitle[2] into two equal groups : half of the templateTitle[2] is older than the templateYLabel[0] templateYLabel[1] and the other half younger . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] 's templateTitle[2] was templateYValue[7] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .
generated: This statistic shows the Median age of the population in Greece from 1950 to 2050 .  The Median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older .  It is a single index that summarizes the age distribution of a population .

Example 303:
titleEntities: {'Subject': ['Summer Olympics'], 'Date': ['1896', '2016']}
title: All-time Summer Olympics medals table 1896 to 2016
X_Axis['Country']: ['United_States', 'Russia', 'Germany', 'Great_Britain', 'France', 'Italy', 'China', 'Australia', 'Sweden', 'Hungary', 'Japan', 'Romania', 'Finland', 'Canada', 'Netherlands', 'Poland', 'South_Korea', 'Cuba', 'Bulgaria', 'Czech_Republic', 'Denmark', 'Switzerland', 'Norway', 'Belgium', 'Spain', 'Brazil', 'Ukraine', 'Greece', 'New_Zealand', 'Kenya', 'Serbia', 'Turkey', 'Austria', 'South_Africa', 'Belarus', 'Jamaica', 'Argentina', 'Kazakhstan', 'Iran', 'Mexico', 'North_Korea', 'Ethiopia', 'Azerbaijan', 'Estonia', 'Croatia', 'Uzbekistan', 'Georgia', 'Ireland', 'Thailand', 'Indonesia']
Y_Axis['Medals', 'won', '(gold,', 'silver,', 'bronze)']: ['2520', '1865', '1681', '847', '713', '577', '543', '497', '494', '491', '439', '306', '303', '301', '285', '282', '264', '220', '214', '197', '194', '193', '152', '148', '148', '128', '124', '117', '117', '100', '110', '93', '87', '86', '85', '78', '69', '69', '68', '67', '54', '53', '44', '34', '33', '33', '32', '32', '31', '30']

gold: The United States is the most successful nation of all time at the Summer Olympic Games , having amassed a total of 2,520 medals since the first Olympics in 1896 . Team USA gets the gold medalOf the 2,520 medals won by Team USA over the years , over one thousand have been gold . The dominance of the United States can be shown in the fact that only two other nations , Russia and Germany , have reached a combined medal tally of 1,000 .
gold_template: The templateXValue[0] is the most successful nation of all time at the templateTitleSubject[0] Olympic Games , having amassed a total of templateYValue[max] templateYLabel[0] since the first templateTitleSubject[0] in templateTitleDate[min] . Team USA gets the gold medalOf the templateYValue[max] templateYLabel[0] templateYLabel[1] by Team USA over the years , over templateYValue[3] thousand have been gold . The dominance of the templateXValue[0] can be shown in the fact that only two other nations , templateXValue[1] and templateXValue[2] , have reached a combined medal tally of 1,000 .

generated_template: This statistic shows the top 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , templateXValue[5] had the highest templateYLabel[0] templateYLabel[1] of a templateYLabel[3] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the top 20 Summer Olympics Summer the Olympics medals table in 1896 .  In that year , Italy had the highest Medals won of a silver, of 2520 (gold, silver, .

Example 304:
titleEntities: {'Subject': ['Italy'], 'Date': ['2016', '2019']}
title: Leading car brand sales in Italy 2016 to 2019
Data:
               0           1       2        3        4      5       6        7      8           9
Response    Fiat  Volkswagen    Ford  Renault  Peugeot   Opel  Toyota  Citroen  Dacia  Jeep/Dodge
2016      385798      139168  124328   118303    94132  95056   71934    60511  47979       40456
2017      402430      144885  134124   133715   104274  98979   85245    78225  58011       49503
2018      323342      162850  129615   125143   108502  95317   87956    82850  61628       84551
2019      285888      176813  122527   113924   110034  96559   90052    86468  84087       81510 

gold: Which brand sold the most cars in Italy in 2019 ? Perhaps unsurprisingly , Fiat topped the country 's sales ranking with sales of nearly 286 thousand cars that year . The Italian car brand was followed by Volkswagen ( roughly 177 thousand passenger cars sold ) and Ford ( approximately 123 thousand units ) . Small cars drive passenger car sales in Italy ... Overall car sales in Italy showed a positive trend in recent years until 2018 , when a decrease of about three percent on the previous year was observed .
gold_template: Which templateTitle[2] sold the most cars in templateTitleSubject[0] in templateTitleDate[max] ? Perhaps unsurprisingly , templateValue[0][0] topped the country 's templateTitle[3] ranking with templateTitle[3] of nearly templateValue[4][max] thousand cars that year . The Italian templateTitle[1] templateTitle[2] was followed by templateValue[0][1] ( roughly templateValue[4][1] thousand passenger cars sold ) and templateValue[0][2] ( approximately templateValue[4][2] thousand units ) . Small cars drive passenger templateTitle[1] templateTitle[3] in templateTitleSubject[0] ... Overall templateTitle[1] templateTitle[3] in templateTitleSubject[0] showed a positive trend in recent years until templateLabel[3][0] , when a templateNegativeTrend of about three templateScale on the previous year was observed .

generated_template: In templateTitleDate[max] , templateValue[0][0] was the most popular templateValue[0][0] employees in the United Kingdom . At that time , templateValue[0][0] accounted for templateValue[2][2] templateScale of all templateValue[0][0] employees , compared to the country . The templateValue[0][0] is the leader among the world 's overall .
generated: In 2019 , Fiat was the most popular Fiat employees in the United Kingdom .  At that time , Fiat accounted for 134124 % of all Fiat employees , compared to the country .  The Fiat is the leader among the world 's overall .

Example 305:
titleEntities: {'Subject': ['Singapore'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Singapore 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.5', '2.35', '2.22', '1.57', '0.99', '0.55', '3.14', '3.7', '2.96', '2.89', '3.9']

gold: Singapore posted a gross domestic product ( GDP ) growth rate of 3.7 percent in 2017 , after adjusting for inflation . While up from the previous two years , this number is expected to decline in 2018 , settling around 3.14 percent . What is GDP ? GDP is a measure of a country 's income , and most economists agree that slow but steady GDP growth is best for a developed economy .
gold_template: templateTitleSubject[0] posted a templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateTitle[5] of templateYValue[7] templateScale in templateXValue[7] , after adjusting for inflation . While up from the templateYLabel[3] templateYValue[1] years , this number is expected to decline in templateXValue[6] , settling around templateYValue[6] templateScale . What is templateYLabel[0] ? templateYLabel[0] is a measure of a country 's income , and most economists agree that slow but steady templateYLabel[0] templateYLabel[1] is best for a developed economy .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Singapore from 2014 to 2018 , with projections up until 2024 .  In 2018 , Singapore 's real Gross domestic product increased by around 3.14 % compared to the previous Year .

Example 306:
titleEntities: {'Subject': ['New England Patriots', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the New England Patriots ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['600', '593', '575', '523', '494', '428', '408', '380', '333', '318', '302', '282', '255', '250', '236', '191', '189', '136']

gold: The statistic depicts the revenue of the National Football League franchise , New England Patriots , from 2001 to 2018 . In 2018 , the franchise generated a revenue of 600 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the National Football League franchise , templateTitleSubject[0] Patriots , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the franchise generated a templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the New England Patriots , a franchise of the National Football League , from 2001 to 2018 .  In 2018 , the Revenue of the New England Patriots was 600 U.S. dollars .

Example 307:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2017']}
title: Unauthorized immigrant population U.S. 1990 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990']
Y_Axis['Number', 'of', 'immigrants', 'in', 'millions']: ['10.5', '10.7', '11.0', '11.1', '11.2', '11.2', '11.5', '11.4', '11.3', '11.7', '12.2', '11.6', '11.1', '8.6', '5.7', '3.5']

gold: This statistic shows the number of unauthorized immigrants in the United States from 1990 to 2017 . In 2017 , an estimated 10.5 million unauthorized immigrants lived in the United States . This is an increase from about 3.5 million unauthorized immigrants in 1990 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , an estimated templateYValue[idxmax(X)] templateScale templateTitle[0] templateYLabel[1] lived in the templateTitle[3] . This is an templatePositiveTrend from about templateYValue[min] templateYValue[idxmin(X)] templateTitle[0] templateYLabel[1] in templateXValue[idxmin(Y)] .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] stood at approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Unauthorized of immigrant population U.S. 1990 of U.S. from 1990 to 2017 .  In 2017 , the Unauthorized of U.S. stood at approximately 10.5 millions .

Example 308:
titleEntities: {'Subject': ['Global'], 'Date': ['2017']}
title: Global leading kiwi producing countries 2017
X_Axis['Country']: ['China_mainland', 'Italy', 'New_Zealand', 'Iran_(Islamic_Republic_of)', 'Greece', 'Chile', 'France', 'Turkey', 'Portugal', 'United_States_of_America']
Y_Axis['Production', 'volume', 'in', 'thousand', 'metric', 'tons']: ['2024.6', '541.15', '411.78', '311.31', '274.6', '224.92', '65.63', '56.16', '35.41', '30.48']

gold: This statistic depicts the production volume of kiwis worldwide in 2017 , by leading country . According to the source , China was the main kiwi producer worldwide in 2017 , with production volume of some 2.02 million metric tons .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] of kiwis worldwide in templateTitleDate[0] , by templateTitle[1] templateXLabel[0] . According to the source , templateXValue[0] was the main templateTitle[2] producer worldwide in templateTitleDate[0] , with templateYLabel[0] templateYLabel[1] of some templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] templateTitle[4] templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] had the largest templateYLabel[0] templateTitle[4] templateTitle[1] templateTitle[2] templateTitle[3] of around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[1] templateTitle[2] .
generated: This statistic shows the Global leading kiwi producing Production countries Global in 2017 .  China_mainland had the largest Production countries leading kiwi producing of around 2024.6 thousand metric of leading kiwi .

Example 309:
titleEntities: {'Subject': ['The'], 'Date': []}
title: The most spoken languages worldwide
X_Axis['Languages']: ['Chinese', 'Spanish', 'English', 'Hindi', 'Arabic', 'Bengali', 'Portuguese', 'Russian', 'Japanese', 'Lahnda']
Y_Axis['Native', 'speaker', 'in', 'millions']: ['1311', '460', '379', '341', '319', '228', '221', '154', '128', '119']

gold: The statistic shows the most spoken languages worldwide . There were around 1.3 billion native Chinese speakers a the time of survey . Spoken languages in the United States The United States does not have an official language , but they use English , specifically American English , for legislation , regulation and other official pronouncements .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateXLabel[0] templateTitle[3] . There were around templateYValue[max] templateScale templateYLabel[0] templateXValue[0] speakers a the time of survey . templateTitle[1] templateXLabel[0] in the country The country does not have an official language , but they use templateXValue[2] , specifically American templateXValue[2] , for legislation , regulation and other official pronouncements .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . The templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was templateXValue[0] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the most spoken languages worldwide The in .  The most spoken languages in The was Chinese with a Native speaker of 1311 millions .

Example 310:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2009', '2019']}
title: Number of births in Sweden 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'births']: ['114523', '115832', '115416', '117425', '114870', '114907', '113593', '113177', '111770', '115641', '111801']

gold: During the period from 2009 to 2019 , the highest number of babies born in Sweden was in 2016 , when over 117 thousand births occurred . In 2019 , about 115 thousand babies were born in Sweden . Within the last decade , there were yearly more baby boys than baby girls being born .
gold_template: During the period from templateXValue[min] to templateXValue[max] , the highest templateYLabel[0] of babies born in templateTitleSubject[0] was in templateXValue[3] , when over templateYValue[max] thousand templateYLabel[1] occurred . In templateXValue[max] , about templateYValue[0] thousand babies were born in templateTitleSubject[0] . Within the last decade , there were yearly more baby boys than baby girls being born .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[1] that templateXLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] templatePositiveTrend by over templateYValue[3] templateScale compared to the templateXLabel[0] .
generated: The statistic shows the Number of the births that Year in Sweden from 2009 to 2019 .  In 2016 , the Number of births in the Sweden increased by over 117425 % compared to the Year .

Example 311:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Household income of white families in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Median', 'income', 'in', '2018', 'CPI-U-RS', 'adjusted', 'U.S.', 'dollars']: ['70642', '69851', '66440', '65133', '62453', '63588', '60979', '60526', '61361', '62374', '63378', '65089', '63892', '63900', '63627', '63832', '64084', '62773', '63609', '63654', '62480', '60548', '59128', '58184', '56297', '55914', '55842', '55568', '56917']

gold: This statistic shows the household income of white families in the U.S. from 1990 to 2018 . The median income in 2018 was at 70,642 U.S. dollars for white , non-Hispanic families . The median household income of the United States can be accessed here .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateYLabel[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[idxmax(X)] templateYLabel[5] templateYLabel[6] for templateTitle[2] , non-Hispanic templateTitle[3] . The templateYLabel[0] templateTitle[0] templateYLabel[1] of the templateTitle[4] can be accessed here .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] in the templateTitle[4] templateNegativeTrend below the following years . In templateXValue[max] , the templateTitle[0] of the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Median of income in the income from 1990 to 2018 .  The Median of income in the U.S. fell below the following years .  In 2018 , the Household of the U.S. amounted to 70642 million .

Example 312:
titleEntities: {'Subject': ['Cattle'], 'Date': ['2012', '2019']}
title: Cattle population worldwide 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Cattle', 'population', 'in', 'million', 'head']: ['1007.26', '996.36', '984.53', '978.77', '969.26', '1008.57', '1005.29', '1001.72']

gold: How many cattle are in the world ? The global cattle population amounted to about 996.36 million head in 2018 , down from over one billion cattle in 2014 . Cows as livestock The domestication of cattle began as early as 10,000 to 5,000 years ago . From ancient times up to the present , cattle are bred to provide meat and dairy .
gold_template: How many templateYLabel[0] are in the world ? The global templateYLabel[0] templateYLabel[1] amounted to about templateYValue[1] templateScale templateYLabel[3] in templateXValue[1] , down from over templateYValue[0] templateScale templateYLabel[0] in templateXValue[5] . Cows as livestock The domestication of templateYLabel[0] began as early as 10,000 to 5,000 years ago . From ancient times up to the present , templateYLabel[0] are bred to provide meat and dairy .

generated_template: The statistic shows the templateYLabel[0] of the employees of the templateTitle[7] templateTitle[2] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] of templateTitleSubject[0] employees amounted to over templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[2] .
generated: The statistic shows the Cattle of the employees of the 2019 worldwide Cattle from 2012 to 2019 .  According to the report , the Cattle of employees amounted to over 984.53 million head in 2017 .

Example 313:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2019']}
title: Sugar beet production in the U.S. 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Production', 'in', 'thousand', 'tons']: ['28600', '33282', '35317', '36920', '35371', '31285', '32789', '35224', '28896', '32034', '29783', '26881', '31834', '34064', '27433', '30021', '30710', '27707', '25708', '32541']

gold: This statistic shows the total U.S. sugar beet production from 2000 to 2019 . According to the report , approximately 28.6 million tons of sugar beet were produced in the United States in 2019 .
gold_template: This statistic shows the total templateTitleSubject[0] templateTitle[0] templateTitle[1] templateYLabel[0] from templateXValue[min] to templateXValue[max] . According to the report , approximately templateYValue[0] templateScale templateYLabel[2] of templateTitle[0] templateTitle[1] were produced in the templateTitle[3] in templateXValue[max] .

generated_template: This statistic shows the total amount of templateTitle[1] cherries produced in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] was templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: This statistic shows the total amount of beet cherries produced in the Sugar from 2000 to 2019 .  According to the report , the Sugar beet production was 28600 thousand tons in 2019 .

Example 314:
titleEntities: {'Subject': ['Japan'], 'Date': ['2024']}
title: National debt of Japan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'billion', 'U.S.', 'dollar']: ['12935.09', '12766.24', '12606.98', '12453.45', '12316.84', '12142.03', '11924.1', '11732.58', '11602.46', '11268.63', '11111.32']

gold: The statistic shows the national debt of Japan from 2014 to 2017 , with projections up until 2024 . The amount of Japan 's national debt in 2017 amounted to about 11.73 trillion U.S. dollar . In a ranking of debt to GDP per country , Japan is thus currently ranked first .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . The amount of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] in templateXValue[7] amounted to about templateYValue[7] templateScale templateYLabel[3] templateYLabel[4] . In a ranking of templateYLabel[1] to GDP per country , templateTitleSubject[0] is thus currently ranked first .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . According to the source , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[6] templateScale .
generated: The statistic shows the National of the debt billion in Japan from 2014 to 2018 , with projections up until 2024 .  According to the source , the National debt in Japan was 11924.1 billion .

Example 315:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. wind power generation 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Net', 'generation', 'in', 'billion', 'kilowatt', 'hours']: ['274.95', '254.3', '226.99', '190.72', '181.66', '167.84', '140.82', '120.18', '94.65', '73.89', '55.36', '34.45', '26.59', '17.81', '14.14', '11.19', '10.35', '6.74', '5.59']

gold: In 2018 , around 275 terawatt hours of wind electricity were generated in the United States . That year , wind power was by far the largest non-hydroelectric renewable energy source used in the United States , followed by solar and thermal . Clean energy on the rise The highest increase in clean energy investments came in the years leading up to the 2008 financial crisis .
gold_template: In templateXValue[max] , around templateYValue[max] terawatt templateYLabel[4] of templateTitle[1] electricity were generated in the templateTitle[0] . That templateXLabel[0] , templateTitle[1] templateTitle[2] was by far the largest non-hydroelectric renewable energy source used in the templateTitle[0] , followed by solar and thermal . Clean energy on the rise The highest templatePositiveTrend in clean energy investments came in the years leading up to the templateXValue[10] financial crisis .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the development of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Net generation of the development of wind power generation 2000 2018 from 2000 to 2018 .  In 2018 , the Net generation of the U.S. was 274.95 billion kilowatt .

Example 316:
titleEntities: {'Subject': ['National Hockey League Chicago'], 'Date': ['2005', '2019']}
title: National Hockey League - Chicago Blackhawks home attendance 2005 to 2019
Data:
                          0        1        2        3        4        5        6        7        8        9       10       11       12       13
Year                2018/19  2017/18  2016/17  2015/16  2014/15  2013/!4  2012/13  2011/12  2010/11  2009/10  2008/09  2007/08  2006/07  2005/06
Total attendance     932098   887794   891827   896240   892532   927545   522619   882874   878356   875596   912155   689377   521809   546075
Average attendance    22734    21653    21751    21859    21769    22623    21775    21533    21423    21356    22247    16814    12727    13318 

gold: This graph depicts the total/average regular season home attendance of the Chicago Blackhawks franchise of the National Hockey League from the 2005/06 season to the 2018/19 season . In 2018/19 , the total regular season home attendance of the franchise was 932,098 .
gold_template: This graph depicts the total/average regular season templateTitle[5] templateTitle[6] of the templateTitleSubject[0] templateTitle[4] franchise of the templateTitleSubject[0] League from the templateValue[0][last] season to the templateValue[0][0] season . In templateValue[0][0] , the templateLabel[1][0] regular season templateTitle[5] templateTitle[6] of the franchise was templateValue[1][0] .

generated_template: This graph depicts the total/average regular season templateTitle[5] templateTitle[6] of the templateTitleSubject[0] templateTitle[4] franchise of the templateTitleSubject[0] League from the templateValue[0][last] season to the templateValue[0][0] season . In templateValue[0][0] , the templateLabel[1][0] regular season templateTitle[5] templateTitle[6] of the franchise was templateValue[1][0] .
generated: This graph depicts the total/average regular season home attendance of the National Hockey League Chicago Blackhawks franchise of the National Hockey League Chicago League from the 2005/06 season to the 2018/19 season .  In 2018/19 , the Total regular season home attendance of the franchise was 932098 .

Example 317:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2019', '2024']}
title: Inflation rate forecast ( CPI ) in the United Kingdom ( UK ) Q3 2019 to Q1 2024
X_Axis['Quarter']: ['Q1_2024', 'Q4_2023', 'Q3_2023', 'Q2_2023', 'Q1_2023', 'Q4_2022', 'Q3_2022', 'Q2_2022', 'Q1_2022', 'Q4_2021', 'Q3_2021', 'Q2_2021', 'Q1_2021', 'Q4_2020', 'Q3_2020', 'Q2_2020', 'Q1_2020', 'Q4_2019', 'Q3_2019']
Y_Axis['Inflation', 'rate', 'forecast']: ['2', '2', '2', '2', '2', '2', '2', '2', '1.99', '1.99', '1.99', '1.99', '1.95', '1.9', '1.88', '1.82', '1.82', '2.01', '2.1']

gold: The consumer price index rate in the United Kingdom is expected to be 1.82 percent in first quarter of 2020 , before rising to 2 percent by the second quarter of 2022 , which is where the rate is anticipated to remain at until at least the first quarter of 2024 . Inflation target of two percent The forecasted inflation rate for the CPI , falls in line with the UK government 's targeted rate of 2 percent . As of October 2019 , the inflation rate for the United Kingdom was 1.5 percent , falling from a high of 3.1 percent almost two years earlier in November 2017 .
gold_template: The consumer price index templateYLabel[1] in the templateTitleSubject[0] is expected to be templateYValue[min] templateScale in first templateXLabel[0] of templateXValue[13] , before templatePositiveTrend to templateYValue[0] templateScale by the second templateXLabel[0] of templateXValue[5] , which is where the templateYLabel[1] is anticipated to remain at until at least the first templateXLabel[0] of templateXValue[0] . templateYLabel[0] target of templateYValue[0] templateScale The forecasted templateYLabel[0] templateYLabel[1] for the templateTitle[3] , falls in line with the templateTitleSubject[1] government 's targeted templateYLabel[1] of templateYValue[0] templateScale . As of October templateXValue[17] , the templateYLabel[0] templateYLabel[1] for the templateTitleSubject[0] was 1.5 templateScale , templateNegativeTrend from a high of 3.1 templateScale almost templateYValue[0] years earlier in 2017 .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . It can be seen that the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Inflation rate forecast of United Kingdom from the first Quarter of 2019 to the fourth Quarter of 2024 .  It can be seen that the Inflation rate forecast Inflation rate forecast of 2.1 forecast .

Example 318:
titleEntities: {'Subject': ['Mobile', 'UK'], 'Date': ['2019']}
title: Mobile phone usage : frequency of making mobile phone calls in the UK 2019 , by gender
Data:
                            0          1                     2                     3                      4                       5      6
Response  Several_times_a_day  Every_day  Several_times_a_week  At_least_once_a_week  At_least_once_a_month  Less_than_once_a_month  Never
Male                       49         21                    16                     6                      4                       2      1
Female                     46         24                    17                     6                      3                       2      1 

gold: Figures covering the frequency of mobile phone use show a relatively even split among genders in the United Kingdom ( UK ) during 2019 . Males were slightly more likely to use their mobile phones to make calls several times a day , while less than one percent of female respondents reported never using a mobile phone to make calls . Mobile call volume increasing The volume of calls made using mobile phones has increased over the past several years .
gold_template: Figures covering the templateTitle[3] of templateTitleSubject[0] templateTitle[1] use show a relatively even split among genders in the United Kingdom ( templateTitleSubject[1] ) during templateTitleDate[0] . Males were slightly more likely to use their templateTitleSubject[0] phones to make templateTitle[7] templateValue[0][0] a templateValue[0][0] , while templateValue[0][5] one templateScale of templateLabel[2][0] respondents reported templateValue[0][6] using a templateTitleSubject[0] templateTitle[1] to make templateTitle[7] . templateTitleSubject[0] call volume templatePositiveTrend The volume of templateTitle[7] made using templateTitleSubject[0] phones has templatePositiveTrend over the past templateValue[0][0] years .

generated_template: This statistic presents the distribution of templateValue[0][0] in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] . During that period of time , templateValue[1][0] templateScale of templateTitle[4] and templateValue[2][0] templateScale of respondents stated that they accessed the templateValue[0][0] templateTitle[4] accessed the templateValue[0][0] .
generated: This statistic presents the distribution of Several_times_a_day in the Mobile who were using Mobile as of 2019 , sorted phone calls .  During that period of time , 49 % of making and 46 % of respondents stated that they accessed the Several_times_a_day making accessed the Several_times_a_day .

Example 319:
titleEntities: {'Subject': ['January'], 'Date': ['2016', '2016']}
title: U.S. price difference between legal and illegal cannabis as of January 2016 , by state
Data:
                        0           1         2         3       4           5
State             Arizona  California  Colorado  Michigan  Oregon  Washington
Dispensary price     290$        299$      223$      301$    214$        238$
Street price         254$        218$      219$      274$    191$        210$ 

gold: This statistic presents the U.S. states with the biggest difference between legal dispensary and illegal street prices for an ounce of marijuana as of January 2016 . California had the biggest price difference between legal and illegal marijuana with an ounce of marijuana from a legal dispensary costing 299 U.S. dollars compared to 218 dollars for illegal street bought marijuana .
gold_template: This statistic presents the templateTitle[0] states with the biggest templateTitle[2] templateTitle[3] templateTitle[4] templateLabel[1][0] and templateTitle[5] templateLabel[2][0] prices for an ounce of marijuana as of templateTitleSubject[0] templateTitleDate[0] . templateValue[0][1] had the biggest templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] and templateTitle[5] marijuana with an ounce of marijuana from a templateTitle[4] templateLabel[1][0] costing 299 templateTitle[0] dollars compared to 218 dollars for templateTitle[5] templateLabel[2][0] bought marijuana .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . According to the source , the templateTitle[0] of templateValue[0][0] , templateValue[1][0] templateScale of templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] were templateValue[2][0] templateScale in templateValue[0][0] .
generated: This statistic shows the U.S. of the price difference between in the legal in 2016 , January 2016 .  According to the source , the U.S. of Arizona , 290$ million of Dispensary price were 254$ million in Arizona .

Example 320:
titleEntities: {'Subject': ['Americans'], 'Date': ['2020']}
title: Gifts Americans are planning to purchase for Valentine 's Day by gender 2020
Data:
                0           1      2               3           4             5                   6                  7        8         9       10       11        12     13            14           15        16     17           18          19                  20         21          22         23    24                       25       26
Race   Dinner_out  Chocolates  Candy  Greeting_cards  Gift_cards  Alcohol/wine  Personalized_gifts  Home-cooked_meals  Flowers  Massages  Jewelry  Outings  Perfumes  Other  Stuffed_toys  Accessories  Holidays  Books  Photo_frame  Home_decor  Electronic_gadgets  Wearables  Toiletries  Cosmetics  Pets  Couple's_gym_membership  Mixtape
Women          53          48     27              24          20            19                  18                  8       17        15       15       12        10      7             6            6         4      4            4           3                   3          3           3          3     2                        1        0
Men            52          54     32              29          17            18                  27                 19       44        28       25        6        18      2             9            6        10      7            8           0                   5         12           1          9     8                        1        2 

gold: This statistic shows the results of a survey among Americans in 2020 regarding what they would gift their Valentine . The results were sorted by gender . Some 53 percent of female respondents stated that they would gift their Valentine a dinner out in 2020 .
gold_template: This statistic shows the results of a survey among templateTitleSubject[0] in templateTitleDate[0] regarding what they would templateValue[0][4] their templateTitle[6] . The results were sorted templateTitle[9] templateTitle[10] . Some templateValue[1][0] templateScale of female respondents stated that they would templateValue[0][4] their templateTitle[6] a templateValue[0][0] in templateTitleDate[0] .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] in the templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . During the survey , it was found that templateValue[2][max] templateScale of respondents templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] were templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] .
generated: This statistic shows the Gifts of the Americans are of planning purchase in the Americans in 2020 and .  During the survey , it was found that 54 % of respondents Women were .

Example 321:
titleEntities: {'Subject': ['Cuba'], 'Date': ['2018']}
title: Distribution of gross domestic product ( GDP ) across economic sectors Cuba 2018
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
Agriculture   3.78    3.8   3.94   3.84   3.94   3.92   3.85    3.6   3.61   3.93   3.82
Industry     25.12  24.36  22.94  22.35  22.15  23.15  23.37  22.26  22.89   22.6  22.39
Services     69.97  70.82  72.05  72.76  72.88  71.77  71.66  73.01  72.47  72.45  72.32 

gold: This statistic shows the distribution of the gross domestic product ( GDP ) across economic sectors in Cuba from 2008 to 2018 . In 2018 , agriculture contributed around 3.78 percent to the GDP of Cuba , 25.12 percent came from the industry and 69.97 percent from the services sector .
gold_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the templateLabel[3][0] sector .

generated_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .
generated: The statistic shows the distribution of in Cuba gross domestic product from 2008 to 2018 .  In 2018 , 3.78 % of the employees in Cuba were active in the agricultural product , 25.12 % in Industry and 69.97 % in the service product .

Example 322:
titleEntities: {'Subject': ['United States'], 'Date': ['1992', '2017']}
title: Shoe store sales in the United States from 1992 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992']
Y_Axis['Billion', 'U.S.', 'dollars']: ['35.5', '36.2', '34.51', '33.45', '32.52', '31.04', '29.26', '27.23', '25.65', '26.98', '26.81', '26.68', '25.26', '23.69', '23.17', '23.2', '22.88', '22.88', '22.69', '22.24', '21.45', '21.24', '20.34', '19.91', '19.03', '18.62']

gold: In 2017 , U.S. shoe store sales amounted to about 35.5 billion U.S. dollars . According to the NAICS definition , this industry is comprised of establishments who are primarily engaged in retailing all types of new footwear , except hosiery and specialty sports footwear , such as golf shoes , bowling shoes , and spiked shoes . Shoe stores and the footwear market Footwear is a multi-billion dollar industry .
gold_template: In templateXValue[max] , templateYLabel[1] templateTitle[0] templateTitle[1] templateTitle[2] amounted to about templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] . According to the NAICS definition , this industry is comprised of establishments who are primarily engaged in retailing all types of new footwear , except hosiery and specialty sports footwear , such as golf shoes , bowling shoes , and spiked shoes . templateTitle[0] stores and the footwear market Footwear is a multi-billion dollar industry .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] were templateTitle[1] templateTitle[2] in the templateTitle[3] .
generated: This statistic shows the Shoe store sales United in United States from 1992 to 2017 .  In 2017 , about 35.5 billion U.S. dollars were store sales in the United .

Example 323:
titleEntities: {'Subject': ['Canada'], 'Date': ['2007', '2019']}
title: Canada 's biodiesel production 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Production', 'in', 'million', 'liters']: ['375', '300', '397', '463', '307', '351', '159', '100', '120', '115', '110', '95', '70']

gold: This statistic shows the annual production of biodiesel in Canada from 2007 to 2017 , with an estimated figure for 2018 , and a forecast for 2019 . The Canadian biodiesel production volume increased from 70 million liters in 2007 , to 397 million liters in 2017 . It is forecast that Canada 's production of biodiesel in 2019 will be 375 million liters .
gold_template: This statistic shows the annual templateYLabel[0] of templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[2] , with an estimated figure for templateXValue[1] , and a forecast for templateXValue[max] . The Canadian templateTitle[2] templateYLabel[0] volume templatePositiveTrend from templateYValue[idxmin(X)] templateScale templateYLabel[2] in templateXValue[idxmin(Y)] , to templateYValue[2] templateScale templateYLabel[2] in templateXValue[2] . It is forecast that templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateTitle[2] in templateXValue[max] will be templateYValue[idxmax(X)] templateScale templateYLabel[2] .

generated_template: In templateXValue[max] , templateTitleSubject[0] produced templateYValue[idxmax(X)] templateScale templateYLabel[2] of templateTitle[1] . In that templateXLabel[0] , 70 templateScale of that templateYLabel[0] was destined to exports . templateTitle[1] is made from the agave plant .
generated: In 2019 , Canada produced 375 million liters of 's .  In that Year , 70 million of that Production was destined to exports .  's is made from the agave plant .

Example 324:
titleEntities: {'Subject': ['Number'], 'Date': ['2017']}
title: Number of eSports games players worldwide 2017
X_Axis['Sport']: ['League_of_Legends', 'Call_of_Duty', 'Hearthstone', 'DOTA_2', 'World_of_Tanks', 'FIFA_17', 'Counter-Strike:_Global_Offensive', "PlayerUnknown's_Battlegrounds", 'StarCraft_II']
Y_Axis['Number', 'of', 'players', 'in', 'millions']: ['100.0', '28.1', '23.9', '12.6', '12.3', '12.3', '11.9', '6.8', '2.4']

gold: The statistic presents information on the number of players of selected eSports games worldwide as of August 2017 . According to the data , League of Legends was a leader based on number of eSports players . In the presented period the game had a player base of 100 million , while World of Tanks ranked fifth with 12.3 million players in the measured period .
gold_template: The statistic presents information on the templateYLabel[0] of templateYLabel[1] of selected templateTitle[1] templateTitle[2] templateTitle[4] as of 2017 . According to the data , templateXValue[0] of templateXValue[0] was a leader based on templateYLabel[0] of templateTitle[1] templateYLabel[1] . In the presented period the game had a player base of templateYValue[max] templateScale , while templateXValue[4] of templateXValue[4] ranked fifth with templateYValue[4] templateScale templateYLabel[1] in the measured period .

generated_template: In templateTitleDate[0] , templateXValue[0] was the templateXValue[last] , with the highest templateYLabel[0] templateYLabel[1] , at templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , followed by templateXValue[1] with templateYValue[1] templateScale templateYLabel[3] . templateXValue[last] was templateXValue[last] , however , templateXValue[3] had the highest templateTitle[2] on popularity .
generated: In 2017 , League_of_Legends was the StarCraft_II , with the highest Number players , at 100.0 millions , followed by Call_of_Duty with 28.1 millions .  StarCraft_II was StarCraft_II , however DOTA_2 had the highest games on popularity .

Example 325:
titleEntities: {'Subject': ['Video'], 'Date': ['2016']}
title: Video game industry 's wealthiest entrepreneurs 2016
X_Axis['Entrepreneur', '(company)']: ['Ma_Huateng_(Tencent)', 'William_Ding_(NetEase)', 'Shi_Yuzhu_(Giant_Interactive)', 'Kwon_Hyuk-Bin_(SmileGate)', 'Kim_Jung-Ju_(Nexon)']
Y_Axis['Net', 'worth', 'in', 'billion', 'U.S.', 'dollars']: ['21.9', '11.5', '5.4', '4.9', '3.5']

gold: The graph shows the estimated net worth of the wealthiest entrepreneurs in the video game industry worldwide as of July 2016 . According to the source , Ma Huateng , the founder and chairman as well as CEO of Tencent , was worth 21.9 billion U.S. dollars in the measured period . Overall , Tencent reported 70.84 billion Chinese yuan revenue from its online games in 2016 .
gold_template: The graph shows the estimated templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] in the templateTitleSubject[0] templateTitle[1] templateTitle[2] worldwide as of 2016 . According to the source , templateXValue[0] , the founder and chairman as well as CEO of Tencent , was templateYLabel[1] templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in the measured period . Overall , Tencent reported 70.84 templateScale Chinese yuan revenue from its online games in templateTitleDate[0] .

generated_template: The statistic presents the distribution of templateTitle[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] in templateTitleDate[0] . According to the source , templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] valued at templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the distribution of Video worth of the wealthiest entrepreneurs in 2016 .  According to the source , Ma_Huateng_(Tencent) had the highest Net of worth valued at 21.9 billion U.S. dollars .

Example 326:
titleEntities: {'Subject': ['American'], 'Date': ['2001', '2019']}
title: American imports of corn 2001 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Imports', 'in', 'million', 'bushels']: ['28', '36', '57', '57', '68', '32', '36', '160', '29', '28', '8', '14', '20', '12', '9', '11', '14', '14', '10']

gold: This statistic shows imports of corn in the United States from 2001 to 2019 . According to the report , U.S. corn imports amounted to approximately 57 million bushels in 2016 , down from 68 million bushels the previous year .
gold_template: This statistic shows templateYLabel[0] of templateTitle[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , U.S. templateTitle[2] templateYLabel[0] amounted to approximately templateYValue[2] templateScale templateYLabel[2] in templateXValue[3] , down from templateYValue[4] templateScale templateYLabel[2] the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Imports million of the 2001 American worldwide from 2001 to 2019 .  In 2019 , the average Imports million of the American 2001 was 28 bushels .

Example 327:
titleEntities: {'Subject': ['Snapchat'], 'Date': ['2014', '2019']}
title: Daily active users of Snapchat 2014 - 2019 , by region
Data:
                    0       1       2       3       4       5       6       7       8       9      10      11      12      13      14      15      16      17      18      19      20      21      22      23
Quarter        Q4_'19  Q3_'19  Q2_'19  Q1_'19  Q4_'18  Q3_'18  Q2_'18  Q1_'18  Q4_'17  Q3_'17  Q2_'17  Q1_'17  Q4_'16  Q3_'16  Q2_'16  Q1_'16  Q4_'15  Q3_'15  Q2_'15  Q1_'15  Q4_'14  Q3_'14  Q2_'14  Q1_'14
North America      86      84      83      80      79      79      80      81      80      77      75      71      68      65      61      54      48      44      40      38      35      32      30      26
Europe             67      65      64      61      60      59      61      62      60      57      57      55      52      49      46      39      34      30      29      27      23      20      18      14
Rest of World      64      61      56      49      47      47      47      48      47      44      42      40      39      39      36      29      24      20      18      16      13      11       9       6 

gold: As of the fourth quarter of 2019 , Snapchat had 86 million daily active users in North America and 67 million DAU in Europe . During the latest reported period , the photo and video sharing app claimed 218 million daily active users worldwide . Snapchat usage Snapchat is one of the most popular social networks worldwide .
gold_template: As of the fourth templateLabel[0][0] of templateTitleDate[max] , templateTitleSubject[0] had templateValue[1][0] templateScale templateTitle[0] templateTitle[1] templateTitle[2] in templateLabel[1][0] templateLabel[1][1] and templateValue[2][0] templateScale DAU in templateLabel[2][0] . During the latest reported period , the photo and video sharing app claimed 218 templateScale templateTitle[0] templateTitle[1] templateTitle[2] worldwide . templateTitleSubject[0] usage templateTitleSubject[0] is one of the most popular social networks worldwide .

generated_template: This statistic shows the average templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[min] to templateTitleDate[max] , templateTitle[7] templateTitle[8] . The second templateLabel[0][0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] stood at templateValue[2][0] templateScale in templateLabel[2][0] .
generated: This statistic shows the average Daily active users Snapchat in the Snapchat ( ) from 2014 to 2019 , region .  The second Quarter of active users Snapchat in stood at 67 % in Europe .

Example 328:
titleEntities: {'Subject': ['Czech Republic'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in the Czech Republic 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.54', '2.54', '2.55', '2.65', '2.62', '2.45', '2.96', '4.35', '2.45', '5.31', '2.72']

gold: The statistic shows the growth in real GDP in the Czech Republic from 2014 to 2018 , with projections up until 2024 . In 2018 , the Czech Republic 's real gross domestic product increased by around 2.96 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Czech Republic from 2014 to 2018 , with projections up until 2024 .  In 2018 , Czech Republic 's real Gross domestic product increased by around 2.96 % compared to the previous Year .

Example 329:
titleEntities: {'Subject': ['Market U.S.'], 'Date': ['2010']}
title: Vending machines : Market share of product categories in the U.S. 2010
Data:
                                 0                          1           2            3          4     5             6                  7                    8          9
Food Type  Packaged_cold_beverages  Snacks_confections_pastry  Hot_drinks  Vended_food  Ice_cream  Milk  Bulk_vending  Cold_drinks_(cup)  Cigarettes_&_Cigars  All_other
2009                          54.2                       22.5         8.4          6.7          2   1.9             1                  1                  0.7        1.6
2010                          56.1                         23         6.8          6.5        2.1   1.8             1                0.6                  0.6        1.5 

gold: This graph depicts the markt share of products sold through vending machines in the U.S. in 2009 and 2010 . Hot drinks accounted for 8.4 percent of total products sold in vending machines in 2009 .
gold_template: This graph depicts the markt templateTitle[3] of products sold through templateValue[0][6] templateTitle[1] in the templateTitleSubject[0] in templateLabel[1][0] and templateTitleDate[0] . templateValue[0][2] accounted for templateValue[1][2] templateScale of total products sold in templateValue[0][6] templateTitle[1] in templateLabel[1][0] .

generated_template: This statistic provides information on the average templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[5] as of 2019 , sorted templateTitle[7] templateTitle[8] . During the survey period , the survey was found that templateValue[2][0] templateScale of templateLabel[2][0] participated in the country .
generated: This statistic provides information on the average Vending machines Market share in the categories as of 2019 , sorted 2010 .  During the survey period , the survey was found that 56.1 % of 2010 participated in the country .

Example 330:
titleEntities: {'Subject': ['Election'], 'Date': ['2012']}
title: Election 2012 exit polls : percentage of votes by ethnicity
Data:
                  0      1         2      3
Race          White  Black  Hispanic  Asian
Barack Obama     39     93        71     73
Mitt Romney      59      6        27     26 

gold: This graph shows the percentage of votes of the 2012 presidential elections in the United States on November 6 , 2012 , by ethnicity . According to the exit polls , about 71 percent of Hispanic voters nationwide have voted for Barack Obama . Elections in the U.S .
gold_template: This graph shows the templateScale of templateTitle[5] of the templateTitleDate[0] presidential elections in the country on 6 , templateTitleDate[0] , templateTitle[6] templateTitle[7] . According to the templateTitle[2] templateTitle[3] , about templateValue[1][2] templateScale of templateValue[0][2] voters nationwide have voted for templateLabel[1][0] templateLabel[1][1] . Elections in the U.S .

generated_template: This graph shows the templateScale of templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] and templateLabel[2][2] in the templateTitle[0] in templateTitleDate[0] , templateTitle[6] templateTitle[7] . During the survey period , templateValue[1][0] templateScale of templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] were templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] .
generated: This graph shows the percentage of Barack Obama and Romney in the Election in 2012 , by ethnicity .  During the survey period , 39 percentage of Barack Obama were Barack Obama .

Example 331:
titleEntities: {'Subject': ['Vodafone'], 'Date': ['2018']}
title: Mobile/fixed market share of Vodafone in its main markets 2018
Data:
                                                 0        1      2      3               4      5
Country        South_Africa/Africa_(Vodacom_Group)  Germany  India  Italy  United_Kingdom  Spain
Mobile market                                 46.3     33.6   31.5   31.1            21.3   17.4
Fixed market                                     0     21.4      0    9.3             6.8   17.4 

gold: The statistic depicts the market share of Vodafone in its main markets of operation in 2018 . In that year , Vodafone held a 21.3 percent share of the mobile market in the United Kingdom , while in South Africa the company held 46.3 percent of the market . Since 2010 , Vodafone 's share of the mobile market in these countries has not substantially changed , however , the brand value of the telecommunications company has decreased in recent years from 29.61 billion U.S. dollars in 2014 to an estimated 21.83 billion in 2017 .
gold_template: The statistic depicts the templateTitle[1] templateTitle[2] of templateTitleSubject[0] in templateTitle[4] templateTitle[5] templateTitle[6] of operation in templateTitleDate[0] . In that year , templateTitleSubject[0] held a templateValue[1][4] templateScale templateTitle[2] of the templateLabel[1][0] templateTitle[1] in the templateValue[0][4] , while in templateValue[0][0] Africa the company held templateValue[1][0] templateScale of the templateTitle[1] . Since 2010 , templateTitleSubject[0] 's templateTitle[2] of the templateLabel[1][0] templateTitle[1] in these countries has not substantially changed , however , the brand value of the telecommunications company has templateNegativeTrend in recent years from 29.61 templateScale U.S. dollars in 2014 to an estimated 21.83 templateScale in 2017 .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[4] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . In templateTitleDate[0] , the templateTitle[0] of templateTitleSubject[0] generated templateValue[2][0] templateScale U.S. dollars in templateValue[0][0] .
generated: This statistic shows the Mobile/fixed of the market share Vodafone in its 2018 , .  In 2018 , the Mobile/fixed of Vodafone generated 0 billion U.S. dollars in South_Africa/Africa_(Vodacom_Group) .

Example 332:
titleEntities: {'Subject': ['Middle East'], 'Date': ['2011', '2019']}
title: Inbound visitor growth in the Middle East 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Year-over-year', 'visitor', 'growth']: ['4.3', '3.2', '3.5', '1.2', '2.5', '8', '4.2', '-3.1', '2.3']

gold: This statistic shows inbound visitor growth in the Middle East from 2011 to 2019 . In 2016 , inbound visitor numbers to the Middle East grew by 1.2 percent and by around 2.5 percent in the previous year , showing a decline compared to eight percent growth in 2014 .
gold_template: This statistic shows templateTitle[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitle[0] templateYLabel[1] numbers to the templateTitleSubject[0] templatePositiveTrend by templateYValue[3] templateScale and by around templateYValue[4] templateScale in the previous templateXLabel[0] , showing a decline compared to templateYValue[max] templateScale templateYLabel[2] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] templatePositiveTrend by templateYValue[idxmin(X)] templateScale over the previous templateXLabel[0] . templateTitle[0] visitors to the region was forecast to grow by templateYValue[idxmax(X)] templateScale in templateXValue[max] .
generated: This statistic shows the Inbound visitor of Middle East from 2011 to 2019 .  In 2011 , the Inbound visitor in Middle East increased by 2.3 % over the previous Year .  Inbound visitors to the region was forecast to grow by 4.3 % in 2019 .

Example 333:
titleEntities: {'Subject': ['Main'], 'Date': ['2017']}
title: Main source of news in selected countries 2017
Data:
                                          0       1        2      3              4      5          6      7       8
Country                      United_Kingdom  France  Germany  Italy  United_States  Japan  Australia  Spain  Sweden
Online (excl. social media)              32      23       21     19             27     29         26     23      39
Online (incl. social media)              41      34       28     29             44     37         43     39      49
TV                                       40      50       52     59             41     44         36     42      29
Print                                    11       6        7      7              6     15          9     10       9
Radio                                     7       7       10      4              5      4          8      8      10 

gold: The statistic shows sources of news for internet users in selected countries in 2017 . Among internet users from the United States , 41 percent named TV as their main source of news , whereas 44 percent stated the internet ( incl . social media ) was their main news source .
gold_template: The statistic shows sources of templateTitle[2] for internet users in templateTitle[3] templateTitle[4] in templateTitleDate[0] . Among internet users from the templateValue[0][0] templateValue[0][4] , templateValue[2][0] templateScale named templateLabel[3][0] as their templateTitleSubject[0] templateTitle[1] of templateTitle[2] , whereas templateValue[2][4] templateScale stated the internet ( incl . templateLabel[1][2] media ) was their templateTitleSubject[0] templateTitle[2] templateTitle[1] .

generated_template: This statistic shows the results of a survey conducted in the templateTitle[0] in templateTitleDate[0] , templateTitle[10] templateTitle[11] . The survey shows that templateValue[1][0] templateScale of respondents in the templateTitle[1] templateTitle[2] at templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] were templateLabel[2][0] .
generated: This statistic shows the results of a survey conducted in the Main in 2017 , .  The survey shows that 32 % of respondents in the source news at Online (excl. social were Online .

Example 334:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2010', '2019']}
title: Twitter : number of monthly active users 2010 to 2019
X_Axis['Quarter']: ["Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10", "Q2_'10", "Q1_'10"]
Y_Axis['Number', 'of', 'monthly', 'active', 'users', 'in', 'millions']: ['330.0', '321.0', '326.0', '335.0', '336.0', '330.0', '330.0', '326.0', '327.0', '318.0', '317.0', '313.0', '310.0', '305.0', '307.0', '304.0', '302.0', '288.0', '284.0', '271.0', '255.0', '241.0', '231.7', '218.0', '204.0', '185.0', '167.0', '151.0', '138.0', '117.0', '101.0', '85.0', '68.0', '54.0', '49.0', '40.0', '30.0']

gold: How many people use Twitter ? As of the first quarter of 2019 , Twitter averaged 330 million monthly active users , a decline from its all-time high of 336 MAU in the first quarter of 2018 . As of the first quarter of 2019 , the company switched its user reporting metric to monetizable daily active users ( mDAU ) . Twitter Twitter is a social networking and microblogging service , enabling registered users to read and post short messages called tweets .
gold_template: How many people use templateTitleSubject[0] ? As of the first templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] averaged templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] , a decline from its all-time high of templateYValue[max] MAU in the first templateXLabel[0] of 2018 . As of the first templateXLabel[0] of templateTitleDate[max] , the company switched its user reporting metric to monetizable daily templateYLabel[2] templateYLabel[3] ( mDAU ) . templateTitleSubject[0] is a social networking and microblogging service , enabling registered templateYLabel[3] to read and post short messages called tweets .

generated_template: This statistic shows a timeline with the amount of templateYLabel[1] templateYLabel[2] templateTitle[4] templateTitleSubject[0] templateYLabel[3] as of the first templateXLabel[0] of templateTitleDate[max] . Excluding the country , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[4] templateTitleSubject[0] templateYLabel[3] amounted to templateYValue[0] templateScale as of the most recent templateXLabel[0] . In total , templateTitleSubject[0] had 330 templateScale global templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows a timeline with the amount of monthly active users Twitter as of the first Quarter of 2019 .  Excluding the country , the Number of monthly active users Twitter amounted to 330.0 millions as of the most recent Quarter .  In total , Twitter had 330 millions global monthly active users .

Example 335:
titleEntities: {'Subject': ['HSBC'], 'Date': ['2007', '2019']}
title: Total assets of HSBC 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Total', 'assets', 'in', 'billion', 'U.S.dollars']: ['2715.15', '2558.12', '2521.77', '2374.99', '2409.66', '2634.14', '2671.3', '2692.5', '2555.6', '2454.7', '2364.5', '2527.5', '2354.3']

gold: This statistic presents the total assets of the British bank HSBC from 2007 to 2019 . In 2019 , the total assets of the HSBC bank reached 2.72 trillion U.S. dollars . HSBC - additional information HSBC Holdings plc is a British multinational bank and financial services organization based in London , United Kingdom .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] of the British bank templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] bank reached templateYValue[max] templateScale U.S. dollars . templateTitleSubject[0] - additional information templateTitleSubject[0] Holdings plc is a British multinational bank and financial services organization based in London , United Kingdom .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] of the templateYLabel[2] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of the templateYLabel[1] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Total of the assets of the billion 2019 from 2007 to 2019 .  In 2019 , the Total of the assets of HSBC amounted to 2715.15 billion U.S.dollars .

Example 336:
titleEntities: {'Subject': ['Tanzania'], 'Date': ['2018']}
title: Urbanization in Tanzania 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['33.78', '33.05', '32.33', '31.62', '30.9', '30.2', '29.49', '28.8', '28.11', '27.44', '26.78']

gold: This statistic shows the degree of urbanization in Tanzania from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 33.78 percent of Tanzania 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Tanzania from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 33.78 % of Tanzania 's total population lived in urban areas and cities .

Example 337:
titleEntities: {'Subject': ['UEFA', 'Union European Football Associations'], 'Date': ['2004', '2018']}
title: UEFA ( Union of European Football Associations ) total revenue 2004 to 2018
X_Axis['Year']: ['2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05']
Y_Axis['Revenue', 'in', 'million', 'euros']: ['2789.8', '2835.9', '4579.8', '2099.41', '1730.4', '1698.9', '2795.7', '1384.1', '1394.7', '907.0', '1925.26', '1151.6', '700.45', '659.2']

gold: This statistic shows the total revenue of the UEFA from the 2004/05 season to the 2017/18 season . The season runs from July 1 to June 30 of the following year . The revenue of the 2017/18 season was 2.79 billion euros .
gold_template: This statistic shows the templateTitle[5] templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The season runs from 1 to 30 of the following templateXLabel[0] . The templateYLabel[0] of the templateXValue[0] season was templateYValue[0] templateScale templateYLabel[2] .

generated_template: This statistic shows the total templateYLabel[0] of the templateTitle[0] templateTitleSubject[0] from the templateXValue[last] to templateXValue[0] . According to the source , the templateXValue[0] season the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[last] templateScale templateYLabel[2] .
generated: This statistic shows the total Revenue of the UEFA from the 2004/05 to 2017/18 .  According to the source , the 2017/18 season the Revenue of the UEFA was 659.2 million euros .

Example 338:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2020']}
title: Employments in the Netherlands 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Labor', 'force', 'in', 'millions']: ['8.94', '8.89', '8.78', '8.58', '8.4', '8.29', '8.21', '8.27', '8.33', '8.28', '8.28']

gold: This statistic shows the size of the employments in the Netherlands from 2010 to 2018 , with projections up until 2020 . In 2018 , the Netherlands had employments of around 8.78 million people .
gold_template: This statistic shows the size of the templateTitle[0] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[2] , with projections up until templateXValue[max] . In templateXValue[2] , the templateTitleSubject[0] had templateTitle[0] of around templateYValue[2] templateScale people .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateXValue[min] and templateXValue[max] . In templateXValue[2] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to around templateYValue[2] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Labor force of the Netherlands 2020 in 2010 and 2020 .  In 2018 , Netherlands 's Labor force amounted to around 8.78 millions .

Example 339:
titleEntities: {'Subject': ['February'], 'Date': ['2014', '2014']}
title: Most popular online dating services in selected countries as of February 2014
Data:
              0          1            2        3      4        5             6       7       8
Country   India  Indonesia  Philippines  Vietnam  Kenya  Nigeria  South_Africa  Brazil  Mexico
Facebook     51         82           70       85     64       64            58      49      62
Twitter      12         41           29       25     29       14            14       9      15
MySpace       0         10            0       13     13        0             0       0       9
Shaadi       14          0            0        0      0        0             0       0       0
Match         0          0           18        0      0        0            17       0       0
Badoo         0          0            0        0      0       17             0       9       0 

gold: The statistic shows the most popular online dating services and social media sites to find a partner in selected countries as of February 2014 . During a survey , nine percent of respondents from Brazil stated they had used Twitter to find a romantic partner .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] and social media sites to find a partner in templateTitle[5] templateTitle[6] as of templateTitleSubject[0] templateTitleDate[0] . During a survey , templateValue[2][7] templateScale of respondents from templateValue[0][7] stated they had used templateLabel[2][0] to find a romantic partner .

generated_template: This statistic displays the leading templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[min] and templateTitleDate[max] . During the survey period , it was found that templateValue[2][0] templateScale of respondents of a templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] templateLabel[1][3] in templateXValue[max] .
generated: This statistic displays the leading February online dating services selected in 2014 and .  During the survey period , it was found that 12 % of respondents a Facebook in .

Example 340:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: YouTube usage penetration in the United States 2019 , by age group
X_Axis['Year']: ['15-25_years', '26-35_years', '36-45_years', '46-55_years', '56+_years']
Y_Axis['Reach']: ['81', '71', '67', '66', '58']

gold: In the third quarter of 2019 , it was found that 81 percent of U.S. internet users aged 15 to 25 years accessed YouTube . YouTube in the United States With over 126 million unique monthly viewers , YouTube is by far the most popular online video property in the United States . The platform 's mobile presence is also significant , as YouTube consistently ranks as the most popular mobile app in the United States based on audience reach .
gold_template: In the third quarter of templateTitleDate[0] , it was found that templateYValue[max] templateScale of templateTitle[4] internet users aged 15 to 25 templateXValue[0] accessed templateTitle[0] . templateTitle[0] in the templateTitleSubject[0] With over 126 templateScale unique monthly viewers , templateTitle[0] is templateTitle[6] far the most popular online video property in the templateTitleSubject[0] . The platform 's mobile presence is also significant , as templateTitle[0] consistently ranks as the most popular mobile app in the templateTitleSubject[0] based on audience templateYLabel[0] .

generated_template: This statistic shows the share of internet users in the templateTitleSubject[1] who were using templateTitleSubject[0] as of the third quarter templateTitleDate[0] , sorted templateTitle[6] templateTitle[7] . Overall , templateYValue[max] templateScale of templateTitle[4] online users aged 26 to 35 templateXValue[0] used the mobile messaging service .
generated: This statistic shows the share of internet users in the United States who were using United States as of the third quarter 2019 , sorted by age .  Overall , 81 % of States online users aged 26 to 35 15-25_years used the mobile messaging service .

Example 341:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009', '2019']}
title: High schools with girls athletics programs in the U.S. by sport 2009 to 2019
Data:
                                 0        1        2        3        4        5        6        7        8        9
Year                       2009/10  2010/11  2011/12  2012/13  2013/14  2014/15  2015/16  2016/17  2017/18  2018/19
Basketball                   17711    17767    17768    17493    17754    17653    17890    17934    18171    18210
Track and Field - Outdoor    15923    16030    16143    15962    16217    16309    16508    16658    16951    17012
Volleyball                   15382    15479    15569    15565    15672    15534    15755    15992    16434    16572
Softball - Fast Pitch        15298    15338    14142    15067    15225    15115    13304    15440    15544    15877
Cross Country                13809    13839    13970    13918    14267    14287    14698    14880    15216    15435
Soccer                       10901    11047    11127    11351    11354    11502    11676    11823    12007    12107
Tennis                       10166    10181    10058    10005    10117    10099    10122    10121    10289    10290
Golf                          9651     9609     9667     9568     9815     9824    10046    10076    10289    10402
Swimming and Diving           7171     7164     7221     7249     7429     7526     7559     7721     7961     8007
Competitive Spirit Squads     4879     4266     4916     4847     4919     5358     5432     6541     6877     7214 

gold: High school sports in the United States has always been a huge industry and figures suggest that it is only getting bigger . During the school year 2018/19 , 18,210 schools in the U.S. had a basketball program for girls , marking a steady increase on the figure from the 2009/10 school year . Indeed , growth was experienced across all major sports , with soccer and competitive spirit squads seeing some of the most significant increases in the number of programs .
gold_template: templateTitle[0] school sports in the templateTitle[6] has always been a huge industry and figures suggest that it is only getting bigger . During the school templateLabel[0][0] templateValue[0][last] , templateValue[1][last] templateTitle[1] in the templateTitleSubject[0] had a templateLabel[1][0] program for templateTitle[3] , marking a steady templatePositiveTrend on the figure from the templateValue[0][0] school templateLabel[0][0] . Indeed , growth was experienced across all major sports , templateTitle[2] templateLabel[6][0] and templateLabel[10][0] templateLabel[10][1] templateLabel[10][2] seeing some of the most significant increases in the number of templateTitle[5] .

generated_template: The statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[4] templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . According to the report , templateValue[1][0] templateScale of all goods goods goods goods and templateValue[3][0] templateScale in templateValue[0][0] .
generated: The statistic shows the High of the schools girls U.S. programs 2018/19 to 2009/10 , sport 2009 .  According to the report , 17711 % of all goods and 15382 % in 2009/10 .

Example 342:
titleEntities: {'Subject': ['Liverpool FC'], 'Date': []}
title: Liverpool FC revenue by stream 2018/19
Data:
                    0        1        2        3        4        5        6        7        8        9       10
Year          2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09
Matchday         94.5     91.6     80.1     75.9     75.0     61.0     52.1     55.9     45.3     52.4     49.9
Broadcasting    299.3    251.3    182.5    168.1    163.8    120.8     74.5     78.2     72.3     97.1     87.6
Commercial      210.9    170.8    161.6    159.8    153.0    124.1    114.0     99.1     85.7     75.8     79.5 

gold: Broadcasting is the largest source of revenue for Liverpool FC . In 2018/2019 , the club earned approximately 299.3 million euros from broadcasting , more than triple than in 2010/2011 . The second biggest revenue stream is the commercial one .
gold_template: templateLabel[2][0] is the largest source of templateTitle[2] for templateTitleSubject[0] . In 2018/2019 , the club earned approximately templateValue[2][0] templateScale euros from templateLabel[2][0] , more than triple than in 2010/2011 . The second biggest templateTitle[2] templateTitle[4] is the templateLabel[3][0] one .

generated_template: templateLabel[2][0] is the largest source of templateTitle[2] for templateTitleSubject[0] . In 2018/2019 , the club earned approximately templateValue[2][0] templateScale euros from templateLabel[2][0] , more than double than in 2010/2011 . The second biggest templateTitle[2] templateTitle[4] is the templateLabel[3][0] templateTitle[4] .
generated: Broadcasting is the largest source of revenue for Liverpool FC .  In 2018/2019 , the club earned approximately 299.3 million euros from Broadcasting , more than double in 2010/2011 .  The second biggest revenue stream is the Commercial stream .

Example 343:
titleEntities: {'Subject': ['Global'], 'Date': ['2012', '2018']}
title: Global organic chocolate market size 2012 to 2018
X_Axis['Year']: ['2018', '2012']
Y_Axis['Market', 'size', 'in', 'million', 'U.S.', 'dollars']: ['886', '734']

gold: The global organic chocolate market was projected to grow in size from 734 million U.S. dollars to 886 million U.S. dollars between 2012 and 2018 . As such , organic chocolate seems to have increased in popularity in recent years . Organic Chocolate Candy A bar of organic chocolate may seem indistinguishable from a bar of regular chocolate ; the biggest difference between the two is that organic chocolate is made from cocoa beans grown without the use of synthetic pesticides or herbicides , as well as with organic sugar and milk .
gold_template: The templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] was projected to grow in templateYLabel[1] from templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] between templateXValue[idxmin(Y)] and templateXValue[idxmax(Y)] . As such , templateTitle[1] templateTitle[2] seems to have templatePositiveTrend in popularity in recent years . templateTitle[1] templateTitle[2] Candy A bar of templateTitle[1] templateTitle[2] may seem indistinguishable from a bar of regular templateTitle[2] ; the biggest difference between the two is that templateTitle[1] templateTitle[2] is made from cocoa beans grown without the use of synthetic pesticides or herbicides , as well as with templateTitle[1] sugar and milk .

generated_template: The statistic depicts the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateYLabel[0] for templateTitle[1] templateTitle[2] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic depicts the Market of organic chocolate size of the Global from 2012 to 2018 .  In 2018 , the global Market for organic chocolate of the Global was 886 million U.S. dollars .

Example 344:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2010', '2019']}
title: Electricity prices for households in Denmark 2010 - 2019 , semi-annually
X_Axis['Year']: ['2019_S1', '2018_S2', '2018_S1', '2017_S2', '2017_S1', '2016_S2', '2016_S1', '2015_S2', '2015_S1', '2014_S2', '2014_S1', '2013_S2', '2013_S1', '2012_S2', '2012_S1', '2011_S2', '2011_S1', '2010_S2', '2010_S1']
Y_Axis['Euro', 'cents', 'per', 'kilowatt-hour']: ['29.84', '31.23', '31.26', '30.1', '30.49', '30.84', '30.88', '30.42', '30.68', '30.35', '30.42', '29.36', '30.0', '29.72', '29.97', '29.75', '29.08', '27.08', '26.7']

gold: Between 2010 and the first half of 2018 , the overall household price for electricity in Denmark increased by approximately 4.53 euro cents per kilowatt-hour to 31.23 euro cents . This was the highest price during this period . Electricity prices per kilowatt-hour were at their lowest in the first half of 2010 at 26.7 euro cents .
gold_template: Between templateXValue[17] and the first half of templateXValue[1] , the overall household price templateTitle[2] templateTitle[0] in templateTitleSubject[0] templatePositiveTrend by approximately 4.53 templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] to templateYValue[1] templateYLabel[0] templateYLabel[1] . This was the highest price during this period . templateTitle[0] templateTitle[1] templateYLabel[2] templateYLabel[3] were at their lowest in the first half of templateXValue[17] at templateYValue[min] templateYLabel[0] templateYLabel[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: This statistic shows the Electricity prices for household end users in Denmark semi-annually from 2010_S2 to 2019_S1 .  In the second half of 2018_S2 , the average Electricity price for households was 31.23 Euro cents per kWh .

Example 345:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2013', '2016']}
title: United Kingdom ( UK ) property market 2013 - 2016 , by submarket value
Data:
                                                               0                                       1               2                    3                             4
Submarket  Residential_property:_owner_occupied_&_social_housing  Residential_property:_privately_rented  Infrastructure  Commercial_property  Other_non-domestic_buildings
2013                                                        3831                                     839               0                  683                           127
2014                                                        4068                                     929             960                  787                           146
2015                                                        4460                                    1015            1061                  871                           163
2016                                                        4805                                    1110            1102                  883                           147 

gold: This statistic shows the value of the real estate market in the United Kingdom ( UK ) from 2013 to 2016 , divided by the value of submarkets . It can be seen that during this period the commercial property market size increased by 200 billion British pounds to a value of 883 billion British pounds in 2016 .
gold_template: This statistic shows the templateTitle[9] of the real estate templateTitle[4] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[min] to templateTitleDate[max] , divided templateTitle[7] the templateTitle[9] of submarkets . It can be seen that during this period the templateValue[0][3] market size templatePositiveTrend templateTitle[7] 200 templateScale British pounds to a templateTitle[9] of templateValue[4][3] templateScale British pounds in templateTitleDate[max] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] , in templateTitleSubject[0] , from templateTitleDate[min] to templateTitleDate[max] , templateTitle[6] templateTitle[7] . The average cost for templateTitle[0] templateTitle[1] in templateTitleSubject[0] in 2017 was templateValue[3][last] U.S. dollars .
generated: This statistic shows the United Kingdom UK property , in United Kingdom , from 2013 to 2016 , by .  The average cost for United Kingdom in United Kingdom 2017 was 163 U.S. dollars .

Example 346:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000']}
title: Number of pet birds in the U.S. 2000 to 2017
X_Axis['Year']: ['2017', '2015', '2014', '2012', '2008', '2006', '2004', '2002', '2000']
Y_Axis['Number', 'of', 'birds', 'in', 'millions']: ['20.6', '14.3', '20.6', '16.2', '15.0', '16.0', '16.6', '17.3', '19.0']

gold: According to a national pet owners survey , the number of birds owned as a pet by households in the United States reached 20.6 million in 2017 . The number of pet birds has fluctuated over the past few years , hitting a low of 14.3 million in 2015 . Americans love their pets Some 68 percent of all households in the United States owned at least one pet in 2017 , this is an increase of over 10 percent since 1988 .
gold_template: According to a national templateTitle[1] owners survey , the templateYLabel[0] of templateYLabel[1] owned as a templateTitle[1] by households in the templateTitle[3] reached templateYValue[max] templateScale in templateXValue[idxmax(Y)] . The templateYLabel[0] of templateTitle[1] templateYLabel[1] has fluctuated over the past few years , hitting a low of templateYValue[min] templateScale in templateXValue[idxmin(Y)] . Americans love their pets Some 68 templateScale of all households in the templateTitle[3] owned at least one templateTitle[1] in templateXValue[max] , this is an templatePositiveTrend of over 10 templateScale since 1988 .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitleSubject[0] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[3] .
generated: This statistic shows the Number birds of the Number U.S. from 2000 to 2017 .  In 2017 , the average Number birds of U.S. was 20.6 millions .

Example 347:
titleEntities: {'Subject': ['West African'], 'Date': ['2016']}
title: Ebola cases and deaths in West African outbreak by country 2016
Data:
              0        1             2        3        4      5              6     7               8      9     10
Country  Guinea  Liberia  Sierra_Leone  Nigeria  Senegal  Spain  United_States  Mali  United_Kingdom  Italy  Total
Cases      3811    10675         14124       20        1      1              4     8               1      1  28646
Deaths     2543     4809          3956        8        0      0              1     6               0      0  11323 

gold: This statistic displays the number of cases and deaths due to Ebola from the outbreak in West Africa which lasts since 2014 . As of March 30 , 2016 , there have been 10,675 cases in Liberia , resulting in 4,809 deaths . The Ebola virus causes extremely severe hemorrhagic fever and is considered a Risk Group 4 Pathogen by the World Health Organization ( WHO ) .
gold_template: This statistic displays the number of templateTitle[1] and templateTitle[2] due to templateTitle[0] from the templateTitle[5] in templateTitleSubject[0] Africa which lasts since 2014 . As of 30 , templateTitleDate[0] , there have been templateValue[1][1] templateTitle[1] in templateValue[0][1] , resulting in templateValue[2][1] templateTitle[2] . The templateTitle[0] virus causes extremely severe hemorrhagic fever and is considered a Risk Group templateValue[1][6] Pathogen templateTitle[6] the World Health Organization ( WHO ) .

generated_template: This statistic displays the number of templateLabel[1][0] and templateLabel[2][0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] related to the 2014 - templateTitleDate[0] templateTitle[0] templateTitle[8] in templateTitleSubject[0] ( and the DR Congo ) . As of 4 , templateTitleDate[0] , there have been templateValue[1][1] templateLabel[1][0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateValue[0][1] , resulting in templateValue[2][1] templateLabel[2][0] . The templateTitle[0] virus causes extremely severe hemorrhagic fever and is considered a Risk Group 4 Pathogen templateTitle[9] the World templateTitle[3] Organization ( WHO ) .
generated: This statistic displays the number of Cases and Deaths West African outbreak related to the 2014 - 2016 Ebola in West African ( and the DR Congo ) .  As of 4 , 2016 there have been 10675 Cases deaths West African outbreak in Liberia , resulting in 4809 Deaths .  The Ebola virus causes extremely severe hemorrhagic fever and is considered a Risk Group 4 Pathogen 2016 the World West Organization ( WHO ) .

Example 348:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2013', '2013']}
title: Reach of fashion blog websites in Great Britain as of August 2013 , by age and gender
X_Axis['Sex']: ['Male', 'Female', 'Under_35', '35_to_49', '50_to_64']
Y_Axis['Share', 'of', 'respondents']: ['2', '6', '7', '3', '2']

gold: This statistic displays the reach of fashion blog websites in Great Britain in 2013 , by demographic group . Among female respondents , 6 percent reported regularly visiting fashion websites or blogs as of August 2013 .
gold_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[8] demographic group . Among templateXValue[1] templateYLabel[1] , templateYValue[1] templateScale reported regularly visiting templateTitle[1] templateTitle[3] or blogs as of templateTitle[6] templateTitleDate[0] .

generated_template: In templateTitleDate[0] , it was found that templateYValue[max] templateScale of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] were templateXValue[0] and templateYValue[min] templateScale of the templateYLabel[1] in the templateTitleSubject[0] . This means that templateYValue[last] templateScale of the internet users in the templateTitle[2] .
generated: In 2013 , it was found that 7 % of Reach fashion blog in the websites were Male and 2 % of the respondents in the Great Britain .  This means that 2 % of the internet users in the blog .

Example 349:
titleEntities: {'Subject': ['Roche'], 'Date': ['2007', '2019']}
title: Roche 's expenditure on research and development 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Expenditure', 'in', 'million', 'Swiss', 'francs']: ['11696', '11047', '10392', '9915', '9300', '8913', '8700', '8475', '8073', '9050', '9509', '8704', '8385']

gold: This statistic shows the expenditure on research and development of pharmaceutical company Roche from 2007 to 2019 . Roche is a Swiss-based pharmaceutical company , and one of the leading pharma companies worldwide . In 2019 , Roche 's expenditure on research and development came to nearly 12 billion Swiss francs .
gold_template: This statistic shows the templateYLabel[0] on templateTitle[3] and templateTitle[4] of pharmaceutical company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] is a Swiss-based pharmaceutical company , and one of the leading pharma companies worldwide . In templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] on templateTitle[3] and templateTitle[4] came to nearly templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Expenditure of million the Roche worldwide from 2007 to 2019 .  In 2019 , the average Expenditure million of the Roche amounted to 11696 Swiss francs .

Example 350:
titleEntities: {'Subject': ['France'], 'Date': ['2006', '2018']}
title: Rural population share in France 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Share', 'of', 'the', 'rural', 'population']: ['19.5', '19.8', '20', '20.3', '20.6', '20.9', '21.1', '21.4', '21.6', '21.9', '22.1', '22.4', '22.6']

gold: In 2018 , the rural population in France reached 19.5 percent of the total . In 2018 , Europe was ranked third continent worldwide in terms of degree of urbanization . 80 percent of the European population was living in cities in 2018 , but this figure is expected to decrease by 2050 .
gold_template: In templateXValue[max] , the templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] reached templateYValue[idxmax(X)] templateScale of the total . In templateXValue[idxmin(Y)] , Europe was ranked third continent worldwide in terms of degree of urbanization . 80 templateScale of the European templateYLabel[2] was living in cities in templateXValue[max] , but this figure is expected to templateNegativeTrend by 2050 .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[7] of the templateTitleSubject[0] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Share rural of the 2018 of the France 2006 from to 2018 .  The Share rural of the France share 2006 was 19.5 population .

Example 351:
titleEntities: {'Subject': ['Hotel'], 'Date': ['2010']}
title: Hotel / restaurant industry : percentage of female employees by region 2010
X_Axis['Country']: ['Latin_America', 'Caribbean', 'Africa', 'Oceania', 'Asia', 'Average']
Y_Axis['Female', 'employees']: ['58.5', '55.4', '47', '46.8', '35.4', '48.62']

gold: This graph shows the percentage of female employees in the hotel and restaurant industry in 2010 . Latin America has 58.5 percent female employees .
gold_template: This graph shows the templateScale of templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] and templateTitle[2] templateTitle[3] in templateTitleDate[0] . templateXValue[0] has templateYValue[max] templateScale templateYLabel[0] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitleSubject[0] worldwide in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In that year , the templateXValue[0] generated approximately templateYValue[max] thousand templateYLabel[2] templateYLabel[3] of templateYLabel[4] .
generated: This statistic shows the Female employees of restaurant industry Hotel worldwide in 2010 , percentage Country .  In that year , the Latin_America generated approximately 58.5 thousand employees of .

Example 352:
titleEntities: {'Subject': ['YouTube U.S. Millennials'], 'Date': ['2016']}
title: Leading YouTube content categories of female U.S. Millennials 2016
X_Axis['Youtube', 'Categories']: ['Beauty_and_style', 'Health_and_fitness', 'Music', 'Shopping', 'Movies_and_tv', 'People', 'Gaming', 'Education_and_reference', 'Hobbies', 'Food_and_drink', 'Other']
Y_Axis['Share', 'of', 'views']: ['62', '6', '5', '4', '4', '4', '3', '2', '2', '1', '7']

gold: This statistic presents a ranking of the most popular YouTube video content categories of female Millennials in the United States . As of April 2016 , beauty and style content accounted for 62 percent of YouTube content views of that particular demographic group .
gold_template: This statistic presents a ranking of the most popular templateXLabel[0] video templateTitle[2] templateXLabel[1] of templateTitle[4] templateTitleSubject[0] in the templateTitle[5] . As of 2016 , templateXValue[0] and templateXValue[0] templateTitle[2] accounted for templateYValue[max] templateScale of templateXLabel[0] templateTitle[2] templateYLabel[1] of that particular demographic group .

generated_template: This statistic presents a ranking of the most popular templateXLabel[0] video templateXLabel[1] templateTitle[2] in the templateTitleSubject[0] in of templateTitle[4] . As of that month , templateXValue[0] accounted for templateYValue[max] templateScale of templateYLabel[1] in that year .
generated: This statistic presents a ranking of the most popular Youtube video Categories content in the YouTube U.S. Millennials in of female .  As of that month , Beauty_and_style accounted for 62 % of views in that year .

Example 353:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Boat ownership by age in the U.S. 2013
X_Axis['Age', '(in', 'years)']: ['Under_16', '16_to_34', '35_to_44', '45_to_64', '65+']
Y_Axis['Own', 'a', 'boat']: ['7.1', '8.2', '7.6', '7.5', '6']

gold: The statistic depicts the boat ownership rate by age in the U.S. in 2013 . 7.5 percent of the respondents aged 45 to 64 years own a boat .
gold_template: The statistic depicts the templateYLabel[1] templateTitle[1] rate templateTitle[2] templateXLabel[0] in the templateTitleSubject[0] in templateTitleDate[0] . templateYValue[3] templateScale of the respondents aged templateXValue[3] to templateXValue[3] years templateYLabel[0] a templateYLabel[1] .

generated_template: This statistic presents the distribution of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] in templateTitleDate[0] , sorted templateTitle[5] templateXLabel[0] . According to the source , it was found that templateYValue[min] templateScale of the templateYLabel[1] in the templateTitleSubject[0] used templateTitle[7] group .
generated: This statistic presents the distribution of Boat ownership by in the U.S. in 2013 , sorted 2013 Age .  According to the source , it was found that 6 % of the boat in the U.S. used 2013 group .

Example 354:
titleEntities: {'Subject': ['Kazakhstan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Kazakhstan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['12992.77', '12048.58', '11007.94', '10291.5', '9672.0', '9139.11', '9401.21', '8970.88', '7662.01', '10435.17', '12713.56', '13789.25', '12300.48', '11553.13', '9005.26', '7116.5', '8349.29', '6733.45', '5261.03', '3753.44', '2862.5', '2062.29', '1657.15', '1491.65', '1230.49', '1132.13', '1445.9', '1457.03', '1349.6', '1058.58', '737.24']

gold: The statistic shows gross domestic product ( GDP ) per capita in Kazakhstan from 1994 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateYLabel[2] templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country .
generated: The statistic shows the capita Gross domestic product ( GDP ) per capita in the Kazakhstan from 1994 to 2017 , with projections up until 2024 .  GDP is the total value of all goods and services produced in a Year .  It is considered to be a very important indicator of the economic strength of a country .

Example 355:
titleEntities: {'Subject': ['Brooklyn Nets'], 'Date': ['2001', '2019']}
title: Brooklyn Nets ' revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['304', '290', '273', '223', '220', '212', '190', '84', '89', '89', '92', '98', '102', '93', '87', '93', '94', '91']

gold: The statistic shows the revenue of the New Jersey Nets franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise is 304 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the New Jersey templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise is templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Brooklyn Nets franchise from the 2001/02 season to the 2018/19 season .  In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 304 million U.S. dollars .

Example 356:
titleEntities: {'Subject': ['NFL'], 'Date': []}
title: Maximum discipline for selected player offenses in the NFL 2011/2012
X_Axis['Fine', 'Types']: ['Unexcused_late_reporting_foror_absence_from_preseason_training', 'Ejection_from_game', 'Unexcused_missed_team_transportation', 'Loss_of_playbook_scouting_reportor_game_plan', 'Throwing_football_into_stands', 'Losing_damaging_or_alteringClub-provided_equipment', 'Failure_to_promptly_report_injury', 'Unexcused_late_reportingfor_team_activity', 'Being_overweight_(per_lb.)']
Y_Axis['Maximum', 'fine', '(in', 'U.S.', 'dollars)']: ['30000', '25000', '9440', '9440', '1770', '1770', '1770', '1770', '470']

gold: This graph depicts the maximum amount a NFL player can be fined for selected offenses in the 2011/2012 season . Throwing a football into the stands can be amerced with a maximum fine of 1,770 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] amount a templateTitleSubject[0] templateTitle[4] can be fined templateTitle[2] templateTitle[3] templateTitle[5] in the templateTitle[7] season . templateXValue[4] a templateXValue[4] the templateXValue[4] can be amerced with a templateYLabel[0] templateXLabel[0] of templateYValue[4] templateYLabel[3] dollars .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . According to the source , templateXValue[0] was ranked first with over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] of people templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: This statistic gives information on the Maximum discipline for U.S. player in NFL , 2011/2012 .  According to the source , Unexcused_late_reporting_foror_absence_from_preseason_training was ranked first with over 30000 million U.S. dollars) of people U.S. dollars) in .

Example 357:
titleEntities: {'Subject': ['Europe'], 'Date': ['2018', '2019']}
title: Average attendance of the main football leagues in Europe 2018 to 2019
X_Axis['League', 'Name']: ['Bundesliga_(Germany)', 'Premier_League_(England)', 'La_Liga_(Spain)', 'Serie_A_(Italy)', 'Ligue_1_(France)']
Y_Axis['Average', 'attendance', 'in', 'thousands', 'of', 'people']: ['42.7', '38.0', '27.2', '24.7', '22.1']

gold: The graph shows the major football leagues in Europe ranked by average attendance per match in 2018 - 2019 . It appears that the German league ( Bundesliga ) had attracted 42,700 spectators in average per game as of February 2019 .
gold_template: The graph shows the major templateTitle[3] templateTitle[4] in templateTitleSubject[0] ranked by templateYLabel[0] templateYLabel[1] per match in templateTitleDate[min] - templateTitleDate[max] . It appears that the German templateXValue[1] ( templateXValue[0] ) had attracted templateYValue[max] spectators in templateYLabel[0] per game as of 2019 .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the templateTitle[4] of templateTitleSubject[0] templateTitle[7] templateTitle[8] in templateTitleDate[0] . The templateTitle[0] of the templateXValue[0] was templateXValue[0] , with a score of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Average attendance main football the leagues of Europe 2019 in 2018 .  The Average of the Bundesliga_(Germany) was , with a score of 42.7 thousands people .

Example 358:
titleEntities: {'Subject': ['Germany'], 'Date': ['2000', '2018']}
title: Number of households in Germany 2000 - 2018 , by size
Data:
                           0      1      2      3      4      5      6      7      8      9     10     11     12     13     14     15     16     17     18
Year                    2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008   2007   2006   2005   2004   2003   2002   2001   2000
One person             17333  17263  16832  16875  16412  16176  15979  15867  16195  15995  15791  15385  15447  14695  14566  14426  14224  14056  13750
Two persons            13983  13850  13915  13956  13837  13748  13665  13514  13793  13741  13636  13496  13375  13266  13335  13169  13059  12904  12720
Three persons           4923   4972   5028   4940   4968   4989   4992   5001   5089   5139   5247   5309   5357   5477   5413   5462   5487   5502   5598
Four persons            3748   3823   3807   3679   3672   3688   3739   3778   3846   3887   3966   4081   4107   4213   4218   4268   4315   4346   4391
Five persons and more   1390   1397   1377   1323   1333   1332   1332   1350   1378   1427   1437   1450   1479   1527   1590   1618   1633   1647   1665 

gold: The number of one-person households in Germany has been increasing in the last decade , while the opposite was true for three-person homes . In 2018 , 17.3 thousand German households had one occupant . Ageing population These trends may be rooted in various reasons , such as population developments , ageing , urbanization , individual lifestyles , flexible living arrangements .
gold_template: The templateTitle[0] of one-person templateTitle[1] in templateTitleSubject[0] has been templatePositiveTrend in the last decade , while the opposite was true for three-person homes . In templateValue[0][0] , templateValue[1][0] thousand German templateTitle[1] had one occupant . Ageing population These trends may be rooted in various reasons , such as population developments , ageing , urbanization , individual lifestyles , flexible living arrangements .

generated_template: This statistic gives information on the most popular templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of all templateTitle[1] were employed in the country between templateValue[0][last] and templateValue[0][0] , templateValue[2][0] templateScale of templateLabel[5][0] .
generated: This statistic gives information on the most popular households Germany of 2000 2018 from 2000 to 2018 .  In 2018 , 17333 % of all households were employed in the country between 2000 and 2018 , 13983 % of Five .

Example 359:
titleEntities: {'Subject': ['Italian'], 'Date': ['2019']}
title: Problems of the Italian school system 2019
X_Axis['Response']: ['Lack_of_opportunities_to_enter_into_the_labor_market', 'Lack_of_funding_and_resources', 'Bad_teachers', 'Lack_of_financial_aid_for_low-income_families_and_students', 'Inadequacy_or_backwardness_of_programs', 'Violence', 'All_these_aspects', 'Others', 'I_do_not_know']
Y_Axis['Share', 'of', 'respondents']: ['26', '21', '13', '13', '11', '7', '4', '2', '3']

gold: In a survey conducted in December 2019 , the largest group of respondents indicated the the lack of opportunities to enter into the labor market as the main problem of Italian schools . Furthermore , 21 percent of interviewees stated that the lack of funding and resources was a relevant problem in the Italian school system .
gold_template: In a survey conducted in 2019 , the largest group of templateYLabel[1] indicated the templateXValue[0] of templateXValue[0] to templateXValue[0] the templateXValue[0] as the main problem of templateTitleSubject[0] schools . Furthermore , templateYValue[1] templateScale of interviewees stated that the templateXValue[0] of templateXValue[1] and templateXValue[1] was a relevant problem in the templateTitleSubject[0] templateTitle[2] templateTitle[3] .

generated_template: This statistic presents the results of a survey among templateTitleSubject[0] adult Americans on the templateTitle[0] of templateTitle[1] . During the survey , it was found that templateYValue[1] templateScale of the templateYLabel[1] stated that they had used templateTitle[0] on the site .
generated: This statistic presents the results of a survey among Italian adult Americans on the Problems of Italian .  During the survey , it was found that 21 % of the respondents stated that they had used Problems on the site .

Example 360:
titleEntities: {'Subject': ['Costa Rica'], 'Date': ['2024']}
title: Inflation rate in Costa Rica 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['3', '3', '3', '3', '3.11', '2.74', '2.22', '1.63', '-0.02', '0.8', '4.52', '5.23', '4.5', '4.88', '5.67', '7.84', '13.42', '9.36', '11.48', '13.79', '12.32', '9.44', '9.17', '11.25', '10.96', '10.05', '11.67', '13.24', '17.52', '23.18', '13.54', '9.78', '21.81', '28.67', '19.07', '16.49', '20.85', '16.81', '11.79', '15.13', '12.01']

gold: This statistic shows the average inflation rate in Costa Rica from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Costa Rica amounted to about 2.22 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Costa Rica from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Costa Rica amounted to about 2.22 % compared to the previous Year .

Example 361:
titleEntities: {'Subject': ['Election'], 'Date': ['2016']}
title: Election 2016 exit polls : percentage of votes by gender
Data:
                    0       1
Sex              Male  Female
Hillary Clinton    41      54
Donald Trump       53      42 

gold: This graph shows the percentage of votes of the 2016 presidential elections in the United States on November 9 , 2016 , by gender . According to the exit polls , about 54 percent of female voters nationwide voted for Hillary Clinton .
gold_template: This graph shows the templateScale of templateTitle[5] of the templateTitleDate[0] presidential elections in the country on 9 , templateTitleDate[0] , templateTitle[6] templateTitle[7] . According to the templateTitle[2] templateTitle[3] , about templateValue[1][last] templateScale of templateValue[0][1] voters nationwide voted for templateLabel[1][0] templateLabel[1][1] .

generated_template: This statistic shows the templateScale of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] . During that period of time , templateValue[1][0] templateScale of templateValue[0][1] respondents stated that they used the social networking site .
generated: This statistic shows the percentage of adults in the Election who were using Election as of 2019 , sorted by gender .  During that period of time , 41 percentage of Female respondents stated that they used the social networking site .

Example 362:
titleEntities: {'Subject': ['Tunisia'], 'Date': ['2024']}
title: Inflation rate in Tunisia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['3.99', '4.16', '4.62', '5.22', '5.44', '6.63', '7.31', '5.31', '3.63', '4.44', '4.63', '5.81', '5.14', '3.55', '5.81', '3.54', '4.92', '3.42', '4.14', '1.96', '3.69', '2.72', '2.71', '1.95', '2.77', '2.77', '3.1', '3.6', '3.73', '6.23', '5.42', '4.04', '5.52', '7.69', '6.5', '7.72', '7.16', '8.22', '6.16', '7.55', '8.6']

gold: This statistic shows the average inflation rate in Tunisia from 1984 to 2016 , with projections up until 2024 . In 2016 , the average inflation rate in Tunisia amounted to about 3.63 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[8] , with projections up until templateXValue[max] . In templateXValue[8] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[8] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Tunisia from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Tunisia amounted to about 7.31 % compared to the previous Year .

Example 363:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Number of households in the U.S. 2015 , by age of householder
X_Axis['Age', 'of', 'householder']: ['15_to_24_years_old', '25_to_29_years_old', '30_to_34_years_old', '35_to_44_years_old', '45_to_54_years_old', '55_to_64_years_old', '65_to_74_years_old', '75_years_old_and_over']
Y_Axis['Number', 'of', 'households', 'in', 'millions']: ['6.37', '9.35', '10.73', '21.12', '23.57', '23.51', '16.89', '13.06']

gold: This graph shows the number of households in the U.S. by age of householder in 2015 . In 2015 , about 10.73 million households were led by a person between 30 and 34 years old .
gold_template: This graph shows the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] templateTitle[4] templateXLabel[0] of templateXLabel[1] in templateTitleDate[0] . In templateTitleDate[0] , about templateYValue[2] templateScale templateYLabel[1] were led templateTitle[4] a person between templateXValue[2] and templateXValue[2] templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] of people in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateXLabel[0] . During that period of time , there were templateYValue[last] templateScale of templateYLabel[1] in templateTitleSubject[0] .
generated: This statistic shows the Number of people in the U.S. who were using Number as of 2019 , sorted householder Age .  During that period of time , there were 13.06 millions of households in U.S. .

Example 364:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: U.S. opinion poll : sexiest musical instrument 2014
X_Axis['Response']: ['Guitar', 'Saxophone', 'Piano', 'Violin', 'Drums', 'Flute']
Y_Axis['Share', 'of', 'respondents']: ['26', '25', '21', '14', '7', '5']

gold: This statistic reveals the sexiest musical instrument according to adults in the United States in 2014 . During a survey , 26 percent of adults found the guitar to be the sexiest musical instrument to play .
gold_template: This statistic reveals the templateTitle[3] templateTitle[4] templateTitle[5] according to adults in the templateTitle[0] in templateTitleDate[0] . During a survey , templateYValue[max] templateScale of adults found the templateXValue[0] to be the templateTitle[3] templateTitle[4] templateTitle[5] to play .

generated_template: As of the third quarter of templateTitleDate[0] , it was found that templateYValue[max] templateScale of templateTitle[0] users in the templateTitleSubject[0] accessed the social platform templateXValue[0] a templateXValue[0] . A further templateYValue[1] templateScale of templateYLabel[1] claimed that they used templateTitle[0] on templateXValue[last] .
generated: As of the third quarter of 2014 , it was found that 26 % of U.S. users in the U.S. accessed the social platform Guitar a .  A further 25 % of respondents claimed that they used U.S. on Flute .

Example 365:
titleEntities: {'Subject': ['Jurassic Park'], 'Date': ['2018']}
title: The Jurassic Park franchise : domestic and global box office revenue 2018
Data:
                            0              1                              2                  3                               4
Movie          Jurassic_World  Jurassic_Park  The_Lost_World:_Jurassic_Park  Jurassic_Park_III  Jurassic_World:_Fallen_Kingdom
North America          652.27         357.07                         229.09             181.17                          416.77
Worldwide              1671.7          983.8                          618.6              368.8                          1304.8 

gold: The statistic presents the box office revenue of the Jurassic Park movie series in North America and worldwide . As of October 2018 , `` Jurassic World '' , the fourth installment in the series released in 2015 , reached a gross of 652.27 million U.S. dollars in 4,291 cinemas across North America . It is currently the fifth most successful movie of all time based on global box office revenue .
gold_template: The statistic presents the templateTitle[5] templateTitle[6] templateTitle[7] of the templateValue[0][0] templateValue[0][1] templateLabel[0][0] series in templateLabel[1][0] templateLabel[1][1] and templateLabel[2][0] . As of October templateTitleDate[0] , `` templateValue[0][0] '' , the fourth installment in the series released in 2015 , reached a gross of templateValue[1][0] templateScale U.S. dollars in 4,291 cinemas across templateLabel[1][0] templateLabel[1][1] . It is currently the fifth most successful templateLabel[0][0] of all time based on templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: The statistic presents the templateTitle[5] templateTitle[6] templateTitle[7] of The templateValue[0][0] movie templateTitle[2] in templateLabel[1][0] templateLabel[1][1] and templateLabel[2][0] . As of 2018 , `` templateValue[0][0] 2 '' , the second installment in the trilogy from the Marvel Cinematic Universe , reached a gross of templateValue[1][1] templateScale U.S. dollars in 3,555 cinemas across templateLabel[1][0] templateLabel[1][1] .
generated: The statistic presents the box office revenue of The Jurassic_World movie franchise in North America and Worldwide .  As of 2018 , `` Jurassic_World 2 '' , the second installment in the trilogy from the Marvel Cinematic Universe , reached a gross of 357.07 million U.S. dollars in 3,555 cinemas across North America .

Example 366:
titleEntities: {'Subject': ['NFL'], 'Date': ['2020']}
title: NFL teams ranked by number of Super Bowl appearances all time 2020
X_Axis['Team']: ['New_England_Patriots', 'Pittsburgh_Steelers', 'Dallas_Cowboys', 'Denver_Broncos', 'San_Francisco_49ers', 'Washington_Redskins', 'Green_Bay_Packers', 'Miami_Dolphins', 'New_York_Giants', 'Oakland_Raiders', 'Buffalo_Bills', 'Minnesota_Vikings', 'Indianapolis_Colts', 'Los_Angeles_Rams', 'Philadelphia_Eagles', 'Kansas_City_Chiefs', 'Seattle_Seahawks', 'Carolina_Panthers', 'Atlanta_Falcons', 'Baltimore_Ravens', 'Cincinnati_Bengals', 'Chicago_Bears', 'New_Orleans_Saints', 'Arizona_Cardinals', 'Tennessee_Titans', 'Tampa_Bay_Buccaneers', 'Los_Angeles_Chargers', 'New_York_Jets']
Y_Axis['Number', 'of', 'Super', 'Bowl', 'appearances']: ['11', '8', '8', '8', '7', '5', '5', '5', '5', '5', '4', '4', '4', '4', '3', '3', '3', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1']

gold: Which team has appeared most often in the Super Bowl ? The New England Patriots have appeared in the National Football League 's annual championship game a record 11 times , winning the Super Bowl six times . The Patriots and the Pittsburgh Steelers share the honor of winning the Super Bowl the highest number of times ( six wins each ) . All of the Patriots six Super Bowl wins have come with Bill Belichick as the head coach and Tom Brady under center at the quarterback position .
gold_template: Which templateXLabel[0] has appeared most often in the templateYLabel[1] templateYLabel[2] ? The templateXValue[0] Patriots have appeared in the National Football League 's annual championship game a record templateYValue[max] times , winning the templateYLabel[1] templateYLabel[2] six times . The templateXValue[0] and the templateXValue[1] share the honor of winning the templateYLabel[1] templateYLabel[2] the highest templateYLabel[0] of times ( six wins each ) . templateTitle[8] of the templateXValue[0] six templateYLabel[1] templateYLabel[2] wins have come with Bill Belichick as the head coach and Tom Brady under center at the quarterback position .

generated_template: The templateXValue[0] had the highest templateTitle[4] templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2019 . The templateTitle[2] templateTitle[3] with a total of templateYValue[max] . It was followed by templateXValue[1] , with templateYValue[1] .
generated: The New_England_Patriots had the highest number Super of 11 Bowl appearances in 2019 .  The ranked by with a total of 11 .  It was followed by Pittsburgh_Steelers , with 8 .

Example 367:
titleEntities: {'Subject': ['Most'], 'Date': ['2016']}
title: Most expensive gTLD auction prices 2016
X_Axis['Top', 'Level', 'Domains']: [".web_(Jul_'16)", ".shop_(Jan_'16)", ".app_(Feb_'15)", ".tech_(Sep_'14)", ".realty_(Oct_'14)", ".salon_(Oct_'14)", ".buy_(Sep_'14)", ".mls_(Dec_'14)", ".baby_(Dec_'14)", ".vip_(Sep_'14)"]
Y_Axis['Sales', 'price', 'in', 'million', 'U.S.', 'dollars']: ['135.0', '41.5', '25.0', '6.76', '5.59', '5.1', '4.59', '3.36', '3.09', '3.0']

gold: The statistic presents a ranking of the most expensive generic top-level domains ( gTLD ) worldwide . In February 2015 , Google purchased the .app domain for 25 million U.S. dollars . Online retailer Amazon spent 4.59 million U.S. dollars for the gTLD .buy in September 2014 .
gold_template: The statistic presents a ranking of the templateTitleSubject[0] templateTitle[1] generic top-level templateXLabel[2] ( templateTitle[2] ) worldwide . In 2015 , Google purchased the templateXValue[2] domain for templateYValue[2] templateScale templateYLabel[3] templateYLabel[4] . Online retailer Amazon spent templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] for the templateTitle[2] templateXValue[6] in 2014 .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] employees in the templateTitle[2] in templateTitleDate[0] . The templateXValue[0] is the templateTitle[0] templateYLabel[0] that divides the templateTitleSubject[0] with over templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Most expensive gTLD employees in the gTLD in 2016 .  The .web_(Jul_'16) is the Most Sales that divides the Most with over 135.0 million U.S. .

Example 368:
titleEntities: {'Subject': ['Armour'], 'Date': ['2009', '2019']}
title: Under Armour 's total workforce worldwide from 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'employees']: ['16400', '15000', '15800', '15200', '13400', '10700', '7800', '5900', '5400', '3900', '3000']

gold: Under Armour is an American sporting goods manufacturer , based in Baltimore , Maryland . In 2019 , Under Armour employed about 16,400 people around the world . Under Armour is one of the largest sportswear and sporting goods manufacturers in the world , however it is still far behind Nike and adidas who are the dominating companies within this industry .
gold_template: templateTitle[0] templateTitleSubject[0] is an American sporting goods manufacturer , based in Baltimore , Maryland . In templateXValue[max] , templateTitle[0] templateTitleSubject[0] employed about templateYValue[idxmax(X)] people around the world . templateTitle[0] templateTitleSubject[0] is one of the largest sportswear and sporting goods manufacturers in the world , however it is still far behind Nike and adidas who are the dominating companies within this industry .

generated_template: This statistic displays the templateYLabel[0] of people employed by templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had a total of templateYValue[idxmax(X)] people as of templateXValue[idxmax(Y)] . templateTitleSubject[0] is one of the largest professional services firms in the world , specializing in auditing and accountancy services .
generated: This statistic displays the Number of people employed by Armour from 2009 to 2019 .  In 2019 , Armour had a total of 16400 people as of 2019 .  Armour is one of the largest professional services firms in the world , specializing in auditing and accountancy services .

Example 369:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Gross premiums written by leading reinsurers in the U.S. 2018
X_Axis['Insurer']: ['National_Indemnity_Company', 'Everest_Reinsurance_Company', 'Munich_Re_America_Corp.', 'XL_Reinsurance_America', 'Swiss_Reinsurance_America_Corporation', 'Transatlantic_Reinsurance_Company', 'Odyssey_Group', 'General_Re_Group', 'SCOR_U.S._Corporation', 'Partner_Reinsurance_Company_of_the_U.S.', 'AXIS_Reinsurance_Company', 'American_Agricultural_Ins._Co.', 'Endurance_Assurance_Corporation', 'Renaissance_Reinsurance_US_Inc.', 'The_Toa_Reinsurance_Company_of_America', 'Sirius_America_Insurance_Company', 'EMC_Reinsurance_Company']
Y_Axis['Amount', 'in', 'billion', 'U.S.', 'dollars']: ['27.12', '6.57', '5.5', '5.47', '4.33', '3.95', '3.09', '2.52', '1.82', '1.57', '1.18', '0.99', '0.96', '0.77', '0.48', '0.29', '0.16']

gold: The leading reinsurer in the United States as of the fourth quarter in 2018 , National Indemnity Company , had 27.12 billion U.S. dollars ' worth of gross premiums written . Reinsurers Reinsurance is a type of insurance that other insurance companies take out to reduce risk . Essentially , it works to restrict the amount of money an insurer can possibly lose and , in turn , protects their own customers from losses .
gold_template: The templateTitle[4] reinsurer in the templateTitle[6] as of the fourth quarter in templateTitleDate[0] , templateXValue[0] Company , had templateYValue[max] templateScale templateXValue[8] templateYLabel[3] ' worth of templateTitle[0] templateTitle[1] templateTitle[2] . templateTitle[5] templateXValue[1] is a type of templateXValue[15] that other templateXValue[15] companies take out to reduce risk . Essentially , it works to restrict the templateYLabel[0] of money an templateXLabel[0] can possibly lose and , in turn , protects their own customers from losses .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] in templateTitleDate[0] . It was templateXValue[0] templateXValue[2] , with over templateYValue[2] templateScale templateYLabel[3] of people .
generated: This statistic shows the Amount of U.S. dollars of the Gross premiums in the U.S. in 2018 .  It was National_Indemnity_Company Munich_Re_America_Corp. , with over 5.5 billion dollars of people .

Example 370:
titleEntities: {'Subject': ['PetSmart'], 'Date': ['2014/15', '2019/20']}
title: PetSmart 's revenue in the U.S. 2014/15 - 2019/20
X_Axis['Year']: ['2019/20', '2018/19', '2017/18', '2016/17', '2015/16', '2014/15']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['7091.1', '6492.5', '5256.5', '5393.7', '5039.7', '4900.8']

gold: PetSmart , formerly known as PetFood Warehouse , is an American retail chain which focuses on the sale of pet products as well as services , such as grooming and training . PetSmart was the leading pet retailer in the United States in 2019 in terms of market share . Their revenue reached approximately 6.49 billion U.S. dollars in the United States in the 2018/19 fiscal year ending on January 31 .
gold_template: templateTitleSubject[0] , formerly known as PetFood Warehouse , is an American retail chain which focuses on the sale of pet products as well as services , such as grooming and training . templateTitleSubject[0] was the leading pet retailer in the templateTitle[3] in 2019 in terms of market share . Their templateYLabel[0] reached approximately templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the templateTitle[3] in the templateXValue[1] fiscal templateXLabel[0] ending on 31 .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the 's revenue PetSmart 2014/15 from to 2019/20 .  The Revenue of the Major League Baseball franchise amounted to 7091.1 million U.S. dollars .

Example 371:
titleEntities: {'Subject': ['Intercity/Amtrak'], 'Date': ['2007', '2018']}
title: U.S. passenger-miles : Intercity/Amtrak 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['U.S.', 'passenger-miles', 'in', 'millions']: ['6361', '6563', '6520', '6536', '6675', '7283', '6752', '6568', '6420', '5914', '6179', '5784']

gold: This graph shows Amtrak/Intercity passenger-miles traveled in the United States from 2007 to 2018 . In 2018 , passengers traveled around 6.36 billion miles by Intercity/Amtrak . Amtrak , also known as the National Railroad Passenger Corporation , is a publicly funded service .
gold_template: This graph shows Amtrak/Intercity templateYLabel[1] traveled in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , passengers traveled around templateYValue[0] templateScale miles by templateTitleSubject[0] . Amtrak , also known as the National Railroad Passenger Corporation , is a publicly funded service .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of the templateTitle[0] that were using templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the U.S. of millions the U.S. that were using Intercity/Amtrak from 2007 to 2018 .  In 2018 , the U.S. of Intercity/Amtrak amounted to 6361 millions .

Example 372:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Top U.S. states by number of hogs and pigs 2019
X_Axis['State']: ['Iowa', 'North_Carolina', 'Minnesota', 'Illinois', 'Indiana', 'Nebraska', 'Missouri', 'Ohio', 'Oklahoma', 'Kansas']
Y_Axis['Number', 'in', 'thousands']: ['23500', '8900', '8700', '5200', '4150', '3550', '3500', '2600', '2170', '2040']

gold: Hogs and pigs are one of the main sources of meat in the United States , and there is a thriving domestic pork market . As of March 2019 , Iowa had an inventory of about 23.5 million hogs and pigs , making it the top producer of swine in the country by a large margin . In that same year , the second largest producer of hogs and pigs was North Carolina , with about 8.9 million head .
gold_template: templateTitle[5] and templateTitle[6] are one of the main sources of meat in the templateTitle[1] , and there is a thriving domestic pork market . As of 2019 , templateXValue[0] had an inventory of about templateYValue[max] templateScale templateTitle[5] and templateTitle[6] , making it the templateTitle[0] producer of swine in the country templateTitle[3] a large margin . In that same year , the second largest producer of templateTitle[5] and templateTitle[6] was templateXValue[1] , with about templateYValue[1] templateScale head .

generated_template: This statistic shows the average templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] of templateYLabel[1] templateTitle[2] in the templateTitle[4] amounted to approximately templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the average Number of states by in the number from 2019 to .  In 2019 , the Number of thousands states in the number amounted to approximately 23500 thousands .

Example 373:
titleEntities: {'Subject': ['Brazil', 'Facebook'], 'Date': ['2017', '2023']}
title: Brazil : number of Facebook users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['91.5', '89.0', '86.1', '82.9', '79.4', '75.6', '71.9']

gold: This statistic shows the number of Facebook users in Brazil from 2017 to 2023 . In 2018 , the number of Facebook users in Brazil reached 75.6 million , up from 71.9 million in 2017 . Facebook users in Brazil – additional information One of the leading online activities worldwide is social networking .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] reached templateYValue[5] templateScale , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] . templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] – additional information One of the leading online activities worldwide is social networking .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around templateYValue[idxmax(X)] templateScale templateTitle[2] templateTitle[3] templateYLabel[1] in the country , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] .
generated: This statistic shows the Number of Facebook users in the Brazil from 2017 to 2023 .  In 2023 , it is estimated that there will be around 91.5 millions Facebook users in the country , up from 71.9 millions in 2017 .

Example 374:
titleEntities: {'Subject': ['England'], 'Date': ['2018']}
title: BMI : adult 's body mass index in England 2018 , by gender and age
Data:
                 0            1            2            3            4            5                  6
Year   16-24_years  25-34_years  35-44_years  45-54_years  55-64_years  65-74_years  75_years_and_over
Men           24.4         26.2         27.4         28.8         29.0         28.6               27.8
Women         24.5         26.9         27.7         28.4         28.3         28.5               27.7 

gold: In 2018 , men aged 55 to 64 years had an average body mass index ( BMI ) of 29 kg/m2 and women aged 65 to 74 years had a BMI of 28.3 kg/m2 , the highest mean BMI among both genders across all the age groups . Apart from individuals aged 16 to 24 years , every demographic in England had an average BMI which is classified as overweight , indicating there is a problem with overweightness in England . An increasing problem It is shown that the mean BMI of individuals for both men and women has been generally increasing year-on-year in England .
gold_template: In templateTitleDate[0] , templateLabel[1][0] aged 55 to 64 templateValue[0][0] had an average templateTitle[3] templateTitle[4] templateTitle[5] ( templateTitle[0] ) of templateValue[1][3] kg/m2 and templateLabel[2][0] aged 65 to 74 templateValue[0][0] had a templateTitle[0] of templateValue[2][4] kg/m2 , the highest mean templateTitle[0] among both genders across all the templateTitle[10] groups . Apart from individuals aged 16 to templateValue[1][0] templateValue[0][0] , every demographic in templateTitleSubject[0] had an average templateTitle[0] which is classified as overweight , indicating there is a problem with overweightness in templateTitleSubject[0] . An templatePositiveTrend problem It is shown that the mean templateTitle[0] of individuals for both templateLabel[1][0] and templateLabel[2][0] has been generally templatePositiveTrend year-on-year in templateTitleSubject[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] templateTitle[6] templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of templateLabel[2][0] were employed in templateLabel[1][0] and templateValue[2][0] templateScale of templateLabel[2][0] respectively .
generated: This statistic shows the BMI adult of 's body mass in the index England 75_years_and_over to 16-24_years .  In 16-24_years , 24.4 % of Women were employed in Men and 24.5 % of Women respectively .

Example 375:
titleEntities: {'Subject': ['New Brunswick', 'Canada'], 'Date': ['2000', '2018']}
title: GDP of New Brunswick , Canada 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['GDP', 'in', 'million', 'chained', '2012', 'Canadian', 'dollars']: ['30446.6', '30223.9', '29546.0', '29275.7', '29039.6', '29017.0', '29117.4', '29467.6', '29404.4', '28802.1', '29215.2', '28962.4', '28787.2', '28281.0', '28008.6', '27240.8', '26653.8', '25444.8', '25058.0']

gold: This statistic shows the gross domestic product ( GDP ) of New Brunswick from 2000 to 2018 . In 2018 , New Brunswick 's GDP was 30.44 billion chained 2012 Canadian dollars .
gold_template: This statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] was 30.44 templateScale templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the gross domestic product ( templateYLabel[0] ) of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateYLabel[2] made in templateTitleSubject[0] was templateYValue[last] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the gross domestic product ( GDP ) of the New Brunswick from 2000 to 2018 .  In 2018 , the GDP of the chained made in New Brunswick was 25058.0 million chained 2012 .

Example 376:
titleEntities: {'Subject': ['region'], 'Date': ['2018']}
title: Most visited amusement and theme parks in the Asia-Pacific region 2018
X_Axis['Theme', 'Park']: ['Tokyo_Disneyland_Japan', 'Tokyo_Disney_Sea_Japan', 'Universal_Studios_Japan', 'Shanghai_Disneyland_China', 'Chimelong_Ocean_Kingdom', 'Hong_Kong_Disneyland_Hong_Kong_S.A.R', 'Lotte_World_South_Korea', 'Nagashima_Spa_Land_Japan', 'Everland_South_Korea', 'Ocean_Park_Hong_Kong_S.A.R.', 'Chimelong_Paradise_China', 'Universal_Studios_Singapore', 'Changzhou_China_Dinosaurs_Park', 'OCT_Window_of_the_World_Shenzen', 'OCT_Happy_Valley_Beijing_China', 'OCT_Happy_Valley_Shenzhen_China', 'Zhengzhou_Fantawild_Adventure_China', 'Ningbo_Fantawild_Oriental_Heritage_China', 'OCT_East_China', 'OCT_Happy_Valley_Chengdu_China']
Y_Axis['Attendance', 'in', 'millions']: ['17.91', '14.65', '14.3', '11.8', '10.83', '6.7', '5.96', '5.92', '5.85', '5.8', '4.68', '4.4', '4.11', '3.99', '3.98', '3.91', '3.8', '3.74', '3.68', '3.1']

gold: This graph depicts the attendance of the 20 most visited theme and amusement parks in the Asia-Pacific region in 2018 . In that year , Tokyo Disneyland had the most number of visitors , at approximately 17.91 million visitors . In 2017 , Universal Studios Japan 's sister park Universal Studios Hollywood had 9.06 million visitors .
gold_template: This graph depicts the templateYLabel[0] of the 20 templateTitle[0] templateTitle[1] templateXLabel[0] and templateTitle[2] templateTitle[4] in the templateTitle[5] templateTitleSubject[0] in templateTitleDate[0] . In that year , templateXValue[0] had the templateTitle[0] number of visitors , at approximately templateYValue[max] templateScale visitors . In 2017 , templateXValue[2] Japan 's sister templateXValue[9] templateXValue[2] Hollywood had 9.06 templateScale visitors .

generated_template: The graph shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[4] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] had the templateTitle[0] templateTitle[1] templateTitle[2] at the other hand , with a total templateYLabel[0] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitle[1] templateTitle[2] templateXValue[1] templatePositiveTrend in second with templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .
generated: The graph shows the Most visited amusement in the parks in 2018 .  In 2018 , Tokyo_Disneyland_Japan had the Most visited amusement at the other hand , with a total Attendance of 17.91 millions .  The visited amusement Tokyo_Disney_Sea_Japan rose in second with 14.65 millions .

Example 377:
titleEntities: {'Subject': ['Cost'], 'Date': ['2019']}
title: Cost of an angioplasty in various countries 2019
X_Axis['Country']: ['United_States', 'South_Korea', 'Costa_Rica', 'Singapore', 'Mexico', 'Malaysia', 'Israel', 'Colombia', 'India', 'Poland', 'Jordan', 'Turkey', 'Thailand']
Y_Axis['Cost', 'in', 'U.S.', 'dollars']: ['28200', '17700', '13800', '13400', '10400', '8000', '7500', '7100', '5700', '5300', '5000', '4800', '4200']

gold: This statistic compares the cost of an angioplasty procedure in selected countries as of 2018 . In the United States , an angioplasty cost 28,200 U.S. dollars , while in Costa Rica such a procedure cost less than half of that price .
gold_template: This statistic compares the templateYLabel[0] of an templateTitle[1] procedure in selected templateTitle[3] as of 2018 . In the templateXValue[0] , an templateTitle[1] templateYLabel[0] templateYValue[max] templateYLabel[1] templateYLabel[2] , while in templateXValue[2] such a procedure templateYLabel[0] less than half of that price .

generated_template: This statistic shows the templateYLabel[0] of monthly price in the templateTitleSubject[0] subscription templateTitle[6] as of 2019 . The data reveals that templateXValue[0] was the templateTitle[2] expensive place in the world to get templateTitleSubject[0] , with a monthly subscription costing templateYValue[min] templateYLabel[1] templateYLabel[2] . In templateXValue[2] , the monthly price templateTitle[3] a templateTitleSubject[0] subscription amounted to templateYValue[2] templateYLabel[1] templateYLabel[2] , and Mexican subscribers paid templateYValue[3] templateYLabel[1] templateYLabel[2] per month to use the streaming service .
generated: This statistic shows the Cost of monthly price in the Cost subscription 2019 as of 2019 .  The data reveals that United_States was the various expensive place in the world to get Cost , with a monthly subscription costing 4200 U.S. dollars .  In Costa_Rica , the monthly price countries a Cost subscription amounted to 13800 U.S. dollars , and Mexican subscribers paid 13400 U.S. dollars per month to use the streaming service .

Example 378:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': ['2004']}
title: Teenage pregnancies in England and Wales resulting in maternities 2004 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Number', 'of', 'pregnancies', 'resulting', 'in', 'maternity']: ['28092', '30518', '32747', '34977', '37887', '42688', '48331', '52470', '56901', '59488', '61075', '59951', '61111', '60682']

gold: This statistic shows teenage conceptions in England and Wales leading to maternities from 2004 to 2017 . The figures show a general decrease in the number of teenage maternities , amounting to under 28.1 thousand in 2017 .
gold_template: This statistic shows templateTitle[0] conceptions in templateTitleSubject[0] and templateTitleSubject[1] leading to templateTitle[5] from templateXValue[min] to templateXValue[max] . The figures show a general templateNegativeTrend in the templateYLabel[0] of templateTitle[0] templateTitle[5] , amounting to under templateYValue[min] thousand in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[2] templateYLabel[3] templateTitle[4] of the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] of the templateTitleSubject[0] stood at approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number of the resulting maternity of the Teenage from 2004 to 2017 .  According to the report , the Teenage of the England stood at approximately 28092 resulting maternity .

Example 379:
titleEntities: {'Subject': ['Amsterdam'], 'Date': ['2009', '2019']}
title: Total population of Amsterdam 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'inhabitants']: ['862965', '854047', '844947', '833624', '821752', '810937', '799278', '790110', '779808', '767457', '755605']

gold: Amsterdam is the largest city in the Netherlands , with a population amounting to nearly 863,000 inhabitants . In the last ten years , Amsterdam 's population increased rapidly , and the end is not yet in sight . By 2030 , the number of inhabitants is forecast to reach over one million .
gold_template: templateTitleSubject[0] is the largest city in the Netherlands , with a templateTitle[1] amounting to nearly 863,000 templateYLabel[1] . In the last ten years , templateTitleSubject[0] 's templateTitle[1] templatePositiveTrend rapidly , and the end is not yet in sight . By 2030 , the templateYLabel[0] of templateYLabel[1] is forecast to reach over templateYValue[max] templateScale .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] of the templateTitle[1] templateTitle[2] of the templateTitleSubject[0] templatePositiveTrend by over 55,000 templateYLabel[1] . In templateXValue[min] , The templateTitleSubject[0] had almost 482,000 templateYLabel[1] .
generated: This statistic shows the Total of the population Amsterdam of the Amsterdam from 2009 to 2019 .  In 2009 , the Total of the population Amsterdam of the Amsterdam increased by over 55,000 inhabitants .  In 2009 , The Amsterdam had almost 482,000 inhabitants .

Example 380:
titleEntities: {'Subject': ['Germany'], 'Date': ['2010', '2018']}
title: Natural gas prices for households in Germany 2010 - 2018 , semi-annually
X_Axis['Year']: ['2018_S1', '2017_S2', '2017_S1', '2016_S2', '2016_S1', '2015_S2', '2015_S1', '2014_S2', '2014_S1', '2013_S2', '2013_S1', '2012_S2', '2012_S1', '2011_S2', '2011_S1', '2010_S2', '2010_S1']
Y_Axis['Euro', 'cents', 'per', 'kilowatt-hour']: ['6.08', '6.09', '6.11', '6.42', '6.61', '6.81', '6.76', '6.81', '6.78', '6.89', '6.61', '6.48', '6.37', '6.4', '5.88', '5.71', '5.65']

gold: This statistic shows the natural gas prices for household end users in Germany semi-annually from 2010 to 2018 . In the first half of 2018 , the average natural gas price for households was 6.08 euro cents per kWh .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] household end users in templateTitleSubject[0] templateTitle[8] from templateXValue[15] to templateXValue[0] . In the first half of templateXValue[0] , the average templateTitle[0] templateTitle[1] price templateTitle[3] templateTitle[4] was templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[0] to templateXValue[last] . In the first half of templateXValue[last] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[last] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: This statistic shows the Natural gas prices household end users in Germany 2018 from 2018_S1 to 2010_S1 .  In the first half of 2010_S1 , the average Natural price prices for was 5.65 Euro cents per kWh .

Example 381:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Leading men 's hair coloring brands in the U.S. 2019
X_Axis['Brand']: ['Just_For_Men', 'Just_For_Men_Autostop', 'Just_For_Men_Control_GX', 'Just_For_Men_Touch_of_Gray', 'Softsheen-Carson_Dark_&_Natural', 'Private_label', 'Grecian_Formula_16', 'Just_For_Men_Original_Formula', 'Creme_of_Nature', 'Grecian_5']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['138.0', '27.2', '18.2', '9.7', '5.7', '2.8', '2.3', '0.5', '0.3', '0.1']

gold: In 2019 , Just For Men was the leading men 's hair coloring brand in the United States with sales of approximately 138 million U.S. dollars . Ranked second , the Just For Men Autostop brand generated sales of around 27.2 million U.S. dollars that year .
gold_template: In templateTitleDate[0] , templateXValue[0] Men was the templateTitle[0] templateXValue[0] templateTitle[2] templateTitle[3] templateTitle[4] templateXLabel[0] in the templateTitle[6] with templateYLabel[0] of approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . Ranked second , the templateXValue[0] Men templateXValue[1] templateXLabel[0] generated templateYLabel[0] of around templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] that year .

generated_template: As of 2019 , templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] market in templateTitleSubject[0] . templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] in the templateTitle[4] with templateYLabel[0] amounting to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the previous year . templateXValue[1] , templateXValue[2] was the second place with templateYLabel[0] of about templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] .
generated: As of 2019 , Just_For_Men was the Leading men 's market in U.S. Just_For_Men was the Leading men 's Brand in the coloring with Sales amounting to 138.0 million U.S. dollars in the previous year .  Just_For_Men_Autostop , Just_For_Men_Control_GX was the second place with Sales of about 18.2 million U.S. dollars .

Example 382:
titleEntities: {'Subject': ['Massachusetts'], 'Date': ['2000', '2018']}
title: Massachusetts - Poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['10', '10.5', '10.4', '11.5', '11.6', '11.9', '11.9', '11.6', '11.4', '10.3', '10', '9.9', '9.9', '10.3', '9.2', '9.4', '8.9', '8.7', '9.6']

gold: This statistic shows the poverty rate in Massachusetts from 2000 to 2018 . In 2018 , 10 percent of Massachusetts ' population lived below the poverty line .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] ' templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the Poverty rate in Massachusetts from 2000 to 2018 .  In 2018 , about 10 percentage of Massachusetts 's population lived below the Poverty line .

Example 383:
titleEntities: {'Subject': ['Finland'], 'Date': ['2007', '2017']}
title: Number of hospitals in Finland 2007 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'hospitals']: ['247', '262', '268', '258', '259', '263', '275', '280', '298', '320', '325']

gold: The number of hospitals in Finland was down at the lowest point of the observed period in 2017 , when there were 247 hospitals . At the beginning of the observed period , in 2007 , the number of hospitals amounted to 325 .
gold_template: The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] was down at the lowest point of the observed period in templateXValue[max] , when there were templateYValue[idxmax(X)] templateYLabel[1] . At the beginning of the observed period , in templateXValue[min] , the templateYLabel[0] of templateYLabel[1] amounted to templateYValue[idxmin(X)] .

generated_template: The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] has been templateNegativeTrend every templateXLabel[0] , amounting to templateYValue[min] in templateXValue[idxmin(Y)] . In comparison , there were templateYValue[max] templateYLabel[1] in templateXValue[idxmax(Y)] . The templateYLabel[0] of hospital beds has been templateNegativeTrend also , from 571,600 in templateXValue[min] to 497,200 recorded in templateXValue[max] .
generated: The Number of hospitals in Finland has been decreasing every Year , amounting to 247 in 2017 .  In comparison , there were 325 hospitals in 2007 .  The Number of hospital beds has been decreased also , from 571,600 in 2007 to 497,200 recorded in 2017 .

Example 384:
titleEntities: {'Subject': ['Fox'], 'Date': ['2011', '2018']}
title: 21st Century Fox 's revenue 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['30.4', '28.5', '27.33', '28.99', '31.87', '27.68', '25.05', '24.23']

gold: Mass media corporation 21st Century Fox generated 30.4 billion U.S. dollars in revenue in the fiscal year 2018 , up from 28.5 billion U.S. dollars in the previous year . The company made over half of its revenue in cable network programming in 2018 and a further 8.75 billion U.S. dollars was derived from its filmed entertainment segment . The acquisition of 21st Century Fox by Disney 21st Century Fox consisted of News Corporations 's former TV and film divisions after the split from the News Corp. in June 2013 .
gold_template: Mass media corporation templateTitle[0] templateTitle[1] templateTitleSubject[0] generated templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] in the fiscal templateXLabel[0] templateXValue[max] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . The company made over half of its templateYLabel[0] in cable network programming in templateXValue[max] and a further 8.75 templateScale templateYLabel[2] templateYLabel[3] was derived from its filmed entertainment segment . The acquisition of templateTitle[0] templateTitle[1] templateTitleSubject[0] by Disney templateTitle[0] templateTitle[1] templateTitleSubject[0] consisted of News Corporations templateTitle[3] former TV and film divisions after the split from the News Corp. in 2013 .

generated_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] between the templateXValue[min] and templateXValue[max] . In templateXValue[max] , the company generated approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] .
generated: This statistic represents Fox 's Revenue between the 2011 and 2018 .  In 2018 , the company generated approximately 30.4 billion U.S. dollars in Revenue .

Example 385:
titleEntities: {'Subject': ['German Bundesliga'], 'Date': ['2019/20', '2019/20']}
title: Kit sponsorship revenue of German Bundesliga clubs 2019/20
X_Axis['Year']: ['FC_Bayern_Munich_(Telekom)', 'FC_Schalke_04_(Gazprom)', 'VfL_Wolfsburg_(Volkswagen)', 'Borussia_Dortmund_(Evonik)', 'RB_Leipzig_(Red_Bull)', 'Borussia_Mönchengladbach_(Postbank)', '1._FC_Köln_(Rewe)', 'Hertha_BSC_Berlin_(Tedi)', 'SV_Werder_Bremen_(Wiesenhof)', 'Eintracht_Frankfurt_(Indeed)', 'Bayer_04_Leverkusen_(Barmenia)', '1899_Hoffenheim_(SAP)', 'FSV_Mainz_05_(Kömmerling)', 'FC_Augsburg_(WWK)', 'Fortuna_Düsseldorf_(Henkel)', 'SC_Freiburg_(Schwarzwaldmilch)', 'Union_Berlin_(Aroundtown)', 'SC_Paderborn_(Sunmaker)']
Y_Axis['Kit', 'sponsorship', 'revenue', 'in', 'million', 'euros']: ['35.0', '24.0', '20.0', '20.0', '9.0', '9.0', '7.5', '7.5', '7.3', '6.7', '6.0', '5.0', '4.0', '4.5', '3.0', '3.0', '2.5', '2.0']

gold: This statistic depicts the kit sponsorship revenue of each German Bundesliga club in the 2019/20 season . FC Bayern Munich will receive 35 million euros from its kit sponsor Telekom in the 2019/20 season .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of each templateTitleSubject[0] club in the templateTitle[6] season . templateXValue[0] Munich will receive templateYValue[max] templateScale templateYLabel[4] from its templateYLabel[0] sponsor Telekom in the templateTitle[6] season .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateYLabel[4] of templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the average templateYLabel[0] templateYLabel[1] of a templateTitle[2] was templateYValue[last] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the average Kit sponsorship of the million euros of German Bundesliga from SC_Paderborn_(Sunmaker) to FC_Bayern_Munich_(Telekom) .  In FC_Bayern_Munich_(Telekom) , the average Kit sponsorship of a revenue was 2.0 revenue million euros .

Example 386:
titleEntities: {'Subject': ['BASF'], 'Date': ['2020', '2024']}
title: BASF 's planned capital expenditure by region 2020 to 2024
X_Axis['Region']: ['Asia_Pacific', 'Europe', 'North_America', 'South_America_Africa_Middle_East', 'Site_alternatives_currently_being_investigated']
Y_Axis['Share', 'of', 'capital', 'expenditure']: ['41', '34', '19', '1', '5']

gold: This statistic shows BASF 's planned capital expenditure between 2020 and 2024 , by region . In that period , some 19 percent of the company 's planned capital expenditure are expected to be spent in North America .
gold_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] between templateTitleDate[min] and templateTitleDate[max] , templateTitle[5] templateXLabel[0] . In that period , some templateYValue[2] templateScale of the company templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] are expected to be spent in templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] as of 2016 , measured in templateScale of templateYLabel[1] templateYLabel[2] templateYLabel[3] . As of that period , the templateXValue[last] had at templateYValue[max] templateYLabel[2] templateYLabel[3] , followed by templateXValue[1] with templateYValue[1] templateScale of templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the BASF of 's planned capital expenditure in BASF as of 2016 , measured in percent of capital expenditure .  As of that period , the Site_alternatives_currently_being_investigated had at 41 expenditure , followed by Europe with 34 % of capital expenditure .

Example 387:
titleEntities: {'Subject': ['Value'], 'Date': ['2014', '2016']}
title: Value of funds raised through crowdfunding globally 2014 to 2016
X_Axis['Year']: ['2016', '2015', '2014']
Y_Axis['Amount', 'in', 'million', 'U.S.', 'dollars']: ['738.9', '762.52', '597.14']

gold: Crowdfunding occurs when a number of private individuals each pays a small amount of money to support a project . As of 2016 , this process had raised 738.9 million U.S. dollars globally . How does crowdfunding work ? There are three main types of crowdfunding : peer-to-peer lending , rewards and donation crowdfunding , and equity crowdfunding .
gold_template: templateTitle[4] occurs when a number of private individuals each pays a small templateYLabel[0] of money to support a project . As of templateXValue[max] , this process had templateTitle[2] templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] templateTitle[5] . How does templateTitle[4] work ? There are three main types of templateTitle[4] : peer-to-peer lending , rewards and donation templateTitle[4] , and equity templateTitle[4] .

generated_template: The statistic presents the templateYLabel[0] of the templateTitle[1] templateTitle[2] and templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . According to the source , the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitle[3] templateTitle[4] templateNegativeTrend by around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] .
generated: The statistic presents the Amount of the funds raised and crowdfunding globally from 2014 to 2016 .  According to the source , the Value funds raised of the through crowdfunding decreased by around 762.52 million U.S. dollars in 2015 .

Example 388:
titleEntities: {'Subject': ['U.S. February'], 'Date': ['2020', '2020']}
title: Mass shootings in the U.S. : legality of shooter 's weapons , as of February 2020
X_Axis['Category', 'Of', 'The', 'Weapon']: ['Legally_obtained', 'Illegally_obtained', 'Unknown/to_be_determined', 'Some_weapons_were_legally_obtained_others_possibly_illegally']
Y_Axis['Number', 'of', 'incidents']: ['82', '16', '18', '1']

gold: 82 of the mass shootings in the United States between 1982 and February 2020 involved weapons which were obtained legally ; a clear majority . Only 16 incidents involved guns that were obtained illegally . Legally owned firearms in the U.S .
gold_template: templateYValue[max] of the templateTitle[0] templateTitle[1] in the templateTitle[2] between 1982 and templateTitleSubject[0] templateTitleDate[0] involved templateXValue[last] which templateXValue[last] templateXValue[0] ; a clear majority . Only templateYValue[1] templateYLabel[1] involved guns that templateXValue[last] templateXValue[0] templateXValue[1] . templateXValue[0] owned firearms in the templateTitle[2] .

generated_template: Since 1982 , an astonishing templateYValue[max] people in the templateYLabel[2] templateYLabel[3] in templateTitleDate[0] included templateXValue[0] the templateTitle[2] . The templateTitle[0] of people have been carried out in the statistic . In this statistic , there were templateYValue[max] templateTitle[2] templateTitle[2] templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] .
generated: Since 1982 , an astonishing 82 people in the incidents in 2020 included Legally_obtained the U.S. The Mass of people have been carried out in the statistic .  In this statistic , there were 82 U.S. in U.S. February 2020 .

Example 389:
titleEntities: {'Subject': ['Number'], 'Date': ['2015', '2022']}
title: Number of connected wearable devices worldwide by region 2015 to 2022
Data:
                                0      1       2      3      4      5
Year                         2022   2021    2020   2017   2016   2015
Asia Pacific                311.0  258.2  194.67  155.0   99.8   30.4
Central and Eastern Europe   68.0   55.6   45.85   28.0   17.5    5.0
Latin America                49.0   39.0   26.08   18.0   12.6   1.83
Middle East and Africa       46.0   37.5   25.42   21.0   14.0   4.09
North America               439.0  378.8  180.96  217.0  127.1  38.65
Western Europe              192.0  159.7  127.64   88.0   54.3  16.75 

gold: North America is forecast to be the region with the most 5G connections made using wearable devices in 2022 . The 439 million connections in North America would be 222 million more than those made to 4G networks in 2017 . Wearables used in North America and Asia Pacific are together forecast to account for around 70 percent of the wearable 5G connections worldwide in 2022 .
gold_template: templateLabel[5][0] templateLabel[3][1] is forecast to be the templateTitle[6] with the most 5G connections made using templateTitle[2] templateTitle[3] in templateValue[0][0] . The templateValue[5][0] templateScale connections in templateLabel[5][0] templateLabel[3][1] would be 222 templateScale more than those made to 4G networks in templateValue[0][3] . Wearables used in templateLabel[5][0] templateLabel[3][1] and templateLabel[1][0] templateLabel[1][1] are together forecast to account for around 70 templateScale of the templateTitle[2] 5G connections templateTitle[4] in templateValue[0][0] .

generated_template: This statistic gives information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] , sorted templateTitle[5] templateTitle[6] . In templateValue[0][1] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] of online templateTitle[2] in templateLabel[4][0] amounted to about templateValue[3][0] templateScale U.S. dollars .
generated: This statistic gives information on the Number connected wearable devices from 2015 to 2022 , sorted by region .  In 2021 , the Number connected wearable of online wearable in Middle amounted to about 49.0 billion U.S. dollars .

Example 390:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1982', '2012']}
title: Adults attending a classical music event in the past 12 months in the U.S. 1982 to 2012
X_Axis['Year']: ['1982', '1992', '2002', '2008', '2012']
Y_Axis['Share', 'of', 'adults']: ['13', '12.5', '11.6', '9.3', '8.8']

gold: This statistic shows the share of adults attending a classical music event at least once in the past 12 months in the United States from 1982 to 2012 . In 2012 , 8.8 percent of U.S. adults had attended a classical music event at least once in the past year .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[1] a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateYValue[2] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateYLabel[1] had attended a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] accessed the templateYLabel[3] , up from templateYValue[min] templateScale in templateXValue[idxmin(Y)] .
generated: This statistic shows the Share of adults in U.S. from 1982 to 2012 .  In 2012 , 8.8 % of U.S. 's adults accessed the adults , up from 8.8 % in 2012 .

Example 391:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2008', '2018']}
title: Unemployment rate in Belgium 2008 - 2018 , by region
Data:
                            0     1     2     3     4     5     6     7     8     9    10
Year                     2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008
Flemish Region            3.5   4.4   4.9   5.2   5.1   5.1   4.6   4.3   5.2     5   3.9
Brussels-Capital Region  13.4    15  16.9  17.5  18.5  19.3  17.5  17.1  17.4  15.9    16
Walloon Region            8.5   9.8  10.6    12    12  11.4  10.1   9.5  11.5  11.2  10.1 

gold: In 2018 , the rate of unemployment in Belgium was six percent – the lowest it had been in a decade . There were real regional differences within the country though . The Flemish region did particularly well economically , with an unemployment rate of 3.5 percent in 2018 .
gold_template: In templateValue[0][0] , the templateTitle[1] of templateTitle[0] in templateTitleSubject[0] was six templateScale – the lowest it had been in a decade . There were real regional differences within the country though . The templateLabel[1][0] templateTitle[6] did particularly well economically , with an templateTitle[0] templateTitle[1] of templateValue[1][0] templateScale in templateValue[0][0] .

generated_template: In templateValue[0][0] , the templateTitle[0] of templateTitleSubject[0] numbered around 11.4 templateScale . Most of these inhabitants lived in the Dutch-speaking templateLabel[1][0] templateTitle[5] , which was home to nearly half of templateTitleSubject[0] 's templateTitle[0] . The templateLabel[3][0] templateTitle[5] , broadly in line with the French-speaking part of templateTitleSubject[0] , numbered roughly templateValue[3][0] templateScale inhabitants , and another templateValue[2][0] templateScale lived in Brussels and the surrounding areas ( which are bilingual ) .
generated: In 2018 , the Unemployment of Belgium numbered around 11.4 % .  Most of these inhabitants lived in the Dutch-speaking Flemish by , which was home to nearly half of Belgium 's Unemployment .  The Walloon by , broadly in line with the French-speaking part of Belgium , numbered roughly 8.5 million inhabitants , and another 13.4 million lived in Brussels and the surrounding areas ( which are bilingual ) .

Example 392:
titleEntities: {'Subject': ['Cisco'], 'Date': ['2010', '2019']}
title: Cisco employees by region 2010 to 2019
Data:
                   0      1      2      3      4      5      6      7      8      9
Year            2010   2011   2012   2013   2014   2015   2016   2017   2018   2019
United States  38350  37300  36052  37275  36725  36222  37550  37000  37800  39000
Rest of World  32350  34525  30587  37774  37317  35611  36150  35900  36400  36900 

gold: This statistic shows the number of employees at Cisco by geography from fiscal year 2010 to 2019 . Cisco employed 39 thousand people in the United States in FY2019 .
gold_template: This statistic shows the number of templateTitle[1] at templateTitleSubject[0] templateTitle[2] geography from fiscal templateLabel[0][0] templateValue[0][0] to templateValue[0][last] . templateTitleSubject[0] employed templateValue[1][last] thousand people in the country in FY2019 .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateValue[0][last] to templateValue[0][0] . According to the source , templateValue[1][0] templateScale of employees were employed in the templateTitle[4] .
generated: This statistic shows the Cisco of the employees by region 2010 in the 2019 from to 2010 .  According to the source , 38350 % of employees were employed in the 2010 .

Example 393:
titleEntities: {'Subject': ['Novartis'], 'Date': ['2017', '2019']}
title: Novartis ' spending on research and development by segment 2017 to 2019
Data:
                            0       1      2
Company  Innovative_Medicines  Sandoz  Alcon
2017                     7.62    0.77   0.58
2018                     7.68    0.81   0.59
2019                     8.15    1.25      0 

gold: This statistic shows the expenditure on research and development of pharmaceutical company Novartis from 2017 to 2019 , by business segment . In 2016 and 2017 , in both years , the company spent around 7.6 billion U.S. dollars for research and development in the Innovative Medicines division .
gold_template: This statistic shows the expenditure on templateTitle[3] and templateTitle[4] of pharmaceutical templateLabel[0][0] templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[5] business templateTitle[6] . In 2016 and templateTitleDate[min] , in both years , the templateLabel[0][0] spent around templateValue[1][max] templateScale U.S. dollars for templateTitle[3] and templateTitle[4] in the templateValue[0][0] division .

generated_template: The statistic shows the number of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateValue[0][0] 's templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to templateValue[0][0] .
generated: The statistic shows the number of Novartis ' spending research development in Novartis from 2017 to 2019 .  In 2019 , Innovative_Medicines 's Novartis ' spending research development amounted to Innovative_Medicines .

Example 394:
titleEntities: {'Subject': ['South Africa'], 'Date': ['2010']}
title: Market share of soft drink companies in South Africa 2010
X_Axis['Company']: ['PepsiCo_Inc', 'Tiger_Brands_Ltd', 'Other', 'The_Coca-Cola_Co']
Y_Axis['Market', 'share', 'in', 'South', 'Africa']: ['5.5', '9.4', '35.3', '49.8']

gold: The statistic shows the market share of soft drink companies in South Africa in 2010 , based on sales value . In that year , Coca Cola 's market share in South Africa amounted to 49.8 percent . The leading 3 soft drink companies in South Africa had a market share of 64.7 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateYLabel[2] templateYLabel[3] in templateTitleDate[0] , based on sales value . In that year , Coca Cola 's templateYLabel[0] templateYLabel[1] in templateYLabel[2] templateYLabel[3] amounted to templateYValue[max] templateScale . The leading 3 templateTitle[2] templateTitle[3] templateTitle[4] in templateYLabel[2] templateYLabel[3] had a templateYLabel[0] templateYLabel[1] of 64.7 templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] , templateTitle[7] on templateXLabel[0] templateXLabel[1] . In that year , templateXValue[4] templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Market share of the Market share soft drink companies in 2010 , on Company .  In that year , The_Coca-Cola_Co PepsiCo_Inc was the Market share soft drink companies with a Market share of 49.8 South Africa .

Example 395:
titleEntities: {'Subject': ['England'], 'Date': ['2017']}
title: Esophageal cancer cases in England 2017 , by age and gender
Data:
           0      1      2      3      4      5      6      7      8      9     10     11     12     13           14
Month  20-24  25-29  30-34  35-39  40-44  45-49  50-54  55-59  60-64  65-69  70-74  75-79  80-84  85-89  90_and_over
Men        4      7     10     18     34    124    248    433    616    850   1037    793    567    383          156
Women      1      1      1      7      9     30     72    106    203    317    372    354    352    278          186 

gold: In 2017 , over 7.5 thousand people in England were diagnosed with esophageal cancer . The most affected age group was among those aged 70 to 74 years with 1,037 diagnoses in men of this age and 372 cases for women . The esophagus is also known as the gullet and is the tube that carries food to the stomach .
gold_template: In templateTitleDate[0] , templateValue[0][14] 7.5 thousand people in templateTitleSubject[0] were diagnosed with templateTitle[0] templateTitle[1] . The most affected templateTitle[6] group was among those aged 70 to 74 years with templateValue[1][10] diagnoses in templateLabel[1][0] of this templateTitle[6] and templateValue[2][10] templateTitle[2] for templateLabel[2][0] . The esophagus is also known as the gullet and is the tube that carries food to the stomach .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] both templateTitle[5] and templateTitle[6] for templateLabel[2][0] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[min] and templateTitleDate[max] . With templateLabel[1][0] templateLabel[1][1] , templateValue[1][0] templateScale of templateLabel[2][0] were templateLabel[2][0] .
generated: This statistic shows the Esophageal cancer of cases England 2017 both by and age for Women in the United Kingdom ( England ) in 2017 and .  With Men Men , 4 % of Women were .

Example 396:
titleEntities: {'Subject': ['Snap'], 'Date': ['2015', '2019']}
title: Snap worldwide net income 2015 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015']
Y_Axis['Income/', 'loss', 'in', 'million', 'U.S.', 'dollars']: ['-1033.66', '-1255.91', '-3445.07', '-514.64', '-372.89']

gold: This statistic presents the annual net loss of mobile messaging platform Snap . In 2019 , the chat app had generated 1.03 billion U.S. dollars in net losses , a slight improvement from 1.56 billion U.S. dollars in losses in the preceding year .
gold_template: This statistic presents the annual templateTitle[2] templateYLabel[1] of mobile messaging platform templateTitleSubject[0] . In templateXValue[max] , the chat app had generated 1.03 templateScale templateYLabel[3] templateYLabel[4] in templateTitle[2] losses , a slight improvement from 1.56 templateScale templateYLabel[3] templateYLabel[4] in losses in the preceding templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[2] templateYLabel[3] of templateYLabel[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of the templateTitleSubject[0] of templateTitleSubject[0] employees amounted to over templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Income/ of the million U.S. of 2015 from to 2019 .  In 2019 , the Snap of the Snap of employees amounted to over -3445.07 million U.S. .

Example 397:
titleEntities: {'Subject': ['Pittsburgh'], 'Date': ['2006', '2019']}
title: Pittsburgh Pirates average ticket price 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['22.81', '23.43', '29.96', '19.99', '18.32', '17.21', '16.11', '15.3', '15.39', '15.39', '17.07', '17.07', '17.08']

gold: This graph depicts the average ticket price for Pittsburgh Pirates games in Major League Baseball from 2006 to 2019 . In 2019 , the average ticket price was at 22.81 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] templateTitle[1] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Pittsburgh games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 22.81 U.S. dollars .

Example 398:
titleEntities: {'Subject': ['Cameroon'], 'Date': ['2008', '2018']}
title: Urbanization in Cameroon 2008 to 2018
X_Axis['Share', 'of', 'total', 'population']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Unnamed:', '1']: ['56.37', '55.78', '55.18', '54.58', '53.98', '53.37', '52.77', '52.16', '51.56', '50.95', '50.35']

gold: This statistic shows the percentage of the total population living in urban areas in Cameroon from 2008 to 2018 . In 2018 , 56.37 percent of the total population of Cameroon was living in urban areas .
gold_template: This statistic shows the templateScale of the templateXLabel[1] templateXLabel[2] living in urban areas in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , templateYValue[max] templateScale of the templateXLabel[1] templateXLabel[2] of templateTitleSubject[0] was living in urban areas .

generated_template: This statistic shows the templateScale of the total templateXLabel[1] living in templateXLabel[0] areas in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , templateYValue[max] templateScale of the total templateXLabel[1] of templateTitleSubject[0] was living in templateXLabel[0] areas .
generated: This statistic shows the percentage of the total living in Share areas in Cameroon from 2008 to 2018 .  In 2018 , 56.37 % of the total of Cameroon was living in Share areas .

Example 399:
titleEntities: {'Subject': ['U.S.'], 'Date': []}
title: U.S. cities with the highest annual temperatures
X_Axis['Us', 'City']: ['Phoenix_Arizona', 'Miami_Florida', 'Orlando_Florida', 'Riverside_California', 'Austin_Texas', 'Houston_Texas', 'San_Antonio_Texas', 'Las_Vegas_Nevada', 'Jacksonville_Florida', 'New_Orleans_Louisiana', 'Tampa_Florida', 'Los_Angeles_California']
Y_Axis['Average', 'temperature', 'in', 'Fahrenheit', 'per', 'year']: ['87.2', '84.3', '82.8', '80.9', '79.8', '79.7', '80.3', '80.1', '79.3', '78.2', '81.7', '75.2']

gold: This statistic shows cities in the United States with the highest average annual temperatures . Data is based on recordings from 1971 to 2000 . In Houston , Texas the average temperature is 80 degreed fahrenheit .
gold_template: This statistic shows templateTitle[1] in the templateTitle[0] templateTitle[2] the templateTitle[3] templateYLabel[0] templateTitle[4] templateTitle[5] . Data is based on recordings from 1971 to 2000 . In templateXValue[5] , templateXValue[4] the templateYLabel[0] templateYLabel[1] is templateYValue[4] degreed templateYLabel[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the largest number of people living in templateTitleSubject[0] in templateTitleDate[0] . The templateTitle[0] of templateXValue[0] was templateXValue[0] , with over templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the U.S. cities the largest number of people living in U.S. The U.S. of Phoenix_Arizona was , with over 87.2 % per .

Example 400:
titleEntities: {'Subject': ['Nigeria'], 'Date': ['2024']}
title: National debt of Nigeria in relation to gross domestic product ( GDP ) 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'relation', 'to', 'GDP']: ['35.98', '35.06', '33.88', '32.64', '31.35', '29.78', '27.26', '25.34', '23.41', '20.33', '17.54']

gold: This statistic shows the national debt of Nigeria from 2014 to 2018 , with projections up until 2024 , in relation to the gross domestic product ( GDP ) . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In 2018 , the national debt of Nigeria amounted to approximately 27.26 percent of the GDP .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] , in templateYLabel[2] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale of the templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateYLabel[2] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale of the templateYLabel[3] .
generated: The statistic shows the National debt of Nigeria from 2014 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 .  The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances .  In 2018 , the National debt of Nigeria amounted to approximately 27.26 % of the GDP .

Example 401:
titleEntities: {'Subject': ['Carrabba', 'Italian'], 'Date': ['2011', '2018']}
title: Number of Carrabba 's Italian Grill restaurants in the U.S. from 2011 to 2018
Data:
                  0     1     2     3     4     5     6     7
Year           2018  2017  2016  2015  2014  2013  2012  2011
Company-owned   224   225   242   244   242   239   234   231
Franchised        3     3     2     3     1     1     1     1 

gold: This statistic shows the number of Carrabba 's Italian Grill restaurants in the United States from 2011 to 2018 , by ownership type . In 2018 , there were 224 company-owned Carrabba 's Italian Grill restaurants in the United States . Carrabba 's Italian Grill is owned by Bloomin ' Brands , Inc. , a U.S.-based restaurant company .
gold_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] templateTitle[2] templateTitleSubject[1] templateTitle[4] templateTitle[5] in the templateTitle[6] templateTitle[7] templateValue[0][last] to templateValue[0][0] , by ownership type . In templateValue[0][0] , there were templateValue[1][0] templateLabel[1][0] templateTitleSubject[0] templateTitle[2] templateTitleSubject[1] templateTitle[4] templateTitle[5] in the templateTitle[6] . templateTitleSubject[0] templateTitle[2] templateTitleSubject[1] templateTitle[4] is owned by Bloomin ' Brands , Inc. , a U.S.-based restaurant company .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] in the templateTitle[4] templateTitle[5] templateValue[0][last] to templateValue[0][0] , by ownership type . In templateValue[0][0] , templateTitleSubject[0] generated templateValue[2][0] templateScale U.S. dollars in templateLabel[2][0] templateTitle[0] templateTitle[1] . templateTitleSubject[0] is owned by Bloomin ' Brands , Inc. , a U.S.-based templateTitle[0] company .
generated: This statistic shows the Number Carrabba of in the Grill restaurants 2011 to 2018 , by ownership type .  In 2018 , Carrabba generated 3 billion U.S. dollars in Franchised Number Carrabba .  Carrabba is owned by Bloomin ' Brands , Inc. a U.S.-based Number company .

Example 402:
titleEntities: {'Subject': ['YouTube'], 'Date': ['2009', '2018']}
title: YouTube : annual beauty content views 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Annual', 'beauty', 'content', 'views', 'in', 'billions']: ['169.0', '104.0', '59.0', '37.0', '25.0', '16.0', '11.0', '7.0', '5.0', '3.0', '2.0', '0.87', '0.13']

gold: Beauty videos are big business on YouTube . In 2018 , beauty-related content generated more than 169 billion views on the video platform . Popular types of YouTube beauty content include tutorials and DIY videos , reviews , haul videos and videos produced by beauty vloggers .
gold_template: templateYLabel[1] videos are big business on templateTitleSubject[0] . In templateXValue[max] , beauty-related templateYLabel[2] generated more than templateYValue[max] templateScale templateYLabel[3] on the video platform . Popular types of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] include tutorials and DIY videos , reviews , haul videos and videos produced by templateYLabel[1] vloggers .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Annual beauty of the views of YouTube from 2006 to 2018 .  In 2018 , the Annual beauty of YouTube amounted to approximately 169.0 views billions .

Example 403:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Average U.S. brand response rate on social media 2017 , by vertical
X_Axis['Month']: ['Utilities', 'Retail', 'Consumer_Goods', 'Banking/Finance', 'Travel/Hospitality', 'Internet/Technology', 'Marketing/Advertising', 'Automotive', 'Real_Estate', 'Healthcare', 'Professional_Services', 'Government', 'Education', 'Nonprofit', 'Media/Entertainment']
Y_Axis['Average', 'response', 'rate']: ['18', '16', '14', '13', '12', '11', '11', '11', '10', '9', '9', '8', '7', '7', '6']

gold: This statistic presents the average brand response rate on social media in the United States as of the third quarter of 2017 , by vertical . According to the findings , the retail industry had an average response rate of 16 percent to communicating back to their consumers on social media , while the consumer goods industry reported in 14 percent .
gold_template: This statistic presents the templateYLabel[0] templateTitle[2] templateYLabel[1] templateYLabel[2] on templateTitle[5] templateTitle[6] in the templateTitle[1] as of the third quarter of templateTitleDate[0] , templateTitle[8] templateTitle[9] . According to the findings , the templateXValue[1] industry had an templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[1] templateScale to communicating back to their consumers on templateTitle[5] templateTitle[6] , while the templateXValue[2] industry reported in templateYValue[2] templateScale .

generated_template: This statistic shows the results of a ranking of the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) as of 2013 . According to the source , the templateXValue[0] are the templateTitle[2] largest templateTitle[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the results of a ranking of the Average U.S. brand in the U.S. ( ) as of 2013 .  According to the source , the Utilities are the brand largest Average of 18 rate .

Example 404:
titleEntities: {'Subject': ['Sonic Corporation'], 'Date': ['2009', '2018']}
title: Revenue of Sonic Corporation in the U.S. from 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['423.59', '477.27', '606.32', '606.09', '552.35', '542.59', '543.73', '545.95', '550.93', '706.28']

gold: This statistic shows the revenue of Sonic Corporation in the United States from 2009 to 2018 . In 2018 , Sonic Corp. generated a revenue of 423.59 million U.S. dollars . Sonic Corporation , founded in Oklahoma , United States in 1953 , is a drive-in fast-food restaurant chain .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] Corp. generated a templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] , founded in Oklahoma , templateTitle[3] in 1953 , is a drive-in fast-food restaurant chain .

generated_template: The statistic presents the templateYLabel[0] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[4] templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic presents the Revenue of the Sonic Corporation worldwide from 2009 to 2018 .  In 2018 , the from Revenue of the Sonic Corporation amounted to 423.59 million U.S. dollars .

Example 405:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019']}
title: Domestic beer consumption in Canada by age group 2019
X_Axis['Year']: ['12_to_17', '18_to_34', '35_to_49', '50_to_64', '65_and_over']
Y_Axis['Share', 'of', 'beer', 'consumption']: ['1', '38.7', '29.9', '22.4', '8']

gold: It was estimated that almost 39 percent of all beer sold in Canada was consumed by Canadians aged between 18 and 34 in 2019 . Those aged between 35 and 49 were the second biggest consumer group , drinking over almost 30 percent of all beer sold in Canada . What types of beer do Canadians prefer ? According to sales figures , canned beer is the most popular type of domestic beer across age groups in Canada .
gold_template: It was estimated that almost templateYValue[max] templateScale of all templateYLabel[1] sold in templateTitleSubject[0] was consumed templateTitle[4] Canadians aged between templateXValue[1] and templateXValue[1] in templateTitleDate[0] . Those aged between templateXValue[2] and templateXValue[2] were the second biggest consumer templateTitle[6] , drinking templateXValue[last] almost templateYValue[2] templateScale of all templateYLabel[1] sold in templateTitleSubject[0] . What types of templateYLabel[1] do Canadians prefer ? According to sales figures , canned templateYLabel[1] is the most popular type of templateTitle[0] templateYLabel[1] across templateTitle[5] groups in templateTitleSubject[0] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of the templateTitle[3] templateTitle[4] of templateTitleSubject[0] templateTitle[7] from templateXValue[0] to templateXValue[last] . In templateTitleDate[0] , the templateTitle[0] of the templateTitleSubject[0] templateTitle[3] templateTitle[4] was templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Domestic Share of the Canada by of Canada 2019 from 12_to_17 to 65_and_over .  In 2019 , the Domestic of the Canada by was 38.7 consumption .

Example 406:
titleEntities: {'Subject': ['Bundesliga'], 'Date': []}
title: Average player salary in the Bundesliga 2019/20 , by team
X_Axis['Month']: ['Bayern_Munich', 'Borussia_Dortmund', 'Bayer_Leverkusen', 'RB_Leipzig', 'Wolfsburg', 'Schalke', 'Borussia_Monchengladbach', 'Hoffenheim', 'Werder_Bremen', 'Eintracht_Frankfurt', 'Hertha_Berlin', 'FC_Koln', 'Augsburg', 'Mainz', 'Fortuna_Dusseldorf', 'SC_Freiburg', 'Union_Berlin', 'Paderborn']
Y_Axis['Average', 'player', 'salary', 'in', 'million', 'U.S.', 'dollars']: ['8.12', '4.97', '3.19', '2.42', '2.41', '2.19', '1.92', '1.7', '1.57', '1.54', '1.3', '1.22', '1.02', '0.85', '0.76', '0.73', '0.68', '0.42']

gold: In the 2019/2020 season , Bayern Munich was the highest paying club in the German Bundesliga . The average annual player salary in that season was 8.12 million U.S. dollars , while in Paderborn , the lowest paying club in the Bundesliga , it was 0.42 million U.S. dollars . 18 clubs consistently play in the first Bundesliga in Germany .
gold_template: In the 2019/2020 season , templateXValue[0] was the highest paying club in the German templateTitleSubject[0] . The templateYLabel[0] annual templateYLabel[1] templateYLabel[2] in that season was templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] , while in templateXValue[last] , the lowest paying club in the templateTitleSubject[0] , it was templateYValue[min] templateScale templateYLabel[4] templateYLabel[5] . 18 clubs consistently play in the first templateTitleSubject[0] in Germany .

generated_template: This statistic shows the templateYLabel[0] annual templateYLabel[1] templateYLabel[2] of the four templateTitle[2] templateTitleSubject[0] worldwide as of 2019 . As of that templateXLabel[0] , templateXValue[0] had an templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Average annual player salary of the four salary Bundesliga worldwide as of 2019 .  As of that Month , Bayern_Munich had an Average player salary of 8.12 million U.S. dollars .

Example 407:
titleEntities: {'Subject': ['UK'], 'Date': ['2014']}
title: Prescription for glasses or contact lenses in the UK 2014 , by country
Data:
               0      1         2                 3               4
Country  England  Wales  Scotland  Northern_Ireland  United_Kingdom
Yes           78     66        84                77              77
No            22     33        16                23              22 

gold: This statistic depicts the share of respondents who have a prescription for glasses or contact lenses in the United Kingdom in 2014 , by country of the UK . Of respondents , 77 percent of individuals in the UK had a prescription for glasses or contact lenses .
gold_template: This statistic depicts the share of respondents who have a templateTitle[0] templateTitle[1] templateTitle[2] or templateTitle[3] templateTitle[4] in the templateValue[0][4] in templateTitleDate[0] , templateTitle[7] templateLabel[0][0] of the templateTitleSubject[0] . Of respondents , templateValue[1][last] templateScale of individuals in the templateTitleSubject[0] had a templateTitle[0] templateTitle[1] templateTitle[2] or templateTitle[3] templateTitle[4] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] in the templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleDate[0] . In that year , templateValue[1][4] templateScale of the respondents said that they used templateTitle[7] at the templateValue[0][1] templateTitle[1] .
generated: This statistic shows the UK for in the contact lenses UK 2014 by in 2014 .  In that year , 77 % of the respondents said that they used by at the Wales for .

Example 408:
titleEntities: {'Subject': ['Yelp'], 'Date': ['2010', '2015']}
title: Yelp : number of unique visitors 2010 to 2015
X_Axis['Quarter']: ["Q1_'10", "Q2_'10", "Q3_'10", "Q4_'10", "Q1_'11", "Q2_'11", "Q3_'11", "Q4_'11", "Q1_'12", "Q2_'12", "Q3_'12", "Q4_'12", "Q1_'13", "Q2_'13", "Q3_'13", "Q4_'13", "Q1_'14", "Q2_'14", "Q3_'14", "Q4_'14", "Q1_'15"]
Y_Axis['Unique', 'visitors', 'in', 'millions']: ['29.82', '32.54', '37.5', '39.36', '46.82', '51.56', '61.1', '65.8', '71.4', '78.0', '84.0', '86.3', '102.1', '108.0', '117.0', '120.0', '132.0', '138.0', '139.0', '135.0', '142.0']

gold: The timeline shows the number of unique visitors per quarter of the website Yelp.com from 2010 to 2015 . The local search and review site 's visitor numbers have displayed a steady growth , reaching 142 million unique visitors in the first quarter of 2015 .
gold_template: The timeline shows the templateTitle[1] of templateYLabel[0] templateYLabel[1] per templateXLabel[0] of the website Yelp.com from templateTitleDate[min] to templateTitleDate[max] . The local search and review site 's visitor numbers have displayed a steady growth , reaching templateYValue[max] templateScale templateYLabel[0] templateYLabel[1] in the first templateXLabel[0] of templateTitleDate[max] .

generated_template: This statistic shows the number of templateTitleSubject[0] templateTitle[1] templateYLabel[0] worldwide from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . As of the last reported templateXLabel[0] , the social network had templateYValue[max] templateScale templateTitleSubject[0] , up from templateYValue[4] templateScale templateYLabel[0] in the preceding templateXLabel[0] .
generated: This statistic shows the number of Yelp number Unique worldwide from the first Quarter of 2010 to the fourth Quarter of 2015 .  As of the last reported Quarter , the social network had 142.0 millions Yelp , up from 46.82 millions Unique in the preceding Quarter .

Example 409:
titleEntities: {'Subject': ['Saudi Arabia'], 'Date': ['2017', '2023']}
title: Saudi Arabia : mobile phone internet users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'mobile', 'phone', 'internet', 'users', 'in', 'millions']: ['22.5', '22.1', '21.7', '21.3', '20.7', '20.2', '19.6']

gold: The statistic shows the number of mobile internet users in Saudi Arabia from 2017 to 2023 . In 2018 , 20.2 million people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 22.5 million mobile phone internet users .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateYValue[5] templateScale people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number of mobile internet users in Saudi Arabia from 2017 to 2023 .  In 2017 , 19.6 millions people accessed the internet through their mobile phone .  In 2023 , this figure is projected to amount 22.5 millions mobile phone internet users .

Example 410:
titleEntities: {'Subject': ['U.S.'], 'Date': []}
title: U.S. women who have given oral sex to a male in their lifetime , by age group
X_Axis['Age', 'group']: ['14-15', '16-17', '18-19', '20-24', '25-29', '30-39', '40-49', '50-59', '60-69', '70_and_older']
Y_Axis['Share', 'of', 'respondents']: ['13', '29', '61', '78', '89', '80', '83', '80', '73', '43']

gold: This statistic shows the share of American women who have ever given oral sex to a male in their lifetime , sorted by age group . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the United States , in 2010 . 89 percent of respondents aged 25 to 29 stated they have given oral sex to a male at some time during their life .
gold_template: This statistic shows the templateYLabel[0] of American templateTitle[1] templateTitle[2] templateTitle[3] ever templateTitle[4] templateTitle[5] templateTitle[6] to a templateTitle[7] in templateTitle[8] templateTitle[9] , sorted templateTitle[10] templateXLabel[0] templateXLabel[1] . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the templateTitle[0] , in 2010 . templateYValue[max] templateScale of templateYLabel[1] aged 25 to templateYValue[1] stated they templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] to a templateTitle[7] at some time during templateTitle[8] life .

generated_template: This statistic shows the templateYLabel[0] of American templateTitle[1] templateTitle[2] templateTitle[3] ever templateTitle[4] templateTitle[5] in templateTitle[6] templateTitle[7] , sorted templateTitle[8] templateXLabel[0] templateXLabel[1] . The findings were acquired in early templateTitleDate[0] and published in the Journal of Sexual Medicine , a publication on sexual behavior in the templateTitle[0] , in 2010 . templateYValue[max] templateScale of templateYLabel[1] aged 25 to 29 stated they templateTitle[3] templateTitle[4] templateTitle[5] at some time during templateTitle[6] life .
generated: This statistic shows the Share of American women who have ever given oral in sex male , sorted their Age group .  The findings were acquired in early and published in the Journal of Sexual Medicine , a publication on sexual behavior in the U.S. , in 2010 .  89 % of respondents aged 25 to 29 stated they have given oral at some time during sex life .

Example 411:
titleEntities: {'Subject': ['Number'], 'Date': []}
title: Number of sexual partners worldwide by country
X_Axis['Country']: ['Global', 'Turkey', 'Australia', 'New_Zealand', 'Iceland', 'South_Africa', 'Finland', 'Norway', 'Italy', 'Sweden', 'Ireland', 'Switzerland', 'Canada', 'United_States', 'Greece', 'Israel', 'Thailand', 'Japan', 'Chile', 'Serbia_&_Montenegro', 'United_Kingdom', 'Austria', 'Denmark', 'Bulgaria', 'Czech_Republic', 'France', 'Croatia', 'Belgium', 'Singapore', 'Netherlands', 'Portugal', 'Taiwan', 'Spain', 'Poland', 'Germany', 'Malaysia', 'Slovakia', 'Indonesia', 'Hong_Kong', 'Vietnam', 'China', 'India']
Y_Axis['Average', 'number', 'of', 'partners']: ['9.0', '14.5', '13.3', '13.2', '13.0', '12.5', '12.4', '12.1', '11.8', '11.8', '11.1', '11.1', '10.7', '10.7', '10.6', '10.6', '10.6', '10.2', '10.1', '10.1', '9.8', '9.7', '9.3', '9.1', '9.0', '8.1', '7.5', '7.5', '7.2', '7.0', '7.0', '6.6', '6.1', '6.0', '5.8', '5.8', '5.4', '5.1', '3.7', '3.2', '3.1', '3.0']

gold: As of 2005 , people in the United States stated that they have had an average of 10.7 sexual partners , which is slightly above the global average of nine sexual partners . Turkey in the lead ? Surveys on sensitive or very personal topics , like sexuality , are often to be taken with a grain of salt , but in this case , Turkey did not only lead the ranking in 2005 , it also topped the one conducted in 2010 on the mean number of sexual partners . So is it likely that the Turkish people are sexually more prolific than everybody else ? Another survey by another source suggests otherwise : It does not include Turkey when it comes to the countries with the most sexually active inhabitants but lists the Greek as the most active ones .
gold_template: As of 2005 , people in the templateXValue[13] stated that they have had an templateYLabel[0] of templateYValue[12] templateTitle[1] templateYLabel[2] , which is slightly above the templateXValue[0] templateYLabel[0] of templateYValue[0] templateTitle[1] templateYLabel[2] . templateXValue[1] in the lead ? Surveys on sensitive or very personal topics , like sexuality , are often to be taken with a grain of salt , but in this case , templateXValue[1] did not only lead the ranking in 2005 , it also topped the one conducted in 2010 on the mean templateYLabel[1] of templateTitle[1] templateYLabel[2] . So is it likely that the Turkish people are sexually more prolific than everybody else ? Another survey templateTitle[4] another source suggests otherwise : It does not include templateXValue[1] when it comes to the countries with the most sexually active inhabitants but lists the Greek as the most active ones .

generated_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] based on templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . In templateTitleDate[0] , the templateXValue[0] had the largest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Number sexual the partners worldwide by based on Average number in .  In , the Global had the largest Average number of 14.5 partners .

Example 412:
titleEntities: {'Subject': ['Georgia'], 'Date': ['2000', '2018']}
title: Georgia - Poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['14.3', '14.9', '16', '17', '18.3', '19', '19.2', '19.1', '17.9', '16.5', '14.7', '14.3', '14.7', '14.4', '14.8', '13.4', '12.7', '11.7', '12.6']

gold: This statistic shows the poverty rate in Georgia from 2000 to 2018 . In 2018 , about 14.3 percent of Georgia 's population lived below the poverty line .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the Poverty rate in Georgia from 2000 to 2018 .  In 2018 , about 14.3 percentage of Georgia 's population lived below the Poverty line .

Example 413:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2008', '2018']}
title: Anthracite coal imports to the United Kingdom ( UK ) 2008 - 2018 , by country
Data:
                    0     1     2     3     4     5     6     7     8     9    10
Year             2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008
EU                 62    33    60    55    42   109   106    96    72    41    11
Australia           0     0     0     0     0     0     0     0    12     0     0
China               4    10    11    23    29    33    13    51    17    14    51
Colombia            0     0     0     0     9     0     0     0    11     0     0
South Africa        0     0     0     6     7     6     7     0     0    20    31
Russia             52    50     8    10    35    12     0     0    43    34    35
USA                 0     1     3     0     0     0     0     0     0     0    16
Other countries     1    22    11     8     5     0     0     0     0     0     1 

gold: Throughout this period the European Union was the key source of anthracite coal to the United Kingdom , with imports peaking in 2013 at 109,000 metric tons . The following year imports from the EU dropped to 42,000 metric tons , and by 2017 Russia emerged as the main exporter . Anthracite is the highest ranking of coal .
gold_template: Throughout this period the European Union was the key source of templateTitle[0] templateTitle[1] to the templateTitleSubject[0] , with templateTitle[2] peaking in templateValue[0][5] at 109,000 metric tons . The following templateLabel[0][0] templateTitle[2] from the templateLabel[1][0] templateNegativeTrend to 42,000 metric tons , and templateTitle[8] templateValue[0][1] templateLabel[6][0] emerged as the main exporter . templateTitle[0] is the highest ranking of templateTitle[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] templateLabel[1][1] had a templateTitle[0] templateTitle[1] of templateValue[1][0] templateScale .
generated: This statistic shows the Anthracite coal of United Kingdom United Kingdom from 2008 to 2018 .  In 2018 , EU had a Anthracite coal of 62 % .

Example 414:
titleEntities: {'Subject': ['UEFA EURO'], 'Date': ['2016']}
title: Stadium construction/renovation costs UEFA EURO 2016
X_Axis['Stadiums']: ['Stade_des_Lumières_Lyon_(new_stadium)', 'Grand_Stade_LMCU_Lille_(new_stadium)', 'Stade_Vélodrome_Marseille_(major_renovation)', 'Nice_Stadium_Nice_(new_stadium)', 'Nouveau_Stade_Bordeaux_(new_stadium)', 'Parc_des_Princes_Paris_(major_renovation)', 'Stade_Bollaert_Lens_(major_renovation)', 'Stade_Geoffroy_Guichard_Saint-Etienne_(major_renovation)', 'Stadium_Municipal_Toulouse_(major_renovation)']
Y_Axis['Construction/renovation', 'costs', 'in', 'million', 'euros']: ['381', '324', '267', '204', '184', '100', '75', '75', '60']

gold: The statistic shows the projected stadium construction/renovation costs for the UEFA EURO 2016 in France . The Parc des Princes stadium in Paris will undergo major renovations that will cost about 100 million euros .
gold_template: The statistic shows the projected templateXValue[3] templateYLabel[0] templateYLabel[1] for the templateTitleSubject[0] 2016 in France . The templateXValue[5] templateXValue[0] templateXValue[5] templateXValue[3] in templateXValue[5] will undergo major renovations that will cost about templateYValue[5] templateScale templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateXLabel[0] templateTitle[3] of the templateTitle[4] templateTitleSubject[0] in templateTitleDate[0] . The templateXValue[0] of templateTitleSubject[0] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Stadium construction/renovation Stadiums UEFA of the EURO UEFA EURO in 2016 .  The Stade_des_Lumières_Lyon_(new_stadium) of UEFA EURO with a Construction/renovation costs of 381 million euros .

Example 415:
titleEntities: {'Subject': ['Arab'], 'Date': ['2018']}
title: Gross domestic product of the Arab world in 2018
X_Axis['Country']: ['Saudi_Arabia', 'United_Arab_Emirates_', 'Egypt', 'Iraq_', 'Qatar', 'Algeria', 'Kuwait_', 'Morocco', 'Oman', 'Lebanon_', 'Jordan', 'Libya_', 'Tunisia_', 'Bahrain', 'Sudan_', 'Yemen', 'Mauritania_', 'Somalia', 'Djibouti', 'Comoros']
Y_Axis['GDP', 'in', 'billion', 'U.S.', 'dollars']: ['786.52', '414.18', '249.56', '224.23', '191.36', '173.76', '141.65', '118.53', '79.28', '56.37', '42.29', '40.95', '39.9', '37.75', '34.28', '27.59', '5.24', '4.72', '2.92', '1.18']

gold: This statistic shows gross domestic product ( GDP ) of the Arab world in 2018 . In 2018 , GDP of Algeria amounted to approximately 173.76 billion U.S. dollars .
gold_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of the templateXValue[1] templateTitle[4] in templateTitleDate[0] . In templateTitleDate[0] , templateYLabel[0] of templateXValue[5] amounted to approximately templateYValue[5] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of the templateTitleSubject[0] templateTitle[4] in templateTitleDate[0] . The templateTitleSubject[0] region in North Africa and the Middle East comprises the templateTitle[4] templateXValue[7] , templateXValue[15] , templateXValue[4] , templateXValue[1] , templateXValue[5] , templateXValue[3] , templateXValue[12] , templateXValue[8] , templateXValue[11] , templateXValue[13] , templateXValue[9] , templateXValue[10] , templateXValue[6] , templateXValue[0] , Syria , templateXValue[14] , templateXValue[2] Emirates and templateXValue[last] . In templateTitleDate[0] , templateYLabel[0] of templateXValue[7] amounted to approximately templateYValue[7] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows Gross domestic product ( GDP ) of the Arab world in 2018 .  The Arab region in North Africa and the Middle East comprises the world Morocco , Yemen Qatar , United_Arab_Emirates_ Algeria , Iraq_ Tunisia_ , Oman Libya_ , Bahrain Lebanon_ , Jordan Kuwait_ , Saudi_Arabia Syria , Sudan_ Egypt Emirates and Comoros .  In 2018 , GDP of Morocco amounted to approximately 118.53 billion U.S. dollars .

Example 416:
titleEntities: {'Subject': ['Pakistan'], 'Date': ['2013', '2018']}
title: Consumer spending in Pakistan 2013 - 2018 , per capita
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['U.S.', 'dollars']: ['1658', '1488', '1335', '1204', '1091', '995']

gold: This statistic depicts consumer spending in Pakistan from 2013 to 2018 , per capita . In 2013 , Pakistan 's consumer spending per capita was 995 U.S. dollars .
gold_template: This statistic depicts templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , templateTitle[5] templateTitle[6] . In templateXValue[min] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] templateTitle[5] templateTitle[6] was templateYValue[idxmin(X)] templateYLabel[0] templateYLabel[1] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in the templateYLabel[2] in the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[0] , an templatePositiveTrend of more than five templateScale compared to the previous templateXLabel[0] .
generated: The statistic shows the U.S. of Pakistan 2018 from 2013 to 2018 .  In 2018 , the U.S. of dollars in the dollars in the Pakistan amounted to 1658 U.S. , an increase of more than five percent compared to the previous Year .

Example 417:
titleEntities: {'Subject': ['PaaS'], 'Date': ['2011', '2016']}
title: Forecast : PaaS market share in 2011 - 2016 , by service
Data:
                                                         0                             1                                      2                                   3                                    4                   5                                      6                                           7                   8                                 9                                              10
Cloud Service  Cloud_Application_platform_services_(aPaaS)  Cloud_ALM_Services_(almPaaS)  Cloud_BPM_platform_services_(bpmPaaS)  Cloud_Integration_services_(iPaaS)  Cloud_Application_Security_Services  Cloud_MFT_Services  Cloud_Application_Services_Governance  Cloud_Business_Analytics_Services_(baPaaS)  Cloud_MOM_Services  Cloud_Database_services_(dbPaas)  Cloud_Horizontal_Portal_Services_(Portal_PaaS)
2011                                                    35                            12                                     11                                  11                                   11                   8                                      4                                           3                   2                                 2                                               1
2016                                                    35                            11                                     10                                  14                                    9                   6                                      3                                           4                   2                                 4                                               2 

gold: This statistic shows the market share of different Platform as a Service ( PaaS ) systems in 2011 and a forecast for 2016 . By 2016 , it is anticipated that almPaaS will account for 11 percent of the market . The source also estimates that the total value of the PaaS market will reach 2.9 billion U.S. by 2016 , up from 900 million in 2011 .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] of different templateValue[0][0] as a templateLabel[0][1] ( templateTitleSubject[0] ) systems in templateTitleDate[min] and a templateTitle[0] for templateTitleDate[max] . templateTitle[6] templateTitleDate[max] , it is anticipated that almPaaS will account for templateValue[1][2] templateScale of the templateTitle[2] . The source also estimates that the total value of the templateTitleSubject[0] templateTitle[2] will reach 2.9 templateScale U.S. templateTitle[6] templateTitleDate[max] , up from 900 templateScale in templateTitleDate[min] .

generated_template: The statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] in the United Kingdom ( templateTitleSubject[0] ) between templateTitleDate[min] and templateTitleDate[max] . The templateValue[0][0] had the highest templateTitle[0] templateTitle[1] templateTitle[2] , with templateValue[1][0] templateScale of respondents .
generated: The statistic shows the Forecast of the PaaS market in the United Kingdom ( PaaS ) between 2011 and 2016 .  The Cloud_Application_platform_services_(aPaaS) had the highest Forecast PaaS market , with 35 % of respondents .

Example 418:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2014']}
title: Leading deodorant brands ranked by sales value in the United Kingdom ( UK ) 2014
X_Axis['Brand']: ['Male_Lynx', 'Female_Sure', 'Female_Dove']
Y_Axis['Sales', 'in', 'million', 'GBP']: ['98.0', '73.6', '57.6']

gold: This statistic shows the leading 3 brands of deodorant sold in the United Kingdom ( UK ) ranked by their total sales in the year to December 2014 . Male Lynx and female Sure deodorant brands ranked as the highest selling brands in the United Kingdom over that period , reaching 98 and 73.6 million British pounds in sales respectively .
gold_template: This statistic shows the templateTitle[0] 3 templateTitle[2] of templateTitle[1] sold in the templateTitleSubject[0] ( templateTitleSubject[1] ) templateTitle[3] templateTitle[4] their total templateYLabel[0] in the year to 2014 . templateXValue[0] and templateXValue[1] deodorant templateTitle[2] templateTitle[3] as the highest selling templateTitle[2] in the templateTitleSubject[0] over that period , reaching templateYValue[max] and templateYValue[1] templateScale British pounds in templateYLabel[0] respectively .

generated_template: This statistic provides information on the templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) as of 2019 , sorted templateTitle[6] templateYLabel[0] . According to the survey period , templateXValue[0] was the templateTitle[0] templateTitle[1] templateXLabel[0] in the United Kingdom , with a total templateYLabel[0] of templateYValue[max] templateScale British pounds .
generated: This statistic provides information on the Leading deodorant the brands ranked in the United Kingdom ( United Kingdom ) as of 2019 , sorted value Sales .  According to the survey period , Male_Lynx was the Leading deodorant Brand in the United Kingdom , with a total Sales of 98.0 million British pounds .

Example 419:
titleEntities: {'Subject': ['New York Times Media Group'], 'Date': ['2012', '2019']}
title: New York Times Media Group 's circulation revenue from Q1 2012 - Q4 2019
X_Axis['Quarter']: ['Q4_2019', 'Q3_2019', 'Q2_2019', 'Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018', 'Q4_2017', 'Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015', 'Q3_2015', 'Q2_2015', 'Q1_2015', 'Q4_2014', 'Q3_2014', 'Q2_2014', 'Q1_2014', 'Q4_2013', 'Q3_2013', 'Q2_2013', 'Q1_2013', 'Q4_2012', 'Q3_2012', 'Q2_2012', 'Q1_2012']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['275.28', '267.3', '270.46', '270.81', '263.55', '257.8', '260.63', '260.59', '269.38', '246.64', '250.04', '242.38', '225.97', '217.1', '219.48', '217.99', '215.16', '210.71', '213.13', '212.79', '210.56', '206.73', '209.82', '209.72', '207.67', '204.16', '206.97', '205.48', '216.12', '194.74', '194.21', '189.97']

gold: The New York Times Media Group 's circulation revenue amounted to 275.28 million U.S. dollars in the final quarter of 2019 , up from 263.5 million in the corresponding quarter of 2018 . The Times ' subscription revenue generally grows steadily over the course of each year . However , for the most part , the company sees a small revenue decrease in the third quarter , which has been an ongoing and consistent trend for the company over the last few years .
gold_template: The templateTitleSubject[0] Times templateTitleSubject[0] 's templateTitle[6] templateYLabel[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the final templateXLabel[0] of templateXValue[0] , up templateTitle[8] 263.5 templateScale in the corresponding templateXLabel[0] of templateXValue[4] . The templateTitleSubject[0] ' subscription templateYLabel[0] generally grows steadily over the course of each year . However , for the most part , the company sees a small templateYLabel[0] templateNegativeTrend in the third templateXLabel[0] , which has been an ongoing and consistent trend for the company over the last few years .

generated_template: The statistic presents the templateTitle[4] of the templateTitle[1] templateTitle[2] templateYLabel[0] worldwide from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateXValue[0] . As of the most recently reported templateXLabel[0] , the templateYValue[last] templateScale templateYLabel[2] templateYLabel[3] , a two templateScale templateYLabel[0] .
generated: The statistic presents the Group of the York Times Revenue worldwide from the first Quarter of 2012 to the fourth Quarter of Q4_2019 .  As of the most recently reported Quarter , the 189.97 million U.S. dollars , a two million Revenue .

Example 420:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: U.S. wildfires - number of fires and acres burned 2019
Data:
                              0        1        2         3           4         5            6         7        8        9      10      11        12       13    14      15        16         17     18        19             20        21         22           23        24       25        26      27             28          29          30        31              32            33    34        35      36            37            38              39            40         41      42     43       44        45          46             47         48       49
State                   Alabama   Alaska  Arizona  Arkansas  California  Colorado  Connecticut  Delaware  Florida  Georgia  Hawaii   Idaho  Illinois  Indiana  Iowa  Kansas  Kentucky  Louisiana  Maine  Maryland  Massachusetts  Michigan  Minnesota  Mississippi  Missouri  Montana  Nebraska  Nevada  New_Hampshire  New_Jersey  New_Mexico  New_York  North_Carolina  North_Dakota  Ohio  Oklahoma  Oregon  Pennsylvania  Rhode_Island  South_Carolina  South_Dakota  Tennessee   Texas   Utah  Vermont  Virginia  Washington  West_Virginia  Wisconsin  Wyoming
Number of fires            1107      720     1869       660        8194       857           88         0     2121     3158     155     960         2       38   153      19       755        361    355       140            289       361       1021          959        67     1474        15     562             16         727         859        79            3872           488   498      1104    2293           547            45             992           346        571    6892   1025       19       364        1394            593        710      486
Number of acres burned    22158  2498159   384942      8602      259148     40392           72         0   122500    12407   10710  284026        41      523  2020   21167     11714       3059    142      1498            248      1128       5862         5473      5091    64835      9478   82282             25       11346       79887       221           14548          4454  1038     67142   79732           691            33            5939          2261       5478  215493  92380       22      2643      169742           7653       1198    41857 

gold: This statistic shows the number of fires and acres burned due to U.S. wildfires in 2019 , by state . In that year , 88 wildfires occurred in Connecticut , which accounted for 72 acres of burned land . In total , there were 50,477 wildfires reported in the United States , burning about 4.66 million acres of land .
gold_template: This statistic shows the templateTitle[2] of templateTitle[3] and templateTitle[4] templateTitle[5] due to templateTitleSubject[0] templateTitle[1] in templateTitleDate[0] , by templateLabel[0][0] . In that year , templateValue[1][6] templateTitle[1] occurred in templateValue[0][6] , which accounted for templateValue[2][6] templateTitle[4] of templateTitle[5] land . In total , there were 50,477 templateTitle[1] reported in the templateTitle[0] , burning about 4.66 templateScale templateTitle[4] of land .

generated_template: This statistic shows the templateTitle[0] of templateValue[0][0] in the templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateTitle[4] templateLabel[0][0] . In templateTitleDate[0] , the templateTitle[0] of templateTitleSubject[0] in templateValue[0][0] was templateValue[0][0] , with templateValue[1][0] U.S. dollars in templateLabel[4][0] .
generated: This statistic shows the U.S. of Alabama in the number fires in 2019 , acres State .  In 2019 , the U.S. of in Alabama was , with 1107 U.S. dollars in Number .

Example 421:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Number of committed crimes , by type U.S. 2018
X_Axis['Type', 'of', 'crime']: ['Property_crime__(total)', 'Larceny-theft', 'Burglary', 'Violent_crime__(total)', 'Aggravated_assault', 'Motor_vehicle_theft', 'Robbery', 'Rape_(revised_definition)', 'Murder_and_nonnegligent_manslaughter']
Y_Axis['Number', 'of', 'committed', 'crimes']: ['7196045', '5217055', '1230149', '1206836', '807410', '748841', '282061', '139380', '16214']

gold: This statistic shows the number of committed crimes in the United States in 2018 , by type of crime . In 2018 , the FBI recorded 139,380 cases of rape .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[5] in templateTitleDate[0] , templateTitle[3] templateXLabel[0] of templateXValue[0] . In templateTitleDate[0] , the FBI recorded templateYValue[7] cases of templateXValue[7] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] employees in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[1] . It was that the average templateYLabel[0] of people of templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number of the Number employees in the U.S. in 2018 , crime .  It was that the average Number of people crimes .

Example 422:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2018']}
title: Ranking of the largest web shops in Sweden 2018 , by revenue
X_Axis['Web', 'Shops']: ['Dustin_/_Dustin_home', 'Cdon.com', 'Ellos', 'Adlibris', 'Boozt.com', 'Nelly.com_/_NLY_Man', 'Komplett.se', 'Apotea_/_Vitaminvaruhuset.se', 'MatHem', 'Bygghemma.se']
Y_Axis['Revenue', 'in', 'thousand', 'SEK']: ['8689669', '1721072', '1619359', '1449069', '1407400', '1195213', '1110272', '967620', '961363', '927830']

gold: The re-seller of IT-products and additional services Dustin and Dustin Home led in the ranking of largest web shops in Sweden in 2018 , by revenue . Dustin had a revenue of roughly 8.7 billion Swedish kronor that year . It was followed by Cdon.com which is a web shop with a variety of products within the sector of sport , fashion , electronics , groceries and other .
gold_template: The re-seller of IT-products and additional services templateXValue[0] and templateXValue[0] led in the templateTitle[0] of templateTitle[1] templateXLabel[0] templateXLabel[1] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateYLabel[0] . templateXValue[0] had a templateYLabel[0] of roughly templateYValue[max] templateScale Swedish kronor that year . It was followed templateTitle[6] templateXValue[1] which is a templateXLabel[0] shop with a variety of products within the sector of sport , fashion , electronics , groceries and other .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] employees in the templateTitle[4] as of 2019 . According to the survey findings , it was found that templateYValue[7] templateScale of templateYLabel[2] templateYLabel[3] .
generated: This statistic gives information on the Ranking largest web employees in the Sweden as of 2019 .  According to the survey findings , it was found that 967620 thousand of SEK .

Example 423:
titleEntities: {'Subject': ['Countries'], 'Date': ['2019']}
title: Countries with the highest inflation rate 2019
X_Axis['Country']: ['Venezuela', 'Zimbabwe', 'Argentina', 'Sudan', 'Islamic_Republic_of_Iran', 'South_Sudan', 'Liberia', 'Haiti', 'Angola', 'Sierra_Leone', 'Turkey', 'Uzbekistan', 'Yemen', 'Ethiopia', 'Egypt', 'Turkmenistan', 'Nigeria', 'Zambia', 'Ghana', 'Mongolia']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['200000', '161.81', '54.44', '50.43', '35.68', '24.47', '22.21', '17.58', '17.21', '15.74', '15.68', '14.74', '14.7', '14.6', '13.87', '13.4', '11.32', '9.95', '9.26', '9.03']

gold: The statistic lists the 20 countries with the highest inflation rate in 2019 . In 2019 , Sudan ranked 4th with an estimated inflation rate of about 50.43 percent compared to the previous year . Inflation in industrialized and in emerging countries Higher inflation rates are more present in third world or developing countries , because they often lack a sufficient central bank , which in turn results in the manipulation of currency to achieve short term economic goals .
gold_template: The statistic lists the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[3] ranked 4th templateTitle[1] an estimated templateYLabel[0] templateYLabel[1] of about templateYValue[3] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] . templateYLabel[0] in industrialized and in emerging templateTitleSubject[0] Higher templateYLabel[0] rates are more present in third world or developing templateTitleSubject[0] , because they often lack a sufficient central bank , which in turn results in the manipulation of currency to achieve short term economic goals .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in selected countries in templateTitle[5] templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] is estimated to be around templateYValue[max] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in selected countries in 2019 .  The Inflation rate is estimated to be around 200000 % compared to the previous Country .

Example 424:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2012', '2016']}
title: Commercial property market value in the United Kingdom ( UK ) 2012 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012']
Y_Axis['Market', 'value', 'in', 'billion', 'GBP']: ['883', '926', '788', '687', '650']

gold: This statistic illustrates the value of the commercial property market in the United Kingdom ( UK ) from 2012 to 2016 . It can be seen that in 2016 the commercial property market reached a value of 883 billion British pounds .
gold_template: This statistic illustrates the templateYLabel[1] of the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . It can be seen that in templateXValue[max] the templateTitle[0] templateTitle[1] templateYLabel[0] reached a templateYLabel[1] of templateYValue[idxmax(X)] templateScale British pounds .

generated_template: This statistic shows the total annual templateYLabel[1] of the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitleSubject[0] from templateXValue[min] , with a templateTitle[0] up to templateXValue[max] . In templateXValue[1] , the templateTitle[0] templateTitle[1] templateYLabel[0] was valued at templateYValue[5] templateScale British pounds . This is templateTitle[0] to grow annually , with a total templateYLabel[0] templateYLabel[1] of templateYValue[idxmax(X)] templateScale by templateXValue[idxmax(Y)] .
generated: This statistic shows the total annual value of the Commercial property Market in the United Kingdom from 2012 , with a Commercial up to 2016 .  In 2015 , the Commercial property Market was valued at 650 billion British pounds .  This is Commercial to grow annually , with a total Market value of 883 billion by 2015 .

Example 425:
titleEntities: {'Subject': ['Most'], 'Date': ['2017']}
title: Most valuable sport athlete brands worldwide 2017
X_Axis['Player']: ['Roger_Federer', 'LeBron_James', 'Usain_Bolt', 'Cristiano_Ronaldo', 'Phil_Mickelson', 'Tiger_Woods', 'Virat_Kohli', 'Rory_Mcllroy', 'Lionel_Messi', 'Stephen_Curry']
Y_Axis['Brand', 'value', 'in', 'million', 'U.S.', 'dollars']: ['37.2', '33.4', '27.0', '21.5', '19.6', '16.6', '14.5', '13.6', '13.5', '13.4']

gold: The statistic shows a ranking of the most valuable sports athletes brands in the world in 2017 . The most valuable athlete brand in 2017 is Roger Federer .
gold_template: The statistic shows a ranking of the templateTitleSubject[0] templateTitle[1] sports athletes templateTitle[4] in the world in templateTitleDate[0] . The templateTitleSubject[0] templateTitle[1] templateTitle[3] templateYLabel[0] in templateTitleDate[0] is templateXValue[0] .

generated_template: With a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , templateXValue[0] was the templateTitleSubject[0] templateXValue[0] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The multinational templateXValue[1] is the second place with a templateYLabel[1] of templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .
generated: With a Brand value of 37.2 million U.S. dollars , Roger_Federer was the Most Roger_Federer with a Brand value of 37.2 million U.S. dollars .  The multinational LeBron_James is the second place with a value of 33.4 million U.S. dollars .

Example 426:
titleEntities: {'Subject': ['British'], 'Date': ['2009', '2014']}
title: Deaths of British tourists abroad 2009 to 2014
X_Axis['Year']: ['2013/2014', '2012/2013', '2011/2012', '2010/2011', '2009/2010']
Y_Axis['Number', 'of', 'deaths']: ['4110', '6193', '6237', '5972', '5930']

gold: This statistic shows the number of deaths of British ( UK ) tourists abroad from 2009 to 2014 . 5,930 deaths were recorded in 2009/2010 , with numbers rising during the following years . Though by 2013/2014 numbers had dropped to 4,110 deaths .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] ( UK ) templateTitle[2] templateTitle[3] from templateTitleDate[min] to templateTitleDate[max] . templateYValue[last] templateYLabel[1] were recorded in templateXValue[last] , with numbers templatePositiveTrend during the following years . Though by templateXValue[0] numbers had templateNegativeTrend to templateYValue[min] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] of the templateTitle[1] templateYLabel[1] in the templateTitle[0] from templateXValue[last] to templateXValue[0] . According to the source , there were around templateYValue[last] templateScale templateYLabel[1] in the templateTitle[1] .
generated: This statistic shows the Number of deaths the British deaths in the Deaths from 2009/2010 to 2013/2014 .  According to the source , there were around 5930 thousand deaths in the British .

Example 427:
titleEntities: {'Subject': ['Manchester Metrolink', 'United Kingdom'], 'Date': ['1992', '2019']}
title: Passenger journeys on the Manchester Metrolink in the United Kingdom 1992 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02', '2000/01', '1999/00', '1998/99', '1997/98', '1996/97', '1995/96', '1994/95', '1993/94', '1992/93']
Y_Axis['Passenger', 'journeys', 'in', 'millions']: ['43.7', '41.2', '37.8', '34.3', '31.2', '29.2', '25.0', '22.3', '19.2', '19.6', '21.1', '20.0', '19.8', '19.9', '19.7', '18.9', '18.8', '18.2', '17.2', '14.2', '13.2', '13.8', '13.4', '12.6', '12.3', '11.3', '8.1']

gold: The Manchester Metrolink recorded 43.7 million passenger journeys in 2018/19 . Since beginning its operation in April 1992 as the United Kingdom 's first modern tram system , the Metrolink has grown to become an integral part of public transportation within the city . The Metrolink is one of currently nine light rail systems in the country , next to those in London 's docklands , Croydon , Birmingham , Sheffield , Newcastle , Nottingham , Blackpool and Edinburgh .
gold_template: The templateTitleSubject[0] recorded templateYValue[max] templateScale templateYLabel[0] templateYLabel[1] in templateXValue[idxmax(Y)] . Since beginning its operation in 1992 as the templateTitleSubject[1] 's first modern tram system , the templateTitleSubject[0] has grown to become an integral part of public transportation within the city . The templateTitleSubject[0] is one of currently nine light rail systems in the country , next to those in London 's docklands , Croydon , Birmingham , Sheffield , Newcastle , Nottingham , Blackpool and Edinburgh .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitleSubject[0] from the templateXValue[last] to templateXValue[0] . In templateXValue[0] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[last] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Passenger journeys of the Passenger Manchester Metrolink from the 1992/93 to 2018/19 .  In 2018/19 , the Passenger journeys of Manchester Metrolink was 8.1 millions .

Example 428:
titleEntities: {'Subject': ['France', 'Spanish'], 'Date': ['2018']}
title: Tour de France : Spanish winners until 2018
X_Axis['Cyclists']: ['Miguel_Induráin_(1991_1992_1993_1994_and_1995)', 'Alberto_Contador_(2007_and_2009)', 'Carlos_Sastre_(2008)', 'Óscar_Pereiro_(2006)', 'Perico_Delgado_(1988)', 'Luis_Ocaña_(1973)', 'Federico_Martín_Bahamontes_(1959)']
Y_Axis['Number', 'of', 'victories']: ['5', '2', '1', '1', '1', '1', '1']

gold: This statistic presents the list of Spanish cyclists who ever won the Tour de France until the 2018 edition . Miguel Induráin , with five consecutive victories between 1991 and 1995 , was the Spaniard who had won the French competition the most . The last Spaniard to win the Tour was Alberto Contador in 2009 .
gold_template: This statistic presents the list of templateTitleSubject[1] templateXLabel[0] who ever won the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[5] the templateTitleDate[0] edition . templateXValue[0] , with templateYValue[max] consecutive templateYLabel[1] between 1991 and 1995 , was the Spaniard who had won the French competition the most . The last Spaniard to win the templateTitle[0] was templateXValue[1] in 2009 .

generated_template: This statistic shows the results of a survey among the templateTitle[0] of people in the templateTitleSubject[0] in templateTitleDate[0] . Of templateYLabel[1] of a survey conducted with templateYValue[1] templateScale of people who were templateXValue[0] templateYLabel[3] in templateTitleDate[0] .
generated: This statistic shows the results of a survey among the Tour of people in the France in 2018 .  Of victories of a survey conducted with 2 % of people who were Miguel_Induráin_(1991_1992_1993_1994_and_1995) victories in 2018 .

Example 429:
titleEntities: {'Subject': ['China'], 'Date': ['2018']}
title: Share of ethnic minorities in the China 's minority autonomous regions 2018
X_Axis['Province']: ['Tibet', 'Hunan', 'Chongqing', 'Qinghai', 'Hebei', 'Sichuan', 'Gansu', 'Xinjiang', 'Guizhou', 'Yunnan', 'Hubei', 'Liaoning', 'Hainan', 'National_total', 'Guangxi', 'Guangdong', 'Ningxia', 'Jilin', 'Inner_Mongolia', 'Heilongjiang', 'Zhejiang']
Y_Axis['Share', 'of', 'ethnic', 'minorities']: ['90.05', '83.5', '74.39', '67.57', '63.75', '63.03', '62.69', '60.22', '60.14', '58.87', '56.78', '54.49', '51.69', '51.07', '44.75', '38.7', '37.39', '34.49', '22.16', '21.87', '11.81']

gold: The graph shows the share of ethnic minorities in the population of China 's minority autonomous regions by province . In 2018 , about 60.22 percent of the population in minority areas in Xinjiang belonged to ethnic minorities .
gold_template: The graph shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the population of templateTitleSubject[0] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] by templateXLabel[0] . In templateTitleDate[0] , about templateYValue[7] templateScale of the population in templateTitle[5] areas in templateXValue[7] belonged to templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , the templateTitle[0] of templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Share ethnic of the Share ethnic minorities China 's in China 2018 , regions Province .  In 2018 , the Share of Tibet had the highest Share ethnic of 90.05 minorities .

Example 430:
titleEntities: {'Subject': ['Georgia'], 'Date': ['1992', '2018']}
title: Georgia - unemployment rate 1992 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Unemployment', 'rate']: ['3.9', '4.7', '5.4', '6', '7.1', '8.2', '9.2', '10.2', '10.5', '9.9', '6.2', '4.5', '4.7', '5.3', '4.8', '4.8', '5', '4', '3.6', '3.9', '4.3', '4.6', '4.7', '4.8', '5.2', '6', '6.9']

gold: This statistic displays the unemployment rate in Georgia from 1992 to 2018 . In 2018 , the unemployment rate in Georgia was at 3.9 percent .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[0] templateScale .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[min] templateScale .
generated: This statistic displays the Unemployment rate in Georgia from 1992 to 2018 .  In 2018 , the Unemployment rate in Georgia was at 3.6 % .

Example 431:
titleEntities: {'Subject': ['Merck KGaA'], 'Date': ['2014', '2018']}
title: Merck KGaA 's revenue by segment 2014 to 2018
Data:
                         0             1                      2
Health Segment  Healthcare  Life_Science  Performance_Materials
2014                  6621          2683                   2060
2015                  6934          3355                   2556
2016                  6855          5658                   2511
2017                  6999          5882                   2446
2018                  6246          6185                   2406 

gold: The statistic depicts the revenue of pharmaceutical and chemical company Merck KGaA ( EMD ) from 2014 to 2018 , distributed by segment . During 2015 , Merck KGaA 's healthcare division generated some 6.63 billion euros of revenue . In the United States , Merck KGaA is also known as `` EMD Chemicals '' or `` German Merck '' .
gold_template: The statistic depicts the templateTitle[3] of pharmaceutical and chemical company templateTitleSubject[0] ( EMD ) from templateTitleDate[min] to templateTitleDate[max] , distributed templateTitle[4] templateLabel[0][1] . During templateLabel[2][0] , templateTitleSubject[0] 's templateValue[0][0] division generated some 6.63 templateScale euros of templateTitle[3] . In the country , templateTitleSubject[0] is also known as `` EMD Chemicals '' or `` German templateTitleSubject[0] '' .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] between templateTitleDate[min] and templateTitleDate[max] , with a forecast for templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] stood at templateValue[3][last] templateScale .
generated: The statistic shows the Merck KGaA 's in the Merck KGaA between 2014 and 2018 , with a forecast for 2018 .  In 2018 , the Merck KGaA 's in the Merck KGaA stood at 2511 million .

Example 432:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1977', '2018']}
title: Sexual orientation : views on nature vs. nurture in the U.S. 1977 to 2018
Data:
                           0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19    20    21
Year                    2018  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  1999  1996  1989  1982  1977
Born with                 50    46    51    42    47    40    40    36    35    41    42    42    38    37    38    40    40    34    31    19    17    13
Upbringing/environment    30    33    30    37    33    35    42    37    42    38    35    37    44    41    44    36    39    44    40    48    52    56
Both                      10    12     8     9     9    11     6    12    12     9    11    11    10    11    11    12     9    13    13    12    13    14 

gold: This statistic shows the shift in Americans ' views from 1977 to 2018 as to whether people are born gay or lesbian , or their sexual orientation develops due to upbringing and environmental factors . In 1977 , 13 percent of respondents stated that they believe people were born gay or lesbian . In 2018 , 50 percent of respondents believed people were born gay or lesbian .
gold_template: This statistic shows the shift in Americans ' templateTitle[2] from templateValue[0][last] to templateValue[0][0] as to whether people are templateLabel[1][0] gay or lesbian , or their templateTitle[0] templateTitle[1] develops due to upbringing and environmental factors . In templateValue[0][last] , templateValue[1][last] templateScale of respondents stated that they believe people were templateLabel[1][0] gay or lesbian . In templateValue[0][0] , templateValue[1][0] templateScale of respondents believed people were templateLabel[1][0] gay or lesbian .

generated_template: This statistic shows the results of a survey concerning the templateTitle[0] of templateValue[0][0] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . The templateTitle[0] of the templateValue[0][0] this figure stood at templateValue[1][0] templateScale templateYLabel[3] templateYLabel[4] in templateValue[0][0] .
generated: This statistic shows the results of a survey concerning the Sexual of 2018 in the U.S. from 1977 to 2018 .  The Sexual of the 2018 this figure stood at 50 % in 2018 .

Example 433:
titleEntities: {'Subject': ['Luxury'], 'Date': ['2007', '2012']}
title: Luxury goods market worldwide by segment share in 2007 and 2012
Data:
                      0                    1                  2            3      4
Race  Fashion_/_Apparel  Perfume_&_Cosmetics  Watches_&_Jewelry  Accessories  Other
2007               44.3                 18.4               16.2         11.1     10
2012               41.9                 17.7               18.2         12.1   10.1 

gold: The statistic shows the luxury goods market worldwide in 2007 and 2012 , by segment share . Watches and jewelry had a share of 16.2 percent in the global luxury goods market in 2007 . This segment is estimated to increase to 18.2 percent by 2012 .
gold_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[min] and templateTitleDate[max] , templateTitle[4] templateTitle[5] templateTitle[6] . templateValue[0][2] and templateValue[0][2] had a templateTitle[6] of templateValue[1][2] templateScale in the global templateTitleSubject[0] templateTitle[1] templateTitle[2] in templateTitleDate[min] . This templateTitle[5] is estimated to templatePositiveTrend to templateValue[2][2] templateScale templateTitle[4] templateTitleDate[max] .

generated_template: This statistic shows the templateScale of templateTitle[1] in the templateTitleSubject[0] and templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[min] and templateTitleDate[max] . During the survey period , it was found that templateValue[1][0] templateScale of templateLabel[1][0] templateLabel[1][1] .
generated: This statistic shows the percentage of goods in the Luxury and market worldwide by segment in 2007 and 2012 .  During the survey period , it was found that 44.3 % of 2007 .

Example 434:
titleEntities: {'Subject': ['England'], 'Date': ['2018']}
title: Leading chemical drugs dispensed in England 2018 , by items
X_Axis['Item']: ['Atorvastatin', 'Levothyroxine_Sodium', 'Omeprazole', 'Amlodipine', 'Ramipril', 'Lansoprazole', 'Simvastatin', 'Bisoprolol_Fumarate', 'Colecalciferol', 'Aspirin', 'Metformin_Hydrochloride', 'Salbutamol', 'Paracetamol', 'Co-Codamol_(Codeine_Phos/Paracetamol)', 'Sertraline_Hydrochloride', 'Citalopram_Hydrobromide', 'Amitriptyline_Hydrochloride', 'Furosemide', 'Influenza', 'Bendroflumethiazide']
Y_Axis['Number', 'of', 'items', 'dispensed', 'in', 'millions']: ['41.8', '32.2', '31.0', '29.1', '28.6', '25.5', '24.3', '23.6', '23.6', '23.4', '21.8', '21.6', '18.5', '15.2', '14.8', '14.1', '13.5', '11.9', '10.3', '10.0']

gold: Atorvastatin was the top dispensed chemical drug in England in 2018 with approximately 41.8 million items supplied . This drug is only available via a prescription in the United Kingdom and is used to lower the cholesterol of individuals who suffer from high cholesterol . Thus , reducing the risk of heart attacks and strokes in the process .
gold_template: templateXValue[0] was the top templateYLabel[2] templateTitle[1] drug in templateTitleSubject[0] in templateTitleDate[0] with approximately templateYValue[max] templateScale templateYLabel[1] supplied . This drug is only available via a prescription in the United Kingdom and is used to lower the cholesterol of individuals who suffer from high cholesterol . Thus , reducing the risk of heart attacks and strokes in the process .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of templateTitleSubject[0] in templateTitleDate[0] . The survey revealed that templateXValue[0] was the templateTitle[0] templateTitle[1] templateXValue[1] with a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the Leading chemical drugs dispensed England Number of England in 2018 .  The survey revealed that Atorvastatin was the Leading chemical Levothyroxine_Sodium with a Number of 41.8 millions .

Example 435:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2013', '2013']}
title: Internet service provider market share in Vietnam fourth quarter 2013
X_Axis['Company']: ['VNPT', 'FPT_Telecom', 'Viettel', 'Others']
Y_Axis['Market', 'share']: ['56.6', '27', '9.8', '6.6']

gold: The statistic depicts a breakdown of the internet service provider ( ISP ) market in Vietnam in the fourth quarter of 2013 . The internet service provider VNPT held a market share of 56.6 percent . The total number of internet service subscribers in Vietnam was at around 5.1 million in the fourth quarter of 2013 .
gold_template: The statistic depicts a breakdown of the templateTitle[0] templateTitle[1] templateTitle[2] ( ISP ) templateYLabel[0] in templateTitleSubject[0] in the templateTitle[6] templateTitle[7] of templateTitleDate[0] . The templateTitle[0] templateTitle[1] templateTitle[2] templateXValue[0] held a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale . The total number of templateTitle[0] templateTitle[1] subscribers in templateTitleSubject[0] was at around 5.1 templateScale in the templateTitle[6] templateTitle[7] of templateTitleDate[0] .

generated_template: This statistic illustrates the templateYLabel[0] templateYLabel[1] breakdown of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . That year , templateXValue[0] held a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale of the templateTitle[1] templateTitle[2] templateXLabel[0] worldwide .
generated: This statistic illustrates the Market share breakdown of Internet service provider market share in 2013 .  That year , VNPT held a Market share of 56.6 % of the service provider Company worldwide .

Example 436:
titleEntities: {'Subject': ['Preferred'], 'Date': ['2017']}
title: Preferred online retail payment methods worldwide 2017
X_Axis['Payment', 'Method']: ['Credit_cards', 'Electronic_payment_(PayPal_if_available)', 'Debit_cards', 'Cash_on_delivery', 'Bank_transfers', 'Gift_cards_or_vouchers', 'Mobile_payment', 'Cryptocurrencies_(Bitcoin)', 'Other']
Y_Axis['Share', 'of', 'online', 'shoppers']: ['42', '39', '28', '23', '20', '15', '14', '3', '3']

gold: Online shoppers have a wide range of payment methods to choose from and during a March 2017 survey of global online shoppers , 42 percent of respondents stated that they preferred to pay via credit card . Credit cards were the most popular payment method , ahead of electronic options such as PayPal . Digital payments Digital and mobile payments have been around for a while but not all shoppers have taken to them equally .
gold_template: templateYLabel[1] templateYLabel[2] have a wide range of templateXValue[1] templateTitle[4] to choose from and during a 2017 survey of global templateYLabel[1] templateYLabel[2] , templateYValue[max] templateScale of respondents stated that they templateTitleSubject[0] to pay via templateXValue[0] card . templateXValue[0] were the most popular templateXValue[1] templateXLabel[1] , ahead of templateXValue[1] options such as PayPal . Digital payments Digital and templateXValue[6] payments have been around for a while but not all templateYLabel[2] have taken to them equally .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the templateTitle[7] templateTitle[8] templateTitle[9] in templateTitleSubject[0] in templateTitleDate[0] . It was templateXValue[0] with a templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Preferred online retail payment the 2017 in Preferred 2017 .  It was Credit_cards with a Share of 42 shoppers .

Example 437:
titleEntities: {'Subject': ['Arizona Cardinals'], 'Date': ['2008', '2019']}
title: Average regular season home attendance of the Arizona Cardinals 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Average', 'attendance']: ['61323', '62013', '64217', '64831', '64185', '61979', '61033', '60890', '61181', '62774', '63142', '64096']

gold: This graph depicts the average regular season home attendance of the Arizona Cardinals from 2008 to 2019 . In 2019 , the average attendance at home games of the Arizona Cardinals was 61,323 .
gold_template: This graph depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitleSubject[0] was templateYValue[0] templateYValue[idxmax(X)]

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitleSubject[0] was templateYValue[0] templateYValue[idxmax(X)]
generated: This statistic shows the Average regular season home attendance of the Arizona Cardinals from 2008 to 2019 .  In 2019 , the Average attendance at home games of the Arizona Cardinals was 61323 . 

Example 438:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2005', '2017']}
title: Mexico : number of households 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2010', '2005']
Y_Axis['Number', 'of', 'households', 'in', 'millions']: ['34.07', '32.9', '31.95', '28.16', '24.8']

gold: The statistic presents a timeline with the number of households in Mexico between 2005 and 2017 . In 2017 , there were more than 34 million households in Mexico , up from nearly 33 million households a year earlier .
gold_template: The statistic presents a timeline with the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were more than templateYValue[max] templateScale templateYLabel[1] in templateTitleSubject[0] , up from nearly templateYValue[1] templateScale templateYLabel[1] a templateXLabel[0] earlier .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateScale templateYLabel[1] in the templateTitleSubject[0] .
generated: This statistic shows the Number of households in the Mexico from 2005 to 2017 .  In 2017 , there were 34.07 millions households in the Mexico .

Example 439:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. consumer usage of mobile payments 2017
X_Axis['Response']: ['Made_a_mobile_payment', 'Paid_a_bill', 'Purchase_in-app_or_online', 'Received_loyalty_points', 'Sent/received_money_(P2P)', 'Paid_in-store', 'Paid_for_parking/taxi/transit', 'Withdrew_money_from_ATM', 'Paid_by_text_message', 'Sent_money_internationally']
Y_Axis['Share', 'of', 'respondents']: ['64', '49', '44', '26', '23', '19', '12', '6', '3', '2']

gold: This statistic gives information on the U.S. consumer usage of mobile payment offerings as of January 2017 . During a survey of smartphone owners , 49 percent of respondents stated they had paid a bill via mobile .
gold_template: This statistic gives information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] of templateXValue[0] offerings as of 2017 . During a survey of smartphone owners , templateYValue[1] templateScale of templateYLabel[1] stated they had templateXValue[1] a templateXValue[1] via templateXValue[0] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateXValue[last] as of 2017 . During the survey period , it was found that templateYValue[1] templateScale of templateYLabel[1] stated that they used templateXValue[1] .
generated: This statistic gives information on the U.S. consumer usage Sent_money_internationally as of 2017 .  During the survey period , it was found that 49 % of respondents stated that they used Paid_a_bill .

Example 440:
titleEntities: {'Subject': ['Deere'], 'Date': ['2013', '2019']}
title: John Deere 's total assets 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Total', 'assets', 'in', 'million', 'U.S.', 'dollars']: ['73011.0', '70108.0', '65786.3', '57981.4', '57947.6', '61336.4', '59521.3']

gold: This statistic represents John Deere 's ( Deere & Company 's ) total assets from 2013 through 2019 . In 2014 , the Fortune Global 500 company held total assets to the value of approximately 61.3 billion U.S. dollars .
gold_template: This statistic represents templateTitle[0] templateTitleSubject[0] templateTitle[2] ( templateTitleSubject[0] & Company templateTitle[2] ) templateYLabel[0] templateYLabel[1] from templateXValue[min] through templateXValue[max] . In templateXValue[5] , the Fortune Global 500 company held templateYLabel[0] templateYLabel[1] to the value of approximately templateYValue[5] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] employees from templateXValue[min] to templateXValue[max] . As of templateXValue[max] , the company 's templateYLabel[0] amounted to over templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Total of the John employees from 2013 to 2019 .  As of 2019 , the company 's Total amounted to over 65786.3 million U.S. .

Example 441:
titleEntities: {'Subject': ['Cyber'], 'Date': ['2019']}
title: Cyber bullying : common types of bullying 2019 , by gender
Data:
                                                     0                                     1                                               2                                       3
Response  I_have_been_cyber_bullied_(previous_30_days)  I_have_been_cyber_bullied_(lifetime)  I_have_cyber_bullied_others_(previous_30_days)  I_have_cyber_bullied_others_(lifetime)
Male                                              17.7                                  34.1                                             8.1                                    16.1
Female                                            17.1                                  38.7                                             4.6                                    13.4 

gold: This statistic presents the percentage of middle and high school students in the United States who were and have cyber bullied , divided by gender of the students . During the April 2019 survey , it was found that 38.7 percent of female students had ever been bullied online . Cyber bullying includes unwanted contact by threatening or insulting the respondent via digital communications as well as sharing private information online .
gold_template: This statistic presents the templateScale of middle and high school students in the country who were and templateValue[0][0] bullied , divided templateTitle[6] templateTitle[7] of the students . During the 2019 survey , it was found that templateValue[2][1] templateScale of templateLabel[2][0] students had ever templateValue[0][0] online . templateValue[0][0] templateTitle[1] includes unwanted contact templateTitle[6] threatening or insulting the respondent via digital communications as well as sharing private information online .

generated_template: This statistic provides information on the average templateYLabel[0] of time templateTitle[4] templateTitle[5] templateTitle[7] in the templateTitle[1] as of 2017 , sorted templateTitle[8] templateTitle[9] . During the survey , it was found that templateValue[2][0] templateScale of templateLabel[2][0] templateValue[0][0] and templateValue[2][0] templateScale of the templateTitle[4] respondents had a templateValue[0][0] .
generated: This statistic provides information on the average of time bullying 2019 gender in the bullying as of 2017 , sorted gender .  During the survey , it was found that 17.1 % of Female I_have_been_cyber_bullied_(previous_30_days) and 17.1 % of the bullying respondents had a I_have_been_cyber_bullied_(previous_30_days) .

Example 442:
titleEntities: {'Subject': ['Somalia'], 'Date': ['2019']}
title: Unemployment rate in Somalia 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['13.96', '13.96', '13.91', '14.26', '14.41', '14.47', '14.63', '14.55', '14.62', '14.57', '14.46', '13.69', '13.82', '14.4', '14.86', '15.09', '15.2', '15.34', '15.18', '15.26', '15.32']

gold: This statistic shows the unemployment rate in Somalia from 1999 to 2019 . In 2019 , the unemployment rate in Somalia was at approximately 13.96 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Somalia from 1999 to 2019 .  In 2019 , the Unemployment rate in Somalia was at approximately 13.96 % .

Example 443:
titleEntities: {'Subject': ['India'], 'Date': ['2019', '2019']}
title: Monthly consumer Price Index ( CPI ) in India July 2019
Data:
                 0         1      2      3      4      5
Month      January  February  March  April    May   June
All India    139.7     139.9  140.4  141.1  142.0  142.9
Urban        138.0     138.6  139.5  140.5  141.5  142.1
Rural        141.1     141.0  141.2  141.7  142.4  143.6 

gold: The Consumer Price Index across India was 142.9 as of June 2019 . This means that the price index has increased by about 42.9 percent since the year 2012 . The index for urban and rural regions differed by about 1.5 percent in the stated time period .
gold_template: The templateTitle[1] templateTitle[2] templateTitle[3] across templateTitleSubject[0] was templateValue[1][last] as of templateValue[0][5] templateTitleDate[0] . This means that the templateTitle[2] templateTitle[3] has templatePositiveTrend by about 42.9 templateScale since the year 2012 . The templateTitle[3] for templateLabel[2][0] and templateLabel[3][0] regions differed by about 1.5 templateScale in the stated time period .

generated_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , distinguished templateTitle[4] templateTitle[5] templateTitle[6] . In templateTitleDate[0] , templateValue[0][0] , the templateTitle[0] of templateLabel[1][0] templateLabel[1][1] amounted to about templateValue[2][3] templateScale U.S. dollars .
generated: This statistic displays the Monthly of consumer Price Index in the United Kingdom ( India ) in 2019 , distinguished CPI India July .  In 2019 , January the Monthly of All India amounted to about 140.5 billion U.S. dollars .

Example 444:
titleEntities: {'Subject': ['YouTube'], 'Date': ['2017']}
title: Global YouTube usage for music consumption 2017 , by country
X_Axis['Country']: ['Mexico', 'Brazil', 'Italy', 'Spain', 'South_Korea', 'Canada', 'Total', 'Germany', 'United_States', 'France', 'Great_Britain', 'Sweden', 'Australia', 'Japan']
Y_Axis['Share', 'of', 'respondents']: ['97', '95', '90', '90', '86', '84', '83', '82', '82', '81', '79', '79', '77', '72']

gold: This statistic shows the share of global users who have accessed YouTube to consume music as of 2017 , sorted by country . During the survey period , 82 percent of respondents from the United States said that they had used YouTube for music .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] users who have accessed templateTitleSubject[0] to consume templateTitle[4] as of templateTitleDate[0] , sorted templateTitle[7] templateXLabel[0] . During the survey period , templateYValue[7] templateScale of templateYLabel[1] from the templateXValue[8] said that they had used templateTitleSubject[0] templateTitle[3] templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] of people from different countries around the world in templateTitleDate[0] , who hold a favorable opinion of the templateXValue[27] States . In templateTitleDate[0] , templateYValue[40] templateScale of templateYLabel[1] in templateXValue[40] held a favorable view of the templateXValue[27] States .
generated: This statistic shows the Share of people from different countries around the world in 2017 , who hold a favorable opinion of the Japan States .  In 2017 , 72 % of respondents in Japan held a favorable view of the Japan States .

Example 445:
titleEntities: {'Subject': ['Canada'], 'Date': ['2015', '2019']}
title: Monthly retail sales of beer , wine and liquor stores in Canada 2015 to 2019
Data:
          0     1     2     3     4     5     6     7     8     9    10    11
Month   Dec   Nov   Oct   Sep   Aug   Jul   Jun   May   Apr   Mar   Feb   Jan
2015   1.82  1.81  1.77  1.84  1.84   1.8  1.77  1.73  1.77  1.77  1.73  1.71
2016   1.86   1.9  1.88  1.85  1.85  1.83  1.86   1.9  1.85  1.88  1.87  1.81
2017   1.94  1.91  1.93  1.91  1.89  1.93  1.91   1.9  1.87  1.87  1.89  1.87
2018   1.98  1.98  2.01  1.97  2.02  1.98  1.97  1.97  1.97  1.94  1.93  1.95
2019      0     0     0     0     0     0     0     0     0     0  2.01  2.02 

gold: Retail sales of beer , wine and liquor stores reached approximately 2.01 billion Canadian dollars in February 2019 , an increase of around 80 million Canadian dollars compared to February in the previous year . Monthly sales have witnessed a steady increase over the past five years . Of these beverages , wine is the most popular among Canadians , followed by domestic and imported beer .
gold_template: templateTitle[1] templateTitle[2] of templateTitle[3] , templateTitle[4] and templateTitle[5] templateTitle[6] reached approximately templateValue[4][2] templateScale Canadian dollars in 2019 , an templatePositiveTrend of around 80 templateScale Canadian dollars compared to in the previous year . templateTitle[0] templateTitle[2] have witnessed a steady templatePositiveTrend over the past five years . Of these beverages , templateTitle[4] is the most popular among Canadians , followed by domestic and imported templateTitle[3] .

generated_template: The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] one dozen , or 12 , templateTitle[4] in templateTitleSubject[0] was templateValue[2][3] Canadian dollars in 2019 . This templateTitle[2] has remained relatively stable over the given time period , not templatePositiveTrend above templateValue[2][max] or below templateValue[2][min] Canadian dollars . The Canadian egg marketCanada produces an templatePositiveTrend number of templateTitle[4] each year .
generated: The Monthly retail sales beer one dozen , or 12 , wine in Canada was 1.85 Canadian dollars in 2019 .  This sales has remained relatively stable over the given time period , not rising above 1.9 or below 1.81 Canadian dollars .  The Canadian egg marketCanada produces an increasing number of wine each year .

Example 446:
titleEntities: {'Subject': ['EU'], 'Date': ['2013', '2017']}
title: EU car sales : share of diesel engines 2013 - 2017 , by brand
Data:
           0    1         2     3        4        5       6           7     8        9     10     11   12    13       14    15    16      17
Brand  Volvo  BMW  Mercedes  Audi  Renault  Peugeot  Nissan  Volkswagen  Ford  Citroen  Skoda  Dacia  Kia  Fiat  Hyundai  Opel  Seat  Toyota
2013      87   81        71    75       60       61      51          56    45       64     46     52   45    31       39    41    44      26
2017      79   68        68    59       49       49      47          47    44       43     41     39   39    36       32    30    29       8 

gold: Volvo had the greatest share of diesel fueled passenger cars in Europe . Despite reducing its share of diesel cars in the past few years , 79 percent of all Volvo cars on European roads were fitted with a diesel engine in 2017 . This was followed by three German companies , BMW , Mercedes and Audi , while Fiat was the only automobile brand listed which had seen its share of diesel cars increase .
gold_template: templateValue[0][0] had the greatest templateTitle[3] of templateTitle[4] fueled passenger cars in Europe . Despite reducing its templateTitle[3] of templateTitle[4] cars in the past few years , templateValue[2][max] templateScale of all templateValue[0][0] cars on European roads were fitted with a templateTitle[4] engine in templateTitleDate[max] . This was followed templateTitle[8] three German companies , templateValue[0][1] , templateValue[0][2] and templateValue[0][3] , while templateValue[0][13] was the only automobile templateLabel[0][0] listed which had seen its templateTitle[3] of templateTitle[4] cars templatePositiveTrend .

generated_template: This statistic shows the templateTitle[1] and templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] , templateTitle[7] templateTitle[8] . In templateTitleDate[max] , the average templateTitle[1] of templateValue[0][0] was templateValue[0][0] with templateValue[1][0] templateScale of respondents aged between 25 to 34 years old reported in templateTitleDate[max] .
generated: This statistic shows the car and sales share diesel in EU 2013 and 2017 , by .  In 2017 , the average car of Volvo was with 87 % of respondents aged between 25 to 34 years old reported in 2017 .

Example 447:
titleEntities: {'Subject': ['Toronto Blue Jays'], 'Date': ['2002', '2019']}
title: Franchise value of the Toronto Blue Jays 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1500', '1350', '1300', '900', '870', '610', '568', '413', '337', '326', '353', '352', '344', '286', '214', '169', '166', '182']

gold: This graph depicts the value of the Toronto Blue Jays franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 1.5 billion U.S. dollars . The Toronto Blue Jays are owned by Rogers Communications , who bought the franchise for 137 million U.S. dollars in 2000 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Jays templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Jays are owned by Rogers Communications , who bought the templateYLabel[0] for 137 templateScale templateYLabel[3] templateYLabel[4] in 2000 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Terry and Kim Pegula .
generated: This graph depicts the Franchise value of the Toronto Blue Jays of the National Football League from 2002 to 2019 .  In 2019 , the Franchise value came to 1500 million U.S. dollars .  The Toronto Blue Jays are owned by Terry and Kim Pegula .

Example 448:
titleEntities: {'Subject': ['Political'], 'Date': []}
title: Political stability - countries
X_Axis['Country']: ['Australia', 'Austria', 'Canada', 'Costa_Rica', 'Czech_Republic', 'Denmark', 'Finland', 'Germany', 'Japan', 'Luxembourg', 'Mauritius', 'New_Zealand', 'Norway', 'Oman', 'Slovenia', 'Sweden', 'Switzerland']
Y_Axis['Index', 'Value']: ['3.6', '3.6', '2.8', '3.5', '3.7', '2.2', '3.2', '3.8', '3.8', '3.6', '3.5', '3.6', '1.2', '3.9', '3.8', '3.2', '3.4']

gold: The statistic shows countries with the highest political stability in the period 2009/2010 . With an index value of 1.2 , Norway was the country with the world 's highest political stability in 2009/2010 .
gold_template: The statistic shows templateTitle[2] with the highest templateTitleSubject[0] templateTitle[1] in the period 2009/2010 . With an templateYLabel[0] templateYLabel[1] of templateYValue[min] , templateXValue[12] was the templateXLabel[0] with the world 's highest templateTitleSubject[0] templateTitle[1] in 2009/2010 .

generated_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] in templateTitleDate[0] . In templateTitleDate[0] , the templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of a templateYLabel[3] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Political stability the countries in .  In , the Australia had the highest Index Value of a Value of 3.9 Value .

Example 449:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2010', '2019']}
title: Electricity prices for households in Belgium 2010 - 2019 , semi-annually
X_Axis['Year']: ['2019_S1', '2018_S2', '2018_S1', '2017_S2', '2017_S1', '2016_S2', '2016_S1', '2015_S2', '2015_S1', '2014_S2', '2014_S1', '2013_S2', '2013_S1', '2012_S2', '2012_S1', '2011_S2', '2011_S1', '2010_S2', '2010_S1']
Y_Axis['Euro', 'cents', 'per', 'kilowatt-hour']: ['28.39', '29.37', '27.33', '28.77', '27.99', '27.45', '25.44', '23.52', '21.27', '20.43', '20.97', '22.15', '21.73', '22.23', '23.27', '21.19', '21.36', '19.74', '19.59']

gold: This statistic shows the electricity prices for household end users in Belgium semi-annually from 2010 to 2019 . In the second half of 2018 , the average electricity price for households was 29.37 euro cents per kWh . This was an increased from the previous period .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh . This was an templatePositiveTrend from the previous period .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: The statistic shows the Electricity prices for household end users in Belgium semi-annually from 2010_S2 to 2019_S1 .  In the second half of 2018_S2 , the average Electricity price for households was 29.37 Euro cents per kWh .

Example 450:
titleEntities: {'Subject': ['United States'], 'Date': ['1990', '2019']}
title: United States - annual real GDP 1990 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['GDP', 'in', 'billion', 'chained', '(2012)', 'U.S.', 'dollars']: ['19072.5', '18638.2', '18108.1', '17688.9', '17403.8', '16912.0', '16495.4', '16197.0', '15840.7', '15598.8', '15208.8', '15604.7', '15626.0', '15338.3', '14912.5', '14406.4', '13879.1', '13493.1', '13262.1', '13131.0', '12610.5', '12038.3', '11521.9', '11031.4', '10630.3', '10352.4', '9951.5', '9684.9', '9355.4', '9365.5']

gold: This statistic shows the development of the Real Gross Domestic Product of the United States of America from 1990 to 2019 in annual numbers chained to the U.S. dollar value of the year 2012 . In 2019 , the United States generated about 19.07 trillion U.S. dollars ( 2012 chained ) . See the US GDP for further information .
gold_template: This statistic shows the development of the templateTitle[3] Gross Domestic Product of the templateTitleSubject[0] of America from templateXValue[min] to templateXValue[max] in templateTitle[2] numbers templateYLabel[2] to the templateYLabel[4] dollar value of the templateXLabel[0] templateXValue[7] . In templateXValue[max] , the templateTitleSubject[0] generated about templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] ( templateXValue[7] templateYLabel[2] ) . See the US templateYLabel[0] for further information .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] of the templateYLabel[2] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of the templateYLabel[1] of the templateTitle[4] came to over templateYValue[2] templateScale templateYLabel[3] .
generated: This statistic shows the GDP of the billion of the chained GDP from 1990 to 2019 .  In 2019 , the United of the billion of the GDP came to over 18108.1 billion (2012) .

Example 451:
titleEntities: {'Subject': ['Black'], 'Date': ['2012']}
title: U.S. and state legislatures : Black elected officials 2012 , by state
X_Axis['State']: ['Florida', 'Minnesota', 'Maine', 'Rhode_Island', 'Alabama', 'New_Mexico', 'Idaho', 'Kentucky', 'New_York', 'District_of_Columbia', 'Massachusetts', 'Oregon', 'Tennessee', 'South_Dakota', 'Vermont', 'Mississippi', 'New_Hampshire', 'North_Dakota', 'Connecticut', 'Arkansas', 'Illinois', 'California', 'West_Virginia', 'Kansas', 'Maryland', 'Iowa', 'Nebraska', 'Ohio', 'Delaware', 'Indiana', 'New_Jersey', 'Washington', 'Pennsylvania', 'Oklahoma', 'Michigan', 'Nevada', 'Colorado', 'Montana', 'Alaska', 'Hawaii', 'Arizona', 'Virginia', 'Wisconsin', 'Utah', 'Texas', 'Georgia', 'North_Carolina', 'Louisiana', 'South_Carolina', 'Wyoming', 'Missouri']
Y_Axis['Black', 'elected', 'officials']: ['56', '49', '44', '37', '33', '30', '30', '27', '25', '25', '21', '20', '19', '18', '18', '18', '17', '17', '13', '13', '12', '9', '8', '8', '7', '7', '7', '6', '6', '5', '3', '3', '3', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1', '0', '0', '0', '0', '0', '0', '0']

gold: The statistic above provides data on the Black elected officials in the United States . In 2012 , 56 Black elected officials were counted in Florida .
gold_template: The statistic above provides data on the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[0] . In templateTitleDate[0] , templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] were counted in templateXValue[0] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateXValue[0] , the templateTitle[3] highest templateYLabel[0] of templateXValue[1] with a templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the U.S. of state in the Black in 2012 , officials State .  In Florida , the Black highest of Minnesota with a elected of 56 officials .

Example 452:
titleEntities: {'Subject': ['Major'], 'Date': ['2015', '2019']}
title: Major countries in bismuth mine production 2015 to 2019
Data:
             0     1      2       3           4         5       6       7        8        9
Country  China  Laos  Japan  Mexico  Kazakhstan  Bulgaria  Canada  Russia  Vietnam  Bolivia
2015      7500     0      0     700           0         0       3      40     5000       10
2016      7400     0      0     700           0         0       3      40     2000       10
2017     13500  2000    525     513         270        50      25       4        0        0
2018     14000  3010    571     333         290        50      25       0        0        0
2019     14000  3000    540     400         270        50      25       0        0        0 

gold: This statistic shows the worldwide bismuth mine production from 2014 to 2019 , by major producing countries . For example , Canada 's bismuth production amounted to approximately three metric tons in 2014 . This figure increased to 25 metric tons in 2019 .
gold_template: This statistic shows the worldwide templateTitle[2] templateTitle[3] templateTitle[4] from 2014 to templateTitleDate[max] , by templateTitleSubject[0] producing templateTitle[1] . For example , templateValue[0][6] 's templateTitle[2] templateTitle[4] amounted to approximately templateValue[1][6] metric tons in 2014 . This figure templatePositiveTrend to templateValue[3][6] metric tons in templateTitleDate[max] .

generated_template: templateValue[0][0] was the leading templateTitle[3] producing templateLabel[0][0] templateTitle[2] as of templateTitleDate[max] . In that year they produced an estimated 700,000 metric tons of templateTitle[3] . templateTitle[3] 's end-uses templateTitle[3] is an allotrope of carbon that is comprised of layers of carbon atoms .
generated: China was the leading mine producing Country bismuth as of 2019 .  In that year they produced an estimated 700,000 metric tons of mine .  mine 's end-uses mine is an allotrope of carbon that is comprised of layers carbon atoms .

Example 453:
titleEntities: {'Subject': ['IPOs U.S.'], 'Date': ['2019']}
title: Leading IPOs in the U.S. 2019
X_Axis['Company', '(Offer', 'date)']: ['Uber_Technologies_(May_9)', 'Avantor_(May_16)', 'Lyft_(Mar_28)', 'XP_(Dec_10)', 'Pinterest_(Apr_17)', 'SmileDirectClub_(Sep_11)', 'Peloton_Interactive_(Sep_25)', 'Tradeweb_Markets_(Apr_3)', 'Chewy_(Jun_13)', 'DouYu_International_Holdings_(Jul_16)']
Y_Axis['Deal', 'size', 'in', 'million', 'U.S.', 'dollars']: ['8100', '2898', '2340', '1958', '1425', '1346', '1160', '1080', '1023', '775']

gold: Uber Technologies went public on May 9 , 2019 . This was the largest initial public offering ( IPO ) in the United States in 2019 , raising 8.1 billion U.S. dollars . As shown in this statistic , the ten largest IPOs that year each raised over a billion dollars in capital .
gold_template: templateXValue[0] went public on 9 , templateTitleDate[0] . This was the largest initial public offering ( IPO ) in the templateTitle[2] in templateTitleDate[0] , raising templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . As shown in this statistic , the ten largest templateTitleSubject[0] that year each raised over a templateScale templateYLabel[4] in capital .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] average templateYLabel[0] of people in the templateTitle[2] in templateTitleDate[0] . According to the source , templateXValue[0] , templateXValue[1] , with templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Leading IPOs average Deal of people in the U.S. in 2019 .  According to the source , Uber_Technologies_(May_9) Avantor_(May_16) , with 2898 million U.S. dollars .

Example 454:
titleEntities: {'Subject': ['UEFA Europa League'], 'Date': ['2019']}
title: UEFA Europa League most titles by club 2019
X_Axis['Soccer', 'Club']: ['Sevilla_FC', 'Atletico_Madrid', 'FC_Internazionale_Milano', 'Liverpool_FC', 'Juventus', 'VfL_Borussia_Mönchengladbach', 'Chelsea_FC', 'IFK_Göteborg', 'Feyenoord', 'Parma_FC', 'Real_Madrid_CF', 'Tottenham_Hotspur_FC', 'FC_Porto', 'Ipswich_Town_FC', 'FC_Zenit', 'PSV_Eindhoven', 'FC_Bayern_Munich', 'SSC_Napoli', 'RSC_Anderlecht', 'CSKA_Moscow', 'Valencia_CF', 'FC_Schalke_04', 'Bayer_04_Leverkusen', 'Manchester_United', 'AFC_Ajax', 'FC_Shakhtar_Donetsk', 'Galatasaray_AS', 'Eintracht_Frankfurt']
Y_Axis['Number', 'of', 'titles']: ['5', '3', '3', '3', '3', '2', '2', '2', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']

gold: This statistic shows the soccer clubs with the most UEFA Cup / UEFA Europa League titles from its inaugural season in 1971 to 2019 . In 2016 , Sevilla FC won the Europa League a record fifth time .
gold_template: This statistic shows the templateXLabel[0] clubs with the templateTitle[3] templateTitleSubject[0] Cup / templateTitleSubject[0] League templateYLabel[1] from its inaugural season in 1971 to templateTitleDate[0] . In 2016 , templateXValue[0] won the templateTitleSubject[0] a record fifth time .

generated_template: The statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[2] templateXLabel[0] templateXLabel[1] templateTitle[5] as of 2017 , templateTitle[6] templateTitle[7] . templateXValue[0] has won the most recently with templateYValue[max] templateTitle[4] templateYLabel[1] in the templateTitleSubject[1] .
generated: The statistic depicts the UEFA Europa League Soccer Club by as of 2017 , club 2019 .  Sevilla_FC has won the most recently with 5 titles in the UEFA Europa League .

Example 455:
titleEntities: {'Subject': ['Zambia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Zambia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['24.4', '23.95', '23.64', '23.42', '23.34', '23.95', '26.72', '25.87', '20.94', '21.24', '27.15', '28.05', '25.5', '23.46', '20.27', '15.33', '17.91', '14.06', '12.76', '8.33', '6.22', '4.9', '4.19', '4.09', '3.6', '3.41', '3.54', '4.3', '3.6', '3.8', '3.66', '3.55', '3.61', '3.69', '4.09', '4.37', '4.1', '2.43', '1.96', '2.85', '3.0']

gold: The statistic shows gross domestic product ( GDP ) in Zambia from 1984 to 2017 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Zambia from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 456:
titleEntities: {'Subject': ['Alibaba'], 'Date': ['2019']}
title: Alibaba : cumulative active online buyers Q4 2014-Q4 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14"]
Y_Axis['Active', 'Alibaba', 'online', 'buyers', 'in', 'millions']: ['711', '693', '674', '654', '636', '601', '576', '552', '515', '488', '466', '454', '443', '439', '434', '423', '407', '386', '367', '350', '334']

gold: The largest Chinese company for e-commerce Alibaba had another strong quarter in 2019 . The annual active consumers on its online shopping properties in China reached 711 million by the end of December , increasing from 693 million at the end of September 2019 . Alibaba business Founded in 1999 , the Alibaba Group grew up to be the leading company providing all kinds of e-commerce services .
gold_template: The largest Chinese company for e-commerce templateYLabel[1] had another strong templateXLabel[0] in templateTitleDate[0] . The annual templateYLabel[0] consumers on its templateYLabel[2] shopping properties in China reached templateYValue[max] templateScale by the end of , templatePositiveTrend from templateYValue[1] templateScale at the end of 2019 . templateYLabel[1] business Founded in 1999 , the templateYLabel[1] Group templatePositiveTrend up to be the leading company providing all kinds of e-commerce services .

generated_template: This statistic shows the total templateYLabel[0] of templateTitleSubject[0] in the first templateXLabel[0] of templateTitleDate[min] and the fourth templateXLabel[0] of templateTitleDate[max] . The company 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] in the first templateXLabel[0] of templateTitleDate[0] .
generated: This statistic shows the total Active of Alibaba in the first Quarter of 2019 and the fourth Quarter of 2019 .  The company 's Active Alibaba amounted to 711 millions buyers in the first Quarter of 2019 .

Example 457:
titleEntities: {'Subject': ['England'], 'Date': ['2010', '2018']}
title: Children participating in cricket in England 2010 to 2018
Data:
                   0        1        2        3        4        5        6        7
Year         2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11
5-10 years       5.3      5.2        4      4.6      6.4      4.7      6.9      6.6
11-15 years     12.6     13.4     18.8     18.1     21.3     22.5     17.6     17.3 

gold: The sport of cricket is on a slight decline in popularity among children in England . Especially among those aged 11 to 15 , whereas in 2013 a total of 22.5 percent played on a monthly basis , in 2018 it was only 12.6 percent . The most popular sport among 11 to 15 year olds was football with 44.7 percent playing at least monthly .
gold_template: The sport of templateTitle[2] is on a slight decline in popularity among templateTitle[0] in templateTitleSubject[0] . Especially among those aged 11 to 15 , whereas in 2013 a total of templateValue[2][max] templateScale played on a monthly basis , in templateTitleDate[max] it was only templateValue[2][min] templateScale . The most popular sport among 11 to 15 templateLabel[0][0] olds was football with 44.7 templateScale playing at least monthly .

generated_template: During the observation period , templateTitle[2] 's popularity has templateNegativeTrend among templateTitle[0] between 11 and templateValue[2][min] templateLabel[1][1] in templateTitleSubject[0] and has templatePositiveTrend among templateTitle[0] between templateValue[1][2] and ten templateLabel[1][1] . In 2017/2018 , templateValue[2][min] templateScale of 11 to templateValue[2][min] templateLabel[0][0] olds participated in templateTitle[2] in the templateValue[1][1] templateLabel[1][1] prior to the survey . Participation peaked in 2011/2012 , when templateValue[2][max] templateScale of the templateTitle[0] in that age group played templateTitle[2] .
generated: During the observation period , cricket 's popularity has decreased among Children between 11 and 12.6 years in England and has increased among Children between 4 and ten years .  In 2017/2018 , 12.6 % of 11 to 12.6 Year olds participated in cricket the 5.2 years prior to the survey .  Participation peaked in 2011/2012 , when 22.5 % of the Children in that age group played cricket .

Example 458:
titleEntities: {'Subject': ['Lithuania'], 'Date': ['2019']}
title: Unemployment rate in Lithuania 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['6.64', '6.01', '7.07', '7.86', '9.12', '10.7', '11.77', '13.36', '15.39', '17.81', '13.79', '5.83', '4.25', '5.78', '8.33', '10.68', '12.88', '13.01', '16.84', '15.93', '13.39']

gold: This statistic shows the unemployment rate in Lithuania from 1999 to 2019 . In 2019 , the unemployment rate in Lithuania was at approximately 6.64 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Lithuania from 1999 to 2019 .  In 2019 , the Unemployment rate in Lithuania was at approximately 6.64 % .

Example 459:
titleEntities: {'Subject': ['Current'], 'Date': []}
title: Current year in various historical and world calendars 2020
X_Axis['Month']: ['Assyrian', 'Hebrew', 'Chinese', 'Julian', 'Buddhist', 'Gregorian', 'Hindu', 'Islamic', 'Iranian', 'French_Revolutionary']
Y_Axis['Current', 'year', '(as', 'of', 'January', '25,', '2020)']: ['6770', '5780', '4718', '2773', '2563', '2020', '1941', '1441', '1398', '228']

gold: Today , the vast majority of the world uses what is known as the Gregorian calendar , Named after Pope Gregory XIII , who introduced it in 1582 . The Gregorian calendar replaced the Julian calendar , which had been the most used calendar in Europe until this point . The Gregorian calendar lasts for approximately 365.24 days ; this means that most years have 365 days , with one extra day being added every fourth year , unless the year is divisible by 100 but not 400 ( for example , the year 2000 was a leap year , whereas 2100 and 2200 will not be ) .
gold_template: Today , the vast majority of the templateTitle[4] uses what is known as the templateXValue[5] calendar , Named after Pope Gregory XIII , who introduced it in 1582 . The templateXValue[5] calendar replaced the templateXValue[3] calendar , which had been the most used calendar in Europe until this point . The templateXValue[5] calendar lasts for approximately 365.24 days ; this means that most years have 365 days , with one extra day being added every fourth templateYLabel[1] , unless the templateYLabel[1] is divisible by 100 but not 400 ( for example , the templateYLabel[1] 2000 was a leap templateYLabel[1] , whereas 2100 and 2200 will not be ) .

generated_template: This statistic gives information on the distribution of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[4] on templateTitle[4] templateYLabel[0] of templateYLabel[1] . As of that time , the templateXValue[0] is the templateTitle[0] templateTitle[1] templateTitle[2] , with a value of templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic gives information on the distribution of the Current year various Current world on Current of year .  As of that time , the Assyrian is the Current year various , with a value of 6770 % January .

Example 460:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000']}
title: Canada : average family size 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Average', 'number', 'of', 'people']: ['2.9', '2.9', '2.9', '2.9', '2.9', '2.9', '2.9', '2.9', '2.9', '2.9', '2.9', '2.9', '2.9', '2.9', '3.0', '3.0', '3.0', '3.0']

gold: This statistic shows the average number of people per family in Canada from 2000 to 2017 . The average Canadian household in 2017 consisted of 2.9 people , compared to 3.0 people in 2000 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] per templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] Canadian household in templateXValue[max] consisted of templateYValue[idxmax(X)] templateYLabel[2] , compared to templateYValue[idxmin(X)] templateYLabel[2] in templateXValue[min] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] and templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] was templateYValue[7] templateYLabel[3] .
generated: The statistic shows the Average number people and 2000 2017 in Canada from 2000 to 2017 .  In 2017 , the average number of people was 2.9 people .

Example 461:
titleEntities: {'Subject': ['Kansas City Royals'], 'Date': ['2006', '2019']}
title: Kansas City Royals average ticket price 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['32.84', '33.58', '26.88', '29.76', '24.73', '19.83', '21.84', '18.95', '19.38', '19.38', '17.54', '14.48', '13.71']

gold: This graph depicts the average ticket price for Kansas City Royals games in Major League Baseball from 2006 to 2019 . In 2019 , the average ticket price was at 32.84 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] Royals games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Kansas City Royals games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 32.84 U.S. dollars .

Example 462:
titleEntities: {'Subject': ['Bitcoin'], 'Date': ['2020']}
title: Price of Bitcoin monthly 2012 to 2020
X_Axis['Month']: ['Jan_20', 'Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16', 'Jun_16', 'Apr_16', 'Mar_16', 'Feb_16', 'Jan_16', 'Dec_15', 'Nov_15', 'Oct_15', 'Sep_15', 'Aug_15', 'Jul_15', 'Jun_15', 'May_15', 'Apr_15', 'Mar_15', 'Feb_15', 'Jan_15', 'Dec_14', 'Nov_14', 'Oct_14', 'Sep_14', 'Aug_14', 'Jul_14', 'Jun_14', 'May_14', 'Apr_14', 'Mar_14', 'Feb_14', 'Jan_14', 'Dec_13', 'Nov_13', 'Oct_13', 'Sep_13', 'Aug_13', 'Jul_13', 'Jun_13', 'May_13', 'Apr_13', 'Mar_13', 'Feb_13', 'Jan_13', 'Dec_12', 'Nov_12', 'Oct_12', 'Sep_12', 'Aug_12', 'Jul_12']
Y_Axis['Bitcoin', 'price', 'index', 'in', 'U.S.', 'dollars']: ['9388.88', '7179.96', '7551.24', '9225.0', '8085.71', '9487.96', '9506.7', '10908.18', '8287.04', '5151.43', '4081.22', '3799.68', '3441.03', '3689.56', '4241.74', '6320.45', '6604.97', '7013.97', '7726.89', '6387.31', '7487.19', '9244.32', '6926.02', '10309.64', '10166.51', '13860.14', '9916.54', '4353.05', '4349.29', '4764.87', '2873.83', '2504.28', '2328.91', '1349.19', '1079.75', '1190.89', '967.67', '968.23', '742.01', '697.37', '608.44', '572.33', '625.88', '672.48', '449.33', '415.16', '436.61', '367.13', '430.05', '376.91', '312.43', '236.25', '230.02', '283.94', '262.48', '230.0', '236.57', '243.39', '253.53', '216.91', '319.7', '377.09', '336.8', '386.27', '478.07', '581.35', '639.36', '623.26', '445.87', '458.5', '543.93', '848.29', '757.5', '1124.76', '198.23', '123.1', '129.46', '97.91', '97.51', '128.82', '139.23', '93.03', '33.38', '20.41', '13.51', '12.56', '11.2', '12.4', '10.16', '9.35']

gold: The bitcoin index value for the end of January 2020 amounted to 9,388.88 U.S. dollars . The bitcoin price index is an average of bitcoin prices across leading global exchanges . More information on bitcoin and other crypto-currencies can be found in our Statista Report 2019 .
gold_template: The templateYLabel[0] templateYLabel[2] value for the end of 2020 amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] is an average of templateYLabel[0] prices across leading global exchanges . More information on templateYLabel[0] and other crypto-currencies can be found in our Statista Report 2019 .

generated_template: This statistic shows the average templateYLabel[0] of the templateTitle[1] templateTitleSubject[0] from the first half of 2013 to 2019 . In the last measured period , the average templateYLabel[0] of people amounted to templateYValue[0] thousand templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the average Bitcoin of the Bitcoin from the first half of 2013 to 2019 .  In the last measured period , the average Bitcoin of people amounted to 9388.88 thousand index U.S. .

Example 463:
titleEntities: {'Subject': ['France'], 'Date': ['2008', '2017']}
title: Consumer spending on clothing in France 2008 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Final', 'consumption', 'expenditure', 'in', 'million', 'euros']: ['36160', '35794', '36254', '36098', '35574', '35580', '35574', '35999', '36146', '37494']

gold: Since 2009 , the final consumption expenditure of French households on clothing has remained relatively stable , ranging at 36 billion euros in 2017 . Fashion purchasing habits Among French respondents in a survey conducted in 2017 , about a third stated purchasing fashion clothing or accessories at least once every three months . In addition to this , 20 percent of surveyed consumers spent between 101 and 200 euros on fashion goods during the 2017 winter seasonal sales .
gold_template: Since templateXValue[8] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of French households on templateTitle[2] has remained relatively stable , ranging at templateYValue[0] templateScale templateYLabel[4] in templateXValue[max] . Fashion purchasing habits Among French respondents in a survey conducted in templateXValue[max] , about a third stated purchasing fashion templateTitle[2] or accessories at least once every three months . In addition to this , 20 templateScale of surveyed consumers spent between 101 and 200 templateYLabel[4] on fashion goods during the templateXValue[max] winter seasonal sales .

generated_template: This statistic presents the templateTitle[0] of templateTitle[1] templateTitle[2] of templateYLabel[2] templateTitle[4] from templateXValue[min] to templateXValue[max] , on templateTitle[1] . According to the source , the templateTitle[0] of templateYLabel[2] in templateTitleSubject[0] has templatePositiveTrend since templateXValue[min] , from templateYValue[idxmin(X)] templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic presents the Consumer of spending clothing of expenditure 2008 from to 2017 , on spending .  According to the source , the Consumer of expenditure in France has increased since 2008 , from 37494 million consumption expenditure .

Example 464:
titleEntities: {'Subject': ['UK'], 'Date': ['2011', '2018']}
title: Average number of minutes waiting in the emergency department in the UK 2011 to 2018
X_Axis['Month']: ['Mar_2018', 'Jan_2018', 'Nov_2017', 'Sep_2017', 'Jul_2017', 'May_2017', 'Mar_2017', 'Jan_2017', 'Nov_2016', 'Sep_2016', 'Jul_2016', 'May_2016', 'Mar_2016', 'Jan_2016', 'Nov_2015', 'Sep_2015', 'Jul_2015', 'May_2015', 'Mar_2015', 'Jan_2015', 'Nov_2014', 'Sep_2014', 'Jul_2014', 'May_2014', 'Mar_2014', 'Jan_2014', 'Nov_2013', 'Sep_2013', 'Jul_2013', 'May_2013', 'Mar_2013', 'Jan_2013', 'Nov_2012', 'Sep_2012', 'Jul_2012', 'May_2012', 'Mar_2012', 'Jan_2012', 'Nov_2011', 'Sep_2011', 'Jul_2011', 'May_2011']
Y_Axis['Number', 'of', 'minutes']: ['64', '57', '60', '58', '60', '59', '58', '57', '59', '59', '62', '62', '69', '58', '58', '55', '53', '53', '56', '46', '54', '55', '56', '56', '56', '47', '50', '50', '53', '51', '57', '50', '53', '53', '54', '53', '55', '49', '51', '53', '53', '57']

gold: In March 2018 , a patient in Accident and Emergency in the United Kingdom would spend an average of 64 minutes waiting before treatment would begin . Since the start of the provided time interval in 2011 this is the second longest average waiting time recorded . The only time it was higher was the average waiting time in March 2016 , which rose to 69 minutes .
gold_template: In 2018 , a patient in Accident and templateTitle[4] in the United Kingdom would spend an templateTitle[0] of templateYValue[0] templateYLabel[1] templateTitle[3] before treatment would begin . Since the start of the provided time interval in templateXValue[38] this is the second longest templateTitle[0] templateTitle[3] time recorded . The only time it was higher was the templateTitle[0] templateTitle[3] time in 2016 , which templatePositiveTrend to templateYValue[max] templateYLabel[1] .

generated_template: Among the 'Best of people who are the most templateXValue[7] templateXValue[13] templateXValue[13] to templateXValue[0] . templateXValue[0] , with over templateYValue[max] templateScale templateYLabel[3] , making it the templateTitle[0] templateTitle[1] in templateTitleSubject[0] . It was followed by templateXValue[2] with over templateYValue[1] templateScale of the templateYLabel[1] templateYLabel[3] .
generated: Among the 'Best of people who are the most Jan_2017 Jan_2016 to Mar_2018 .  Mar_2018 , with over 69 million minutes , making it the Average number in UK .  It was followed by Nov_2017 with over 57 % of the minutes .

Example 465:
titleEntities: {'Subject': ['America'], 'Date': ['2019']}
title: The richest people in America as of 2019
X_Axis['Month']: ['Jeff_Bezos_&_family_(Amazon.com)', 'Bill_Gates_(Microsoft)', 'Warren_Buffett_(Berkshire_Hathaway)', 'Larry_Ellison_(Oracle)', 'Mark_Zuckerberg_(Facebook)', 'Michael_Bloomberg_(Bloomberg_LP_)', 'Larry_Page_(Google)', 'Charles_Koch_(Koch_Industries)', 'David_Koch_(Koch_Industries)', 'Sergey_Brin_(Google)', 'Jim_Walton_(Walmart)', 'Alice_Walton_(Walmart)', 'S._Robson_Walton_(Walmart)', 'Steve_Ballmer_(Microsoft)', 'Sheldon_Adelson_(Las_Vegas_Sands)', 'Michael_Dell_(Dell_Technologies)', 'Phil_Knight_&_family_(Nike)', 'Jacqueline_Mars_(Mars)', 'John_Mars_(Mars)', 'Elon_Musk_(Tesla_Motors_SpaceX)']
Y_Axis['Net', 'worth', 'in', 'billion', 'U.S.', 'dollars']: ['131.0', '96.5', '82.5', '62.5', '62.3', '55.5', '50.8', '50.5', '50.5', '49.8', '44.6', '44.4', '44.3', '41.2', '35.1', '34.3', '33.4', '23.9', '23.9', '22.3']

gold: This statistic presents the estimated net worth of the 20 richest people in America as of 2019 . At this time Jeff Bezos was the richest man in the United States with an estimated net worth of 131 billion U.S. dollars . Richest people in the United States - additional information Every year since 1982 , the American business magazine Forbes has been compiling lists of the 400 richest people in the United States , known as the `` Forbes 400 .
gold_template: This statistic presents the estimated templateYLabel[0] templateYLabel[1] of the 20 templateTitle[0] templateTitle[1] in templateTitleSubject[0] as of templateTitleDate[0] . At this time templateXValue[0] was the templateTitle[0] man in the country with an estimated templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . templateTitle[0] templateTitle[1] in the country - additional information Every year since 1982 , the American business magazine Forbes has been compiling lists of the 400 templateTitle[0] templateTitle[1] in the country , known as the `` Forbes 400 .

generated_template: The statistic gives information on the templateYLabel[3] templateTitle[2] of templateTitleSubject[0] as of 2019 . As of that time , templateXValue[0] had the highest templateYLabel[0] of templateTitle[4] templateXValue[0] with over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic gives information on the U.S. America of as 2019 .  As of that time , Jeff_Bezos_&_family_(Amazon.com) had the highest Net of 2019 Jeff_Bezos_&_family_(Amazon.com) with over 131.0 billion U.S. dollars .

Example 466:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Vehicle sales in the United States 2019
X_Axis['Vehicles']: ['Light_vehicles_(total)', 'Thereof_light_trucks', 'Thereof_autos', 'Heavy_trucks', 'Motor_vehicles_(total)']
Y_Axis['Vehicle', 'retail', 'sales', 'in', 'thousands']: ['16965.2', '12237.8', '4727.3', '529.5', '17494.6']

gold: In 2019 , a little under 17.5 million heavy trucks , light trucks , and cars were sold to customers in the United States . Light trucks accounted for about 70 percent of U.S. motor vehicle sales that year .
gold_template: In templateTitleDate[0] , a little under templateYValue[max] templateScale templateXValue[3] templateXValue[1] , templateXValue[0] templateXValue[1] , and cars were sold to customers in the templateTitleSubject[0] . templateXValue[0] templateXValue[1] accounted for about 70 templateScale of templateTitle[3] templateXValue[last] templateYLabel[0] templateYLabel[2] that year .

generated_template: As of 2019 , it was found that templateYValue[max] templateScale of people templateYLabel[3] on templateXValue[0] accounted for up to templateYValue[1] templateScale of the templateYLabel[1] templateTitle[4] . The templateTitle[0] of all templateYLabel[2] templateYLabel[3] can be linked to various various statistic on the platform .
generated: As of 2019 , it was found that 17494.6 thousands of people thousands on Light_vehicles_(total) accounted for up to 12237.8 thousands of the retail 2019 .  The Vehicle of all sales thousands can be linked to various statistic on the platform .

Example 467:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2018']}
title: Leading Youtubers from the Netherlands based on subscribers 2018
X_Axis['Month']: ['Trap_City', 'NikkieTutorials', 'Martin_Garrix', 'Kwebbelkop', 'Jelly', 'Hardwell', 'Armada_Music', 'saskiasdansschool', 'NiceNienke', 'Tiësto']
Y_Axis['Number', 'of', 'subscribers', 'in', 'thousands']: ['9333.53', '8883.0', '8102.89', '7898.74', '7643.67', '4084.03', '3261.56', '3032.43', '2622.09', '2485.82']

gold: 'Music channel Trap City had more than nine million subscriptions in April 2018 , making it the most subscribed YouTube channel that comes from the Netherlands . The ranking also suggests that Dutch EDM artists or channels are popular to subscribe to , with entries for DJs Martin Garrix ( 3rd in this ranking ) , Hardwell ( 6th ) and Tiësto ( 10th ) , as well as dance label Armada Music ( 7th ) . Note , however , that the numbers provided here concern worldwide subscribers .
gold_template: 'Music channel templateXValue[0] had more than templateYValue[max] templateScale subscriptions in 2018 , making it the most subscribed YouTube channel that comes templateTitle[2] the templateTitleSubject[0] . The ranking also suggests that Dutch EDM artists or channels are popular to subscribe to , with entries for DJs templateXValue[2] ( 3rd in this ranking ) , templateXValue[5] ( 6th ) and templateXValue[last] ( 10th ) , as well as dance label templateXValue[6] ( 7th ) . Note , however , that the numbers provided here concern worldwide templateYLabel[1] .

generated_template: As of 2019 , templateXValue[0] accounted for over templateYValue[max] templateScale of people . It was followed by templateXValue[1] with templateYValue[1] templateScale of the templateYLabel[1] having templateTitle[4] . More specifically that time , it was followed by templateXValue[2] and templateYValue[2] templateScale of the people .
generated: As of 2019 , Trap_City accounted for over 9333.53 thousands of people .  It was followed by NikkieTutorials with 8883.0 thousands of the subscribers having based .  More specifically that time , it was followed by Martin_Garrix and 8102.89 thousands of the people .

Example 468:
titleEntities: {'Subject': ['North America'], 'Date': ['2019']}
title: Life expectancy in North America 2019
Data:
                     0       1    2
Country  North_America  Canada  USA
Males               76      80   76
Females             81      84   81 

gold: This statistic shows the average life expectancy in North America for those born in 2019 , by gender and region . In Canada , the average life expectancy was 80 years for males and 84 years for females in 2019 . Life expectancy in North America Of those considered in this statistic , the life expectancy of female Canadian infants born in 2019 was the longest , at 84 years .
gold_template: This statistic shows the average templateTitle[0] templateTitle[1] in templateValue[0][0] for those born in templateTitleDate[0] , by gender and region . In templateValue[0][1] , the average templateTitle[0] templateTitle[1] was templateValue[1][1] years for templateLabel[1][0] and templateValue[2][1] years for templateLabel[2][0] in templateTitleDate[0] . templateTitle[0] templateTitle[1] in templateValue[0][0] Of those considered in this statistic , the templateTitle[0] templateTitle[1] of female Canadian infants born in templateTitleDate[0] was the longest , at templateValue[2][1] years .

generated_template: This statistic shows the average templateTitleSubject[0] templateTitle[1] at birth templateTitle[2] templateTitle[3] in templateTitleDate[0] . The average templateTitleSubject[0] templateTitle[1] for templateLabel[2][0] in templateTitleDate[0] was templateValue[1][1] years for templateLabel[1][0] and templateValue[2][1] years for templateLabel[1][0] and templateValue[2][1] years for templateLabel[2][0] in templateTitleDate[0] .
generated: This statistic shows the average North America expectancy at birth North America in 2019 .  The average North America expectancy for Females in 2019 was 80 years for Males and 84 years for Males and 84 years for Females in 2019 .

Example 469:
titleEntities: {'Subject': ['Chicago Blackhawks', 'NHL'], 'Date': ['2014/15', '2014/15']}
title: Average ticket price Chicago Blackhawks ( NHL ) 2014/15
X_Axis['Year']: ['2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['78.8', '72.95', '62.88', '55.72', '55.39', '55.39', '46.8', '52.22', '34.88', '34.88']

gold: This graph depicts the average ticket price of Chicago Blackhawks games within the National Hockey League from 2005/06 to 2014/15 In the 2005/06 season , the average ticket price was 34.88 U.S. dollars . The Blackhawks play their home games at the United Center .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] games within the National Hockey League from templateXValue[last] to templateXValue[0] In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] play their home games at the United Center .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Chicago Blackhawks games of the National Basketball Association from 2005/06 to 2014/15 .  In the 2005/06 season , the Average ticket price was 34.88 U.S. dollars .

Example 470:
titleEntities: {'Subject': ['eSports'], 'Date': ['2015', '2020']}
title: Brand spend on eSports sponsorship and advertising worldwide 2015 to 2020
X_Axis['Year']: ['2020', '2017', '2016', '2015']
Y_Axis['Spending', 'in', 'million', 'U.S.', 'dollars']: ['1220', '517', '350', '230']

gold: The statistic provides information on brands ' spending on eSports sponsorship and advertising worldwide from 2015 to 2020 . According to the estimates , brands will invest 517 million U.S. dollars in marketing and sponsoring eSports related events in 2017 .
gold_template: The statistic provides information on brands ' templateYLabel[0] on templateTitleSubject[0] templateTitle[3] and templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . According to the estimates , brands will invest templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in marketing and sponsoring templateTitleSubject[0] related events in templateXValue[1] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Spending of the spend eSports and sponsorship advertising from 2015 to 2020 .  In 2015 , the eSports spend sponsorship advertising amounted to 230 million U.S. dollars .

Example 471:
titleEntities: {'Subject': ['Mobile', 'United Kingdom', 'UK'], 'Date': ['2018']}
title: Mobile operators : carrier market shares in the United Kingdom ( UK ) 2018
X_Axis['Mobile', 'Carrier']: ['BT_(including_EE)', 'O2', 'Vodafone', 'Three', 'Tesco_Mobile', 'Virgin_Mobile', 'Talk_Talk', 'iD_Mobile', 'Sky']
Y_Axis['Market', 'share']: ['28', '26', '21', '12', '6', '4', '1', '1', '1']

gold: This statistic shows the market shares held by mobile operators in the United Kingdom ( UK ) as at 31 December 2017 . BT , including Everything Everywhere ( EE ) held the greatest share of the market with 28 percent followed by O2 who held 26 percent . The four main mobile network operators ( MNO ) in the UK are Vodafone , Three ( 3 ) , EE and O2 .
gold_template: This statistic shows the templateYLabel[0] templateTitle[4] held by templateXValue[4] templateTitle[1] in the templateTitleSubject[1] ( templateTitleSubject[2] ) as at 31 2017 . templateXValue[0] , including Everything Everywhere ( EE ) held the greatest templateYLabel[1] of the templateYLabel[0] with templateYValue[max] templateScale followed by templateXValue[1] who held templateYValue[1] templateScale . The templateYValue[5] main templateXValue[4] network templateTitle[1] ( MNO ) in the templateTitleSubject[2] are templateXValue[2] , templateXValue[3] ( templateXValue[3] ) , EE and templateXValue[1] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] breakdown of the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . The templateXValue[0] accounted for templateYValue[max] templateScale of the templateTitleSubject[1] 's templateYLabel[0] in templateTitleDate[0] .
generated: This statistic shows the Market share breakdown of the Mobile in 2018 , United Mobile .  The BT_(including_EE) accounted for 28 % of the United Kingdom 's Market in 2018 .

Example 472:
titleEntities: {'Subject': ['Brazil'], 'Date': []}
title: Fertility rate in Brazil
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['1.74', '1.75', '1.75', '1.76', '1.76', '1.77', '1.78', '1.8', '1.82', '1.85', '1.88']

gold: This statistic shows the fertility rate in Brazil from 2007 to 2017 . The fertility rate is the average number of children born by one woman while being of child-bearing age . In 2017 , the fertility rate among Brazil 's population amounted to 1.74 children per woman .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] among templateTitleSubject[0] 's population amounted to templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] a templateYLabel[4] will have during her child-bearing years . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] 's population was templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate of Brazil from 2007 to 2017 .  The Fertility rate is the average Number of children a woman will have during her child-bearing years .  In 2017 , the Fertility rate of Brazil 's population was 1.74 children per woman .

Example 473:
titleEntities: {'Subject': ['Philippines'], 'Date': ['2018']}
title: Infant mortality rate in the Philippines 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['22.5', '22.9', '23.4', '23.7', '24.0', '24.2', '24.4', '24.7', '25.0', '25.3', '25.5']

gold: The statistic shows the infant mortality rate in the Philippines from 2008 to 2018 . In 2018 , the infant mortality rate in the Philippines was at about 22.5 deaths per 1,000 live births .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality rate in Philippines from 2008 to 2018 .  In 2018 , the Infant mortality rate in Philippines was at about 22.5 Deaths per 1,000 live births .

Example 474:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the lowest infant mortality rate 2017
X_Axis['Country']: ['Monaco', 'Japan', 'Iceland', 'Singapore', 'Norway', 'Finland', 'Bermuda', 'Sweden', 'Czech_Republic', 'Hong_Kong', 'South_Korea', 'Macau', 'France', 'Spain', 'Anguilla', 'Italy', 'Luxembourg', 'Austria', 'Belgium', 'Germany']
Y_Axis['Child', 'deaths', 'in', 'the', 'first', 'year', 'of', 'life', 'per', '1,000', 'live', 'births']: ['1.8', '2.0', '2.1', '2.4', '2.5', '2.5', '2.5', '2.6', '2.6', '2.7', '3.0', '3.1', '3.2', '3.3', '3.3', '3.3', '3.4', '3.4', '3.4', '3.4']

gold: This statistic shows the 20 countries  with the lowest infant mortality rate in 2017 . Estimated 1.8 out of 1,000 live births died in the first year of life in Monaco in 2017 . Infant mortality Infant mortality rates are often used as an indicator of the health and well-being of a nation .
gold_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . Estimated templateYValue[min] out of 1,000 templateYLabel[7] templateYLabel[8] died in the templateYLabel[2] templateYLabel[3] of templateYLabel[4] in templateXValue[0] in templateTitleDate[0] . templateTitle[3] templateTitle[4] templateTitle[3] templateTitle[4] rates are often used as an indicator of the health and well-being of a nation .

generated_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , the templateXValue[0] in the templateTitle[1] of the templateTitle[2] templateTitle[3] templateTitle[4] in templateXValue[1] stood at templateYValue[1] .
generated: This statistic shows the 20 Countries lowest the infant mortality rate in 2017 .  In that year , the Monaco in the lowest of the infant mortality rate in Japan stood at 2.0 .

Example 475:
titleEntities: {'Subject': ['New York Knicks', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price New York Knicks ( NBA ) games 2015/16
X_Axis['Year']: ['2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['129.38', '129.38', '129.38', '123.22', '117.47', '88.66', '68.04', '70.51', '70.51', '70.51']

gold: This graph depicts the average ticket price for New York Knicks games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 70.51 U.S. dollars . With an average price of more than 120 U.S. dollars , tickets for New York Knicks games are the most expensive in the league .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] Knicks templateTitle[7] of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] . With an templateYLabel[0] templateYLabel[2] of more than 120 templateYLabel[3] templateYLabel[4] , tickets for templateTitleSubject[0] Knicks templateTitle[7] are the most expensive in the league .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for New York Knicks games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Average ticket price was 68.04 U.S. dollars .

Example 476:
titleEntities: {'Subject': ['Spotify'], 'Date': ['2020']}
title: Spotify : most streamed weekly tracks worldwide 2020
X_Axis['Songs']: ['Dance_Monkey_-_Tones_and_I', 'Yummy_-_Justin_Bieber', 'ROXANNE_-_Arizona_Zervas', 'The_Box_-_Roddy_Ricch', 'Tusa_-_KAROL_G', 'Blinding_Lights_-_The_Weeknd', 'Falling_-_Trevor_Daniel', "Don't_Start_Now_-_Dua_Lipa", 'Memories_-_Maroon_5', 'Circles_-_Post_Malone']
Y_Axis['Number', 'of', 'streams', 'in', 'millions']: ['50.24', '36.77', '33.55', '32.33', '29.52', '29.2', '28.25', '27.34', '27.01', '26.37']

gold: As of the week ending January 9 , 2020 , Tones and I 's 'Dance Monkey ' was the most-streamed track on Spotify with 50.24 million streams worldwide , followed by ‘ Yummy ' by Justin Bieber with 36.77 million . How do music artists get so many streams on Spotify ? Firstly , Spotify is one of the most successful and popular music streaming services in the United States , and as of the first half of 2018 had the biggest share of music streaming subscribers in the world . With Spotify 's vast audience , featuring on the platform is a good start for emerging and popular artists hoping to make an impact .
gold_template: As of the week ending 9 , templateTitleDate[0] , templateXValue[0] and templateXValue[0] 's 'Dance templateXValue[0] ' was the most-streamed track on templateTitleSubject[0] with templateYValue[max] templateScale templateYLabel[1] templateTitle[5] , followed by ‘ templateXValue[1] ' by templateXValue[1] with templateYValue[1] templateScale . How do music artists get so many templateYLabel[1] on templateTitleSubject[0] ? Firstly , templateTitleSubject[0] is one of the templateTitle[1] successful and popular music streaming services in the country , and as of the first half of 2018 had the biggest share of music streaming subscribers in the world . With templateTitleSubject[0] 's vast audience , featuring on the platform is a good templateXValue[7] for emerging and popular artists hoping to make an impact .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the United Kingdom ( templateTitleSubject[0] ) as of templateTitle[7] templateTitleDate[0] . According to the source , templateXValue[0] accounted for over templateYValue[max] templateScale of people templateYLabel[3] of people .
generated: This statistic shows the Spotify most streamed weekly the United Kingdom ( Spotify ) as of 2020 .  According to the source , Dance_Monkey_-_Tones_and_I accounted for over 50.24 millions of people millions of people .

Example 477:
titleEntities: {'Subject': ['Millennial U.S. July'], 'Date': ['2016', '2016']}
title: Profile of Millennial travelers in the U.S. as of July 2016
X_Axis['Profile', 'Type']: ['Cultural_interest', 'Exploration_hungry', 'Food_and_cuisine_driven_traveler', 'Wants_the_real_destination_not_commercial_tourism_product', 'Connected_during_leisure_travel_and_its_planning', 'Intensively_plans_leisure_travel', 'Nature_lovers', 'Activity_seekers', 'Price_sensitivity']
Y_Axis['Average', 'Traveler', 'Psychographic', 'Intensity', 'Index', 'score']: ['70.3', '69.5', '68.5', '65.2', '65.1', '60.5', '58.8', '56.9', '55.4']

gold: This statistic shows the profile of Millennial travelers in the United States as of July 2016 . Millennial travelers were characterized most by their cultural interest with an average Traveler Psychographic Intensity Index score of 70.3 in this category . Millennial travelers were the generation group whose activity choices were most influenced by what was posted on social media .
gold_template: This statistic shows the templateXLabel[0] of templateTitleSubject[0] templateTitle[2] in the templateTitle[3] as of templateTitleSubject[0] templateTitleDate[0] . templateTitleSubject[0] templateTitle[2] were characterized most by their templateXValue[0] with an templateYLabel[0] templateXValue[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of templateYValue[max] in this category . templateTitleSubject[0] templateTitle[2] were the generation group whose templateXValue[7] choices were most influenced by what was posted on social media .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] as of templateTitleSubject[0] templateTitleDate[0] . templateTitleSubject[0] 's templateYLabel[0] of templateTitleSubject[0] templateTitleDate[0] was around templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] in templateTitleSubject[0] .
generated: This statistic shows the Profile Millennial travelers U.S. Millennial U.S. July as of Millennial U.S. July 2016 .  Millennial U.S. July 's Average of Millennial U.S. July 2016 was around 70.3 % Index score in Millennial U.S. July .

Example 478:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2018']}
title: Mexico : religion affiliation share 2018 , by type
X_Axis['Response']: ['Catholic', 'Evangelist_(unspecified)', "Jehova's_witness", 'Pentecostal_evangelist', 'Adventist', 'Mormon', 'Baptist_evangelist', 'None', 'Believer_without_formal_affiliation_to_any_religion', 'Other', 'Do_not_know', 'Do_not_answer', 'Agnostic', 'Atheist']
Y_Axis['Share', 'of', 'respondents']: ['80.8', '1.3', '1.2', '0.8', '0.2', '0.2', '0.1', '10.6', '2.8', '0.8', '0.5', '0.3', '0.2', '0.2']

gold: Roman Catholic was the most common religion affiliation in Mexico in 2018 . In a survey carried out between July and August of 2018 , almost 81 percent of Mexican respondents claimed to be of catholic faith , whereas the second most chosen religion was evangelism , with only 1.3 percent of the people interviewed . More than 10 percent of respondents answered they did n't profess any religion and a total of 0.4 percent defined themselves either as atheists or agnostics .
gold_template: Roman templateXValue[0] was the most common templateXValue[8] in templateTitleSubject[0] in templateTitleDate[0] . In a survey carried out between and of templateTitleDate[0] , almost templateYValue[max] templateScale of Mexican templateYLabel[1] claimed to be of templateXValue[0] faith , whereas the second most chosen templateXValue[8] was evangelism , with only templateYValue[1] templateScale of the people interviewed . More than 10 templateScale of templateYLabel[1] answered they did n't profess templateXValue[8] and a total of 0.4 templateScale defined themselves either as atheists or agnostics .

generated_template: This statistic shows the distribution of adults in the templateTitle[4] in templateTitleDate[0] . According to the survey , templateYValue[max] templateScale of templateYLabel[1] said that they templateXValue[0] .
generated: This statistic shows the distribution of adults in the 2018 in .  According to the survey , 80.8 % of respondents said that they Catholic .

Example 479:
titleEntities: {'Subject': ['Venezuela'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Venezuela 2024
X_Axis['Year']: ['2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['-5', '-10', '-35', '-18', '-15.67', '-17.04', '-6.22', '-3.89', '1.34', '5.63', '4.18']

gold: The statistic shows the growth in real GDP in Venezuela from between 2011 to 2018 , with projections up until 2021 . In 2018 , Venezuela 's real gross domestic product decreased by 18 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from between templateXValue[min] to templateXValue[3] , with projections up until templateXValue[max] . In templateXValue[3] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templateNegativeTrend by 18 templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Venezuela from 2011 to 2015 , with projections up until 2021 .  In 2015 , Venezuela 's real Gross domestic product increased by around -6.22 % compared to the previous Year .

Example 480:
titleEntities: {'Subject': ['BNP Paribas'], 'Date': ['2003', '2018']}
title: Return on equity of BNP Paribas 2003 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Return', 'on', 'equity']: ['8.2', '8.9', '9.3', '8.3', '7.7', '6.1', '8.9', '8.8', '12.3', '10.8', '6.6', '19.6', '21.2', '20.2', '16.8', '15.3']

gold: The statistic shows the return on equity of BNP Paribas from 2003 to 2018 . In 2018 , the return on equity of the global banking group headquartered in Paris was equal to 8.2 percent .
gold_template: The statistic shows the templateYLabel[0] on templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] on templateYLabel[1] of the global banking group headquartered in Paris was equal to templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[7] of the templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of a templateYLabel[4] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Return equity of the 2018 of the BNP Paribas 2003 from to 2018 .  In 2018 , the Return equity of a equity of 21.2 equity .

Example 481:
titleEntities: {'Subject': ['Saudi Arabia'], 'Date': ['2015']}
title: Median age of the population in Saudi Arabia 2015
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Age', 'median', 'in', 'years']: ['40.2', '38.9', '37.6', '36.3', '35.0', '33.3', '31.8', '30.0', '26.0', '23.7', '21.3', '19.4', '19.5', '19.3', '18.4', '18.0', '18.0', '18.2', '18.5', '18.8', '19.0']

gold: This statistic shows the median age of the population in Saudi Arabia from 1950 to 2050 . The median age of a population is an index that divides the population into two equal groups : half of the population is older than the median age and the other half younger . In 2015 , the median age of Saudi Arabia 's population was 30 years .
gold_template: This statistic shows the templateYLabel[1] templateYLabel[0] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[1] templateYLabel[0] of a templateTitle[2] is an index that divides the templateTitle[2] into two equal groups : half of the templateTitle[2] is older than the templateYLabel[1] templateYLabel[0] and the other half younger . In templateXValue[7] , the templateYLabel[1] templateYLabel[0] of templateTitleSubject[0] 's templateTitle[2] was templateYValue[7] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .
generated: This statistic shows the Age median of the population in Saudi Arabia from 1950 to 2050 .  The Age median is the median that divides a population into two numerically equal groups ; that is , half the people are younger than this median and half are older .  It is a single index that summarizes the median distribution of a population .

Example 482:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['1970', '2018']}
title: Deep and surface mining coal production in the United Kingdom ( UK ) 1970 to 2018
Data:
                    0     1     2     3     4      5      6      7      8      9     10     11     12     13      14      15      16
Year             2018  2017  2016  2015  2014   2013   2012   2011   2010   2005   2000   1995   1990   1985    1980    1975    1970
Deep mined       7390  7520  8096  7674  9444   9563  12542  15633  16391  17347  17188  35150  72899  75289  112430  117412  136686
Surface mining  10426  9854  9509  8866  8635  10445  11993  12126  13148  14166  13412  16369  19863  18822   17667   11271   10509 

gold: The volume of coal produced from deep mining has fallen considerably since 1970 . In that year , 136.6 million metric tons was produced , but by 2018 this had fallen to just 7,390 metric tons . Production volumes since 2015 have been higher from surface mining than deep mining .
gold_template: The volume of templateTitle[3] produced from templateTitle[0] templateTitle[2] has fallen considerably since templateValue[0][last] . In that templateLabel[0][0] , 136.6 templateScale metric tons was produced , but by templateValue[0][0] this had fallen to just templateValue[1][0] metric tons . templateTitle[4] volumes since templateValue[0][3] have been higher from templateTitle[1] templateTitle[2] than templateTitle[0] templateTitle[2] .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[4] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitle[0] of the employees in templateTitleSubject[0] amounted to templateValue[1][2] templateScale compared to the previous templateLabel[0][0] .
generated: This statistic shows the Deep of the surface mining production in United Kingdom from 1970 to 2018 .  In 2018 , the Deep of the employees in United Kingdom amounted to 8096 % compared to the previous Year .

Example 483:
titleEntities: {'Subject': ['Chicago'], 'Date': ['2008', '2019']}
title: Average regular season home attendance of the Chicago Bears 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Average', 'attendance']: ['61916', '61815', '61142', '60368', '62035', '61681', '62358', '62329', '62145', '62195', '62250', '62034']

gold: This graph depicts the average regular season home attendance of the Chicago Bears from 2008 to 2019 . In 2019 , the average attendance at home games of the Chicago Bears was 61,916 .
gold_template: This graph depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitleSubject[0] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitleSubject[0] templateTitle[6] was templateYValue[idxmax(X)] .

generated_template: This graph depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitleSubject[0] was templateYValue[0] templateYValue[idxmax(X)]
generated: This graph depicts the Average regular season home attendance of the Chicago from 2008 to 2019 .  In 2019 , the Average attendance at home games of the Chicago was 61916 . 

Example 484:
titleEntities: {'Subject': ['IT IT FY2005-FY2016'], 'Date': []}
title: Share of IT budgets spent on IT security FY2005-FY2016
X_Axis['Fiscal', 'Year']: ['FY2005', 'FY2006', 'FY2007', 'FY2008', 'FY2009', 'FY2010', 'FY2011', 'FY2012', 'FY2013', 'FY2014', 'FY2015', 'FY2016', 'FY2017']
Y_Axis['Share', 'of', 'total', 'IT', 'budget']: ['7.5', '7.2', '7.5', '7.9', '9.1', '8.6', '8.8', '9.1', '9.9', '9.2', '10', '10.2', '10.6']

gold: The statistic shows the average share of IT budgets that is put towards IT security , from FY2005 to FY2017 . As of January 2018 , according to respondents , enterprises currently spend an average of 10.6 percent of their overall IT budgets on IT security . This figure has risen relatively steadily since 2005 .
gold_template: The statistic shows the average templateYLabel[0] of IT templateTitle[1] that is put towards IT templateTitle[3] , from templateXValue[0] to templateXValue[last] . As of 2018 , according to respondents , enterprises currently spend an average of templateYValue[max] templateScale of their overall IT templateTitle[1] on IT templateTitle[3] . This figure has risen relatively steadily since 2005 .

generated_template: As of 2019 , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was templateXValue[0] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] . This statistic gives over 70 templateScale of the employees who were using the templateTitle[2] in templateTitleSubject[0] .
generated: As of 2019 , the Share budgets spent in IT IT FY2005-FY2016 was FY2005 with a Share total of 10.6 budget .  This statistic gives over 70 % of the employees who were using the spent in IT IT FY2005-FY2016 .

Example 485:
titleEntities: {'Subject': ['PND'], 'Date': ['2005', '2015']}
title: Global PND and car navigation market size forecast 2005 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Market', 'size', 'in', 'thousand', 'units']: ['51420', '51600', '51510', '50485', '49361', '48306', '44449', '45502', '34905', '21103', '12963']

gold: The statistic illustrates a market size forecast of the worldwide portable navigation device ( PND ) and car navigation market from 2005 to 2015 . The global PND and car navigation market size is projected to be 51 million units in 2015 .
gold_template: The statistic illustrates a templateYLabel[0] templateYLabel[1] templateTitle[6] of the worldwide portable templateTitle[3] device ( templateTitleSubject[0] ) and templateTitle[2] templateTitle[3] templateYLabel[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitleSubject[0] and templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] is projected to be templateYValue[0] templateScale templateYLabel[3] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] at templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . According to the source , there were templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateTitle[4] in templateTitleSubject[0] in templateXValue[max] , up from templateYValue[1] in the previous templateXLabel[0] .
generated: This statistic shows the Market of thousand units at PND car navigation market 2005 to 2015 .  According to the source , there were 51420 thousand units market in PND 2015 , up from 51600 in the previous Year .

Example 486:
titleEntities: {'Subject': ['National Basketball Association'], 'Date': ['1946', '2020']}
title: National Basketball Association all-time rebounding leaders 1946 to 2020
X_Axis['NBA', 'player']: ['Wilt_Chamberlain', 'Bill_Russell', 'Kareem_Abdul-Jabbar', 'Elvin_Hayes', 'Moses_Malone', 'Tim_Duncan', 'Karl_Malone', 'Robert_Parish', 'Kevin_Garnett', 'Nate_Thurmond', 'Walt_Bellamy', 'Wes_Unseld', 'Hakeem_Olajuwon', 'Dwight_Howard', "Shaquille_O'Neal", 'Buck_Williams', 'Jerry_Lucas', 'Bob_Petit', 'Charles_Barkley', 'Dikembe_Mutombo', 'Paul_Silas', 'Charles_Oakley', 'Dennis_Rodman', 'Kevin_Willis', 'Patrick_Ewing', 'Dirk_Nowitzki', 'Elgin_Baylor', 'Pau_Gasol', 'Dolph_Schayes', 'Bill_Bridges']
Y_Axis['Number', 'of', 'rebounds']: ['23924', '21620', '17440', '16279', '16212', '15091', '14968', '14715', '14662', '14464', '14241', '13769', '13748', '13587', '13099', '13017', '12942', '12849', '12546', '12359', '12357', '12546', '11954', '11901', '11607', '11489', '11463', '11305', '11256', '11054']

gold: Wilt Chamberlain is the all-time rebound leader of the National Basketball Association with a total of 23,924 rebounds in his career . The highest ranked player still active in the NBA is Dwight Howard of the Washington Wizards , who has a total of 13,587 assists to his name . Basketball legends In basketball , a rebound is defined as a player either retrieving the ball after a missed field goal or free throw or tipping in a missed shot on an offensive play .
gold_template: templateXValue[0] is the templateTitle[3] rebound leader of the templateTitleSubject[0] Association with a total of templateYValue[max] templateYLabel[1] in his career . The highest ranked templateXLabel[1] still active in the templateXLabel[0] is templateXValue[13] of the Washington Wizards , who has a total of templateYValue[13] assists to his name . templateTitleSubject[0] legends In templateTitleSubject[0] , a rebound is defined as a templateXLabel[1] either retrieving the ball after a missed field goal or free throw or tipping in a missed shot on an offensive play .

generated_template: This statistic shows the distribution of templateYLabel[2] templateYLabel[3] of the templateTitle[7] templateTitle[8] templateTitle[9] in templateTitleSubject[0] as of templateTitleDate[0] . At that time , templateXValue[0] was the templateTitle[0] of America with a score of templateYValue[max] templateYLabel[2] templateYLabel[3] , followed by templateXValue[1] with templateYValue[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the distribution of rebounds the 2020 in National Basketball Association as of 1946 .  At that time , Wilt_Chamberlain was the National of America with a score of 23924 rebounds , followed by Bill_Russell with 21620 rebounds .

Example 487:
titleEntities: {'Subject': ['CVS Health'], 'Date': ['2005', '2018']}
title: CVS Health 's number of stores 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Number', 'of', 'stores']: ['9967', '9846', '9750', '9681', '7866', '7702', '7508', '7388', '7248', '7095', '6997', '6301', '6205', '5474']

gold: This statistic depicts CVS Health 's number of stores from 2005 to 2018 . The CVS Health Corporation is a U.S. drug retailing company , with three major segments : Pharmacy Services , Retail/LTC , and Corporate . CVS Health is headquartered in Woonsocket , Rhode Island .
gold_template: This statistic depicts templateTitleSubject[0] 's templateYLabel[0] of templateYLabel[1] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] Corporation is a U.S. drug retailing company , with three major segments : Pharmacy Services , Retail/LTC , and Corporate . templateTitleSubject[0] is headquartered in Woonsocket , Rhode Island .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[idxmax(X)] templateYLabel[1] in templateTitleSubject[0] .
generated: This statistic shows the Number of stores the CVS Health stores from 2005 to 2018 .  In 2018 , there were a total of 9967 stores in CVS Health .

Example 488:
titleEntities: {'Subject': ['Finland'], 'Date': ['2018']}
title: Density of the population of Finland in 2018 , by region
X_Axis['Finnish', 'Region']: ['Uusimaa', 'Southwest_Finland', 'Pirkanmaa', 'Päijät-Häme', 'Kymenlaakso', 'Kanta-Häme', 'Satakunta', 'South_Karelia', 'Ostrobothnia', 'Åland', 'Central_Finland', 'North_Savo', 'South_Ostrobothnia', 'Central_Ostrobothnia', 'North_Ostrobothnia', 'South_Savo', 'North_Karelia', 'Kainuu', 'Lapland']
Y_Axis['Number', 'of', 'inhabitants', 'per', 'square', 'kilometer']: ['183.7', '44.9', '40.9', '39.2', '33.7', '33.0', '28.0', '24.2', '23.3', '19.2', '16.5', '14.6', '14.1', '13.6', '11.2', '10.1', '9.1', '3.6', '1.9']

gold: The statistic shows the population density of Finland in 2018 , by region . The most densely populated region was Uusimaa with a density of approximately 184 inhabitants per square kilometer . Lapland was the most scarcely populated with roughly two inhabitants per square kilometer .
gold_template: The statistic shows the templateTitle[1] templateTitle[0] of templateXValue[1] in templateTitleDate[0] , templateTitle[4] templateXLabel[1] . The most densely populated templateXLabel[1] was templateXValue[0] with a templateTitle[0] of approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateXValue[last] was the most scarcely populated with roughly templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] average templateYLabel[0] of templateXValue[7] templateTitle[4] templateYLabel[1] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[min] to templateTitleDate[max] . The templateTitle[0] templateTitle[1] is defined as the trademark and changes within a ranking of one templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Density population average Number of South_Karelia by inhabitants in the United Kingdom ( Finland ) from 2018 to .  The Density population is defined as the trademark and changes within a ranking of one hundred square kilometer .

Example 489:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2012']}
title: Car rental in the U.S. - total cars in service 2002 to 2012
X_Axis['Year']: ['2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012']
Y_Axis['Cars', 'in', 'service', 'in', 'thousands']: ['1643', '1617', '1665', '1714', '1768', '1861', '1813', '1637', '1629', '1761', '1857']

gold: This statistic represents the US rental car industry 's total number of cars in service between 2002 and 2012 . In 2012 , there were about 1.86 million rental cars in service , while the industry 's revenue grew to around 23.6 billion US dollars .
gold_template: This statistic represents the US templateTitle[1] templateTitle[0] industry 's templateTitle[3] number of templateYLabel[0] in templateYLabel[1] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were about templateYValue[max] templateScale templateTitle[1] templateYLabel[0] in templateYLabel[1] , while the industry 's revenue templatePositiveTrend to around 23.6 templateScale US dollars .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of a templateTitle[2] in templateTitleSubject[0] was templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Cars service of the total cars of U.S. from 2002 to 2012 .  In 2012 , the average Cars service of a U.S. in was 1861 thousands .

Example 490:
titleEntities: {'Subject': ['Rice'], 'Date': ['2011', '2018']}
title: Rice export prices worldwide 2011 - 2018 , by variety
Data:
                                     0     1     2     3     4     5     6     7
Year                              2018  2017  2016  2015  2014  2013  2012  2011
U.S. Long Grain 2.4                531   456   438   490   571   628   567   577
U.S. California Medium Grain 1.4   888   673   651   857  1007   692   718   821
Thai Parboiled 100                 365   421   410   392   435   530   594   563
India 25                           374   361   333   337   377   402   391   409
Pak Basmati                       1023  1131   795   849  1324  1372  1137  1060 

gold: This statistic shows rice export prices worldwide from 2011 to 2018 , by selected varieties . In 2016 , one metric ton of U.S. California Medium Grain rice had an average export price of 651 U.S. dollars .
gold_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] , templateTitle[6] selected varieties . In templateValue[0][2] , one metric ton of templateLabel[1][0] templateLabel[2][1] templateLabel[2][2] templateLabel[1][2] templateTitleSubject[0] had an average templateTitle[1] price of templateValue[2][2] templateLabel[1][0] dollars .

generated_template: The statistic shows the templateTitle[0] of templateTitleSubject[0] employed templateTitle[3] templateTitle[4] from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . The templateTitle[0] of templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] generated approximately 3.3 templateScale templateYLabel[3] in templateValue[0][0] .
generated: The statistic shows the Rice of employed worldwide 2011 from to 2018 , variety .  The Rice of prices Rice generated approximately 3.3 billion in 2018 .

Example 491:
titleEntities: {'Subject': ['Ghana'], 'Date': ['2019']}
title: Youth unemployment rate in Ghana in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['13.69', '13.7', '13.7', '14.07', '14.1', '13.51', '13.37', '12.55', '12.17', '11.15', '10.83', '9.53', '9.35', '9.31', '10.63', '11.89', '13.33', '14.52', '15.14', '16.26', '15.83']

gold: The statistic shows the youth unemployment rate in Ghana from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Ghana was at 13.69 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Youth unemployment rate in Ghana from 1999 and 2019 .  According to the source , the data are ILO estimates .  In 2019 , the estimated Youth unemployment rate in Ghana was at 13.69 % .

Example 492:
titleEntities: {'Subject': ['France'], 'Date': ['2019']}
title: Grocery market share in France 2019
X_Axis['Grocery', 'Stores']: ['E._Leclerc_Group', 'Carrefour_Group', 'Intermarché_Group', 'Système_U_Group', 'Casino_Group', 'Auchan_Group', 'Lidl_Group', 'Delhaize_Group', 'Aldi_Group', 'Other']
Y_Axis['Market', 'share', 'of', 'total', 'grocers']: ['21.6', '19.8', '15.3', '10.7', '10.6', '10', '6.2', '2.8', '2.3', '0.7']

gold: This statistic shows the market share of grocery stores in France for the 12 weeks ending December 17 , 2019 . E. Leclerc holds the largest market share with 21.6 percent , followed by Carrefour with a 19.8 percent share . The grocery retail landscape in France has been dominated by the Leclerc Group and Carrefour group , who have been fighting neck in neck for the lead position .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateXLabel[0] templateXLabel[1] in templateTitleSubject[0] for the 12 weeks ending 17 , templateTitleDate[0] . templateXValue[0] holds the largest templateYLabel[0] templateYLabel[1] with templateYValue[max] templateScale , followed by templateXValue[1] with a templateYValue[1] templateScale templateYLabel[1] . The templateXLabel[0] retail landscape in templateTitleSubject[0] has been dominated by the templateXValue[0] and templateXValue[1] templateXValue[0] , who have been fighting neck in neck for the lead position .

generated_template: This statistic illustrates the templateTitle[0] templateTitle[1] templateYLabel[0] of the United Kingdom ( templateTitleSubject[0] ) . The templateTitle[0] templateTitle[1] templateTitle[2] in the United Kingdom was templateXValue[0] , with over templateYValue[max] templateScale of the templateYLabel[1] , followed by templateXValue[1] with templateYValue[1] templateScale of the templateYLabel[1] .
generated: This statistic illustrates the Grocery market of the United Kingdom ( France ) .  The Grocery market share in the United Kingdom was E._Leclerc_Group , with over 21.6 % of the share , followed by Carrefour_Group with 19.8 % of the share .

Example 493:
titleEntities: {'Subject': ['NASCAR'], 'Date': ['2017']}
title: Racing teams of NASCAR ranked by revenue 2017
X_Axis['Teams']: ['Hendrick_Motorsports', 'Richard_Childress_Racing', 'Joe_Gibbs_Racing', 'Stewart-Haas_Racing', 'Roush_Fenway_Racing', 'Team_Penske', 'Chip_Ganassi_Racing', 'Front_Row_Motorsports']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['180', '145', '131', '109', '97', '63', '49', '19']

gold: The statistic ranks the racing teams of NASCAR according to revenue in 2017 . Hendrick Motorsports generated 180 million U.S. dollars in revenue in 2017 .
gold_template: The statistic ranks the templateXValue[1] templateXLabel[0] of templateTitleSubject[0] according to templateYLabel[0] in templateTitleDate[0] . templateXValue[0] generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] in templateTitleDate[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] in the templateTitle[5] in templateTitleDate[0] . The templateXValue[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .
generated: The statistic shows the Racing teams NASCAR ranked by Revenue in the revenue in 2017 .  The Hendrick_Motorsports amounted to 180 million U.S. dollars in the Hendrick_Motorsports season .

Example 494:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Most popular social media apps in the U.S. 2019 , by audience
X_Axis['Platform']: ['Facebook_(main)', 'Instagram_(main)', 'Facebook_Messenger', 'Twitter_(main)', 'Pinterest', 'Reddit', 'Snapchat', 'WhatsApp', 'Messenger_by_Google', 'Tumblr', 'Discord_-_Chat_for_Games', 'Google_Hangouts_(main)', 'GroupMe', 'Kik', 'TikTok_(main)', 'Skype_(main)', 'TikTok', 'Telegram', 'LINE', 'Google+', 'WeChat']
Y_Axis['Million', 'unique', 'visitors']: ['169.76', '121.23', '106.4', '81.47', '66.88', '47.87', '45.98', '25.58', '17.46', '16.74', '15.01', '14.19', '10.75', '7.72', '6.24', '5.53', '3.76', '3.47', '3.15', '2.62', '1.48']

gold: Despite a tumultuous 2018 , Facebook is still the top social network in the United States , with 169.76 million mobile users accessing the Facebook app in September 2019 . The company 's other properties Instagram and Facebook Messenger ranked second and third with 121 and 106 million users respectively . Cambridge Analytica scandal 2018 The biggest social media company in the world had a difficult 2018 .
gold_template: Despite a tumultuous 2018 , templateXValue[0] is still the top templateTitle[2] network in the templateTitle[5] , with templateYValue[max] templateScale mobile users accessing the templateXValue[0] app in 2019 . The company 's other properties templateXValue[1] and templateXValue[0] templateXValue[2] ranked second and third with templateYValue[1] and templateYValue[2] templateScale users respectively . Cambridge Analytica scandal 2018 The biggest templateTitle[2] templateTitle[3] company in the world had a difficult 2018 .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[7] templateTitle[8] in templateTitleDate[0] , based on templateTitle[7] templateTitle[8] . In templateXValue[0] was ranked first with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Most popular social media apps U.S. by audience in 2019 , based on by audience .  In Facebook_(main) was ranked first with a Million unique of 169.76 visitors .

Example 495:
titleEntities: {'Subject': ['Germany'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Germany 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['1.17', '1.24', '1.34', '1.44', '1.25', '0.54', '1.52', '2.47', '2.23', '1.74', '2.22']

gold: In 2018 , Germany 's real gross domestic product grew by around 1.52 percent compared to the previous year . By 2024 , the German GDP is expected to increase by 1.17 percent compared to the previous year . Keeping it real Real gross domestic product is , by definition , a measure of the value of economic output adjusted for inflation .
gold_template: In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . By templateXValue[max] , the German templateYLabel[0] is expected to templatePositiveTrend by templateYValue[idxmax(X)] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . Keeping it real Real templateTitle[0] templateTitle[1] templateTitle[2] is , by definition , a measure of the value of economic output adjusted for inflation .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitleSubject[0] ' real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in the Germany from 2014 to 2018 , with projections up until 2024 .  In 2018 , the Germany ' real Gross domestic product increased by around 1.52 % compared to the previous Year .

Example 496:
titleEntities: {'Subject': ['PRH', 'Hong Kong'], 'Date': ['2007', '2019']}
title: Average living space of PRH tenants Hong Kong 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2014', '2012', '2009', '2007']
Y_Axis['Average', 'living', 'space', 'in', 'square', 'meters', 'per', 'person']: ['13.3', '13.3', '13.2', '13.0', '12.9', '12.5', '12.2']

gold: This statistic illustrates the average living space of public rental housing tenants in Hong Kong in selected years from 2007 to 2019 . As of March 2019 , the average living space in public rental housing in Hong Kong was around 13.3 square meters per person .
gold_template: This statistic illustrates the templateYLabel[0] templateYLabel[1] templateYLabel[2] of public rental housing templateTitle[4] in templateTitleSubject[1] in selected years from templateXValue[min] to templateXValue[max] . As of templateXValue[idxmax(Y)] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in public rental housing in templateTitleSubject[1] was around templateYValue[max] templateYLabel[3] templateYValue[idxmax(X)] templateYLabel[5] templateYLabel[6] .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] of templateTitle[1] templateTitleSubject[0] reached templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateScale templateYLabel[3] in the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] of the highest in templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] .
generated: In 2019 , the Average living of the square of living PRH reached 13.3 million square meters , up from 13.3 million square in the previous Year .  The Average living of the highest in PRH was 13.3 square meters in 2019 .

Example 497:
titleEntities: {'Subject': ['Oil'], 'Date': []}
title: Oil spills - worldwide
X_Axis['Oil', 'Spill', 'Event']: ['Itox_Uno_(Gulf_of_Mexico_1979_oil_rig)', 'Gulf_War_(Persian_Gulf_1991_oil_platform)', 'Amoco_Cadiz_(Atlantic_1978_tanker)', 'Pipeline_(Komi_1994_pipeline)_', 'Sea_Empress_(Atlantic_1996_tanker)', 'Torrey_Canyon_(Atlantic_1967_tanker)', 'Urquijo_(Atlantic_1976_tanker)', 'Prestige_(Atlantic_2002_tanker)_', 'Aegean_Sea_(Atlantic_1992_tanker)', 'Andros_Patria_(Atlantic_1978_tanker)', 'Exxon_Valdez_(Pacific_1989_tanker)', 'Erika_(Atlantic_1999_tanker)']
Y_Axis['Amount', 'of', 'leaked', 'oil', '(in', 'tons)']: ['1000000', '1000000', '230000', '200000', '147000', '120000', '100000', '77000', '70000', '50000', '45000', '20000']

gold: The statistic depicts the world 's worst oil spills based on the amount of leaked oil from 1967 to 2002 . During the Prestige oil spill in 2002 , 77.000 tons of oil leaked into the Atlantic .
gold_template: The statistic depicts the world 's worst templateXValue[0] templateTitle[1] based on the templateYLabel[0] of templateYLabel[1] templateXValue[0] from templateXValue[5] to templateXValue[7] . During the templateXValue[7] templateXValue[0] templateXLabel[1] in templateXValue[7] , 77.000 tons of templateXValue[0] templateYLabel[1] into the Atlantic .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of the templateTitle[2] in the templateTitleSubject[0] in templateTitleDate[0] . The templateTitle[0] templateYLabel[0] of templateXValue[2] was templateYValue[2] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Oil Amount of the worldwide in the Oil in .  The Oil Amount of Amoco_Cadiz_(Atlantic_1978_tanker) was 230000 oil (in .

Example 498:
titleEntities: {'Subject': ['National Basketball Association'], 'Date': ['2020', '2020']}
title: Value of National Basketball Association franchises 2020
X_Axis['Nba', 'Team']: ['New_York_Knicks', 'Los_Angeles_Lakers', 'Golden_State_Warriors', 'Chicago_Bulls', 'Boston_Celtics', 'Los_Angeles_Clippers', 'Brooklyn_Nets', 'Houston_Rockets', 'Dallas_Mavericks', 'Toronto_Raptors', 'Philadelphia_76ers', 'Miami_Heat', 'Portland_Trail_Blazers', 'San_Antonio_Spurs', 'Sacramento_Kings', 'Washington_Wizards', 'Phoenix_Suns', 'Denver_Nuggets', 'Milwaukee_Bucks', 'Oklahoma_City_Thunder', 'Utah_Jazz', 'Indiana_Pacers', 'Atlanta_Hawks', 'Cleveland_Cavaliers', 'Charlotte_Hornets', 'Detroit_Pistons', 'Orlando_Magic', 'Minnesota_Timberwolves', 'New_Orleans_Pelicans', 'Memphis_Grizzlies']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['4600', '4400', '4300', '3200', '3100', '2600', '2500', '2475', '2400', '2100', '2000', '1950', '1850', '1800', '1775', '1750', '1625', '1600', '1580', '1575', '1550', '1525', '1520', '1510', '1500', '1450', '1430', '1375', '1350', '1300']

gold: The statistic depicts the franchise value of all National Basketball Association teams in 2020 . The New York Knicks franchise was valued at 4.6 billion U.S. dollars in 2020 . NBA franchise value – additional information For the 2018/19 season , all NBA teams generated combined revenues of almost 8.8 billion U.S. dollars – regular season ticketing accounted for over 22 percent of the total .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of all templateTitleSubject[0] Association teams in templateTitleDate[0] . The templateXValue[0] Knicks templateYLabel[0] was valued at templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateTitleDate[0] . templateXLabel[0] templateYLabel[0] templateYLabel[1] – additional information For the 2018/19 season , all templateXLabel[0] teams generated combined revenues of almost 8.8 templateScale templateYLabel[3] templateYLabel[4] – regular season ticketing accounted for over 22 templateScale of the total .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of all National Basketball Association in templateTitleDate[0] . The templateXValue[0] had an estimated templateYLabel[0] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in the National Basketball Association .
generated: This graph depicts the Franchise value of all National Basketball Association in 2020 .  The New_York_Knicks had an estimated Franchise of 4600 million U.S. dollars in the National Basketball Association .

Example 499:
titleEntities: {'Subject': ['Boston Bruins'], 'Date': ['2005', '2019']}
title: Boston Bruins ' revenue 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['228', '191', '176', '169', '158', '164', '114', '129', '125', '110', '108', '97', '87', '86']

gold: The statistic shows the revenue of the Boston Bruins from the 2005/06 season to the 2018/19 season . The revenue of the Boston Bruins amounted to 228 million U.S. dollars in the 2018/19 season .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .
generated: This graph depicts the annual National Hockey League Revenue of the Boston Bruins from the 2005/06 season to the 2018/19 season .  The Revenue of the Boston Bruins amounted to 228 million U.S. dollars in the 2018/19 season .

Example 500:
titleEntities: {'Subject': ['Chile'], 'Date': ['2018']}
title: Urbanization in Chile 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['87.56', '87.49', '87.42', '87.36', '87.3', '87.25', '87.19', '87.13', '87.07', '87.02', '86.96']

gold: This statistic shows the degree of urbanization in Chile from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 87.56 percent of Chile 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Chile from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 87.56 % of Chile 's total population lived in urban areas and cities .

Example 501:
titleEntities: {'Subject': ['Walt Disney Company'], 'Date': ['2010', '2019']}
title: Operating income of the Walt Disney Company 2010 - 2019 , by region
Data:
                              0             1       2                         3
Region  Latin_America_and_other  Asia_Pacific  Europe  United_States_and_Canada
2010                        217           620    1275                      5474
2011                        293           627    1517                      6388
2012                        446           835    1692                      6991
2013                        476          1016    1361                      7871
2014                        488          1342    1581                      9594
2015                        532          1365    1964                     10820
2016                        443          1324    1815                     12139
2017                        375          1326    1812                     10962
2018                        502          1869    1922                     11413
2019                        237          2167    2433                     10031 

gold: The Walt Disney Company 's worldwide operating income amounted to 14.86 billion U.S. dollars in 2019 , of which 2.43 billion U.S. dollars were derived from operations in Europe . Operating income from Latin America and other areas came to 237 million U.S. dollars that year .
gold_template: The templateTitleSubject[0] Company 's worldwide templateTitle[0] templateTitle[1] amounted to 14.86 templateScale U.S. dollars in templateTitleDate[max] , of which 2.43 templateScale U.S. dollars were derived from operations in templateValue[0][2] . templateTitle[0] templateTitle[1] from templateValue[0][0] and templateValue[0][0] areas came to templateValue[10][min] templateScale U.S. dollars that year .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the leading templateXLabel[0] templateTitle[5] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateXValue[0] ranked first with a templateTitle[0] templateTitle[1] templateTitle[2] of templateYValue[max] templateScale U.S. dollars .
generated: This statistic shows the Operating income Walt Disney the leading 2010 in the United Kingdom ( Walt Disney Company ) from 2010 to 2019 .  In 2019 , the ranked first with a Operating income Walt of million U.S. dollars .

Example 502:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2024']}
title: Ratio of government expenditure to gross domestic product ( GDP ) in Mexico 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'government', 'expenditure', 'to', 'GDP']: ['25.13', '24.93', '24.87', '24.67', '24.9', '25.66', '25.67', '25.75', '27.37', '27.53', '27.97']

gold: The statistic shows the ratio of government expenditure to the gross domestic product ( GDP ) in Mexico from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in Mexico amounted to 25.67 percent of the gross domestic product .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to the templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to templateYValue[6] templateScale of the templateTitle[3] templateTitle[4] templateTitle[5] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to the templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[min] templateScale of the templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to the gross domestic product ( GDP ) in Mexico from 2014 to 2018 , with projections up until 2024 .  In 2018 , government expenditure in Mexico amounted to about 24.67 % of the gross domestic product .

Example 503:
titleEntities: {'Subject': ['Zalando'], 'Date': ['2018']}
title: Annual net income/loss of Zalando 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Net', 'income/loss', 'in', 'million', 'euros']: ['51.2', '101.6', '120.5', '121.5', '47.1', '-116.6', '-85.07', '-59.74', '-23.1']

gold: Berlin-based online fashion retailer Zalando is one of the fastest-growing companies not just in Germany , but in Europe . While its active customer base expands with each year , Zalando 's annual net income suffered a major blow in the financial year 2018 , going from 101.6 million to 51.2 million euros between 2017 and 2018 . A great turnaround in 2014 Zalando delivered its sunniest results in 2015 , when its net income equaled 121.5 million euros as displayed in this chart .
gold_template: Berlin-based online fashion retailer templateTitleSubject[0] is one of the fastest-growing companies not just in Germany , but in Europe . While its active customer base expands with each templateXLabel[0] , templateTitleSubject[0] 's templateTitle[0] templateYLabel[0] income suffered a major blow in the financial templateXLabel[0] templateXValue[max] , going from templateYValue[1] templateScale to templateYValue[idxmax(X)] templateScale templateYLabel[3] between templateXValue[1] and templateXValue[max] . A great turnaround in templateXValue[4] templateTitleSubject[0] delivered its sunniest results in templateXValue[3] , when its templateYLabel[0] income equaled templateYValue[max] templateScale templateYLabel[3] as displayed in this chart .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the development of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company employed approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net income/loss of the development of Zalando from 2010 to 2018 .  In 2018 , the company employed approximately 51.2 million euros .

Example 504:
titleEntities: {'Subject': ['Miami Heat'], 'Date': ['2001', '2019']}
title: Miami Heat 's revenue 2001 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['294', '259', '253', '210', '180', '188', '188', '150', '158', '124', '126', '131', '131', '132', '119', '93', '91', '96']

gold: The statistic shows the revenue of the Miami Heat franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 294 million U.S. dollars . Miami Heat The Miami Heat is a professional basketball team of the National Basketball Association ( NBA ) .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the templateXValue[last] season to the templateXValue[0] season . In templateXValue[0] , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] The templateTitleSubject[0] is a professional basketball team of the National Basketball Association ( NBA ) .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the templateXValue[last] season to the templateXValue[0] season . In templateXValue[0] , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Miami Heat franchise from the 2001/02 season to the 2018/19 season .  In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 294 million U.S. dollars .

Example 505:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2020']}
title: U.S. - average salary for graduates , by discipline 2020
X_Axis['Discipline']: ['Computer_Science', 'Math_&_Sciences', 'Engineering', 'Business']
Y_Axis['Average', 'salary', 'in', 'U.S.', 'dollars']: ['79793', '79717', '77298', '75197']

gold: In 2020 , it was found that people in the United States with a Master 's degree in Computer Science had the highest average starting salary , at 79,793 U.S. dollars . People who held a Master 's degree in Math and Sciences had the second highest starting salary , at 79,717 U.S. dollars . An abundance of Masters As higher education in the United States has become more common , and even expected , the number of Master 's degrees awarded has increased .
gold_template: In templateTitleDate[0] , it was found that people in the templateTitle[0] with a Master 's degree in templateXValue[0] had the highest templateYLabel[0] starting templateYLabel[1] , at templateYValue[max] templateYLabel[2] templateYLabel[3] . People who held a Master 's degree in templateXValue[1] and templateXValue[1] had the second highest starting templateYLabel[1] , at templateYValue[1] templateYLabel[2] templateYLabel[3] . An abundance of Masters As higher education in the templateTitle[0] has become more common , and even expected , the number of Master 's degrees awarded has templatePositiveTrend .

generated_template: This statistic shows the projected templateYLabel[0] starting salaries templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[0] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] had the largest templateTitle[0] of templateYValue[max] templateScale of all templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the projected Average starting salaries for graduates by discipline 2020 in the U.S. in 2020 .  In 2020 , Computer_Science had the largest U.S. of 79793 % of all U.S. dollars .

Example 506:
titleEntities: {'Subject': ['Countries'], 'Date': ['2019']}
title: Countries with the lowest inflation rate 2019
X_Axis['Country']: ['Eritrea', 'Guinea-Bissau', 'United_Arab_Emirates', 'Niger', 'Saudi_Arabia', 'Qatar', 'Benin', 'Iraq', 'Puerto_Rico', 'Panama', 'Brunei_Darussalam', 'Mali', 'Solomon_Islands', 'Ecuador', 'Korea', 'Switzerland', 'Marshall_Islands', 'St._Kitts_and_Nevis', 'Greece', 'Morocco']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['-27.6', '-2.58', '-1.5', '-1.3', '-1.05', '-0.36', '-0.28', '-0.27', '-0.06', '-0.01', '0.07', '0.17', '0.35', '0.38', '0.46', '0.57', '0.57', '0.61', '0.63', '0.65']

gold: The statistic lists the 20 countries with the lowest inflation rate in 2019 . In 2019 , Eritrea ranked 1st with a negative inflation rate of about 27.6 percent compared to the previous year . Inflation rates and the financial crisis Due to relatively stagnant worker wages as well as a hesitation from banks to so easily distribute loans to the ordinary citizen , inflation has remained considerably low .
gold_template: The statistic lists the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] ranked 1st templateTitle[1] a negative templateYLabel[0] templateYLabel[1] of about 27.6 templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] . templateYLabel[0] rates and the financial crisis Due to relatively stagnant worker wages as well as a hesitation from banks to so easily distribute loans to the ordinary citizen , templateYLabel[0] has remained considerably low .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] of templateTitleDate[0] , templateTitle[5] templateTitle[6] . In templateTitleDate[0] , the templateXValue[0] had an templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Inflation rate in the Countries of 2019 , .  In 2019 , the Eritrea had an Inflation rate of 0.65 % compared to the previous year .

Example 507:
titleEntities: {'Subject': ['Forecast'], 'Date': ['2015', '2020']}
title: Forecast of nickel consumption worldwide 2015 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Consumption', 'in', 'thousand', 'metric', 'tons']: ['2376', '2288', '2199', '2117', '2050', '1963']

gold: This statistic shows a forecast of the volume of nickel consumption globally from 2015 to 2020 . By 2020 , it is predicted that nickel consumption will reach nearly 2.4 million metric tons worldwide . Nickel is most commonly used for making steels , nonferrous alloys , and electroplating .
gold_template: This statistic shows a templateTitleSubject[0] of the volume of templateTitle[1] templateYLabel[0] globally from templateXValue[min] to templateXValue[max] . By templateXValue[max] , it is predicted that templateTitle[1] templateYLabel[0] will reach nearly templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] templateTitle[3] . templateTitle[1] is most commonly used for making steels , nonferrous alloys , and electroplating .

generated_template: This statistic shows the total annual templateYLabel[0] of the templateTitle[1] templateYLabel[2] templateYLabel[3] of templateTitle[1] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateYLabel[0] of the templateTitle[1] templateTitle[2] stood at templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the total annual Consumption of the nickel metric tons of nickel in the Forecast from 2015 to 2020 .  In 2019 , the Consumption of the nickel consumption stood at 2288 thousand metric tons .

Example 508:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Population of metropolitan areas in the U.S. 2018
X_Axis['Metropolitan', 'Areas']: ['New_York-Newark-Jersey_City_NY-NJ-PA', 'Los_Angeles-Long_Beach-Anaheim_CA', 'Chicago-Naperville-Elgin_IL-IN-WI', 'Dallas-Fort_Worth-Arlington_TX', 'Houston-The_Woodlands-Sugar_Land_TX', 'Washington-Arlington-Alexandria_DC-VA-MD-WV', 'Miami-Fort_Lauderdale-West_Palm_Beach_FL', 'Philadelphia-Camden-Wilmington_PA-NJ-DE-MD', 'Atlanta-Sandy_Springs-Roswell_GA', 'Boston-Cambridge-Newton_MA-NH', 'Phoenix-Mesa-Scottsdale_AZ', 'San_Francisco-Oakland-Hayward_CA', 'Riverside-San_Bernardino-Ontario_CA', 'Detroit-Warren-Dearborn_MI', 'Seattle-Tacoma-Bellevue_WA', 'Minneapolis-St._Paul-Bloomington_MN-WI', 'San_Diego-Carlsbad_CA', 'Tampa-St._Petersburg-Clearwater_FL', 'Denver-Aurora-Lakewood_CO', 'St._Louis_MO-IL', 'Baltimore-Columbia-Towson_MD', 'Orlando-Kissimmee-Sanford_FL', 'Charlotte-Concord-Gastonia_NC-SC', 'San_Antonio-New_Braunfels_TX', 'Portland-Vancouver-Hillsboro_OR-WA', 'Sacramento--Roseville--Arden-Arcade_CA', 'Pittsburgh_PA', 'Las_Vegas-Henderson-Paradise_NV', 'Cincinnati_OH-KY-IN', 'Austin-Round_Rock_TX', 'Kansas_City_MO-KS', 'Columbus_OH', 'Cleveland-Elyria_OH', 'Indianapolis-Carmel-Anderson_IN', 'San_Jose-Sunnyvale-Santa_Clara_CA', 'Nashville-Davidson--Murfreesboro--Franklin_TN', 'Virginia_Beach-Norfolk-Newport_News_VA-NC', 'Providence-Warwick_RI-MA', 'Milwaukee-Waukesha-West_Allis_WI', 'Jacksonville_FL', 'Oklahoma_City_OK', 'Raleigh_NC', 'Memphis_TN-MS-AR', 'Richmond_VA', 'Louisville/Jefferson_County_KY-IN', 'New_Orleans-Metairie_LA', 'Salt_Lake_City_UT', 'Hartford-West_Hartford-East_Hartford_CT', 'Birmingham-Hoover_AL', 'Buffalo-Cheektowaga-Niagara_Falls_NY']
Y_Axis['Number', 'of', 'residents', 'in', 'thousands']: ['19979.48', '13291.49', '9498.72', '7539.71', '6997.38', '6249.95', '6198.78', '6096.37', '5949.95', '4875.39', '4857.96', '4729.48', '4622.36', '4326.44', '3939.36', '3629.19', '3343.36', '3142.66', '2932.42', '2805.47', '2802.79', '2572.96', '2569.21', '2518.04', '2478.81', '2345.21', '2324.74', '2231.65', '2190.21', '2168.32', '2143.65', '2106.54', '2057.01', '2048.7', '1999.11', '1930.96', '1728.73', '1621.34', '1576.11', '1534.7', '1396.45', '1362.54', '1350.62', '1306.17', '1297.3', '1270.4', '1222.54', '1206.3', '1151.8', '1130.15']

gold: With 19.97 million residents , the New York-Newark-Jersey City metropolitan area was the most populous in the United States in 2018 . The Los Angeles , Chicago , Dallas , and Houston metropolitan areas rounded out the top five in the U.S. What is a metropolitan area ? It may seem strange that a metropolitan area can contain cities and areas in different states , such as the New York-Newark-Jersey City metro area , but there is a specific reason as to why that is : According to the U.S. Census Bureau , a metropolitan statistical area is either one or more counties that has a city in it with a population of 50,000 inhabitants or more , or is an area defined by the Census with a population of 100,000 or more . Metropolitan areas are particularly helpful in tracking demographic change over time in large communities and allow officials to see where the largest pockets of inhabitants are in the country .
gold_template: With 19.97 templateScale templateYLabel[1] , the templateXValue[0] City templateXLabel[0] area was the most populous in the templateTitle[3] in templateTitleDate[0] . The templateXValue[1] Angeles , Chicago , Dallas , and Houston templateXLabel[0] templateXLabel[1] rounded out the top five in the templateTitleSubject[0] What is a templateXLabel[0] area ? It may seem strange that a templateXLabel[0] area can contain cities and templateXLabel[1] in different states , such as the templateXValue[0] City metro area , but there is a specific reason as to why that is : According to the templateTitleSubject[0] Census Bureau , a templateXLabel[0] statistical area is either one or more counties that has a templateXValue[0] in it with a templateTitle[0] of 50,000 inhabitants or more , or is an area defined by the Census with a templateTitle[0] of 100,000 or more . templateXLabel[0] templateXLabel[1] are particularly helpful in tracking demographic change over time in large communities and allow officials to see where the largest pockets of inhabitants are in the country .

generated_template: This statistic shows the templateXValue[0] templateTitle[4] of the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] in templateTitleDate[0] , based on templateYLabel[0] templateYLabel[1] . The templateYLabel[0] templateTitle[2] templateNegativeTrend by far the most templateYLabel[1] in that year , with templateYValue[2] templateScale compared to the previous year .
generated: This statistic shows the New_York-Newark-Jersey_City_NY-NJ-PA 2018 of the Population metropolitan areas in the U.S. in 2018 , based on Number residents .  The Number areas dropped by far the most residents in that year , with 9498.72 thousands compared to the previous year .

Example 509:
titleEntities: {'Subject': ['Premier League'], 'Date': ['2010', '2019']}
title: Premier League total broadcasting payments to clubs 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Broadcasting', 'payments', 'in', 'million', 'GBP']: ['2456.01', '2419.6', '2398.5', '1633.9', '1605.3', '1563.0', '1061.0', '1055.0', '953.0']

gold: The statistic depicts the broadcasting payments to Premier League clubs from 2010/11 to 2018/19 . In the 2018/19 season , all Premier League clubs combined received a total of 2.46 billion British Pounds in broadcasting payments .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] to templateTitleSubject[0] clubs from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , all templateTitleSubject[0] clubs combined received a templateTitle[2] of templateYValue[max] templateScale British Pounds in templateYLabel[0] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] of the templateYLabel[2] templateTitle[4] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , the templateTitle[0] templateYLabel[0] of the templateTitleSubject[0] was templateYValue[last] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Broadcasting of the Premier of the million payments Premier League from 2010/11 to 2018/19 .  In the 2018/19 season , the Premier Broadcasting of the Premier League was 953.0 million GBP .

Example 510:
titleEntities: {'Subject': ['DDoS'], 'Date': ['2017']}
title: Leading industries targeted by DDoS attack traffic 2017
X_Axis['Industry']: ['Gaming', 'Internet_and_telecom', 'Financial_services', 'Software_and_technology', 'Education', 'Retail_and_consumer_goods', 'Media_and_entertainment', 'Other']
Y_Axis['Percentage', 'of', 'attack', 'traffic']: ['79', '6', '4', '4', '3', '3', '1', '1']

gold: The statistic shows the percentage of worldwide denial of service attack traffic in the fourth quarter of 2017 , sorted by industry . During that period of time , 79 percent of DDoS attack traffic were directed towards the gaming industry . Internet and telecom ranked second with six percent of DDoS attack traffic .
gold_template: The statistic shows the templateScale of worldwide denial of service templateYLabel[1] templateYLabel[2] in the fourth quarter of templateTitleDate[0] , sorted templateTitle[3] templateXLabel[0] . During that period of time , templateYValue[max] templateScale of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] were directed towards the templateXValue[0] templateXLabel[0] . templateXValue[1] and templateXValue[1] ranked second with templateYValue[1] templateScale of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateScale of templateYLabel[1] templateYLabel[2] templateYLabel[3] between 2017 and 2018 , sorted by templateXLabel[0] . During the measured period , it was found that templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] templateYLabel[3] had between IP addresses in the templateXValue[0] .
generated: This statistic shows the percentage of attack traffic between 2017 and 2018 , sorted by Industry .  During the measured period , it was found that 79 percentage of attack traffic had between IP addresses in the Gaming .

Example 511:
titleEntities: {'Subject': ['Miami Heat', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Miami Heat ( NBA ) games 2015/16
X_Axis['Year']: ['2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['76.73', '78.3', '78.3', '72.5', '67.0', '60.5', '58.55', '58.55', '58.55', '56.55']

gold: This graph depicts the average ticket price for Miami Heat games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 56.55 U.S. dollars . Miami Heat - additional information The Miami Heat are an American professional basketball team playing in the National Basketball Association 's ( NBA ) Southeast Division of the Eastern Conference .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] - additional information The templateTitleSubject[0] are an American professional basketball team playing in the National Basketball Association 's ( templateTitleSubject[1] ) Southeast Division of the Eastern Conference .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Miami Heat games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Average ticket price was 56.55 U.S. dollars .

Example 512:
titleEntities: {'Subject': ['Class'], 'Date': ['2007', '2018']}
title: Class 8 truck manufacturers - sales 2007 to 2018
Data:
                   0      1      2       3      4      5      6      7      8      9     10     11
Year            2018   2017   2016    2015   2014   2013   2012   2011   2010   2009   2008   2007
Freightliner   90.86  72.17  77.46  100.21  82.11  70.58   66.6  54.37  36.35  29.58  42.64  51.71
International  34.33   21.9  21.37   28.84  31.13  34.82  34.82  35.93  26.94  26.58   32.4  29.68
Kenworth       37.38  29.34  29.47    37.2  31.16  25.94  27.78  22.58  11.62  11.65  15.86   19.3
Mack           18.45  16.01  17.15   19.83  19.65  16.29  17.33  12.93   9.21   7.63  11.79  13.44
Peterbilt      36.97  30.47  26.53   31.72  29.73  24.72  27.26  24.58  12.98  12.28  17.61  19.95
Volvo Truck    26.79   17.1  20.54   30.93  26.56  20.66  20.92  20.96  10.04   7.07  13.06  16.06 

gold: This statistic represents U.S. Class 8 truck sales between 2007 and 2018 , by brand . In 2018 , Daimler 's Freightliner division sold more than 90,860 Class 8 trucks . With a 36 percent U.S. Class 8 truck market share , Freightliner is also the market leader in the United States .
gold_template: This statistic represents U.S. templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[4] between templateValue[0][last] and templateValue[0][0] , by brand . In templateValue[0][0] , Daimler 's templateLabel[1][0] division sold more than 90,860 templateTitleSubject[0] templateTitle[1] trucks . With a templateValue[1][8] templateScale U.S. templateTitleSubject[0] templateTitle[1] templateTitle[2] market share , templateLabel[1][0] is also the market leader in the country .

generated_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] employees templateTitle[4] from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . In templateValue[0][0] , the templateTitle[0] of templateTitleSubject[0] generated approximately templateValue[3][0] templateScale U.S. dollars in templateLabel[1][0] templateLabel[1][1] .
generated: This statistic shows the Class of employees sales from 2007 to 2018 , .  In 2018 , the Class of generated approximately 37.38 billion U.S. dollars in Freightliner .

Example 513:
titleEntities: {'Subject': ['Countries'], 'Date': ['2019']}
title: Countries with the lowest national debt 2019
X_Axis['Country']: ['Hong_Kong_SAR', 'Macao_SAR', 'Brunei_Darussalam', 'Afghanistan', 'Estonia', 'Solomon_Islands', 'Botswana', 'Democratic_Republic_of_the_Congo', 'Kuwait', 'Russia', 'Zimbabwe', 'Kosovo', 'Micronesia', 'Bulgaria', 'Azerbaijan', 'United_Arab_Emirates', 'Kazakhstan', 'Luxembourg', 'Tuvalu', 'Kiribati']
Y_Axis['National', 'debt', 'in', 'relation', 'to', 'GDP']: ['0', '0', '2.8', '7.55', '8.2', '10.91', '12.31', '13.49', '15.25', '16.49', '17.67', '18.18', '18.49', '19.16', '19.71', '20.14', '20.76', '21.34', '22.6', '22.62']

gold: The statistic shows the 20 countries with the lowest national debt in 2019 in relation to the gross domestic product ( GDP ) . The data refer to the debts of the entire state , including the central government , the provinces , municipalities , local authorities and social insurance . In 2019 , Russia 's estimated level of national debt reached about 16.49 percent of the GDP , ranking 10th of the countries with the lowest national debt .
gold_template: The statistic shows the templateYValue[14] templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] in templateYLabel[2] to the gross domestic product ( templateYLabel[3] ) . The data refer to the debts of the entire state , including the central government , the provinces , municipalities , local authorities and social insurance . In templateTitleDate[0] , templateXValue[9] 's estimated level of templateYLabel[0] templateYLabel[1] reached about templateYValue[9] templateScale of the templateYLabel[3] , ranking 10th of the templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In templateTitleDate[0] , templateYValue[max] templateScale of the templateYLabel[1] of templateXValue[0] were the most important templateTitle[1] templateTitle[2] .
generated: This statistic shows the National debt of Countries in 2019 , .  In 2019 , 22.62 % of the debt of Hong_Kong_SAR were the most important lowest national .

Example 514:
titleEntities: {'Subject': ['NFL'], 'Date': ['2019']}
title: NFL Monday Night Football average viewers 2015 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015']
Y_Axis['Viewers', 'in', 'millions']: ['12.6', '11.6', '10.76', '11.36', '12.9']

gold: Monday Night Football averaged 12.6 million viewers in the United States during the 2019 NFL season , an increase of around one million on the previous year . Although viewing figures have increased , they are still down on the 12.9 million viewers in 2015 . Sunday , Monday , happy nights The NFL has prime-time games that are broadcast across the entire United States by one major network on Thursday night , Sunday night , and Monday night – these games start at around 8:30 pm Eastern Time .
gold_template: templateTitle[1] templateTitle[2] templateTitle[3] averaged templateYValue[0] templateScale templateYLabel[0] in the country during the templateXValue[max] templateTitleSubject[0] season , an templatePositiveTrend of around one templateScale on the previous templateXLabel[0] . Although viewing figures have templatePositiveTrend , they are still down on the templateYValue[max] templateScale templateYLabel[0] in templateXValue[idxmax(Y)] . Sunday , templateTitle[1] , happy nights The templateTitleSubject[0] has prime-time games that are broadcast across the entire country by one major network on Thursday templateTitle[2] , Sunday templateTitle[2] , and templateTitle[1] templateTitle[2] – these games start at around 8:30 pm Eastern Time .

generated_template: The statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] to templateTitleSubject[0] in templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] amounted to templateYValue[idxmax(X)] templateScale .
generated: The statistic presents the Viewers of millions the NFL Monday Night Football to NFL in 2015 and 2019 .  In 2019 , the Viewers of millions amounted to 12.6 millions .

Example 515:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2000', '2018']}
title: Mexico : internet penetration 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'individuals', 'using', 'internet']: ['65.8', '63.9', '59.54', '57.43', '44.39', '43.46', '39.75', '37.18', '31.05', '26.34', '21.71', '20.81', '19.52', '17.21', '14.1', '12.9', '11.9', '7.04', '5.08']

gold: This statistic presents a timeline of the internet penetration in Mexico from 2000 to 2018 . In 2018 , 65.8 percent of the Mexican population used the internet , up from 7.04 percent in 2001 .
gold_template: This statistic presents a timeline of the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the Mexican population used the templateYLabel[3] , up from templateYValue[17] templateScale in templateXValue[17] .

generated_template: The statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the Brazilian population accessed the templateYLabel[3] , up from templateYValue[15] templateScale in templateXValue[16] .
generated: The statistic gives information on the internet penetration in Mexico from 2000 to 2018 .  In 2018 , 65.8 percentage of the Brazilian population accessed the internet , up from 12.9 percentage in 2002 .

Example 516:
titleEntities: {'Subject': ['Reebok'], 'Date': ['2012', '2017']}
title: Brand value of Reebok 2012 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Brand', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['0.8', '0.8', '0.83', '0.88', '1.1', '1.2']

gold: The statistic shows the brand value of the sports company Reebok from 2012 to 2017 . Reebok had a brand value of 0.8 billion U.S. dollars in 2017 .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the sports company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] had a templateYLabel[0] templateYLabel[1] of templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmin(Y)] .

generated_template: This statistic illustrates the templateYLabel[0] templateYLabel[1] of the sports company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] had a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] .
generated: This statistic illustrates the Brand value of the sports company Reebok from 2012 to 2017 .  Reebok had a Brand value of 1.2 billion U.S. dollars in 2012 .

Example 517:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009', '2016']}
title: Share of mobile banking users among mobile phone owners in the U.S. 2009 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Share', 'of', 'mobile', 'banking', 'users']: ['51', '46', '42', '38', '33', '29', '19', '18']

gold: This statistic presents the share of mobile banking users among mobile phone owners in the United States from 2009 to 2011 and a forecast thereof until 2016 . In 2011 , 29 percent of the mobile phone owners accessed banking services on their mobile phones and this number was predicted to increase to 51 percent in 2016 . Mobile banking – additional information Mobile phones are one of the most popular devices owned , and are used for personal and business purposes by people worldwide .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[4] templateYLabel[1] templateTitle[6] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[5] and a forecast thereof until templateXValue[max] . In templateXValue[5] , templateYValue[5] templateScale of the templateYLabel[1] templateTitle[6] templateTitle[7] accessed templateYLabel[2] services on their templateYLabel[1] phones and this number was predicted to templatePositiveTrend to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] . templateYLabel[1] templateYLabel[2] – additional information templateYLabel[1] phones are one of the most popular devices owned , and are used for personal and business purposes by people worldwide .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Share mobile of the mobile banking users among U.S. from 2009 to 2016 .  The Share of U.S. banking users among was 51 banking users .

Example 518:
titleEntities: {'Subject': ['Volvo', 'Sweden'], 'Date': ['2009', '2019']}
title: Volvo car sales in Sweden 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Units', 'sold']: ['65282', '69943', '75506', '71238', '71221', '61496', '53939', '52800', '58862', '53741', '41808']

gold: In 2019 , over 65 thousand Volvo cars were sold in Sweden . This was a decrease of more than four thousand vehicles when compared to the previous year , and the second time since 2012 that the company did not experience a rise in sales . Nevertheless , Volvo was actually the leading passenger car brand in Sweden that year , surpassing Volkswagen by more than 16 thousand sold passenger cars .
gold_template: In templateXValue[max] , over templateYValue[0] thousand templateTitleSubject[0] cars were templateYLabel[1] in templateTitleSubject[1] . This was a templateNegativeTrend of more than four thousand vehicles when compared to the previous templateXLabel[0] , and the second time since templateXValue[7] that the company did not experience a rise in templateTitle[2] . Nevertheless , templateTitleSubject[0] was actually the leading passenger templateTitle[1] brand in templateTitleSubject[1] that templateXLabel[0] , surpassing Volkswagen by more than 16 thousand templateYLabel[1] passenger cars .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[1] of the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the source , the templateTitle[0] of templateTitle[2] in templateTitleSubject[0] has been templatePositiveTrend , amounting to templateYValue[0] templateScale in templateXValue[max] .
generated: The statistic shows the Units of the sold of the car sales in Volvo from 2009 to 2019 .  According to the source , the Volvo of sales in Volvo has been increasing , amounting to 65282 million in 2019 .

Example 519:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005', '2018']}
title: Average wine consumption per U.S. resident 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Consumption', 'per', 'resident', 'in', 'gallons']: ['2.95', '2.94', '2.93', '2.86', '2.82', '2.83', '2.78', '2.72', '2.58', '2.54', '2.45', '2.47', '2.36', '2.32']

gold: Wine consumption has been increasing in the United States . Since 2005 , the average annual consumption per resident has increased by 27 percent to a high of 2.95 gallons in 2018 .
gold_template: templateTitle[1] templateYLabel[0] has been templatePositiveTrend in the templateTitle[4] . Since templateXValue[min] , the templateTitle[0] annual templateYLabel[0] templateYLabel[1] templateYLabel[2] has templatePositiveTrend by 27 templateScale to a high of templateYValue[max] templateYLabel[3] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Consumption per of the consumption in the Average from 2005 to 2018 .  In 2018 , the per of U.S. was approximately 2.95 resident gallons .

Example 520:
titleEntities: {'Subject': ['World Warcraft'], 'Date': ['2015', '2023']}
title: Estimated number of World of Warcraft subscribers 2015 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Subscribers', 'in', 'millions']: ['4.46', '4.59', '4.74', '4.88', '5.03', '5.19', '5.42', '5.67', '5.5']

gold: How many people play World of Warcraft ? In 2015 , when Activision Blizzard last reported on WoW 's subscriptions , the game had 5.5 million global subscribers . It is projected that the numbers will be gradually decreasing to reach 4.46 million in 2023 . The game reached the peak of its popularity in the second half of 2010 , when Activision Blizzard 's estimates put the global number of WoW subscribers at 12 million .
gold_template: How many people play templateTitleSubject[0] of templateTitleSubject[0] ? In templateXValue[min] , when Activision Blizzard last reported on WoW 's subscriptions , the game had templateYValue[idxmin(X)] templateScale global templateYLabel[0] . It is projected that the numbers will be gradually templateNegativeTrend to reach templateYValue[min] templateScale in templateXValue[idxmin(Y)] . The game reached the peak of its popularity in the second half of 2010 , when Activision Blizzard 's estimates put the global templateTitle[1] of WoW templateYLabel[0] at 12 templateScale .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Subscribers of millions the number World Warcraft in World Warcraft from 2015 to 2023 .  In 2023 , the Subscribers millions in World Warcraft was 4.46 millions .

Example 521:
titleEntities: {'Subject': ['Ontario'], 'Date': ['2000', '2018']}
title: Farm value per liter of maple syrup in Ontario 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Farm', 'value', 'per', 'liter', 'in', 'Canadian', 'dollars']: ['11.31', '12.91', '12.87', '15.59', '15.46', '15.11', '14.22', '14.68', '14.69', '13.53', '12.99', '11.87', '11.31', '11.09', '11.03', '10.84', '10.62', '10.72', '10.5']

gold: In 2018 , the farm value of maple syrup in Ontario amounted to 11.31 Canadian dollars per liter . The farm gate value of one liter of maple syrup in Ontario has fluctuated over the past eighteen years , having hit a high of 15.59 Canadian dollars in 2015 . The volume of maple syrup produced in Ontario has also varied quite dramatically in the last few years .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[4] templateYLabel[5] templateYLabel[2] templateYLabel[3] . The templateYLabel[0] gate templateYLabel[1] of one templateYLabel[3] of templateTitle[4] templateTitle[5] in templateTitleSubject[0] has fluctuated over the past eighteen years , having hit a high of templateYValue[max] templateYLabel[4] templateYLabel[5] in templateXValue[idxmax(Y)] . The volume of templateTitle[4] templateTitle[5] produced in templateTitleSubject[0] has also varied quite dramatically in the last few years .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Farm value of the liter of Ontario from 2000 to 2018 .  In 2018 , the average Farm value of Ontario was approximately 11.31 liter Canadian .

Example 522:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2001', '2018']}
title: Annual imports of cement into Great Britain by importer 2001 to 2018
Data:
                     0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17
Year              2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001
by manufacturers   935  1114     0   635   227   117    61    86    61    99   283   255   124   306   609   576   966  1182
by others         1663  1932  1503  1425  1590  1322  1122  1173  1153  1085  1084  1121  1089   971   825   646   452   360 

gold: This statistic shows the amount of cement imported into Great Britain from 2001 to 2018 , categorized by importer and measured in thousand metric tons . While the amount of cement imported by manufacturers has decreased over the years , it has shown considerable increases in the past four years . On the other side , the amount of cement imported by other entities has steadily increased .
gold_template: This statistic shows the amount of templateTitle[2] imported templateTitle[3] templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , categorized templateTitle[6] templateTitle[7] and measured in thousand metric tons . While the amount of templateTitle[2] imported templateTitle[6] templateLabel[1][1] has templateNegativeTrend over the years , it has shown considerable increases in the past four years . On the other side , the amount of templateTitle[2] imported templateTitle[6] other entities has steadily templatePositiveTrend .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in the United Kingdom ( templateTitleSubject[0] ) from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . The templateTitle[0] of templateTitle[1] in templateTitleSubject[0] stood at templateValue[1][0] U.S. dollars in templateValue[0][0] .
generated: This statistic shows the Annual of imports in the United Kingdom ( Great Britain ) from 2001 to 2018 , importer 2001 .  The Annual of imports in Great Britain stood at 935 U.S. dollars in 2018 .

Example 523:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000']}
title: Retail price for granulated sugar in the U.S. 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Price', 'per', 'pound', 'in', 'U.S.', 'cents']: ['64.76', '64.19', '65.36', '60.9', '64.32', '69.41', '68.3', '62.86', '57.03', '52.91', '51.48', '49.58', '43.54', '42.64', '42.68', '43.1', '43.42', '42.41']

gold: In the United States , the average retail price per pound of granulated sugar was roughly 65 U.S. cents in 2017 . The price of sugar has fluctuated considerably over the past seventeen years , peaking at almost 70 U.S. cents per pound in 2012 . Sugar production Within the 2018/2019 period , a total of just under 180 million metric tons of sugar was produced worldwide .
gold_template: In the templateTitle[5] , the average templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] was roughly templateYValue[0] templateYLabel[3] templateYLabel[4] in templateXValue[max] . The templateYLabel[0] of templateTitle[4] has fluctuated considerably over the past seventeen years , peaking at almost 70 templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] in templateXValue[5] . templateTitle[4] production Within the 2018/2019 period , a total of just under 180 templateScale metric tons of templateTitle[4] was produced worldwide .

generated_template: This statistic shows the average templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] as templateTitle[2] templateTitle[3] templateYLabel[3] templateTitle[5] from templateXValue[min] to templateXValue[max] . In the templateXValue[min] calendar templateXLabel[0] , a templateYLabel[3] templateTitle[0] farmer templateTitle[2] an average templateYLabel[0] of templateYValue[idxmin(X)] templateYLabel[4] templateYLabel[1] one templateYLabel[2] of upland templateTitle[0] .
generated: This statistic shows the average Retail Price per pound as for granulated U.S. from 2000 to 2017 .  In the 2000 calendar Year , a U.S. Retail farmer for an average Price of 42.41 cents per one pound of upland Retail .

Example 524:
titleEntities: {'Subject': ['Manchester City'], 'Date': []}
title: Manchester City revenue by stream 2018/19
Data:
                    0        1        2        3        4        5        6        7        8        9       10
Year          2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09
Matchday         62.4     63.9     60.4     70.2     57.0     56.8     46.2     38.1     29.5     29.8     24.4
Broadcasting    287.2    238.8    236.8    215.8    178.0    159.3    103.1    109.0     76.1     66.0     56.7
Commercial      261.0    265.7    230.5    238.9    228.5    198.3    166.9    138.5     64.0     57.0     21.1 

gold: The commercial revenue stream is the largest source of revenue for Manchester City . In 2018/2019 , the football club earned approximately 261 million euros from sponsorship , merchandising , stadium tours and other commercial operations , broadcasting , more than four times more than in 2010/2011 . The biggest revenue stream was broadcasting .
gold_template: The templateLabel[3][0] templateTitle[2] templateTitle[4] is the largest source of templateTitle[2] for templateTitleSubject[0] . In 2018/2019 , the football club earned approximately templateValue[3][0] templateScale euros from sponsorship , merchandising , stadium tours and other templateLabel[3][0] operations , templateLabel[2][0] , more than four times more than in 2010/2011 . The biggest templateTitle[2] templateTitle[4] was templateLabel[2][0] .

generated_template: templateLabel[2][0] is the largest source of templateTitle[2] for templateTitleSubject[0] . In 2018/2019 , the club earned approximately templateValue[2][0] templateScale euros from templateLabel[2][0] , more than double than in 2010/2011 . The second biggest templateTitle[2] templateTitle[4] is the templateLabel[3][0] templateTitle[4] .
generated: Broadcasting is the largest source of revenue for Manchester City .  In 2018/2019 , the club earned approximately 287.2 million euros from Broadcasting , more than double in 2010/2011 .  The second biggest revenue stream is the Commercial stream .

Example 525:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2007', '2018']}
title: U.S. household expenditure on sewing machines 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Average', 'annual', 'expenditure', 'in', 'U.S.', 'dollars']: ['8.18', '1.54', '3.95', '5.68', '5.46', '4.88', '2.54', '2.43', '2.82', '8.19', '5.09', '6.25']

gold: This statistic shows the average annual expenditure on sewing machines per consumer unit in the United States from 2007 to 2018 . In 2018 , the country 's average expenditure on sewing machines amounted to 8.18 U.S. dollars per consumer unit .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] templateTitle[4] per consumer unit in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the country 's templateYLabel[0] templateYLabel[2] on templateTitle[3] templateTitle[4] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] per consumer unit .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] and templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the country 's templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] and templateTitle[4] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] per consumer unit .
generated: The statistic shows the Average annual expenditure on sewing and machines 2007 in the U.S. from 2007 to 2018 .  In 2018 , the country 's Average annual expenditure on sewing and machines amounted to 8.18 U.S. dollars per consumer unit .

Example 526:
titleEntities: {'Subject': ['Boston Celtics', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Boston Celtics ( NBA ) games 2015/16
X_Axis['Year']: ['2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['66.49', '70.79', '72.64', '72.96', '68.55', '68.55', '68.55', '68.55', '65.43', '57.04']

gold: This graph depicts the average ticket price for Boston Celtics games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 57.04 U.S. dollars . Boston Celtics The Boston Celtics are a professional basketball team of the National Basketball Association ( NBA ) playing in the Eastern Conference of the league .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] The templateTitleSubject[0] are a professional basketball team of the National Basketball Association ( templateTitleSubject[1] ) playing in the Eastern Conference of the league .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Boston Celtics games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Average ticket price was 57.04 U.S. dollars .

Example 527:
titleEntities: {'Subject': ['India'], 'Date': ['2008', '2019']}
title: Cement consumption volume in India 2008 to 2019
X_Axis['Fiscal', 'Annually']: ['FY_2019', 'FY_2018', 'FY_2017', 'FY_2016', 'FY_2015', 'FY_2014', 'FY_2013', 'FY_2012', 'FY_2011', 'FY_2010', 'FY_2009']
Y_Axis['Consumption', 'in', 'million', 'metric', 'tons']: ['328', '269', '272', '269', '256', '245', '239', '221', '166', '206', '193']

gold: In 2018 , India was the second largest producer of cement in the world . The country had about eight percent of the global installed capacity that year . Consumption of cement at this time stood at around 270 million metric tons .
gold_template: In templateXValue[1] , templateTitleSubject[0] was the second largest producer of templateTitle[0] in the world . The country had about eight templateScale of the global installed capacity that year . templateYLabel[0] of templateTitle[0] at this time stood at around 270 templateScale templateYLabel[2] templateYLabel[3] .

generated_template: In fiscal year templateTitleDate[0] , the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] across templateTitleSubject[0] was templateXValue[0] , with a total of templateYValue[max] templateYLabel[2] templateYLabel[3] . It was followed by templateXValue[1] , with templateYValue[1] templateYLabel[2] templateYLabel[3] that year .
generated: In fiscal year 2008 , the Cement consumption volume in the India across was FY_2019 , with a total of 328 metric tons .  It was followed by FY_2018 , with 269 metric tons that year .

Example 528:
titleEntities: {'Subject': ['Premier League England'], 'Date': ['2019']}
title: Brand value of Premier League ( England ) football teams 2019
Data:
                      0     1     2     3     4     5     6     7     8
Year               2011  2012  2013  2014  2015  2016  2017  2018  2019
Manchester United   661   853   837   739  1206  1170  1733  1895  1651
Chelsea             314   398   418   502   795   776  1248  1195  1085
Manchester City     170   302   332   510   800   905  1021  1331  1407
Arsenal             301   388   410   505   703   858   941  1083   993
Liverpool           250   367   361   469   577   748   908  1204  1336
Tottenham Hotspur   127   255   219   248   360   441   696   764   850 

gold: The statistic depicts the brand value of the most valuable English football teams from 2011 to 2019 . Manchester United had a brand value of 1.65 billion U.S. dollars in 2019 . A brand is defined here as the trademark and associated intellectual property .
gold_template: The statistic depicts the templateTitle[0] templateTitle[1] of the most valuable English templateTitle[5] templateTitle[6] from templateValue[0][0] to templateValue[0][last] . templateLabel[1][0] templateLabel[1][1] had a templateTitle[0] templateTitle[1] of 1.65 templateScale U.S. dollars in templateValue[0][last] . A templateTitle[0] is defined here as the trademark and associated intellectual property .

generated_template: The timeline shows the templateTitle[0] of templateTitle[1] templateTitle[2] and templateTitle[4] templateTitle[5] in the templateTitle[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][last] templateScale of templateTitleSubject[0] templateTitle[0] on templateTitle[2] .
generated: The timeline shows the Brand of value Premier and England football in the Brand from 2019 to 2011 .  In 2011 , about 1651 % of Premier League England Brand on Premier .

Example 529:
titleEntities: {'Subject': ['North American'], 'Date': ['2009']}
title: North American sports market size 2009 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Sports', 'market', 'revenue', 'in', 'billion', 'U.S.', 'dollars']: ['83.1', '80.5', '77.88', '75.71', '73.04', '71.06', '69.14', '67.29', '63.56', '60.23', '56.45', '53.47', '52.01', '49.99', '48.73']

gold: The statistic shows the sports market size in North America from 2009 to 2018 , with forecasts from 2019 to 2023 . In 2018 , the North American sports market had a value of about 71.06 billion U.S. dollars . This figure is expected to rise to 83.1 billion by 2023 .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[4] in templateTitleSubject[0] America from templateXValue[min] to templateXValue[5] , with forecasts from templateXValue[4] to templateXValue[max] . In templateXValue[5] , the templateTitleSubject[0] sports templateYLabel[1] had a value of about templateYValue[5] templateScale templateYLabel[4] templateYLabel[5] . This figure is expected to rise to templateYValue[max] templateYValue[idxmax(X)] by templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of a templateTitleSubject[0] of templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Sports market of the billion size 2009 in North American from 2009 to 2023 .  In 2023 , the average Sports market of a North American of 83.1 revenue billion .

Example 530:
titleEntities: {'Subject': ['ROI'], 'Date': ['2017']}
title: Perceived ROI level from digital marketing channels worldwide 2017
Data:
                    0                  1                                          2                                3                       4                        5                        6                       7                                           8
Response          SEO  Content_marketing  E-mail_marketing_and_marketing_automation  Paid_search_marketing_(AdWords)  Social_media_(organic)  Socila_media_(paid_ads)  Website_personalization  Online_PR_and_outreach  Display_advertising_including_programmatic
Lowest ROI         15                 17                                         17                               19                      33                       24                       18                      28                                          26
Medium ROI         41                 40                                         43                               37                      39                       36                       26                      37                                          31
Highest ROI        32                 30                                         30                               20                      20                       17                       16                      15                                          10
Don't use or N/A   12                 13                                         10                               24                       8                       23                       40                      20                                          33 

gold: Among the many digital marketing channels to choose from , SEO , content marketing and e-mail marketing together with automation commanded the largest share of marketers claiming that they generated the highest return on investment . Roughly a third of surveyed industry professionals believed that these three tactics were highly beneficial . Consequently , more than half of global marketers declared an increase in their budgets on all three channels in the same period .
gold_template: Among the many templateTitle[4] templateValue[0][1] templateTitle[6] to choose templateTitle[3] , templateValue[0][0] , templateValue[0][1] and templateValue[0][2] templateValue[0][1] together with templateValue[0][2] commanded the largest share of marketers claiming that they generated the templateLabel[3][0] return on investment . Roughly a third of surveyed industry professionals believed that these three tactics were highly beneficial . Consequently , more than half of global marketers declared an templatePositiveTrend in their budgets on all three templateTitle[6] in the same period .

generated_template: This statistic shows the results of a survey conducted in the templateTitle[0] in templateTitleDate[0] , the templateTitle[7] templateTitle[8] . The survey results were templateValue[1][1] templateScale of the respondents stated that they used templateValue[0][0] a templateTitle[7] templateTitle[8] templateTitle[9] .
generated: This statistic shows the results of a survey conducted in the Perceived in 2017 , the worldwide 2017 .  The survey results were 17 % of the respondents stated that they used SEO a worldwide 2017 .

Example 531:
titleEntities: {'Subject': ['Singapore'], 'Date': ['2005', '2019']}
title: Number of immigrants in Singapore 2005 to 2019
X_Axis['Year']: ['2019', '2017', '2015', '2010', '2005']
Y_Axis['Number', 'of', 'immigrants', 'in', 'thousands']: ['2155.65', '2623.0', '2544.0', '2165.0', '1711.0']

gold: In 2019 , there were about 2.16 million immigrants ( here classified as people living in a country in which they were not born in ) in Singapore out of a total population of about 5.7 million . Immigrants made up a significant share of the population and play a crucial role in Singapore , where the birth rate was among the lowest in the world . Singapore 's immigrant population The immigrant population of Singapore made up slightly more than 40 percent of the total population of the country in 2018 .
gold_template: In templateXValue[max] , there were about templateYValue[0] templateScale templateYLabel[1] ( here classified as people living in a country in which they were not born in ) in templateTitleSubject[0] out of a total population of about 5.7 templateScale . templateYLabel[1] made up a significant share of the population and play a crucial role in templateTitleSubject[0] , where the birth rate was among the lowest in the world . templateTitleSubject[0] 's immigrant population The immigrant population of templateTitleSubject[0] made up slightly more than 40 templateScale of the total population of the country in 2018 .

generated_template: As of templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] amounted to approximately templateYValue[max] templateScale . As one of the biggest economies in Southeast Asia , templateTitleSubject[0] is an attractive destination for workers from neighboring countries seeking better wages and employment . Indonesians make up the majority of templateYLabel[1] With their shared culture , religion and a very similar language , Indonesia was the single biggest contributor of the country .
generated: As of 2019 , the Number of immigrants in Singapore amounted to approximately 2623.0 thousands .  As one of the biggest economies in Southeast Asia , Singapore is an attractive destination for workers from neighboring countries seeking better wages and employment .  Indonesians make up the majority of immigrants With their shared culture , religion and a very similar language , Indonesia was the single biggest contributor of the country .

Example 532:
titleEntities: {'Subject': ['National Hockey League Carolina'], 'Date': ['2005', '2019']}
title: National Hockey League - Carolina Hurricanes home attendance 2005 to 2019
Data:
                          0        1        2        3        4        5        6        7        8        9       10       11       12       13
Year                2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09  2007/08  2006/07  2005/06
Total attendance     587222   546142   482829   500363   516375   634832   421401   657747   656611   624873   679488   681962   712861   639454
Average attendance    14322    13320    11776    12203    12594    15483    17558    16042    16415    15240    16572    16633    17386    15596 

gold: This graph depicts the total/average regular season home attendance of the Carolina Hurricanes franchise of the National Hockey League from the 2005/06 season to the 2018/19 season . In 2018/19 , the total regular season home attendance of the franchise was 587,222 .
gold_template: This graph depicts the total/average regular season templateTitle[5] templateTitle[6] of the templateTitleSubject[0] templateTitle[4] franchise of the templateTitleSubject[0] League from the templateValue[0][last] season to the templateValue[0][0] season . In templateValue[0][0] , the templateLabel[1][0] regular season templateTitle[5] templateTitle[6] of the franchise was templateValue[1][0] .

generated_template: This graph depicts the total/average regular season templateTitle[5] templateTitle[6] of the templateTitleSubject[0] templateTitle[4] franchise of the templateTitleSubject[0] League from the templateValue[0][last] season to the templateValue[0][0] season . In templateValue[0][0] , the templateLabel[1][0] regular season templateTitle[5] templateTitle[6] of the franchise was templateValue[1][0] .
generated: This graph depicts the total/average regular season home attendance of the National Hockey League Carolina Hurricanes franchise of the National Hockey League Carolina League from the 2005/06 season to the 2018/19 season .  In 2018/19 , the Total regular season home attendance of the franchise was 587222 .

Example 533:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2014']}
title: U.S. TV viewership selected games 2014 FIFA World Cup in Brazil
Data:
                                                                                      0                                                         1                                                            2                                                           3                                                           4                                                            5                                                                   6                                                                    7
2014 World Cup Matches  Opening_match_-_Brazil_vs._Croatia_(06/12/14)(Univision_&_ESPN)  Group_match_-_USA_vs._Ghana_(06/17/14)(Univision_&_ESPN)  Group_match_-_USA_vs._Portugal_(06/22/14)(Univision_&_ESPN)  Group_match_-_USA_vs._Germany_(06/22/14)(Univision_&_ESPN)  Round_of_16_-_USA_vs._Belgium_(07/01/14)(Univision_&_ESPN)  Semifinal_-_Brazil_vs._Germany_(07/08/14)(Univision_&_ESPN)  Semifinal_-_Argentina_vs._Netherlands_(07/09/14)(Univision_&_ESPN)  World_Cup_Final_-_Germany_vs._Argentina_(07/13/14)(Univision_&_ABC)
ESPN/ABC                                                                            4.4                                                      11.1                                                         18.2                                                        10.8                                                        16.5                                                          6.6                                                                 6.8                                                                 17.3
Univision                                                                           5.1                                                       4.8                                                          6.5                                                         3.4                                                         5.1                                                          5.8                                                                 5.4                                                                  9.2 

gold: The graph shows the TV viewership of selected games at the 2014 FIFA World Cup in Brazil . The opening match on June 12 between Brazil and Croatia had a total TV viewership of 9.5 million viewers in the United States .
gold_template: The graph shows the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] at the templateLabel[0][0] templateTitle[6] templateValue[0][7] in templateValue[0][0] . The templateValue[0][0] on 12 between templateValue[0][0] and templateValue[0][0] had a total templateTitle[1] templateTitle[2] of 9.5 templateScale viewers in the templateTitle[0] .

generated_template: The statistic shows the distribution of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the templateTitle[4] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . The survey shows that templateValue[1][0] templateScale of the people between the templateValue[0][0] to templateValue[0][0] .
generated: The statistic shows the distribution of U.S. TV viewership selected the games in 2014 , World Cup .  The survey shows that 4.4 % of the people between the Opening_match_-_Brazil_vs._Croatia_(06/12/14)(Univision_&_ESPN) to .

Example 534:
titleEntities: {'Subject': ['UK'], 'Date': ['2000', '2019']}
title: Employees with zero hours contracts in the UK 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Employees', 'in', 'thousands']: ['896', '780', '883', '903', '747', '624', '585', '252', '190', '168', '189', '143', '166', '147', '119', '108', '124', '156', '176', '225']

gold: This statistic shows the number of people in thousands on a zero-hour contract in the United Kingdom from 2000 to 2019 . In 2000 , there were 225 thousand people on zero-hour contracts , with this number increasing to approximately 896 thousand by 2019 .
gold_template: This statistic shows the number of people in thousands on a zero-hour contract in the United Kingdom from templateXValue[min] to templateXValue[max] . In templateXValue[min] , there were templateYValue[idxmin(X)] thousand people on zero-hour templateTitle[4] , templateTitle[1] this number templatePositiveTrend to approximately templateYValue[idxmax(X)] thousand by templateXValue[max] .

generated_template: This statistic shows the total of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] in the United Kingdom ( UK ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[0] templateTitle[3] templateTitle[4] was templateYValue[idxmax(X)] templateYLabel[1] , an templatePositiveTrend of over the previous templateXLabel[0] .
generated: This statistic shows the total of UK thousands contracts UK 2000 in the United Kingdom ( UK ) from 2000 to 2019 .  In 2019 , the Employees of UK contracts was 896 thousands , an increase of over the previous Year .

Example 535:
titleEntities: {'Subject': ['Saudi Arabia'], 'Date': ['2015', '2023']}
title: Saudi Arabia : number of internet users 2015 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Number', 'of', 'internet', 'users', 'in', 'millions']: ['35.0', '34.1', '33.0', '31.7', '30.2', '28.5', '26.4', '22.42', '21.54']

gold: This statistic provides information on the number of internet users in Saudi Arabia from 2015 to 2023 . In 2018 , Saudi Arabia had 28.5 million internet users . This figure is projected to grow to 35 million internet users in 2023 .
gold_template: This statistic provides information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateTitleSubject[0] had templateYValue[5] templateScale templateYLabel[1] templateYLabel[2] . This figure is projected to grow to templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[idxmax(Y)] .

generated_template: This statistic provides information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateTitleSubject[0] had templateYValue[5] templateScale templateYLabel[1] templateYLabel[2] . This figure is projected to grow to templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[idxmax(Y)] .
generated: This statistic provides information on the Number of internet users in Saudi Arabia from 2015 to 2023 .  In 2018 , Saudi Arabia had 28.5 millions internet users .  This figure is projected to grow 35.0 millions internet users in 2023 .

Example 536:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: U.S. consumers who use marijuana 2019 , by age group
X_Axis['Year']: ['18-29_years', '30-49_years', '50-64_years', '65+_years']
Y_Axis['Share', 'of', 'respondents']: ['22', '11', '12', '3']

gold: Eighteen to twenty-nine-year-olds were the largest group of current marijuana smokers in the United States in 2019 . Roughly a quarter of the age group were current smokers , with usage dipping among older age groups . Seniors were the least likely , with only three percent stating that they were current smokers .
gold_template: Eighteen to twenty-nine-year-olds were the largest templateTitle[8] of current templateTitle[4] smokers in the templateTitle[0] in templateTitleDate[0] . Roughly a quarter of the templateTitle[7] templateTitle[8] were current smokers , with usage dipping among older templateTitle[7] groups . Seniors were the least likely , with only templateYValue[min] templateScale stating that they were current smokers .

generated_template: This statistic shows the distribution of templateTitle[1] the templateTitleSubject[0] templateTitle[2] templateTitle[3] times in templateTitleDate[0] , templateTitle[8] templateTitle[9] templateYLabel[1] . According to the survey , templateYValue[max] templateScale of the templateYLabel[1] reported accessing the templateTitle[7] templateTitle[8] templateXValue[0] .
generated: This statistic shows the distribution of consumers the U.S. who use times in 2019 , group respondents .  According to the survey , 22 % of the respondents reported accessing the age group 18-29_years .

Example 537:
titleEntities: {'Subject': ['Hollywood'], 'Date': ['2019']}
title: Highest grossing Hollywood actors of all time in the U.S. 2019
X_Axis['Month']: ['Samuel_L._Jackson', 'Robert_Downey_Jr.', 'Scarlett_Johannsson', 'Harrison_Ford', 'Tom_Hanks', 'Bradley_Cooper', 'Morgan_Freeman', 'Don_Cheadle', 'Andy_Serkis', 'Zoe_Saldana', 'Chris_Pratt', 'Anthony_Daniels', 'Chris_Evans', 'Chris_Hemsworth', 'Tom_Cruise', 'Idris_Elba', 'Johnny_Depp', 'Eddie_Murphy', 'Stanley_Tucci', 'Mark_Ruffalo']
Y_Axis['Box', 'office', 'grosses', 'in', 'million', 'U.S.', 'dollars']: ['7107.8', '5818.8', '5244.1', '5121.8', '5039.3', '4803.8', '4631.0', '4422.7', '4357.7', '4193.6', '4145.4', '4040.9', '4019.8', '4018.8', '3997.6', '3852.6', '3846.8', '3811.1', '3778.2', '3764.7']

gold: This statistic shows the highest-grossing actors of all time in the United States , as of September 2019 , ranked by their domestic box office results . Owing mostly to his role as Han Solo in the Star Wars franchise , and his role as the eponymous hero in the Indiana Jones series of films , Harrison Ford is fourth in the list with a lifetime domestic box office revenue of approximately 5.12 billion U.S. dollars . The highest actress on the list is Scarlett Johannsson , who ranked third , with a lifetime gross of over 5.24 billion U.S. dollars .
gold_template: This statistic shows the highest-grossing templateTitle[3] of templateTitle[4] templateTitle[5] in the templateTitle[6] , as of 2019 , ranked by their domestic templateYLabel[0] templateYLabel[1] results . Owing mostly to his role as Han Solo in the Star Wars franchise , and his role as the eponymous hero in the Indiana Jones series of films , templateXValue[3] is fourth in the list with a lifetime domestic templateYLabel[0] templateYLabel[1] revenue of approximately templateYValue[3] templateScale templateYLabel[4] templateYLabel[5] . The templateTitle[0] actress on the list is templateXValue[2] , who ranked third , with a lifetime gross of over templateYValue[2] templateScale templateYLabel[4] templateYLabel[5] .

generated_template: This statistic presents the distribution of employees employees on the templateTitle[0] making it the largest templateXValue[0] templateYLabel[3] . As of that time , templateXValue[last] , the templateTitle[0] of templateXValue[0] , with over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , followed by templateXValue[1] with templateYValue[1] templateScale .
generated: This statistic presents the distribution of employees on the Highest making it the largest Samuel_L._Jackson million .  As of that time , Mark_Ruffalo the Highest of Samuel_L._Jackson , with over 7107.8 million grosses , followed by Robert_Downey_Jr. with 5818.8 million .

Example 538:
titleEntities: {'Subject': ['Ethiopia'], 'Date': ['2018']}
title: Age structure in Ethiopia 2018
Data:
                        0      1      2      3      4     5      6      7      8      9     10
Year                 2018   2017   2016   2015   2014  2013   2012   2011   2010   2009   2008
0-14 years          40.78  41.25  41.72  42.18  42.74  43.3  43.86  44.41  44.93  45.37  45.75
15-64 years         55.72  55.26  54.81  54.37  53.83  53.3  52.77  52.26  51.77  51.37  51.02
65 years and older    3.5   3.49   3.47   3.45   3.43   3.4   3.37   3.34    3.3   3.26   3.22 

gold: This statistic shows the age structure in Ethiopia from 2008 to 2018 . In 2018 , about 40.78 percent of Ethiopia 's total population were aged 0 to 14 years .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][min] templateScale of templateTitleSubject[0] 's total population were aged 0 to 14 templateLabel[1][1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][min] templateScale of templateTitleSubject[0] 's total population were aged 0 to 14 templateLabel[1][1] .
generated: This statistic shows the Age structure in Ethiopia from 2008 to 2018 .  In 2018 , about 40.78 % of Ethiopia 's total population were aged 0 to 14 years .

Example 539:
titleEntities: {'Subject': ['Canada', 'United States'], 'Date': ['2000', '2018']}
title: Rate of homicide in Canada and the United States 2000 to 2018
Data:
                  0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18
Year           2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  2017  2018
Canada         1.78  1.78  1.86  1.74  1.95  2.06  1.86  1.81  1.84  1.81  1.63  1.74  1.56  1.46  1.47  1.71  1.69  1.82  1.76
United States   5.7   5.5   5.6   5.6   5.7   5.5   5.6   5.8   5.7   5.4   5.0   4.8   4.7   4.7   4.5   4.4   4.9   5.4   5.3 

gold: This statistic shows the rate of homicides in Canada and the United States from 2000 to 2018 . There were roughly 5.3 homicides in the U.S. and 1.76 homicides in Canada per 100,000 residents in 2018 .
gold_template: This statistic shows the templateTitle[0] of homicides in templateTitleSubject[0] and the templateTitleSubject[1] from templateValue[0][0] to templateValue[0][last] . There were roughly templateValue[2][last] homicides in the templateTitle[4] and templateValue[1][last] homicides in templateTitleSubject[0] per 100,000 residents in templateValue[0][last] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] and templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , there were templateValue[1][0] templateLabel[1][0] templateTitle[3] templateTitle[4] in the templateTitle[5] .
generated: This statistic shows the Rate of homicide and Canada United States in the 2000 from 2018 to 2000 .  In 2000 , there were 1.78 Canada United States in the 2000 .

Example 540:
titleEntities: {'Subject': ['National Basketball Association'], 'Date': ['1946', '2020']}
title: National Basketball Association all-time minutes leaders 1946 to 2020
X_Axis['NBA', 'player']: ['Kareem_Abdul-Jabbar', 'Karl_Malone', 'Dirk_Nowitzki', 'Kevin_Garnett', 'Jason_Kidd', 'Elvin_Hayes', 'Kobe_Bryant', 'Wilt_Chamberlain', 'John_Stockton', 'Reggie_Miller', 'LeBron_James', 'Tim_Duncan', 'Gary_Payton', 'John_Havlicek', 'Ray_Allen', 'Vince_Carter', 'Paul_Pierce', 'Robert_Parish', 'Moses_Malone', 'Joe_Johnson', 'Hakeem_Olajuwon', 'Oscar_Robertson', 'Clifford_Robinson', 'Buck_Williams', 'Jason_Terry', "Shaquille_O'Neal", 'Scottie_Pippen', 'Michael_Jordan', 'Pau_Gasol', 'Bill_Russell']
Y_Axis['Number', 'of', 'minutes', 'played']: ['57446', '54852', '51367', '50412', '50116', '50000', '48643', '47859', '47766', '47620', '47971', '47367', '47117', '46471', '46350', '46234', '45879', '45704', '45071', '44233', '44218', '43886', '42560', '42470', '42034', '41917', '41068', '41010', '41001', '40726']

gold: This statistic shows the NBA all-time minutes played leaders from 1946 to 2020 . As of February 2020 , Dirk Nowitzki was third on the list with a total of 51,367 minutes played in regular season NBA games . He became only the sixth player in NBA history to play more than 50,000 NBA regular season minutes in a game against the Los Angeles Clippers in February 2018 .
gold_template: This statistic shows the templateXLabel[0] templateTitle[3] templateYLabel[1] templateYLabel[2] templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . As of 2020 , templateXValue[2] was third on the list with a total of templateYValue[2] templateYLabel[1] templateYLabel[2] in regular season templateXLabel[0] games . He became only the sixth templateXLabel[1] in templateXLabel[0] history to play more than templateYValue[5] templateXLabel[0] regular season templateYLabel[1] in a game against the Los Angeles Clippers in 2018 .

generated_template: This statistic shows the distribution of templateYLabel[2] templateYLabel[3] with the templateTitle[7] templateTitle[8] templateTitle[9] in templateTitleDate[0] . templateXValue[0] is the templateTitle[0] , leader in the templateTitle[4] , with templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the distribution of played with the 2020 in 1946 .  Kareem_Abdul-Jabbar is the National , leader in the minutes , with 57446 played .

Example 541:
titleEntities: {'Subject': ['World War II Memorial U.S.'], 'Date': ['2008', '2019']}
title: Number of visitors to the World War II Memorial in the U.S. 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'visitors', 'in', 'millions']: ['4.83', '4.65', '4.88', '4.86', '5.07', '4.23', '3.93', '4.16', '3.75', '3.96', '4.12', '4.15']

gold: This statistic shows the number of recreational visitors to the World War II Memorial in the United States from 2008 to 2019 . The number of visitors to the World War II Memorial amounted to approximately 4.83 million in 2019 .
gold_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[1] to the templateTitleSubject[0] II templateTitleSubject[0] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] to the templateTitleSubject[0] II templateTitleSubject[0] amounted to approximately templateYValue[0] templateYValue[idxmax(X)] in templateXValue[max] .

generated_template: templateTitleSubject[0] Park in the templateTitle[5] – the country 's oldest templateTitleSubject[0] - attracted a total of approximately templateYValue[0] templateScale templateYLabel[1] in templateXValue[max] , down from the previous templateXLabel[0] 's total of templateYValue[1] templateScale . The templateTitleSubject[0] received over templateYValue[6] templateScale templateYLabel[1] annually since templateXValue[min] and reached its visitation peak in templateXValue[3] with templateYValue[max] templateScale templateYLabel[1] .
generated: World War II Memorial U.S. Park in the Memorial – the country 's oldest World War II Memorial U.S. - attracted a total of approximately 4.83 millions visitors in 2019 , down from the previous Year 's total of 4.65 millions .  The World War II Memorial U.S. received over 3.93 millions visitors annually since 2008 and reached its visitation peak in 2016 with 5.07 millions visitors .

Example 542:
titleEntities: {'Subject': ['Vending'], 'Date': ['2010']}
title: Vending machines : sales volume of vended products 2010
X_Axis['Year']: ['1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010']
Y_Axis['Vended', 'volume', '(in', 'billion', 'U.S.', 'dollars)']: ['36.6', '38.7', '41.0', '41.1', '42.2', '44.2', '46.0', '46.8', '47.5', '45.6', '42.9', '42.2']

gold: This graph depicts the total sales volume of products sold through vending machines in the U.S. from 1999 to 2010 . In 1999 , the sales volume was 36.6 billion U.S. dollars .
gold_template: This graph depicts the total templateTitle[2] templateYLabel[1] of templateTitle[5] sold through templateTitleSubject[0] templateTitle[1] in the templateYLabel[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[2] templateYLabel[1] was templateYValue[idxmin(X)] templateScale templateYLabel[4] dollars .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[4] was templateYValue[idxmax(X)] templateYLabel[3] .
generated: The statistic shows the Vended volume of the volume vended of Vending from 1999 to 2010 .  In 2010 , the average Vended volume of the Vending vended was 42.2 billion .

Example 543:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2010', '2019']}
title: Number of nursery schools in the United Kingdom 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Number', 'of', 'schools']: ['3.04', '3.04', '3.02', '3.07', '2.97', '3.03', '3.09', '3.1', '3.13']

gold: In the academic year 2018/19 there were approximately 3.04 thousand nursery schools in the United Kingdom , around 900 fewer than there were in the 2010/11 academic year . During 2017/18 nursery schools had the worst teacher to pupil ratio in the UK , with around 22 children for every one teacher .
gold_template: In the academic templateXLabel[0] templateXValue[0] there were approximately templateYValue[0] thousand templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] , around 900 fewer than there were in the templateXValue[last] academic templateXLabel[0] . During templateXValue[1] templateTitle[1] templateYLabel[1] had the worst teacher to pupil ratio in the UK , with around 22 children for every one teacher .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitleSubject[0] from the templateXValue[last] to templateXValue[0] . The templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] amounted to approximately templateYValue[0] thousand templateYLabel[3] .
generated: This statistic shows the Number schools of the Number United Kingdom from the 2010/11 to 2018/19 .  The Number schools of the United Kingdom amounted to approximately 3.04 thousand schools .

Example 544:
titleEntities: {'Subject': ['Germany'], 'Date': ['2017']}
title: Life expectancy at birth in Germany 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Life', 'expectancy', 'at', 'birth', 'in', 'years']: ['80.99', '80.99', '80.64', '81.09', '80.49', '80.54', '80.44', '79.99', '79.84', '79.74', '79.53']

gold: This statistic shows the average life expectancy at birth in Germany from 2007 to 2017 . A child born in the year 2017 had an average life expectancy of 80.99 years .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . A child born in the templateXLabel[0] templateXValue[max] had an average templateYLabel[0] templateYLabel[1] of templateYValue[idxmax(X)] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of women at templateYLabel[2] in templateTitleSubject[0] was about templateYValue[max] templateYLabel[3] .
generated: The statistic shows the Life expectancy of birth in Germany from 2007 to 2017 .  In 2017 , the average Life expectancy of women at birth in Germany was about 81.09 years .

Example 545:
titleEntities: {'Subject': ['Daily'], 'Date': ['2011', '2021']}
title: Daily internet usage per capita worldwide 2011 - 2021 , by device
Data:
            0     1     2     3     4     5     6     7      8      9    10
Year     2011  2012  2013  2014  2015  2016  2017  2018  2019_  2020_  2021
Desktop    43    47    49    47    42    40    42    39     39     38    37
Mobile     32    36    45    63    81    94   109   122    132    143   155 

gold: This statistic presents the average daily time spent online by internet users worldwide from 2011 to 2021 , sorted by device . According to Zenith Optimedia , in 2018 , the average daily minutes of desktop internet consumption per capita amounted to 39 minutes and is projected to slowly decline until 2020 . However , daily mobile internet consumption is set to increase to 155 minutes in 2021 .
gold_template: This statistic presents the average templateTitleSubject[0] time spent online templateTitle[8] templateTitle[1] users templateTitle[5] from templateValue[0][0] to templateValue[0][last] , sorted templateTitle[8] templateTitle[9] . According to Zenith Optimedia , in templateValue[0][7] , the average templateTitleSubject[0] minutes of templateLabel[1][0] templateTitle[1] consumption templateTitle[3] templateTitle[4] amounted to templateValue[1][7] minutes and is projected to slowly decline until templateValue[0][9] . However , templateTitleSubject[0] templateLabel[2][0] templateTitle[1] consumption is set to templatePositiveTrend to templateValue[2][last] minutes in templateValue[0][last] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in the templateTitleSubject[0] templateTitle[3] from templateValue[0][last] to templateValue[0][0] , sorted templateTitle[6] templateTitle[7] . In templateValue[0][0] , it was estimated that the templateTitle[0] of templateTitle[2] templateTitle[3] in the United Kingdom ( UK ) amounted to templateValue[1][0] templateScale British pounds .
generated: This statistic shows the Daily of internet in the Daily per from 2021 to 2011 , sorted 2011 2021 .  In 2011 , it was estimated that the Daily of usage per in the United Kingdom ( UK ) amounted to 43 billion British pounds .

Example 546:
titleEntities: {'Subject': ['June'], 'Date': ['2017', '2017']}
title: Facebook : global penetration rage as of June 2017 , by region
X_Axis['Country']: ['North_America', 'Latin_America_/_Caribbean', 'Oceania_/_Australia', 'Europe', 'Middle_East', 'Worldwide', 'Asia', 'Africa']
Y_Axis['Share', 'of', 'population', 'using', 'Facebook']: ['72.4', '57.3', '48.1', '41.7', '34.6', '26.3', '13.8', '12.7']

gold: This statistic provides information on the share of the global population using Facebook as of June 2017 , by region . As of that period , North America had the highest active Facebook usage rate with 72.4 percent of the population accessing the social network .
gold_template: This statistic provides information on the templateYLabel[0] of the templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] as of templateTitleSubject[0] templateTitleDate[0] , templateTitle[6] templateTitle[7] . As of that period , templateXValue[0] had the highest active templateYLabel[3] usage rate with templateYValue[max] templateScale of the templateYLabel[1] accessing the social network .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[3] templateTitleSubject[0] worldwide in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . According to the source , the templateXValue[0] had the largest amount of templateYValue[max] templateYLabel[2] templateYLabel[3] in that year .
generated: This statistic shows the Share of the global rage June worldwide in 2017 , June Country .  According to the source , the North_America had the largest amount of 72.4 using Facebook in that year .

Example 547:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Leading U.S. states based on the total number of active physicians 2019
X_Axis['State']: ['California', 'New_York', 'Texas', 'Florida', 'Pennsylvania', 'Illinois', 'Ohio', 'Michigan', 'Massachusetts', 'New_Jersey']
Y_Axis['Number', 'of', 'specialist', 'physicians']: ['112906', '89500', '64602', '56499', '51069', '43377', '42373', '38819', '36506', '30502']

gold: The states with the most active physicians include California , New York , Texas , and Florida . California currently has the most active physicians of any U.S. state , with around 112,906 physicians . In California , the specialty areas with the most physicians are psychiatry , anesthesiology , and emergency medicine .
gold_template: The templateTitle[2] with the most templateTitle[6] templateYLabel[2] include templateXValue[0] , templateXValue[1] , templateXValue[2] , and templateXValue[3] . templateXValue[0] currently has the most templateTitle[6] templateYLabel[2] of any templateTitleSubject[0] templateXLabel[0] , with around templateYValue[max] templateYLabel[2] . In templateXValue[0] , the specialty areas with the most templateYLabel[2] are psychiatry , anesthesiology , and emergency medicine .

generated_template: This statistic shows the templateScale of the total templateTitle[4] of the templateTitle[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . It was that templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] at templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the percentage of the total of the Leading in 2019 , physicians State .  It was that California had the highest Number of specialist physicians at 112906 physicians .

Example 548:
titleEntities: {'Subject': ['India'], 'Date': ['2000', '2018']}
title: Foreign exchange earnings from tourism in India 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Foreign', 'exchange', 'earnings', 'in', 'billion', 'U.S.', 'dollars']: ['28.59', '27.31', '22.92', '21.1', '19.7', '18.39', '17.74', '16.56', '14.19', '11.39', '11.83', '10.73', '8.63', '7.49', '6.17', '4.46', '3.1', '3.2', '3.46']

gold: In 2018 , foreign exchange earnings from tourism industry across India amounted to over 28 billion U.S. dollars , an increase from the previous year . An exponential rise in the foreign exchange earnings was seen from 2009 in the country .
gold_template: In templateXValue[max] , templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] industry across templateTitleSubject[0] amounted to over 28 templateScale templateYLabel[4] templateYLabel[5] , an templatePositiveTrend templateTitle[3] the previous templateXLabel[0] . An exponential rise in the templateYLabel[0] templateYLabel[1] templateYLabel[2] was seen templateTitle[3] templateXValue[9] in the country .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateYLabel[3] ( templateTitle[3] ) of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of the templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Foreign exchange of the Foreign exchange billion ( from ) of the India from 2000 to 2018 .  In 2018 , the Foreign of the India was 28.59 earnings billion .

Example 549:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1995', '2018']}
title: Research and development expenditure : U.S. pharmaceutical industry 1995 to 2018
Data:
                                   0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19    20    21    22    23
Year                            2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  2000  1999  1998  1997  1996  1995
Total pharmaceutical industry      0     0     0     0     0     0     0     0  67.4  65.9  63.7  63.2  56.1  51.8  47.6     0     0     0     0     0     0     0     0     0
PhRMA member companies          79.6  71.4  65.5  59.6  53.3  51.6  49.6  48.6  50.7  46.4  47.4  47.9  43.0  39.9  37.0  34.5  31.0  29.8  26.0  22.7  21.0  19.0  16.9  15.2 

gold: This statistic depicts the research and development expenditure of the entire pharmaceutical industry of the United States from 1995 to 2018 . In 2004 , the expenditure of the entire U.S. pharmaceutical industry on research and development came to 47.6 billion U.S. dollars . In 2018 , R & D spending of PhRMA members stood at 79.6 billion U.S. dollars .
gold_template: This statistic depicts the templateTitle[0] and templateTitle[1] templateTitle[2] of the entire templateTitle[4] templateTitle[5] of the templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][14] , the templateTitle[2] of the entire templateTitleSubject[0] templateTitle[4] templateTitle[5] on templateTitle[0] and templateTitle[1] came to templateValue[1][14] templateScale templateTitleSubject[0] dollars . In templateValue[0][0] , R & D spending of templateLabel[2][0] members stood at templateValue[2][0] templateScale templateTitleSubject[0] dollars .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , broken down templateTitle[7] templateTitle[8] . The templateTitle[0] of templateTitleSubject[0] employees in templateValue[0][0] amounted to templateValue[1][0] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Research of development in the U.S. from 1995 to 2018 , broken down 2018 .  The Research of U.S. employees in 2018 amounted to 0 % .

Example 550:
titleEntities: {'Subject': ['Medium'], 'Date': ['2023', '2023']}
title: Medium term forecast for global fertilizer demand by nutrient to 2023
Data:
                                0
Year                         2023
Urea (CH4N2O)                 184
Phosphorus pentoxide (P2O5)  63.9
Potassium oxide (K2O)        45.7 

gold: This statistic displays the medium-term forecast of the global demand for fertilizer by nutrient to the period of 2023 . It is forecasted that the demand for urea fertilizers will reach some 184 megatons by 2023 . Increasing crop prices lead to increased fertilizer demands and has been especially noted in recent years in South Asia .
gold_template: This statistic displays the medium-term templateTitle[2] of the templateTitle[4] templateTitle[6] templateTitle[3] templateTitle[5] templateTitle[7] templateTitle[8] to the period of templateValue[0][last] . It is forecasted that the templateTitle[6] templateTitle[3] templateLabel[1][0] fertilizers will reach some templateValue[1][last] megatons templateTitle[7] templateValue[0][last] . templatePositiveTrend crop prices lead to templatePositiveTrend templateTitle[5] demands and has been especially noted in recent years in South Asia .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , there were around templateValue[1][0] templateScale templateYLabel[3] of the templateTitle[3] in the templateTitle[2] , an templatePositiveTrend from templateYValue[min] templateScale in templateXValue[idxmin(Y)] .
generated: This statistic shows the of term forecast for global in the Medium from 2023 to .  In 2023 , there were around 184 % of the for in the forecast , an increase from hundreds in 2023 .

Example 551:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000', '2018']}
title: Canada : total university enrollment 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Number', 'of', 'students', 'in', 'thousands']: ['1341.35', '1320.56', '1306.04', '1303.74', '1298.96', '1281.69', '1260.22', '1234.14', '1199.45', '1112.59', '1072.9', '1066.93', '1050.83', '1020.68', '994.24', '935.86', '885.94', '850.42', '846.6']

gold: This statistic shows the total number of students enrolled in Canadian universities from 2000 to 2018 . In 2018 , around 1.34 million students were enrolled in Canadian universities .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] of templateYLabel[1] enrolled in Canadian universities from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[max] templateScale templateYLabel[1] were enrolled in Canadian universities .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[2] templateYLabel[3] templateTitle[4] templateYLabel[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the data from the templateTitle[0] of the templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Number of the thousands 2000 between and 2018 .  In 2018 , the data from the Canada of the Canada was 1341.35 thousands .

Example 552:
titleEntities: {'Subject': ['Lithuania'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Lithuania 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['25936.96', '24437.92', '23066.81', '21708.62', '20355.0', '19266.79', '18994.38', '16845.33', '14988.57', '14299.1', '16571.43', '15695.74', '14354.29', '14386.61', '12010.68', '11866.63', '15047.25', '12313.17', '9246.51', '7880.35', '6706.03', '5505.59', '4146.11', '3530.2', '3297.45', '3113.64', '3166.96', '2830.75', '2328.22', '1845.67']

gold: The statistic shows gross domestic product ( GDP ) per capita in Lithuania from 1995 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateYLabel[2] templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country .
generated: The statistic shows the capita Gross domestic product ( GDP ) per capita in the Lithuania from 1995 to 2017 , with projections up until 2024 .  GDP is the total value of all goods and services produced in a Year .  It is considered to be a very important indicator of the economic strength of a country .

Example 553:
titleEntities: {'Subject': ['Southeast Asia'], 'Date': ['2013', '2017']}
title: Southeast Asia gaming revenue share 2013 - 2017 , by country
Data:
                0     1     2     3     4
Year         2017  2016  2015  2014  2013
Indonesia      21    19    18    17    16
Malaysia       18    19    19    20    19
Philippines    11    10     9     9     9
Singapore      12    14    17    18    19
Thailand       22    22    21    21    21
Vietnam        14    14    14    14    15
Rest of SEA     2     2     2     1     1 

gold: The statistic presents the gaming revenue share in Southeast Asia from 2013 to 2014 , with a forecast until 2017 , broken down by country . According the calculations , the gaming industry in Southeast Asia will be worth nearly 2.22 billion U.S. dollars in 2017 , with Thailand 's gaming revenue accounting for 22 percent of that sum . In 2016 , Indonesia was to be the largest mobile gaming market in the region , generating over 330 million U.S. dollars in revenue , while Thailand was said to rank second that year .
gold_template: The statistic presents the templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][3] , with a forecast until templateValue[0][0] , broken down templateTitle[7] templateTitle[8] . According the calculations , the templateTitle[2] industry in templateTitleSubject[0] will be worth nearly 2.22 templateScale U.S. dollars in templateValue[0][0] , with templateLabel[5][0] 's templateTitle[2] templateTitle[3] accounting for templateValue[5][0] templateScale of that sum . In templateValue[0][1] , templateLabel[1][0] was to be the largest mobile templateTitle[2] market in the region , generating over 330 templateScale U.S. dollars in templateTitle[3] , while templateLabel[5][0] was said to rank second that templateLabel[0][0] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in the templateTitleSubject[0] templateTitle[3] from templateValue[0][0] to templateValue[0][last] , templateTitle[8] templateTitle[9] . The estimated templateValue[1][0] templateScale of the templateTitleSubject[0] templateTitle[1] were templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] to the templateTitle[4] templateTitle[8] in templateValue[0][last] .
generated: This statistic shows the Southeast of Asia in the Southeast Asia revenue from 2017 to 2013 , country .  The estimated 21 % of the Southeast Asia Asia were Indonesia to the share country in 2013 .

Example 554:
titleEntities: {'Subject': ['Zimbabwe'], 'Date': ['2024']}
title: Inflation rate in Zimbabwe 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['3', '3', '3', '3.7', '49.7', '161.81', '10.61', '0.91', '-1.56', '-2.41', '-0.21', '1.63', '3.72', '3.47', '3.04', '6.22', '156.96', '-72.73', '32.97', '-31.52', '113.57', '-8.57', '-34.45', '-37.2', '4.48', '-13.43', '-28.02', '-0.97', '6.01', '15.23', '-2.76', '0.38', '-3', '-13.14', '1.35', '-3.85', '-1.06', '12.79', '10.67', '-16.01', '-1.86']

gold: Inflation in Zimbabwe rose to 10.6 percent in 2018 , and is projected to jump dramatically to 161.81 percent in 2019 . After that , estimates predict a 3 percent equilibrium for now - however , given Zimbabwe 's history of poor monetary policy , including one of the worst instances of hyperinflation , this seems unrealistic . Inflation history Inflation depends significantly on economic expectations of it , making it hard to reduce inflation once it has hit higher levels .
gold_template: templateYLabel[0] in templateTitleSubject[0] templatePositiveTrend to templateYValue[6] templateScale in templateXValue[6] , and is projected to jump dramatically to templateYValue[max] templateScale in templateXValue[idxmax(Y)] . After that , estimates predict a templateYValue[0] templateScale equilibrium for now - however , given templateTitleSubject[0] 's history of poor monetary policy , including templateYValue[7] of the worst instances of hyperinflation , this seems unrealistic . templateYLabel[0] history templateYLabel[0] depends significantly on economic expectations of it , making it hard to reduce templateYLabel[0] once it has hit higher levels .

generated_template: The statistic shows the average templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the average Inflation rate in the Zimbabwe from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in the Zimbabwe amounted to about 10.61 % compared to the previous Year .

Example 555:
titleEntities: {'Subject': ['Qatar'], 'Date': ['2013', '2022']}
title: Inbound tourism in Qatar from 2013 to 2022 
X_Axis['Year']: ['2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['1.0', '1.2', '1.4', '1.6', '1.8', '2.1', '2.4', '2.8', '3.2', '3.7']

gold: This statistic shows inbound tourism in Qatar from 2013 to 2022 . In 2013 , the number of inbound tourists in Qatar was one million . This figure was forecasted to increase to 3.7 million by 2022 .
gold_template: This statistic shows templateTitle[0] templateTitle[1] in templateTitleSubject[0] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] of templateTitle[0] tourists in templateTitleSubject[0] was templateYValue[min] templateScale . This figure was forecasted to templatePositiveTrend to templateYValue[idxmax(X)] templateScale by templateXValue[idxmax(Y)] .

generated_template: templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] have been steadily templatePositiveTrend each templateXLabel[0] since the start of the decade . In templateXValue[max] , a record templateYValue[idxmax(X)] templateScale tourists ( including residents and non-residents ) arrived at templateTitle[3] in templateTitleSubject[0] . Tourism in templateTitleSubject[0] In line with trends across Europe , tourism in templateTitleSubject[0] has experienced a boost in the last ten years .
generated: Qatar arrivals at from establishments in Qatar have been steadily increasing each Year since the start of the decade .  In 2022 , a record 3.7 millions tourists ( including residents and non-residents ) arrived at from in Qatar .  Tourism in Qatar line with trends across Europe , tourism in Qatar has experienced a boost in the last ten years .

Example 556:
titleEntities: {'Subject': ['Malawi'], 'Date': ['2024']}
title: Inflation rate in Malawi 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['5.05', '5.44', '6.19', '7.13', '8.37', '8.78', '9.22', '11.54', '21.73', '21.86', '23.78', '28.28', '21.3', '7.62', '7.41', '8.42', '8.72', '8.52', '8.34', '8.16', '7.98', '7.83', '7.71', '7.64', '29.6', '44.76', '29.78', '9.14', '37.73', '83.15', '34.66', '22.78', '23.24', '8.23', '11.86', '12.45', '33.84', '25.05', '14.18', '10.64', '19.75']

gold: This statistic shows the average inflation rate in Malawi from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Malawi amounted to about 9.22 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Malawi from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Malawi amounted to about 9.22 % compared to the previous Year .

Example 557:
titleEntities: {'Subject': ['Lead'], 'Date': ['2019']}
title: Lead reserves worldwide by country 2019
X_Axis['Country']: ['Australia', 'China', 'Russia', 'Peru', 'Mexico', 'United_States', 'India', 'Kazakhstan', 'Bolivia', 'Sweden', 'Turkey', 'Other_countries']
Y_Axis['Reserves', 'in', 'million', 'metric', 'tons']: ['36.0', '18.0', '6.4', '6.3', '5.6', '5.0', '2.5', '2.0', '1.6', '1.1', '0.86', '0.0']

gold: Australia had the world 's largest reserves of lead reserves as of 2019 , amounting to 36 million metric tons that year . Lead and its uses Lead is a naturally occurring element that is soft and malleable . It is a post-transition metal that is very dense and is easily tarnished to a dull grey color .
gold_template: templateXValue[0] had the world 's largest templateYLabel[0] of templateTitleSubject[0] templateYLabel[0] as of templateTitleDate[0] , amounting to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] that year . templateTitleSubject[0] and its uses templateTitleSubject[0] is a naturally occurring element that is soft and malleable . It is a post-transition metal that is very dense and is easily tarnished to a dull grey color .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitleSubject[0] worldwide in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In that year , the templateXValue[0] had the largest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Reserves million of worldwide by Lead worldwide in 2019 , country .  In that year , the Australia had the largest Reserves million of 36.0 metric tons .

Example 558:
titleEntities: {'Subject': ['Japanese'], 'Date': ['2017', '2017']}
title: Japanese video games adapted into movies - box office revenue 2017
X_Axis['Film', 'title', '(original', 'writer', 'company)']: ['Resident_Evil:_The_Final_Chapter_(Capcom)', 'Resident_Evil:_Afterlife_(Capcom)', 'Resident_Evil:_Retribution_(Capcom)', 'Resident_Evil:_Extinction_(Capcom)', 'Resident_Evil:_Apocalypse_(Capcom)', 'Resident_Evil_(Capcom)', 'Street_Fighter_(Capcom)', 'Silent_Hill_(Konami)', 'Final_Fantasy:_The_Spirits_Within_(Square)', 'Super_Mario_Bros._(Nintendo)', 'House_Of_The_Dead_(Sega)']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['312.42', '300.23', '240.0', '148.41', '129.34', '102.98', '99.42', '97.61', '85.13', '20.92', '13.82']

gold: The statistic presents data on the global box office revenue of movies based on Japanese video games as of June 30 , 2017 . 'Resident Evil : The Final Chapter ' generated 312.42 million U.S. dollars in the measured period .
gold_template: The statistic presents data on the global templateTitle[6] templateTitle[7] templateYLabel[0] of templateTitle[5] based on templateTitleSubject[0] templateTitle[1] templateTitle[2] as of 30 , templateTitleDate[0] . 'Resident templateXValue[5] : The templateXValue[0] ' generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the measured period .

generated_template: The statistic presents a ranking of the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitle[4] as of 2019 . According to the source , templateValue[1][0] templateScale templateYLabel[2] templateYLabel[3] of templateXValue[0] accounted for templateYValue[3] templateScale of people .
generated: The statistic presents a ranking of the Japanese video games Revenue in the into as of 2019 .  According to the source , million U.S. dollars of Resident_Evil:_The_Final_Chapter_(Capcom) accounted for 148.41 million of people .

Example 559:
titleEntities: {'Subject': ['Fiat', 'United Kingdom', 'UK'], 'Date': ['2016', '2019']}
title: Fiat car sales in the United Kingdom ( UK ) 2016 to 2019
X_Axis['Month']: ['Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16']
Y_Axis['Units', 'sold']: ['1833', '1605', '1582', '4765', '881', '1475', '2404', '2497', '1892', '8325', '955', '1676', '1977', '2530', '1963', '5851', '1099', '2286', '3613', '2623', '2166', '8524', '1306', '1714', '1645', '1914', '2463', '6540', '1283', '1935', '5062', '4075', '1981', '12930', '1426', '3221', '3929', '3387', '3397', '11319', '1719', '3506']

gold: This statistic shows the monthly amount of cars sold by Fiat in the United Kingdom ( UK ) between July 2016 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December 2019 , 1,833 new Fiat cars had been sold .
gold_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 and 2019 . Peaks in registration numbers were recorded in and of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In 2019 , templateYValue[0] new templateTitleSubject[0] cars had been templateYLabel[1] .

generated_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 and 2019 . Peaks in registration numbers were recorded in and of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In 2019 , templateYValue[5] new templateTitleSubject[0] templateTitle[1] templateYLabel[0] had been templateYLabel[1] , a templateNegativeTrend of roughly ten templateScale in comparison to templateYValue[17] templateYLabel[0] as of 2018 .
generated: This statistic shows the monthly amount of cars sold by Fiat car in the United Kingdom ( UK ) between 2016 and 2019 .  Peaks in registration numbers were recorded in and of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months .  In 2019 , 1475 new Fiat car Units had been sold , a decrease of roughly ten percent in comparison to 2286 Units as of 2018 .

Example 560:
titleEntities: {'Subject': ['Japan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Japan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'rate', 'compared', 'to', 'previous', 'year']: ['0.5', '0.5', '0.52', '0.45', '0.47', '0.89', '0.81', '1.94', '0.61', '1.22', '0.38']

gold: The statistic shows the growth rate of the real gross domestic product ( GDP ) in Japan from 2014 to 2018 , with projections up until 2024 . In 2018 , Japan 's GDP increased by 0.81 percent compared to the previous year . For comparison , the GDP growth rate of China had reached about 7.3 percent that same year.Gross domestic product growth rate in Japan GDP serves as one of the most heavily relied upon indicators to gauge the state and health of a country 's economy .
gold_template: The statistic shows the templateYLabel[1] templateYLabel[2] of the real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] templatePositiveTrend by templateYValue[6] templateScale templateYLabel[3] to the templateYLabel[4] templateXLabel[0] . For comparison , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of China had reached about 7.3 templateScale that same year.Gross templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] templateYLabel[0] serves as templateYValue[0] of the most heavily relied upon indicators to gauge the state and health of a country 's economy .

generated_template: The statistic shows the templateYLabel[1] templateYLabel[2] of the real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the growth rate of the real Gross domestic product ( GDP ) in the Japan from 2014 to 2018 , with projections up until 2024 .  GDP refers to the total market value of all goods and services that are produced within a country per Year .  It is an important indicator of the economic strength of a country .

Example 561:
titleEntities: {'Subject': ['US'], 'Date': ['2018']}
title: Murder in the US - number of victims by weapon 2018
X_Axis['Weapon', 'Used']: ['Handguns', 'Firearms_type_not_stated', 'Knives_or_cutting_instruments', 'Other_weapons_or_weapons_not_stated', 'Personal_weapons_(hands_fists_feet_etc.)', 'Blunt_objects_(clubs_hammers_etc.)', 'Rifles', 'Shotguns', 'Other_guns', 'Asphyxiation', 'Narcotics', 'Fire', 'Strangulation', 'Drowning', 'Poison', 'Explosives']
Y_Axis['Number', 'of', 'murder', 'victims']: ['6603', '2963', '1515', '900', '672', '443', '297', '235', '167', '90', '78', '72', '70', '9', '5', '4']

gold: Which weapon is predominantly used in murder cases ? In the United States , handguns are predominantly used in murder cases , with 6,603 cases in 2018 alone . This is followed by firearms ( type not stated ) and knives or cutting instruments with 2,963 and 1,515 cases respectively in 2018 . Murder in the U.S .
gold_template: Which templateXLabel[0] is predominantly templateXLabel[1] in templateYLabel[1] cases ? In the country , templateXValue[0] are predominantly templateXLabel[1] in templateYLabel[1] cases , with templateYValue[max] cases in templateTitleDate[0] alone . This is followed templateTitle[4] templateXValue[1] ( templateXValue[1] stated ) and templateXValue[2] or templateXValue[2] with templateYValue[1] and templateYValue[2] cases respectively in templateTitleDate[0] . templateYLabel[1] in the U.S .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[2] in the templateTitle[2] templateTitle[4] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] . At that time , templateXValue[0] had templateYLabel[2] in the highest templateYLabel[0] of templateYLabel[2] during that year .
generated: This statistic displays the Number of victims in the number by murder victims in 2018 .  At that time , Handguns had victims in the highest Number of victims during that year .

Example 562:
titleEntities: {'Subject': ['Florida'], 'Date': ['1992', '2018']}
title: Florida - unemployment rate 1992 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Unemployment', 'rate']: ['3.6', '4.2', '4.8', '5.5', '6.3', '7.2', '8.5', '10', '11.1', '10.4', '6.3', '4', '3.2', '3.7', '4.6', '5.2', '5.6', '4.7', '3.7', '3.9', '4.3', '4.8', '5.2', '5.5', '6.4', '7.1', '8.2']

gold: This statistic displays the unemployment rate in Florida from 1992 to 2018 . In 2018 , unemployment rate in Florida was 3.6 percent . You can access the monthly unemployment rate for the United States here .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] templateScale . You can access the monthly templateYLabel[0] templateYLabel[1] for the country here .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[0] templateScale .
generated: This statistic displays the Unemployment rate in Florida from 1992 to 2018 .  In 2018 , Unemployment in Florida was 3.6 % .

Example 563:
titleEntities: {'Subject': ['Distribution'], 'Date': ['2018']}
title: Distribution of global animal health care market by species 2018
X_Axis['Animal', 'Type']: ['Food_animal', 'Companion_animal']
Y_Axis['Distribution', 'of', 'animal', 'health', 'care', 'market', 'volume']: ['62', '38']

gold: This statistic shows the distribution of the global animal health care market volume in 2018 , by species . In that year , health products for companion animals generated some 38 percent of the total global animal health market volume .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateXValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateTitleDate[0] , templateTitle[6] templateTitle[7] . In that year , templateYLabel[2] products for templateXValue[last] animals generated some templateYValue[min] templateScale of the total templateTitle[1] templateXValue[0] templateYLabel[2] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] rate templateYLabel[0] of people templateYLabel[2] templateYLabel[3] as of 2019 . During the survey period , it was found that templateXValue[0] accounted for templateYValue[max] templateScale of all templateYLabel[2] templateYLabel[3] .
generated: This statistic gives information on the Distribution global rate Distribution of people health care as of 2019 .  During the survey period , it was found that Food_animal accounted for 62 % of all health care .

Example 564:
titleEntities: {'Subject': ['Computer'], 'Date': ['2017']}
title: Computer tomography scanner density by country 2017
X_Axis['Country']: ['Australia', 'Iceland', 'United_States', 'Denmark', 'Switzerland', 'South_Korea', 'Greece', 'Latvia', 'Germany', 'Italy', 'Austria', 'Chile', 'Finland', 'Ireland', 'Spain', 'New_Zealand', 'Estonia', 'Poland', 'France', 'Slovak_Rep.', 'Luxembourg', 'Czech_Rep.', 'Canada', 'Slovenia', 'Turkey', 'Netherlands', 'Israel', 'Hungary', 'Mexico']
Y_Axis['CT', 'scanners', 'per', 'million', 'population']: ['64.35', '44.33', '42.64', '39.83', '38.93', '38.18', '36.66', '36.23', '35.17', '34.29', '29.07', '24.27', '24.2', '19.23', '18.26', '17.9', '17.48', '17.33', '17.33', '17.31', '16.77', '15.52', '15.28', '15.01', '14.53', '13.04', '9.65', '8.86', '6.12']

gold: Computer tomography ( CT ) scanners are vital medical technology used in the diagnosis and monitoring of various medical conditions . CT scanner utilize x-ray technology to make images of bones , vessels and other internal organs . As of 2017 , Australia had the largest number of CT scanners per one million population with 64.35 scanners per million people .
gold_template: templateTitleSubject[0] templateTitle[1] ( templateYLabel[0] ) templateYLabel[1] are vital medical technology used in the diagnosis and monitoring of various medical conditions . templateYLabel[0] templateTitle[2] utilize x-ray technology to make images of bones , vessels and other internal organs . As of templateTitleDate[0] , templateXValue[0] had the largest number of templateYLabel[0] templateYLabel[1] templateYLabel[2] one templateScale templateYLabel[4] with templateYValue[max] templateYLabel[1] templateYLabel[2] templateScale people .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . In that year , the templateTitle[2] templateTitle[3] templateTitle[4] in templateXValue[0] was the world with a templateYLabel[0] value of templateYValue[max] templateYLabel[2] templateYLabel[3] of templateYLabel[4] .
generated: This statistic shows the CT scanners of the scanner density by country in 2017 .  In that year , the scanner density by in Australia was the world with a CT value of 64.35 per million of population .

Example 565:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Malaysia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['505.16', '470.16', '438.68', '409.53', '381.52', '365.3', '358.58', '318.96', '301.26', '301.36', '342.87', '323.28', '314.44', '297.96', '255.02', '208.91', '238.65', '199.96', '168.08', '148.25', '133.97', '118.34', '108.3', '99.64', '100.72', '85.0', '77.51', '107.57', '108.3', '95.4', '79.99', '71.84', '63.52', '52.77', '46.58', '41.1', '37.32', '34.05', '29.88', '33.61', '36.57']

gold: The statistic shows gross domestic product ( GDP ) in Malaysia from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Malaysia from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 566:
titleEntities: {'Subject': ['UK'], 'Date': ['2014', '2019']}
title: Monthly hours of sunlight in UK 2014 to 2019
X_Axis['Month']: ["Dec_'19", "Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sep_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17", "Dec_'16", "Nov_'16", "Oct_'16", "Sep_'16", "Aug_'16", "Jul_'16", "Jun_'16", "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16", "Dec_'15", "Nov_'15", "Oct_'15", "Sep_'15", "Aug_'15", "Jul_'15", "Jun_'15", "May_'15", "Apr_'15", "Mar_'15", "Feb_'15", "Jan_'15", "Dec_'14", "Nov_'14", "Oct_'14", "Sep_'14", "Aug_'14", "Jul_'14", "Jun_'14", "May_'14", "Apr_'14", "Mar_'14", "Feb_'14", "Jan_'14"]
Y_Axis['Number', 'of', 'hours']: ['46.2', '48.1', '87.9', '144.0', '173.6', '173.2', '160.8', '188.5', '168.9', '115.6', '100.6', '47.9', '37.6', '63.0', '113.2', '134.1', '147.4', '237.6', '239.9', '246.0', '132.6', '85.0', '95.6', '48.7', '45.3', '71.1', '72.7', '109.0', '155.5', '168.7', '155.7', '208.3', '158.0', '119.7', '55.0', '55.1', '40.7', '74.7', '105.3', '119.9', '181.7', '156.4', '136.5', '209.6', '160.8', '117.3', '84.9', '37.1', '29.2', '35.6', '91.2', '157.8', '148.8', '160.6', '189.7', '174.4', '212.9', '121.9', '76.0', '58.5', '57.1', '51.9', '82.8', '123.3', '171.0', '223.0', '178.4', '149.6', '144.9', '126.7', '75.0', '42.8']

gold: In the period of consideration , the total monthly hours of sunlight in the UK followed a similar pattern each year . The most notable change occurred in 2018 , when the hours of sunlight shot up in May , June and July to 246 , 240 and 238 hours respectively . Unsurprisingly it was the end of each year when sunlight hours were lowest .
gold_template: In the period of consideration , the total templateTitle[0] templateYLabel[1] of templateTitle[2] in the templateTitleSubject[0] followed a similar pattern each year . The most notable change occurred in 2018 , when the templateYLabel[1] of templateTitle[2] shot up in templateXValue[7] , and to templateYValue[max] , templateYValue[18] and templateYValue[17] templateYLabel[1] respectively . Unsurprisingly it was the end of each year when templateTitle[2] templateYLabel[1] were lowest .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of the templateTitle[3] templateTitle[4] of templateTitleSubject[0] from 2015 to 2019 . The templateTitle[0] of templateTitle[2] templateTitleSubject[0] templateTitle[4] was templateXValue[0] , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Monthly Number of the UK 2014 of UK from 2015 to 2019 .  The Monthly of sunlight UK 2014 was Dec_'19 , with a Number hours of 246.0 hours .

Example 567:
titleEntities: {'Subject': ['North America'], 'Date': ['2010']}
title: Religious diversity in North America in 2010 , by religion
X_Axis['Religion']: ['Christians', 'Unaffiliated', 'Jews', 'Muslims', 'Other_religions', 'Hindus', 'Buddhists']
Y_Axis['Percentage', 'of', 'population']: ['77', '17', '2', '1', '1', '1', '1']

gold: This statistic shows religious diversity in North America in 2010 , by share of religious population . In 2010 , about 77 percent of population were Christians .
gold_template: This statistic shows templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] share of templateTitle[0] templateYLabel[1] . In templateTitleDate[0] , about templateYValue[max] templateScale of templateYLabel[1] were templateXValue[0] .

generated_template: This statistic shows templateTitle[0] templateTitle[1] in the templateTitle[2] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] share of templateTitle[0] templateYLabel[1] . In templateTitleDate[0] , around templateYValue[max] templateScale of templateYLabel[1] , in templateTitle[2] countries , identified as Hindu .
generated: This statistic shows Religious diversity in the North North America in 2010 , by share of Religious population .  In 2010 , around 77 percentage of population , in North countries , identified as Hindu .

Example 568:
titleEntities: {'Subject': ['England'], 'Date': ['2018']}
title: Population of regions in England in 2018
X_Axis['Region']: ['South_East', 'London', 'North_West', 'East', 'West_Midlands', 'South_West', 'Yorkshire_And_The_Humber', 'East_Midlands', 'North_East']
Y_Axis['Number', 'of', 'inhabitants', '(in', 'millions)']: ['9.13', '8.9', '7.29', '6.2', '5.9', '5.56', '5.48', '4.8', '2.66']

gold: In England , the region with the highest population was the South East at approximately 9.13 million people , followed by the London region which had around 8.9 million people . In the North of England the region with the highest population was the North West at 7.29 million inhabitants whereas the North East had the lowest population at 2.66 million inhabitants . Four countries of the UK Within the UK , England has the highest population of the four countries that compose the United Kingdom at over 55 million people , compared with Scotland ( 5.44 million ) Wales ( 3.14 million ) and Northern Ireland ( 1.88 million people ) which have comparatively small populations .
gold_template: In templateTitleSubject[0] , the templateXLabel[0] with the highest templateTitle[0] was the templateXValue[0] at approximately templateYValue[max] templateScale people , followed by the templateXValue[1] templateXLabel[0] which had around templateYValue[1] templateScale people . In the templateXValue[2] of templateTitleSubject[0] the templateXLabel[0] with the highest templateTitle[0] was the templateXValue[2] at templateYValue[2] templateScale templateYLabel[1] whereas the templateXValue[2] templateXValue[0] had the lowest templateTitle[0] at templateYValue[min] templateScale templateYLabel[1] . Four countries of the UK Within the UK , templateTitleSubject[0] has the highest templateTitle[0] of the four countries that compose the United Kingdom at over 55 templateScale people , compared with Scotland ( 5.44 templateScale ) Wales ( 3.14 templateScale ) and Northern Ireland ( 1.88 templateScale people ) which have comparatively small populations .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In that year , the average annual templateYLabel[1] of templateXValue[0] in templateTitleSubject[0] amounted to about templateYValue[max] templateScale templateYLabel[3] .
generated: The statistic shows the Population regions England in 2018 , Region .  In that year , the average annual inhabitants of South_East in England amounted to about 9.13 million millions) .

Example 569:
titleEntities: {'Subject': ['Croatia'], 'Date': ['2006', '2018']}
title: Croatia : Number of road deaths 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['317', '331', '307', '348', '308', '368', '393', '418', '426', '548', '664', '619', '614']

gold: This statistic illustrates the number of road traffic fatalities per year in Croatia between 2006 and 2018 . In the period of consideration , road fatalities presented an overall trend of decline . The year with the lowest amount of fatalities was 2016 , with a total of 207 road traffic fatalities in Croatia .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[2] traffic templateYLabel[1] per templateXLabel[0] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In the period of consideration , templateTitle[2] templateYLabel[1] presented an overall trend of decline . The templateXLabel[0] with the lowest amount of templateYLabel[1] was templateXValue[2] , with a total of 207 templateTitle[2] traffic templateYLabel[1] in templateTitleSubject[0] .

generated_template: In templateXValue[max] , there were templateYValue[idxmax(X)] recorded traffic templateYLabel[1] in templateTitleSubject[0] . The Scandinavian country had seen the templateYLabel[0] of templateTitle[1] templateTitle[2] drop significantly in the last ten years and by templateXValue[idxmin(Y)] reported 20 incidents per one templateScale inhabitants . This was the lowest templateTitle[1] fatality prevalence in Europe .
generated: In 2018 , there were 317 recorded traffic fatalities in Croatia .  The Scandinavian country had seen the Number of road drop significantly in the last ten years and by 2016 reported 20 incidents per one million inhabitants .  This was the lowest Number fatality prevalence in Europe .

Example 570:
titleEntities: {'Subject': ['Catalonia', 'Spain'], 'Date': ['2003', '2017']}
title: Gross domestic product in Catalonia and Spain 2003 to 2017
Data:
                0       1       2        3        4        5        6        7        8        9       10       11       12       13       14
Year         2003    2004    2005     2006     2007     2008     2009     2010     2011     2012     2013     2014     2015     2016     2017
Catalonia  156600  169776  183507   199169   212391   216922   208115   209792   209716   203157   202638   207156   215722   224751   234651
Spain      803472  861420  930566  1007974  1080807  1116225  1079052  1080935  1070449  1039815  1025693  1037820  1079998  1118255  1163662 

gold: This statistic compares the gross domestic product ( GDP ) in Catalonia and the whole of Spain between 2003 and 2017 . GDP refers to the total market value of all goods and services that are produced within a country per year . It is an important indicator of the economic strength of a country .
gold_template: This statistic compares the templateTitle[0] templateTitle[1] templateTitle[2] ( GDP ) in templateTitleSubject[0] and the whole of templateTitleSubject[1] between templateValue[0][0] and templateValue[0][last] . GDP refers to the total market value of all goods and services that are produced within a country per templateLabel[0][0] . It is an important indicator of the economic strength of a country .

generated_template: This statistic gives information on the templateTitle[0] of templateTitle[1] templateTitle[2] in the templateTitle[4] from templateValue[0][last] to templateValue[0][0] , sorted templateTitle[7] templateTitle[8] . As of templateValue[0][0] , it was found that templateValue[1][0] templateScale of the employees of templateTitle[4] made up from templateValue[1][last] templateScale in the previous templateLabel[0][0] .
generated: This statistic gives information on the Gross of domestic product in the Spain from 2017 to 2003 , sorted 2017 .  As of 2003 , it was found that 156600 % of the employees of Spain made up from 234651 % in the previous Year .

Example 571:
titleEntities: {'Subject': ['Pinterest'], 'Date': ['2017']}
title: Number of U.S. Pinterest users 2017 , by gender
X_Axis['Sex']: ['Female', 'Male']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['58.9', '32.6']

gold: This statistic shows the gender distribution of visitors to Pinterest.com , divided by visitors from the United States and from the United Kingom as of September 2013 . Pinterest 's usership in the United States is predominantly female as 67 percent of all users are women .
gold_template: This statistic shows the templateTitle[6] distribution of visitors to Pinterest.com , divided templateTitle[5] visitors from the templateTitle[1] and from the United Kingom as of 2013 . templateTitleSubject[0] 's usership in the templateTitle[1] is predominantly templateXValue[0] as 67 templateScale of all templateYLabel[1] are women .

generated_template: As of 2019 , there were more than templateYValue[max] templateScale templateTitle[1] templateYLabel[1] in the templateTitleSubject[1] in templateTitleDate[0] , compared to a survey conducted in the previous year . templateYValue[min] templateScale of templateYLabel[1] in the templateTitleSubject[0] stated that they used a templateTitle[2] templateTitle[3] were templateXValue[0] .
generated: As of 2019 , there were more than 58.9 millions U.S. users in the Pinterest in 2017 , compared to a survey conducted in the previous year .  32.6 millions of users in the Pinterest stated that they used a Pinterest users were Female .

Example 572:
titleEntities: {'Subject': ['New York'], 'Date': ['2016', '2017']}
title: Quarterly average daily rate of hotels in New York 2016 to 2017
Data:
           0    1    2    3
Quarter   Q4   Q3   Q2   Q1
2016     389  350  351  260
2017       0    0    0  257 

gold: This statistic shows the quarterly average daily rate of hotels in New York in 2016 and 2017 . In the first quarter of 2017 , the average daily rate of hotels in New York in the United States was 257 U.S. dollars . Accommodation in New York - additional information The average daily rate of hotels in New York appears to be much higher in the fourth quarter of the year , reaching 389 U.S. dollars in 2016 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . In the first templateLabel[0][0] of templateTitleDate[max] , the templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in the country was templateValue[2][max] U.S. dollars . Accommodation in templateTitleSubject[0] - additional information The templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] appears to be much higher in the fourth templateLabel[0][0] of the year , reaching templateValue[1][max] U.S. dollars in templateTitleDate[min] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . In the first templateLabel[0][0] of templateTitleDate[max] , the templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in the country was templateValue[2][max] U.S. dollars .
generated: This statistic shows the Quarterly average daily rate of hotels in New York 2016 and 2017 .  In the first Quarter of 2017 , the average daily rate of hotels in New York the country was 257 U.S. dollars .

Example 573:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2011', '2016']}
title: Revenue of the U.S. employee wellness market 2011 to 2016
X_Axis['Year']: ['2011', '2016']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['1.45', '2.9']

gold: The statistic shows the revenue of the U.S. employee wellness market in 2011 provides a forecast for 2016 . 1.45 billion U.S. dollars in total revenue was generated by the U.S. employee wellness market in 2011 .
gold_template: The statistic shows the templateYLabel[0] of the templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] in templateXValue[min] provides a forecast for templateXValue[max] . templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] in total templateYLabel[0] was generated by the templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the worldwide templateYLabel[0] for the templateTitleSubject[0] templateTitle[1] templateTitle[2] and templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateTitle[2] had a templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateScale the previous templateXLabel[0] .
generated: This statistic shows the worldwide Revenue for the U.S. employee and wellness from 2011 to 2016 .  In 2016 , the global employee had a Revenue of 2.9 billion U.S. dollars , up from 2.9 billion the previous Year .

Example 574:
titleEntities: {'Subject': ['Video'], 'Date': ['2011', '2021']}
title: Video game consumer market value worldwide 2011 - 2021 , by distribution
Data:
                   0     1     2     3     4     5     6     7     8     9    10
Year            2021  2020  2019  2018  2017  2016  2015  2014  2013  2012  2011
DLC market      28.9  24.6  22.1  19.0  13.2  10.3   9.3   8.7   7.4   7.1   5.3
Package market   8.4  87.0   9.8  10.9  11.2  10.9  12.4  12.9  16.3  18.4  22.4 

gold: This statistic shows the value of the video game consumer market , both package and digitally distributed , worldwide from 2011 to 2018 and offers a forecast until 2021 . The packaged market value in 2018 amounted to 10.9 billion U.S. dollars , down from 11.2 billion a year earlier . Recent video game sales revenue information can be found here .
gold_template: This statistic shows the templateTitle[4] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] , both templateLabel[2][0] and digitally distributed , templateTitle[5] from templateValue[0][last] to templateValue[0][3] and offers a forecast until templateValue[0][0] . The packaged templateTitle[3] templateTitle[4] in templateValue[0][3] amounted to templateValue[2][3] templateScale U.S. dollars , down from templateValue[2][4] templateScale a templateLabel[0][0] earlier . Recent templateTitleSubject[0] templateTitle[1] sales revenue information can be found here .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitle[0] of the templateTitle[1] in templateTitleSubject[0] amounted to templateValue[2][0] templateScale templateYLabel[3] .
generated: This statistic shows the Video of the game consumer market value in Video from 2011 to 2021 .  In 2021 , the Video of the game in Video amounted to 8.4 million .

Example 575:
titleEntities: {'Subject': ['Top'], 'Date': ['2014']}
title: Top countries by pharmaceutical industry employment 2014
X_Axis['Country']: ['China', 'India', 'United_States', 'Germany', 'Brazil', 'Japan', 'Russian_Federation', 'Indonesia', 'Italy', 'Mexico', 'Thailand', 'Egypt', 'France', 'Spain', 'Switzerland', 'United_Kingdom', 'Vietnam', 'Poland', 'Pakistan', 'Turkey']
Y_Axis['Number', 'of', 'employees']: ['2301534', '567469', '251995', '128000', '105253', '91529', '66523', '64059', '59300', '51125', '48000', '47753', '47000', '43723', '43258', '41690', '39749', '38000', '36336', '35100']

gold: This statistic displays the top 20 countries worldwide based on the number of people employed in the pharmaceutical industry as of 2014 . The pharmaceutical industry in Japan counted over 91,500 employees that year .
gold_template: This statistic displays the templateTitleSubject[0] 20 templateTitle[1] worldwide based on the templateYLabel[0] of people employed in the templateTitle[3] templateTitle[4] as of templateTitleDate[0] . The templateTitle[3] templateTitle[4] in templateXValue[5] counted over 91,500 templateYLabel[1] that year .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitle[0] templateTitle[1] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . According to the source , templateXValue[0] had over templateYValue[max] templateScale of the largest templateTitleSubject[0] , followed templateTitle[5] templateXValue[1] with templateYValue[1] templateScale .
generated: This statistic shows the Number of employees the Top countries in 2014 , industry Country .  According to the source , China had over 2301534 million of the largest Top , followed employment India with 567469 million .

Example 576:
titleEntities: {'Subject': ['Russia'], 'Date': ['2024']}
title: Total population of Russia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['145.74', '146.02', '146.27', '146.47', '146.62', '146.73', '146.8', '146.9', '146.8', '146.5', '146.3']

gold: This statistic shows the total population of Russia from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population of Russia was around 146.8 million people . Only a fraction of them live in the major Russian cities .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was around templateYValue[6] templateScale people . Only a fraction of them live in the major Russian cities .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale templateYLabel[0] .
generated: The statistic shows the Total population of the Russia from 2014 to 2024 .  In 2018 , the Total population of the Russia amounted to approximately 146.8 millions Inhabitants .

Example 577:
titleEntities: {'Subject': ['Nuclear'], 'Date': ['2030']}
title: Nuclear energy - global market size by segment through 2030
Data:
                             0                     1                                 2        3                        4                                                   5
Power Type  Nuclear_generation  Radiation_management  Nuclear_power_plant_construction  Service  Front_end_of_fuel_cycle  Spent_nuclear_fuel_reprocessing_and_secondary_fuel
2011                       133                    69                                31       18                       17                                                   3
2030                       300                   267                                53       22                       26                                                   9 

gold: The global nuclear energy market is expected to grow over the next years , with the nuclear generation segment reaching 300 billion U.S. dollars . In addition to the already operable nuclear reactors all over the world , there are also nuclear reactors currently under construction , most notably in China , Russia , and India . In these countries , there are also several reactors being planned and in various stages of approval , funding , or major commitments already in place .
gold_template: The templateTitle[2] templateValue[0][0] templateTitle[1] templateTitle[3] is expected to grow over the next years , with the templateValue[0][0] segment reaching templateValue[2][max] templateScale U.S. dollars . In addition to the already operable templateValue[0][0] reactors all over the world , there are also templateValue[0][0] reactors currently under templateValue[0][2] , most notably in China , Russia , and India . In these countries , there are also several reactors being planned and in various stages of approval , funding , or major commitments already in place .

generated_template: The statistic shows the distribution of templateTitle[1] in the templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] , templateTitle[7] templateTitle[8] . According to the survey , templateValue[1][1] templateScale of the people in the country were living in the world .
generated: The statistic shows the distribution of energy in the Nuclear in 2030 and , through 2030 .  According to the survey , 69 % of the people in the country were living in the world .

Example 578:
titleEntities: {'Subject': ['Canada'], 'Date': ['2008', '2019']}
title: Maple syrup production in Canada 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Production', 'in', 'million', 'gallons']: ['13.2', '9.8', '12.51', '12.16', '8.91', '9.49', '10.05', '7.86', '8.55', '7.27', '9.08', '5.13']

gold: This statistic shows the maple syrup production in Canada from 2008 to 2019 . Approximately 13.2 million gallons of maple syrup was produced in Canada in 2019 , up from 9.8 million gallons the previous year . Maple syrup in Canada - additional information Maple syrup is most commonly associated with Canada , the product 's largest producer .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] of templateTitle[0] templateTitle[1] was produced in templateTitleSubject[0] in templateXValue[idxmax(Y)] , up from templateYValue[1] templateScale templateYLabel[2] the previous templateXLabel[0] . templateTitle[0] templateTitle[1] in templateTitleSubject[0] - additional information templateTitle[0] templateTitle[1] is most commonly associated with templateTitleSubject[0] , the product 's largest producer .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale templateYLabel[2] of templateTitle[0] templateTitle[1] were produced . templateTitle[0] templateTitle[1] is a templateTitle[0] , low-calorie dessert , which is often served in a large variety of flavors .
generated: The statistic shows the Maple syrup Production in the Canada from 2008 to 2019 .  In 2019 , about 13.2 million gallons of Maple syrup were produced .  Maple syrup is a Maple , low-calorie dessert , which is often served in a large variety of flavors .

Example 579:
titleEntities: {'Subject': ['Countries'], 'Date': ['2018']}
title: Countries with the highest rates of autism among children worldwide as of 2018
X_Axis['Country']: ['United_States', 'Japan', 'Canada', 'United_Kingdom', 'Ireland', 'Denmark', 'Australia', 'Hong_Kong_(SAR_of_China)', 'Brazil', 'Portugal']
Y_Axis['Number', 'of', 'children', 'with', 'autism', 'per', '10,000', 'children', 'studied']: ['168.0', '161.0', '152.0', '100.0', '100.0', '69.0', '67.0', '49.0', '27.0', '9.2']

gold: In 2018 , it was estimated that around 168 per 10,000 children in the United States had autism spectrum disorder , one of the highest prevalence rates in the world . Autism spectrum disorder ( ASD ) includes a variety of disorders . The `` spectrum '' denotes the range of symptoms as well as their severity .
gold_template: In templateTitleDate[0] , it was estimated that around templateYValue[max] templateYLabel[4] 10,000 templateYLabel[1] in the templateXValue[0] had templateYLabel[3] spectrum disorder , one of the templateTitle[2] prevalence templateTitle[3] in the world . templateYLabel[3] spectrum disorder ( ASD ) includes a variety of disorders . The `` spectrum '' denotes the range of symptoms as well as their severity .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] worldwide in templateTitleDate[0] . In that year , the templateTitle[2] templateTitle[3] templateTitle[4] was estimated to amount to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number children of the rates autism among worldwide in 2018 .  In that year , the rates autism among was estimated to amount approximately 168.0 autism per .

Example 580:
titleEntities: {'Subject': ['Costco'], 'Date': ['2014', '2019']}
title: Costco membership worldwide 2014 to 2019
X_Axis['Year']: ['2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['Total', 'number', 'of', 'card', 'holders']: ['76.4', '81.3', '86.7', '90.3', '94.3', '98.5']

gold: This statistic presents the total number of Costco card holders worldwide from 2014 to 2019 . For the 52 weeks ended on September 1 , 2019 , there were approximately 98.5 million Costco cardholders all over the world .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateYLabel[2] templateYLabel[3] templateTitle[2] from templateXValue[min] to templateXValue[max] . For the 52 weeks ended on 1 , templateXValue[max] , there were approximately templateYValue[idxmax(X)] templateYValue[idxmax(X)] templateTitleSubject[0] cardholders all over the world .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[0] employees amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] , down from templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in the previous templateXLabel[0] .
generated: This statistic shows the Total of number the Costco worldwide from 2014 to 2019 .  In 2019 , the Total of Costco employees amounted to approximately 98.5 million holders , down from 81.3 million holders in the previous Year .

Example 581:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2024']}
title: Inflation rate in Denmark 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2', '2', '2', '1.8', '1.5', '1.3', '0.71', '1.06', '0.02', '0.23', '0.35', '0.52', '2.35', '2.66', '2.2', '1.04', '3.64', '1.66', '1.88', '1.68', '0.89', '2', '2.37', '2.31', '2.72', '2.05', '1.31', '1.98', '2.08', '2.04', '1.84', '0.87', '1.94', '2.17', '6.84', '4.78', '4.53', '3.98', '3.72', '4.66', '6.34']

gold: In 2018 , the inflation rate in Denmark amounted to 0.71 percent compared to the previous year . Forecasts see it rising to around two percent , the rate of inflation considered stable by economists . Denmark 's economy in short Denmark is a market economy with a consistent trade surplus , with most of its workforce employed in the services sector .
gold_template: In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . Forecasts see it templatePositiveTrend to around templateYValue[0] templateScale , the templateYLabel[1] of templateYLabel[0] considered stable by economists . templateTitleSubject[0] 's economy in short templateTitleSubject[0] is a market economy with a consistent trade surplus , with most of its workforce employed in the services sector .

generated_template: The statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the average Inflation rate in Denmark from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Denmark amounted to about 0.71 % compared to the previous Year .

Example 582:
titleEntities: {'Subject': ['Activision Blizzard'], 'Date': ['2005', '2018']}
title: Activision Blizzard 's net income/loss from 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['1813', '273', '966', '892', '835', '1010', '1149', '1085', '418', '113', '-107', '227', '139', '45']

gold: This statistic shows the annual net income/loss of Activision Blizzard worldwide from 2005 to 2018 . In 2018 , Activision Blizzard 's net income amounted to 1.8 billion U.S. dollars .
gold_template: This statistic shows the annual templateYLabel[0] templateTitle[4] of templateTitleSubject[0] worldwide templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . The company reported templateYLabel[0] templateYLabel[1] of approximately templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: This statistic shows the Net income of the U.S. 's net Activision Blizzard worldwide from 2005 to 2018 .  The company reported Net income of approximately 1813 million U.S. dollars in 2018 .

Example 583:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2017']}
title: Facebook : monthly brand profile follower growth 2017
X_Axis['Month']: ["Dec_'17", "Nov_'17", "Oct_'17", "Sep_'17", "Aug_'17"]
Y_Axis['Growth', 'rate']: ['0.61', '0.1', '-0.1', '-3.28', '0.82']

gold: This statistic presents the monthly brand fan growth of Facebook profiles from August to December 2017 . In the last measured month , Facebook brand pages gained an average of 0.6 percent more followers than in the previous month .
gold_template: This statistic presents the templateTitle[1] templateTitle[2] fan templateYLabel[0] of templateTitleSubject[0] profiles from to 2017 . In the last measured templateXLabel[0] , templateTitleSubject[0] templateTitle[2] pages templatePositiveTrend an average of templateYValue[0] templateScale more followers than in the previous templateXLabel[0] .

generated_template: This statistic presents the templateTitle[1] templateTitle[2] templateTitle[4] templateYLabel[0] of templateTitleSubject[0] profiles from to 2017 . In the last measured templateXLabel[0] , templateTitleSubject[0] templateTitle[2] profiles templatePositiveTrend an average of templateYValue[max] templateScale more followers than in the previous templateXLabel[0] .
generated: This statistic presents the monthly brand follower Growth of Facebook profiles from to 2017 .  In the last measured Month , Facebook brand profiles gained an average of 0.82 million more followers than in the previous Month .

Example 584:
titleEntities: {'Subject': ['Manchester'], 'Date': ['2019']}
title: Players with most appearances for Manchester United 2019
X_Axis['Month']: ['Ryan_Giggs', 'Sir_Bobby_Charlton', 'Paul_Scholes', 'Bill_Foulkes', 'Gary_Neville', 'Wayne_Rooney', 'Alex_Stepney', 'Tony_Dunne', 'Denis_Irwin', 'Joe_Spence', 'Arthur_Albiston', 'Roy_Keane', 'Brian_McClair', 'George_Best', 'Mark_Hughes', 'Michael_Carrick', 'Bryan_Robson', 'Martin_Buchan', 'Rio_Ferdinand', 'Jack_Silcock', 'Gary_Pallister']
Y_Axis['Number', 'of', 'appearances']: ['963', '758', '718', '688', '602', '559', '539', '535', '529', '510', '485', '480', '471', '470', '467', '464', '461', '456', '455', '449', '437']

gold: Ryan Giggs holds the all-time record for most appearances for Manchester United , playing in 963 games for the club between 1991 and 2014 . Sir Bobby Charlton and Paul Scholes are ranked second and third in the list respectively.The Welsh wizard Ryan Giggs made his debut for Manchester United aged 17-years-old as a substitute against Everton at Old Trafford in March 1991 . Giggs scored 168 goals during his 23-years as a player for the club , a tally that puts him among the list of the all-time top goalscorers for Manchester United .
gold_template: templateXValue[0] holds the all-time record templateTitle[4] templateTitle[2] templateYLabel[1] templateTitle[4] templateTitleSubject[0] templateTitle[6] , playing in templateYValue[max] games templateTitle[4] the club between 1991 and 2014 . templateXValue[1] Charlton and templateXValue[2] are ranked second and third in the list respectively.The Welsh wizard templateXValue[0] made his debut templateTitle[4] templateTitleSubject[0] templateTitle[6] aged 17-years-old as a substitute against Everton at Old Trafford in 1991 . templateXValue[0] scored 168 goals during his 23-years as a player templateTitle[4] the club , a tally that puts him among the list of the all-time top goalscorers templateTitle[4] templateTitleSubject[0] templateTitle[6] .

generated_template: As of 2019 , templateXValue[0] had the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] with the highest templateYLabel[0] of templateYLabel[1] with templateYValue[max] templateScale templateYLabel[3] . templateXValue[1] was followed with templateYValue[1] templateScale of templateYLabel[1] .
generated: As of 2019 , Ryan_Giggs had the Players most appearances in the for with the highest Number of appearances with 963 % appearances .  Sir_Bobby_Charlton was followed with 758 % of appearances .

Example 585:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2008', '2018']}
title: Average life expectancy at birth in Sweden 2008 - 2018 , by gender
Data:
               0          1          2          3          4          5          6
Year   2014-2018  2013-2017  2012-2016  2011-2015  2010-2014  2009-2013  2008-2012
Men        80.55      80.41      80.24      80.08      79.93      79.72      79.52
Women       84.1      83.99      83.88      83.79      83.69      83.55      83.43 

gold: This statistic depicts the average life expectancy in Sweden from 2008 to 2018 , by gender . Women had a higher life expectancy than men in Sweden . In 2014 to 2018 the life expectancy for women was expected to be slightly more than 84 years old .
gold_template: This statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[7] templateTitle[8] . templateLabel[2][0] had a higher templateTitle[1] templateTitle[2] than templateLabel[1][0] in templateTitleSubject[0] . In 2014 to templateTitleDate[max] the templateTitle[1] templateTitle[2] for templateLabel[2][0] was expected to be slightly more than templateValue[2][0] years old .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] at templateTitle[2] of templateLabel[2][0] in the templateTitleSubject[0] as of templateTitleDate[0] , templateTitle[4] templateTitle[5] . During the survey , it was found that templateValue[2][0] templateScale of templateLabel[1][0] spending on templateTitle[4] templateValue[0][0] .
generated: The statistic shows the Average life at expectancy of Women in the Sweden as of 2008 , Sweden 2008 .  During the survey , it was found that 84.1 % of Men spending on Sweden 2014-2018 .

Example 586:
titleEntities: {'Subject': ['Amazon'], 'Date': ['2016', '2020']}
title: U.S. Amazon retail market share 2016 and 2020
X_Axis['Year']: ['2020', '2016']
Y_Axis['Market', 'share']: ['50', '34']

gold: This statistic presents a projection of Amazon 's retail market share in the United States in 2016 and 2020 , based on GMV . In 2016 , the online retail platform was estimated to account for 34 percent of the U.S. retail GMV . This share is set to increase to 50 percent in 2020 .
gold_template: This statistic presents a projection of templateTitleSubject[0] 's templateTitle[2] templateYLabel[0] templateYLabel[1] in the templateTitle[0] in templateXValue[min] and templateXValue[max] , based on GMV . In templateXValue[min] , the online templateTitle[2] platform was estimated to account for templateYValue[idxmin(X)] templateScale of the templateTitle[0] templateTitle[2] GMV . This templateYLabel[1] is set to templatePositiveTrend to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the global templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] is estimated to be templateYValue[min] templateScale .
generated: This statistic shows the global Amazon retail share of the share 2016 2020 from 2016 to 2020 .  In 2016 , the Amazon retail Market is estimated to be 34 % .

Example 587:
titleEntities: {'Subject': ['Europe'], 'Date': ['2017']}
title: Bottled water consumption per capita in Europe 2017 , by country
X_Axis['Country']: ['Italy', 'Germany', 'Hungary', 'Belgium', 'France', 'Spain', 'Portugal', 'EU_Average', 'Poland', 'Austria', 'Greece', 'Romania', 'Bulgaria', 'Croatia', 'Slovakia', 'Slovenia', 'Latvia', 'Czech_Republic', 'Lithuania', 'Ireland', 'United_Kingdom', 'Estonia', 'Netherlands', 'Denmark', 'Finland', 'Sweden']
Y_Axis['Litres', 'consumed', 'per', 'capita']: ['188', '175', '126', '130', '125', '126', '125', '117', '99', '92', '93', '90', '80', '74', '67', '61', '62', '50', '48', '48', '36', '32', '25', '21', '14', '10']

gold: This statistic shows the per capita consumption of bottled water in Europe in 2017 . In this year , Italy was the leading market for the consumption of bottled water with 188 liters of bottled water consumed per person and Germany was the second largest consumer of bottled water with 175 liters consumed per person . Bottled water in the European Union is predominantly made up of the natural mineral water category.Germany is the market with the largest amount of different mineral water brands .
gold_template: This statistic shows the templateYLabel[2] templateYLabel[3] templateTitle[2] of templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] . In this year , templateXValue[0] was the leading market for the templateTitle[2] of templateTitle[0] templateTitle[1] with templateYValue[max] liters of templateTitle[0] templateTitle[1] templateYLabel[1] templateYLabel[2] person and templateXValue[1] was the second largest consumer of templateTitle[0] templateTitle[1] with templateYValue[1] liters templateYLabel[1] templateYLabel[2] person . templateTitle[0] templateTitle[1] in the European Union is predominantly made up of the natural mineral templateTitle[1] category.Germany is the market with the largest amount of different mineral templateTitle[1] brands .

generated_template: This statistic represents the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] had the highest templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] with templateYValue[max] certified templateTitle[1] templateTitle[2] water sources .
generated: This statistic represents the Bottled of water consumption per in Europe 2017 .  Italy had the highest Bottled of water consumption per with 188 certified water consumption sources .

Example 588:
titleEntities: {'Subject': ['UK'], 'Date': ['2019']}
title: Internet clothing and footwear retail sales trend monthly in the UK 2013 to 2019
X_Axis['Month']: ['Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16', 'Jun_16', 'May_16', 'Apr_16', 'Mar_16', 'Feb_16', 'Jan_16', 'Dec_15', 'Nov_15', 'Oct_15', 'Sep_15', 'Aug_15', 'Jul_15', 'Jun_15', 'May_15', 'Apr_15', 'Mar_15', 'Feb_15', 'Jan_15', 'Dec_14', 'Nov_14', 'Oct_14', 'Sep_14', 'Aug_14', 'Jul_14', 'Jun_14', 'May_14', 'Apr_14', 'Mar_14', 'Feb_14', 'Jan_14', 'Dec_13', 'Nov_13', 'Oct_13', 'Sep_13', 'Aug_13', 'Jul_13', 'Jun_13', 'May_13', 'Apr_13', 'Mar_13', 'Feb_13', 'Jan_13']
Y_Axis['Percentage', 'change']: ['6.6', '8.5', '13.2', '17.9', '6.5', '15.9', '12.3', '12', '11', '13.4', '19.2', '13.8', '18.7', '11.8', '16', '18.9', '24', '24.5', '13.2', '10.9', '18', '20.5', '15.6', '19.1', '24.9', '22.2', '18.4', '17.3', '13.4', '9.7', '25.9', '16.5', '12.9', '8.2', '10.2', '7.2', '-1.2', '-3', '9.6', '-0.5', '12.7', '4.4', '3.1', '12.7', '9.9', '10.2', '18.9', '15.6', '9', '12.7', '6', '17.1', '1.8', '16', '7.9', '8.9', '13.9', '20.4', '20.3', '16.9', '14.3', '34', '20.8', '22.1', '22.8', '11.6', '6.7', '14', '17.2', '4.8', '29.9', '3', '20.4', '6.4', '6.4', '21.8', '15.1', '18.3', '21.2', '12.6', '10.7']

gold: This statistic displays the monthly trend in the value of retail internet apparel sales in the United Kingdom ( UK ) from January 2013 to September 2019 . In September 2019 , sales were up 6.6 percent with respect to the same month a year earlier .
gold_template: This statistic displays the templateTitle[6] templateTitle[5] in the value of templateTitle[3] templateTitle[0] apparel templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) from 2013 to 2019 . In 2019 , templateTitle[4] were up templateYValue[0] templateScale with respect to the same templateXLabel[0] a year earlier .

generated_template: This statistic displays the templateTitle[5] templateTitle[4] of the templateTitle[3] of templateTitle[1] templateTitle[0] templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from 2013 to 2019 . In 2019 , templateTitle[1] templateTitle[2] templatePositiveTrend by templateYValue[0] templateScale .
generated: This statistic displays the trend sales of the retail of clothing Internet footwear in the UK ( ) from 2013 to 2019 .  In 2019 , clothing footwear increased by 6.6 percentage .

Example 589:
titleEntities: {'Subject': ['UK'], 'Date': ['2018']}
title: UK : length of wait for security screening in Luton Airport 2018
X_Axis['Year']: ['0_mins', '1-5_mins', '6-10_mins', '11-20_mins', '21-30_mins']
Y_Axis['Share', 'of', 'respondents']: ['14', '48', '25', '12', '1']

gold: The greatest share of passengers at Luton Airport waited between one to five minutes to be security screened . Only 25 percent stated that they had queued for longer than six minutes , although none of the respondents waited for longer than 30 minutes . 85 percent of passengers at Luton airport were satisfied with their security screening experience .
gold_template: The greatest templateYLabel[0] of passengers at templateTitle[6] templateTitle[7] waited between templateYValue[min] to five minutes to be templateTitle[4] screened . Only templateYValue[2] templateScale stated that they had queued templateTitle[3] longer than six minutes , although none of the templateYLabel[1] waited templateTitle[3] longer than 30 minutes . 85 templateScale of passengers at templateTitle[6] templateTitle[7] were satisfied with their templateTitle[4] templateTitle[5] experience .

generated_template: templateTitle[3] templateTitle[4] at templateTitle[5] templateTitle[6] had an average waiting time of 10 minutes . templateYValue[max] templateScale of templateYLabel[1] from a templateTitleDate[0] survey reported queuing approximately templateYValue[min] - 5 minutes . Only templateYValue[min] templateScale of templateYLabel[1] stated that they had to templateTitle[1] more than 30 minutes before being able to continue to their gate .
generated: for security at screening Luton had an average waiting time of 10 minutes .  48 % of respondents from a 2018 survey reported queuing approximately 1 - 5 minutes .  Only 1 % of respondents stated that they had to length more than 30 minutes before being able to continue their gate .

Example 590:
titleEntities: {'Subject': ['Instagram.com'], 'Date': ['2019']}
title: Distribution of Instagram.com traffic 2019 , by country
X_Axis['Country']: ['United_States', 'Brazil', 'Russia', 'United_Kingdom', 'Turkey']
Y_Axis['Share', 'of', 'desktop', 'traffic']: ['17.77', '8.22', '7.06', '3.6', '3.24']

gold: As of October 2019 , the United States generated 17.77 percent of Instagram desktop traffic , while visitors from the United Kingdom accounted for 3.6 percent of site visits . Instagram is one of the most popular social networks in the United States with a 31 percent membership reach among online adults in the United States . During a February 2019 survey , it was found that 93 percent of adults in the United States were aware of the photo sharing app .
gold_template: As of October templateTitleDate[0] , the templateXValue[0] generated templateYValue[max] templateScale of Instagram templateYLabel[1] templateYLabel[2] , while visitors from the templateXValue[0] templateXValue[3] accounted for templateYValue[3] templateScale of site visits . Instagram is one of the most popular social networks in the templateXValue[0] with a 31 templateScale membership reach among online adults in the templateXValue[0] . During a 2019 survey , it was found that 93 templateScale of adults in the templateXValue[0] were aware of the photo sharing app .

generated_template: This statistic shows the 20 templateTitle[0] of templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In that year , the templateXValue[0] was ranked first , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the 20 Distribution of Instagram.com 2019 in .  In that year , the United_States was ranked first , with a Share desktop of 17.77 million traffic .

Example 591:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2013']}
title: Most popular crisps and savoury snack brands in the United Kingdom ( UK ) 2013
X_Axis['Snack', 'Brand']: ['Walkers', 'Doritos', 'Hula_Hoops', 'Pringles', 'McCoys', 'Own_brand_crisps', 'KP_nuts']
Y_Axis['Share', 'of', 'respondents', 'currently', 'purchasing']: ['42', '32', '26', '26', '25', '24', '22']

gold: This statistic displays the leading crisp and savory snack brands in the United Kingdom ( UK ) , ranked by most purchased . According to the survey conducted in November 2013 , the leading six products were all crisps . Walkers ranked highest with 42 percent of respondents purchasing their crisps at the time of survey , followed by Doritos at 32 percent .
gold_template: This statistic displays the leading crisp and savory templateXLabel[0] templateTitle[5] in the templateTitleSubject[0] ( templateTitleSubject[1] ) , ranked by templateTitle[0] purchased . According to the survey conducted in 2013 , the leading six products were all templateXValue[5] . templateXValue[0] ranked highest with templateYValue[max] templateScale of templateYLabel[1] templateYLabel[3] their templateXValue[5] at the time of survey , followed by templateXValue[1] at templateYValue[1] templateScale .

generated_template: The statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[4] as of 2019 . According to the survey , it was found that templateYValue[max] templateScale of the people who were living in the templateTitle[4] .
generated: The statistic gives information on the Most popular crisps in the snack as of 2019 .  According to the survey , it was found that 42 % of the people who were living in the snack .

Example 592:
titleEntities: {'Subject': ['De Beers'], 'Date': ['2011', '2018']}
title: De Beers ' revenue 2011 to 2018
X_Axis['Year']: ['2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['3320', '4028', '6404', '7114', '4671', '6068', '5841', '6082']

gold: In 2018 , the revenue of diamond mining company De Beers was about 6.1 billion U.S. dollars . De Beers De Beers was founded by Cecil Rhodes in 1888 , a British businessman and politician in Southern Africa . The company is now headquarted in London .
gold_template: In templateXValue[max] , the templateYLabel[0] of diamond mining company templateTitleSubject[0] was about templateYValue[5] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] De templateTitleSubject[0] was founded by Cecil Rhodes in 1888 , a British businessman and politician in Southern Africa . The company is now headquarted in London .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the De Beers , a franchise of the National Football League , from 2011 to 2018 .  In 2018 , the Revenue of the De Beers was 7114 6082 U.S. dollars .

Example 593:
titleEntities: {'Subject': ['Volkswagen'], 'Date': ['2014', '2018']}
title: Sales figures for Volkswagen by brand 2014 to 2018
Data:
                              0     1     2     3     4
Year                       2014  2015  2016  2017  2018
Volkswagen Passenger Cars  4583  4424  4347  3573  3715
Audi                       1444  1529  1534  1530  1467
Skoda                       796   800   814   937   957
Seat                        501   544   548   595   608
VW Commercial Vehicles      442   456   478   498   469
Porsche                     187   219   239   248   253
MAN Commercial Vehicles     120   102   102   114   137
Scania                       80    78    83    92    97
Bentley                      11    11    11    11    10 

gold: Volkswagen Group 's top brand is its namesake Volkswagen passenger car brand , which saw sales drop by some 868,000 units between 2014 and 2018 , to reach 3.7 million units in 2018 . Despite the sales slump , it still accounted for almost half of motor vehicles sold by the automaker outside China and about one third of all vehicles sold by Volkswagen Group in 2018 . Other brands have noticeably increased in sales , such as Skoda , Seat , and Porsche .
gold_template: templateTitleSubject[0] Group 's top templateTitle[5] is its namesake templateTitleSubject[0] templateLabel[1][1] car templateTitle[5] , which saw templateTitle[0] drop templateTitle[4] some 868,000 units between templateValue[0][0] and templateValue[0][last] , to reach 3.7 templateScale units in templateValue[0][last] . Despite the templateTitle[0] slump , it still accounted templateTitle[2] almost half of motor templateLabel[5][2] sold templateTitle[4] the automaker outside China and about one third of all templateLabel[5][2] sold templateTitle[4] templateTitleSubject[0] Group in templateValue[0][last] . Other brands have noticeably templatePositiveTrend in templateTitle[0] , such as templateLabel[3][0] , templateLabel[4][0] , and templateLabel[6][0] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] directly spent a templateTitle[0] in the United Kingdom ( templateTitleSubject[0] ) from templateValue[0][last] to templateValue[0][0] , templateTitle[5] templateTitle[6] . It was found that templateValue[1][0] templateScale of the population of templateTitleSubject[0] templateTitle[3] in templateValue[0][0] .
generated: This statistic shows the Sales of figures directly spent a Sales in the United Kingdom ( Volkswagen ) from 2018 to 2014 , brand 2014 .  It was found that 4583 % of the population of Volkswagen in 2014 .

Example 594:
titleEntities: {'Subject': ['Nepal'], 'Date': ['2024']}
title: Total population of Nepal 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['30.36', '29.97', '29.59', '29.2', '28.83', '28.46', '28.09', '27.63', '27.26', '27.02', '26.91']

gold: This statistic represents the total population of Nepal from 2014 to 2015 , with projections up until 2024 . In 2018 , the estimated total population of Nepal amounted to around 28.09 million people .
gold_template: This statistic represents the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[9] , with projections up until templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateScale people .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was estimated at approximately templateYValue[6] templateScale templateYLabel[0] .
generated: This statistic shows the Total population of Nepal from 2014 to 2024 .  In 2018 , the Total population of Nepal was estimated at approximately 28.09 millions Inhabitants .

Example 595:
titleEntities: {'Subject': ['Portugal'], 'Date': ['2008', '2018']}
title: Share of economic sectors in the GDP in Portugal 2008 to 2018
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
Agriculture   2.05    2.1   2.07    2.1   2.08    2.1   1.92   1.83   1.94   1.95   1.96
Industry     19.16  19.12  19.33  19.46  18.94  18.87  19.11  19.34  19.95  19.86  20.39
Services     65.26  65.36  65.47  65.53  66.32   66.9  66.44  66.35  66.06  66.87  64.84 

gold: This statistic shows the share of economic sectors in gross domestic product ( GDP ) in Portugal from 2007 to 2018 . In 2018 , the share of agriculture in Portugal 's gross domestic product was 2.05 percent , industry contributed approximately 19.16 percent and the services sector contributed about 65.26 percent .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] in gross domestic product ( templateTitle[3] ) in templateTitleSubject[0] from 2007 to templateValue[0][0] . In templateValue[0][0] , the templateTitle[0] of templateLabel[1][0] in templateTitleSubject[0] 's gross domestic product was templateValue[1][0] templateScale , templateLabel[2][0] contributed approximately templateValue[2][0] templateScale and the templateLabel[3][0] sector contributed about templateValue[3][0] templateScale .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the service sector .
generated: This statistic shows the Share of the economic sectors GDP ( Portugal ) 2008 2018 in Portugal from 2008 to 2018 .  In 2018 , Agriculture contributed around 2.05 % to the Portugal of , 19.16 % came from the Industry and 65.26 % from the service sector .

Example 596:
titleEntities: {'Subject': ['U.S'], 'Date': []}
title: Alzheimer 's in the U.S : lifetime risks , by age and gender
Data:
                0         1
Country  45_years  65_years
Men          10.3      19.5
Women        11.6      21.1 

gold: This statistic depicts the estimated lifetime risk for getting Alzheimer 's disease in the United States as of 2015 , by age and gender . It is estimated that men aged 65 have a 11.6 percent risk of developing Alzheimer 's during their life .
gold_template: This statistic depicts the estimated templateTitle[3] risk for getting templateTitle[0] templateTitle[1] disease in the country as of 2015 , templateTitle[5] templateTitle[6] and templateTitle[7] . It is estimated that templateLabel[1][0] aged templateValue[0][1] have a templateValue[2][0] templateScale risk of developing templateTitle[0] templateTitle[1] during their life .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateLabel[2][0] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . During the survey period , it was found that templateValue[2][0] templateScale of templateLabel[2][0] had a templateValue[0][0] of templateValue[2][1] templateScale of the templateTitle[7] .
generated: This statistic shows the Alzheimer 's U.S lifetime of Women in the U.S ( ) in .  During the survey period , it was found that 11.6 % of Women had a 45_years of 21.1 % of the gender .

Example 597:
titleEntities: {'Subject': ['Germany'], 'Date': ['2008', '2017']}
title: Share of people owning microwave ovens in Germany 2008 to 2017
X_Axis['Year']: ['2017', '2015', '2013', '2010', '2008']
Y_Axis['Share', 'of', 'respondents']: ['65', '70', '76', '80', '80']

gold: The figure shows the share of households owning microwave ovens in Germany from 2008 to 2017 . Over the time period under consideration , microwave oven ownership has decreased from 80 percent of respondents in 2008 to 65 percent in 2017 .
gold_template: The figure shows the templateYLabel[0] of households templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Over the time period under consideration , templateTitle[3] oven ownership has templateNegativeTrend from templateYValue[idxmin(X)] templateScale of templateYLabel[1] in templateXValue[min] to templateYValue[idxmax(X)] templateScale in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateScale of British templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] at home to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the templateYLabel[1] reported accessing the templateTitle[0] , up from templateYValue[min] templateScale in templateXValue[idxmin(Y)] .
generated: This statistic shows the percentage of British owning microwave ovens Germany at home to 2017 .  In 2017 , 65 % of the respondents reported accessing the Share , up from 65 % in 2017 .

Example 598:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2007', '2018']}
title: U.S. household expenditure on hair care products 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Average', 'annual', 'expenditure', 'in', 'U.S.', 'dollars']: ['73.4', '89.95', '83.07', '73.43', '69.53', '64.49', '61.69', '67.88', '62.0', '65.14', '68.27', '56.1']

gold: The amount spent on hair care products annually varies over the years in the United States . In 2018 , American consumers spent between 73.4 U.S. dollars in a year on hair care products . Where Americans Buy Cosmetics According to a 2017 survey , over half of Americans have bought cosmetics from a Walmart or Walmart Supercenter , a higher share than any other retailer in the United States .
gold_template: The amount spent on templateTitle[3] templateTitle[4] templateTitle[5] annually varies over the years in the templateTitle[0] . In templateXValue[max] , American consumers spent between templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] in a templateXLabel[0] on templateTitle[3] templateTitle[4] templateTitle[5] . Where Americans Buy Cosmetics According to a templateXValue[1] survey , over half of Americans have bought cosmetics from a Walmart or Walmart Supercenter , a higher share than any other retailer in the templateTitle[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] and templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the country 's templateYLabel[0] templateYLabel[2] on templateTitle[3] and templateTitle[4] amounted to templateYValue[idxmax(X)] templateYLabel[3] .
generated: This statistic shows the Average annual expenditure on hair and care products in the U.S. from 2007 to 2018 .  In 2018 , the country 's Average expenditure on hair and care amounted to 73.4 U.S. .

Example 599:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2015']}
title: Number of cigarettes smoked daily in the United Kingdom ( UK ) 2015
X_Axis['Number', 'of', 'cigarettes']: ['1_to_5', '6_to_10', '11_to_15', '16_to_20', '21_to_25', '26_to_30', '31_to_50', '51_and_over']
Y_Axis['Share', 'of', 'respondents']: ['23', '24', '22', '19', '6', '4', '2', '3']

gold: This statistic displays the number of cigarettes smoked per day in the United Kingdom ( UK ) in 2015 . Approximately 69 percent of respondents smoked between one and 15 cigarettes daily in that year .
gold_template: This statistic displays the templateXLabel[0] of templateXLabel[1] templateTitle[2] per day in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . Approximately 69 templateScale of templateYLabel[1] templateTitle[2] between templateXValue[0] and templateXValue[2] templateXLabel[1] templateTitle[3] in that year .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateXValue[last] as of the third quarter of templateTitleDate[0] . During the survey period , it was found that templateYValue[max] templateScale of templateYLabel[1] stated that they had made templateXValue[0] a templateXValue[last] .
generated: This statistic gives information on the Number cigarettes smoked 51_and_over as of the third quarter of 2015 .  During the survey period , it was found that 24 % of respondents stated that they had made 1_to_5 a 51_and_over .

Example 600:
titleEntities: {'Subject': ['Finland'], 'Date': ['2010/11', '2017/18']}
title: Ice hockey players in Finland 2010/11 - 2017/18
X_Axis['Year']: ['2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Number', 'of', 'players']: ['73374', '76387', '74150', '75871', '73682', '66636', '56626', '65251']

gold: The statistics shows the number of registered ice hockey players in Finland from 2010/11 to 2017/18 . The number of registered ice hockey players in 2017/18 amounted to nearly 73.4 thousand . The highest player number was reported in the previous season ( 2016/17 ) with over 76 thousand players .
gold_template: The statistics shows the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . The templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateXValue[0] amounted to nearly templateYValue[0] thousand . The highest player templateYLabel[0] was reported in the previous season ( templateXValue[1] ) with over templateYValue[max] thousand templateYLabel[1] .

generated_template: The statistics depicts the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[0] registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] according to the International templateTitle[0] templateTitle[1] Federation .
generated: The statistics depicts the Number of registered Ice hockey players in Finland from 2010/11 to 2017/18 .  In the 2017/18 season , there were a total of 73374 registered Ice hockey players in Finland according to the International Ice hockey Federation .

Example 601:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2018', '2019']}
title: Household rent to income ratio in the United Kingdom ( UK ) 2018 - 2019 , by region
Data:
              0           1           2           3             4                              5      6              7           8              9        10                11                  12          13
Region   London  South_West  South_East  UK_Average  East_England  UK_Average_(Excluding_London)  Wales  East_Midlands  North_West  West_Midlands  Scotland  Northern_Ireland  Yorkshire_&_Humber  North_East
Oct' 18    36.6        32.8        31.6          31          30.3                           29.4     29           28.7        28.6           28.5      27.5              26.6                  26        24.4
Oct '19    35.6        31.5        31.6        30.6          30.3                           29.2   29.3           28.8        27.1           29.2        27              25.9                26.3        23.7 

gold: The average cost of rent to household income ratio in the United Kingdom ( UK ) remained consistent between October 2018 and October 2019 . London had the highest household rent to income ratio despite a slight decrease between October 2018 and October 2019 . The North East of England saw the lowest rent to income ratio across the UK of 23.7 percent in October 2019 .
gold_template: The templateValue[0][3] cost of templateTitle[1] to templateTitle[0] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateValue[0][3] ) remained consistent between October templateTitleDate[min] and October templateTitleDate[max] . templateValue[0][0] had the highest templateTitle[0] templateTitle[1] to templateTitle[2] templateTitle[3] despite a slight templateNegativeTrend between October templateTitleDate[min] and October templateTitleDate[max] . The templateValue[0][8] templateValue[0][2] of templateValue[0][4] saw the lowest templateTitle[1] to templateTitle[2] templateTitle[3] across the templateValue[0][3] of templateValue[2][last] templateScale in October templateTitleDate[max] .

generated_template: This statistic shows the proportion of templateTitle[0] templateTitle[1] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[min] and templateTitleDate[max] , templateTitle[3] templateLabel[0][0] . During the survey period , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] at templateValue[1][0] templateScale U.S. dollars in templateValue[0][0] .
generated: This statistic shows the proportion of Household rent in the United Kingdom ( United Kingdom ) in 2018 and 2019 , ratio Region .  During the survey period , the Household rent income ratio at 36.6 million U.S. dollars in London .

Example 602:
titleEntities: {'Subject': ['GSK'], 'Date': ['2012', '2018']}
title: GSK : revenue of Seretide/Advair globally by region 2012 to 2018
Data:
                     0     1     2     3     4     5     6
Year              2018  2017  2016  2015  2014  2013  2012
Total             2422  3130  3485  3681  4229  5274  5046
United States     1097  1610  1829  1865  1972  2769  2533
Europe             599   736   835  1014  1330  1458  1447
Emerging markets     0     0     0     0   400   429   417
Rest of world      726   784   821   802   527   618   649 

gold: This statistic depicts the global revenue of Seretide/Advair around the world from 2012 to 2018 , by region . Seretide/Advair generated a revenue of 1.83 billion British pounds in 2016 in the United States alone . Advair is the brand name marketed in the U.S. and Canada but is known as Seretide in many other countries .
gold_template: This statistic depicts the global templateTitle[1] of templateTitle[2] around the templateLabel[5][1] from templateValue[0][last] to templateValue[0][0] , templateTitle[4] templateTitle[5] . templateTitle[2] generated a templateTitle[1] of 1.83 templateScale British pounds in templateValue[0][2] in the country alone . Advair is the brand name marketed in the U.S. and Canada but is known as Seretide in many other countries .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitle[2] of the templateTitleSubject[0] templateTitle[1] were templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] and templateValue[2][0] templateScale in templateLabel[2][0] .
generated: This statistic shows the GSK revenue Seretide/Advair globally by region in the 2012 from to 2018 .  In 2018 , the Seretide/Advair of the GSK revenue were Total and 1097 million in United .

Example 603:
titleEntities: {'Subject': ['Hong Kong'], 'Date': ['2015']}
title: Median age of the population in Hong Kong 2015
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Median', 'age', 'in', 'years']: ['53.4', '52.7', '51.3', '49.7', '47.9', '46.4', '44.8', '43.2', '41.2', '39.1', '36.2', '34.0', '31.0', '28.3', '25.7', '23.1', '21.7', '21.3', '23.4', '24.0', '23.7']

gold: This statistic shows the median age of the population in Hong Kong from 1950 to 2050  . The median age of a population is an index that divides the population into two equal groups : half of the population is older than the median age and the other half younger . In 2015 , the median age of Hong Kong 's population was 43.2 years .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of a templateTitle[2] is an index that divides the templateTitle[2] into two equal groups : half of the templateTitle[2] is older than the templateYLabel[0] templateYLabel[1] and the other half younger . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] 's templateTitle[2] was templateYValue[7] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .
generated: This statistic shows the Median age of the population in Hong Kong from 1950 to 2050 .  The Median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older .  It is a single index that summarizes the age distribution of a population .

Example 604:
titleEntities: {'Subject': ['Major'], 'Date': ['2014', '2019']}
title: Major countries in lithium mine production 2014 to 2019
Data:
                 0      1      2          3         4         5       6        7
Country  Australia  Chile  China  Argentina  Zimbabwe  Portugal  Brazil  Namibia
2014         13300  11500   2300       3200       900       300     160        0
2016         14300  12000   2000       5700       900       200     200        0
2018         58800  17000   7100       6400      1600       800     300      500
2019         42000  18000   7500       6400      1600      1200     300        0 

gold: This statistic depicts the countries with the largest mine production of lithium worldwide from 2014 to 2019 . For example , Australia 's lithium mine production came to approximately 42,000 metric tons in 2019 . Lithium is a soft alkali metal with uses in batteries , ceramics and glass , electronics , metallurgy , and more .
gold_template: This statistic depicts the templateTitle[1] with the largest templateTitle[3] templateTitle[4] of templateTitle[2] worldwide from templateTitleDate[min] to templateTitleDate[max] . For example , templateValue[0][0] 's templateTitle[2] templateTitle[3] templateTitle[4] came to approximately templateValue[4][max] metric tons in templateTitleDate[max] . templateTitle[2] is a soft alkali metal with uses in batteries , ceramics and glass , electronics , metallurgy , and more .

generated_template: This statistic shows the templateTitle[2] templateTitleSubject[0] templateTitle[1] with the highest templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[min] and templateTitleDate[max] . In templateTitleDate[max] , templateValue[0][0] 's templateTitle[1] templateTitle[2] in templateTitleDate[max] amounted to templateValue[1][max] templateScale metric tons .
generated: This statistic shows the lithium Major countries with the highest production 2014 2019 in 2014 and 2019 .  In 2019 , Australia 's countries lithium in 2019 amounted to 13300 million metric tons .

Example 605:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Urbanization in Canada 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population']: ['81.41', '81.35', '81.3', '81.26', '81.22', '81.18', '81.14', '81.1', '80.94', '80.76', '80.58']

gold: The statistic shows the degree of urbanization in Canada from 2008 to 2018 and details the percentage of the entire population , living in urban areas . In 2018 , 81.41 percent of the total population in Canada lived in cities . Canada 's rural exodus Canada is one of the largest countries in the world regarding land area , second behind Russia .
gold_template: The statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities . templateTitleSubject[0] 's rural exodus templateTitleSubject[0] is one of the largest countries in the world regarding land area , second behind Russia .

generated_template: The statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: The statistic shows the degree of Urbanization in Canada from 2008 to 2018 .  In 2018 , about 81.41 % of the total population in Canada lived in cities .

Example 606:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Sales of lotteries in the U.S. 2016 , by state
X_Axis['State']: ['New_York', 'California', 'Florida', 'Massachusetts', 'Texas', 'Georgia', 'Pennsylvania', 'Ohio', 'New_Jersey', 'Michigan', 'Maryland', 'Illinois', 'North_Carolina', 'Virginia', 'Tennessee', 'South_Carolina', 'Missouri', 'Oregon', 'Connecticut', 'Indiana', 'West_Virginia', 'Kentucky', 'Rhode_Island', 'Arizona', 'Washington', 'Delaware', 'Wisconsin', 'Minnesota', 'Colorado', 'Louisiana', 'Arkansas', 'Iowa', 'New_Hampshire', 'Kansas', 'Maine', 'South_Dakota', 'Idaho', 'District_of_Columbia', 'Oklahoma', 'Nebraska', 'New_Mexico', 'Vermont', 'Montana', 'North_Dakota', 'Wyoming']
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['9.69', '6.28', '6.06', '5.22', '5.07', '4.56', '4.14', '3.93', '3.29', '3.1', '3.05', '2.86', '2.38', '2.01', '1.63', '1.6', '1.32', '1.23', '1.23', '1.21', '1.14', '1.0', '0.88', '0.87', '0.69', '0.63', '0.63', '0.59', '0.59', '0.51', '0.46', '0.37', '0.3', '0.27', '0.27', '0.26', '0.24', '0.23', '0.19', '0.18', '0.15', '0.12', '0.06', '0.04', '0.03']

gold: This statistic presents the annual sales of lotteries in the United States in 2016 , by state . In 2016 , U.S. sales of lotteries in the state of New York reached approximately 9.69 billion U.S. dollars .
gold_template: This statistic presents the annual templateYLabel[0] of templateTitle[1] in the templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , templateYLabel[2] templateYLabel[0] of templateTitle[1] in the templateXLabel[0] of templateXValue[0] reached approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The graph shows the templateScale templateYLabel[2] templateYLabel[3] in the templateTitle[1] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , the had a total of templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in that year .
generated: The graph shows the billion U.S. dollars in the lotteries in 2016 , state .  In 2016 , the had a total of 6.28 billion U.S. dollars in that year .

Example 607:
titleEntities: {'Subject': ['Outback Steakhouse'], 'Date': ['2015', '2018']}
title: Average sales per unit of Outback Steakhouse restaurants in the U.S. 2015 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015']
Y_Axis['Average', 'sales', 'in', 'thousand', 'U.S.', 'dollars']: ['3580', '3542', '3354', '3430']

gold: This statistic shows the average sales per unit of Outback Steakhouse restaurants in the United States from 2015 to 2018 . Average sales per Outback Steakhouse restaurant amounted to approximately 3.58 million U.S. dollars in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] restaurants in the templateTitle[7] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitleSubject[0] restaurant amounted to approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[0] employees amounted to templateYValue[idxmax(X)] templateYLabel[3] .
generated: This statistic shows the Average of thousand U.S. of Outback Steakhouse from 2015 to 2018 .  In 2018 , the Average of Outback Steakhouse employees amounted to 3580 U.S. .

Example 608:
titleEntities: {'Subject': ['Lithuania'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Lithuania 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.29', '2.3', '2.39', '2.46', '2.72', '3.41', '3.49', '4.14', '2.35', '2.02', '3.54']

gold: The statistic shows the growth in real GDP in Lithuania from 2014 to 2018 , with projections up until 2024 . In 2018 , Lithuania 's real gross domestic product grew by around 3.49 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Lithuania from 2014 to 2018 , with projections up until 2024 .  In 2018 , Lithuania 's real Gross domestic product increased by around 3.49 % compared to the previous Year .

Example 609:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Consumers who have purchased a meal from a food truck in the U.S. 2014 , by age
X_Axis['Year']: ['All_adults', '18_to_34_years', '35_to_44_years', '45_to_54_years', '55_to_64_years', '65_years_and_over']
Y_Axis['Share', 'of', 'respondents']: ['47', '56', '54', '48', '48', '30']

gold: This statistic shows the share of consumers who have purchased a meal from a food truck in the United States as of November 2014 , by age group . During the survey , 48 percent of respondents aged between 45 and 54 years said they had bought food from a food truck . In 2015 , the value of the food truck industry in the U.S. reached 856.7 million U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] a templateTitle[4] templateTitle[5] a templateTitle[6] templateTitle[7] in the templateTitle[8] as of 2014 , templateTitle[10] templateTitle[11] group . During the survey , templateYValue[3] templateScale of templateYLabel[1] aged between templateXValue[3] and templateYValue[2] templateXValue[1] said they had bought templateTitle[6] templateTitle[5] a templateTitle[6] templateTitle[7] . In 2015 , the value of the templateTitle[6] templateTitle[7] industry in the templateTitleSubject[0] reached 856.7 templateScale templateTitleSubject[0] dollars .

generated_template: This statistic presents the templateScale of online consumers in the templateTitle[0] who are templateTitleSubject[0] templateTitle[2] members . During the most recent survey period in 2016 , it was found that templateYValue[max] templateScale of responding Millennials had an templateTitleSubject[0] templateTitle[2] templateTitle[3] .
generated: This statistic presents the percentage of online consumers in the Consumers who are U.S. have members .  During the most recent survey period in 2016 , it was found that 56 % of responding Millennials had an U.S. have purchased .

Example 610:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. arms exports 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Export', 'value', 'in', 'TIV', 'in', 'million', 'constant', '(1990)', 'U.S.', 'dollars']: ['10508', '12485', '9955', '9931', '9601', '7571', '9086', '9022', '8074', '6913', '6800', '7850', '7523', '6772', '6845', '5665', '4953', '5615', '7576']

gold: The statistic shows total U.S. arms exports in the years 2000 to 2018 expressed in TIV . The TIV is based on the known unit production costs of a core set of weapons and is intended to represent the transfer of military resources rather than the financial value of the transfer . The TIV is expressed here in million constant U.S. dollars as of 1990 .
gold_template: The statistic shows total templateYLabel[6] templateTitle[1] templateTitle[2] in the years templateXValue[min] to templateXValue[max] expressed in templateYLabel[2] . The templateYLabel[2] is based on the known unit production costs of a core set of weapons and is intended to represent the transfer of military resources rather than the financial templateYLabel[1] of the transfer . The templateYLabel[2] is expressed here in templateScale templateYLabel[4] templateYLabel[6] templateYLabel[7] as of 1990 .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of the templateTitle[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[last] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Export of TIV million of the U.S. of from 2000 to 2018 .  According to the report , the U.S. Export value of U.S. was 7576 TIV million .

Example 611:
titleEntities: {'Subject': ['Subaru', 'United Kingdom', 'UK'], 'Date': ['2016', '2019']}
title: Subaru car sales in the United Kingdom ( UK ) 2016 to 2019
X_Axis['Month']: ['Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16']
Y_Axis['Units', 'sold']: ['708', '157', '88', '526', '52', '103', '244', '138', '101', '632', '74', '174', '193', '145', '135', '829', '100', '112', '265', '231', '153', '761', '62', '155', '246', '216', '99', '510', '44', '152', '202', '155', '123', '706', '48', '178', '330', '219', '256', '762', '69', '148']

gold: This statistic shows the monthly amount of cars sold by Subaru in the United Kingdom ( UK ) between July 2016 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December 2019 , 708 new Subaru cars had been sold .
gold_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 and 2019 . Peaks in registration numbers were recorded in and of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In 2019 , templateYValue[0] new templateTitleSubject[0] cars had been templateYLabel[1] .

generated_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 and 2019 . Peaks in registration numbers were recorded in and of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In 2019 , templateYValue[5] new templateTitleSubject[0] templateTitle[1] templateYLabel[0] had been templateYLabel[1] , a templateNegativeTrend of roughly ten templateScale in comparison to templateYValue[17] templateYLabel[0] as of 2018 .
generated: This statistic shows the monthly amount of cars sold by Subaru car in the United Kingdom ( UK ) between 2016 and 2019 .  Peaks in registration numbers were recorded in and of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months .  In 2019 , 103 new Subaru car Units had been sold , a decrease of roughly ten percent in comparison to 112 Units as of 2018 .

Example 612:
titleEntities: {'Subject': ['Starwood Hotels', 'Resorts'], 'Date': ['2009', '2015']}
title: Number of Starwood Hotels and Resorts hotels worldwide 2009 to 2015
X_Axis['Year']: ['2009', '2010', '2011', '2012', '2013', '2014', '2015']
Y_Axis['Number', 'of', 'hotels']: ['942', '979', '1041', '1077', '1121', '1161', '1207']

gold: This statistic shows the number of Starwood Hotels and Resorts hotels worldwide from 2009 to 2015 . There were 1,161 Starwood Hotels and Resorts properties worldwide as of January 1 , 2014 , up from 1,121 in 2013 .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] and templateTitleSubject[1] templateYLabel[1] templateTitle[5] from templateXValue[min] to templateXValue[max] . There were templateYValue[5] templateTitleSubject[0] templateYLabel[1] and templateTitleSubject[1] properties templateTitle[5] as of 1 , templateXValue[5] , up from templateYValue[4] in templateXValue[4] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateYLabel[1] reported in templateTitleSubject[0] .
generated: This statistic shows the Number of Starwood Hotels in Starwood Hotels from 2009 to 2015 .  In 2015 , there were 1207 hotels reported in Starwood Hotels .

Example 613:
titleEntities: {'Subject': ['Refugees'], 'Date': ['2001', '2018']}
title: Refugees worldwide 2001 to 2018
Data:
                                  0      1      2      3      4      5      6     7     8     9    10    11    12    13    14    15    16    17
Year                           2018   2017   2016   2015   2014   2013   2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001
Refugees                      20.36  19.94  16.53  15.48  13.69   11.0   9.88  15.2  15.4  15.2  15.2  16.0  14.3  13.0  13.8  13.7  14.6  16.0
Internally displaced persons  41.43  39.12  36.63  37.49  32.27  23.93  17.67  26.4  27.5  27.1  26.0  26.0  24.4  23.7  25.3  24.6  25.0  25.0
Asylum-seekers                  3.5   3.09   6.51   2.32    1.8   1.17   0.94   0.9   0.8   1.0   0.8   0.7   0.7   0.8   0.9   1.0   1.1   1.1 

gold: The statistic shows the number of refugees worldwide from 2001 to 2018 . At the end of 2018 , 41.43 million people were internally displaced worldwide . Internally displaced persons are people or groups of individuals who have been forced to leave their homes or places of habitual residence , and who have not crossed an international border .
gold_template: The statistic shows the number of templateTitleSubject[0] templateTitle[1] from templateValue[0][last] to templateValue[0][0] . At the end of templateValue[0][0] , templateValue[2][0] templateScale people were templateLabel[2][0] templateLabel[2][1] templateTitle[1] . templateLabel[2][0] templateLabel[2][1] templateLabel[2][2] are people or groups of individuals who have been forced to leave their homes or places of habitual residence , and who have not crossed an international border .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the average templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] was templateValue[1][0] templateScale U.S. dollars .
generated: The statistic shows the Refugees worldwide 2001 2018 in the 2018 from 2001 to 2018 .  In 2018 , the average Refugees worldwide 2001 in the Refugees was 20.36 billion U.S. dollars .

Example 614:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016', '2018']}
title: Crude oil export destination countries of the United Kingdom ( UK ) 2016 to 2018
Data:
           0      1        2        3       4        5       6      7      8           9           10      11      12                   13           14     15      16      17    18             19       20
Year  Canada  China  Denmark  Finland  France  Germany  Greece  India  Italy  Martinique  Netherlands  Norway  Poland  Republic_of_Ireland  South_Korea  Spain  Sweden  Turkey   USA  UK_refineries  Unknown
2016     418   4685      114        0    1204     5146       0     80    482          79        12092      85      78                   54         3055   1390     323     157   709          13846       79
2017     315   5675      188        0    1257     4345       0      0     76         129        13980     158      40                  545         5187    764     216      38   651           8178      331
2018     570   6188       78       56     728     3751     130     91    399           0        16797    1144     589                  583         3791    552     447       0  1821           6761      908 

gold: This statistic shows the main destination countries of crude oil disposal exports from the United Kingdom ( UK ) from 2016 to 2018 . In terms of exported crude oil , the Netherlands received the largest amount of crude oil of all countries shown here .
gold_template: This statistic shows the main templateTitle[3] templateTitle[4] of templateTitle[0] templateTitle[1] disposal exports from the templateTitleSubject[0] ( templateValue[0][19] ) from templateTitleDate[min] to templateTitleDate[max] . In terms of exported templateTitle[0] templateTitle[1] , the templateValue[0][10] received the largest amount of templateTitle[0] templateTitle[1] of all templateTitle[4] shown here .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] and templateTitle[2] templateTitle[3] the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[7] templateTitle[8] . In templateTitleDate[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in the country was templateValue[2][max] U.S. dollars .
generated: This statistic shows the Crude of oil and export destination the United Kingdom from 2016 to 2018 , UK 2016 .  In 2018 , the Crude oil export destination of countries in United Kingdom the country was 13980 U.S. dollars .

Example 615:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global sulfur production by country 2019
X_Axis['Country']: ['China', 'United_States', 'Russia', 'Saudi_Arabia', 'Canada', 'Other', 'Kazakhstan', 'United_Arab_Emirates', 'India', 'Japan', 'South_Korea', 'Iran', 'Qatar', 'Chile', 'Poland', 'Finland', 'Kuwait', 'Australia', 'Germany', 'Venezuela', 'Italy', 'Netherlands', 'Brazil']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'ton']: ['17400', '8800', '7100', '6600', '5300', '3900', '3600', '3400', '3400', '3400', '3100', '2200', '2100', '1500', '1230', '940', '900', '900', '870', '700', '550', '520', '500']

gold: In 2019 , China produced around 17.4 megatons of sulfur , which makes China the world 's leading sulfur producer . China 's sulfur production includes byproduct elemental sulfur recovered from natural gas and petroleum , the estimated sulfur content of byproduct sulfuric acid from metallurgy , and the sulfur content of sulfuric acid from pyrite .
gold_template: In templateTitleDate[0] , templateXValue[0] produced around 17.4 megatons of templateTitle[1] , which makes templateXValue[0] the world 's leading templateTitle[1] producer . templateXValue[0] 's templateTitle[1] templateTitle[2] includes byproduct elemental templateTitle[1] recovered from natural gas and petroleum , the estimated templateTitle[1] content of byproduct sulfuric acid from metallurgy , and the templateTitle[1] content of sulfuric acid from pyrite .

generated_template: This statistic shows the top ten templateTitleSubject[0] templateTitle[1] templateTitle[3] in templateTitleDate[0] , based on templateYLabel[0] templateYLabel[1] . In templateTitleDate[0] , the templateXValue[0] was ranked first , with a total of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the top ten Global sulfur by in 2019 , based on Price U.S. In 2019 , the China was ranked first , with a total of 17400 dollars per .

Example 616:
titleEntities: {'Subject': ['adidas Group'], 'Date': ['2019']}
title: adidas Group - revenue from H1 2017 to H1 2019 , by region
Data:
                              0              1              2
Half              1st_half_2019  1st_half_2018  1st_half_2017
Asia-Pacific               4011           3582           3240
Europe                     2972           3023           2969
North America              2370           2122           2001
Latin America               779            847            895
Emerging Markets            611            560            644
Russia/CIS                  307            297            341 

gold: The graph illustrates adidas ' revenue from the first half of 2017 to the first half of 2019 , by region . In the first half of 2019 , the European region generated a revenue of about 2.97 billion euros for adidas . In total , the adidas Group generated a global 1st half revenue of approximately 11.4 billion euros in 2019 .
gold_template: The graph illustrates templateTitleSubject[0] ' templateTitle[2] templateTitle[3] the first templateValue[0][0] of templateValue[0][2] to the first templateValue[0][0] of templateValue[0][0] , templateTitle[8] templateTitle[9] . In the first templateValue[0][0] of templateValue[0][0] , the European templateTitle[9] generated a templateTitle[2] of about 2.97 templateScale euros for templateTitleSubject[0] . In total , the templateTitleSubject[0] generated a global templateValue[0][0] revenue of approximately 11.4 templateScale euros in templateValue[0][0] .

generated_template: This statistic gives information on the projected of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . In that year , templateTitleSubject[0] had a total of templateYValue[1] templateScale metric tons in that year .
generated: This statistic gives information on the projected of adidas Group Group revenue from in 2019 , sorted 2017 H1 .  In that year , adidas Group had a total of million metric tons in that year .

Example 617:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1999', '2017']}
title: U.S. automobile registrations 1999 to 2017
X_Axis['Year']: ['2017', '2011', '2005', '1999']
Y_Axis['Number', 'of', 'passenger', 'cars']: ['111177029', '126966714', '136568083', '127083019']

gold: This statistic represents the number of registered automobiles in the United States from 1999 through 2017 . In 2017 , there were about 111 million automobiles registered in the United States . In 2017 , a total of around 272 million motor vehicles were registered in the U.S .
gold_template: This statistic represents the templateYLabel[0] of registered automobiles in the templateTitle[0] from templateXValue[min] through templateXValue[max] . In templateXValue[max] , there were about templateYValue[min] templateScale automobiles registered in the templateTitle[0] . In templateXValue[max] templateXValue[idxmin(Y)] a total of around 272 templateScale motor vehicles were registered in the templateTitle[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of the templateYLabel[1] templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the source , there were over templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] at templateTitle[1] templateTitle[2] templateYLabel[3] templateTitle[4] .
generated: This statistic shows the Number of cars the passenger U.S. from 1999 to 2017 .  According to the source , there were over 136568083 million cars at automobile registrations cars 2017 .

Example 618:
titleEntities: {'Subject': ['Polish'], 'Date': ['2015']}
title: Polish banks ranked by brand value in 2015
X_Axis['Banking', 'Institution']: ['PKO_Bank_Polski', 'Bank_Pekao', 'Bank_Zachodni_WBK', 'mbank', 'Millennium']
Y_Axis['Brand', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1594', '956', '902', '339', '295']

gold: This statistic illustrates five leading banks domiciled in Poland , ranked by the brand value as of 2015 . The highest position in the ranking was occupied by PKO Bank Polski , with a brand rank value of 1.6 billion U.S. dollars . The fifth in the ranking was Millennium , with the total brand value of 295 million U.S. dollars .
gold_template: This statistic illustrates five leading templateTitle[1] domiciled in Poland , templateTitle[2] templateTitle[3] the templateYLabel[0] templateYLabel[1] as of templateTitleDate[0] . The highest position in the ranking was occupied templateTitle[3] templateXValue[0] Polski , with a templateYLabel[0] rank templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The fifth in the ranking was templateXValue[last] , with the total templateYLabel[0] templateYLabel[1] of templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitleSubject[0] templateTitle[4] templateTitle[5] in templateTitleDate[0] . According to the source , templateXValue[0] was the templateTitle[0] templateXLabel[0] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Brand value of the ranked Polish brand value in 2015 .  According to the source , PKO_Bank_Polski was the Polish Banking with a Brand value of 1594 million U.S. dollars .

Example 619:
titleEntities: {'Subject': ['Forbes', 'Italy'], 'Date': ['2019']}
title: Forbes ranking of the 10 richest people in Italy 2019
X_Axis['Month']: ['Leonardo_Del_Vecchio', 'Giovanni_Ferrero', 'Giorgio_Armani', 'Stefano_Pessina', 'Massimiliana_Landini_Aleotti', 'Silvio_Berlusconi', 'Augusto_e_Giorgio_Perfetti', 'Paolo_and_Gianfelice_Rocca', 'Piero_Ferrari', 'Ennio_Doris']
Y_Axis['Wealth', 'in', 'billion', 'U.S.', 'dollars']: ['24.4', '21.9', '11.2', '10.4', '8.1', '7.0', '6.9', '3.7', '3.4', '3.3']

gold: Leonardo Del Vecchio , founder of the eyewear company Luxottica , podiumed in the yearly ranking of Italian billionaires , published by the American business magazine Forbes . With the total net worth estimated at 24.4 billion dollars , Del Vecchio put ahead other Italian entrepreneurs - Giovanni Ferrero , the CEO of the Italian confectionary company Ferrero and the fashion designer Giorgio Armani , whose fortunes reached respectively 21.9 and 11.2 billion dollars . Largest eyewear company in the world Luxottica is the largest company in the eyewear sector worldwide .
gold_template: templateXValue[0] Vecchio , founder of the eyewear company Luxottica , podiumed in the yearly templateTitle[1] of Italian billionaires , published by the American business magazine templateTitleSubject[0] . With the total net worth estimated at templateYValue[max] templateScale templateYLabel[3] , templateXValue[0] put ahead other Italian entrepreneurs - templateXValue[1] , the CEO of the Italian confectionary company templateXValue[1] and the fashion designer templateXValue[2] , whose fortunes reached respectively templateYValue[1] and templateYValue[2] templateScale templateYLabel[3] . Largest eyewear company in the world Luxottica is the largest company in the eyewear sector worldwide .

generated_template: The statistic presents the distribution of templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleDate[0] . As of that templateXLabel[0] , templateXValue[0] had more than templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] , followed templateTitle[4] templateXValue[1] with templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic presents the distribution of Forbes ranking 10 in 2019 .  As of that Month , Leonardo_Del_Vecchio had more than 24.4 billion U.S. dollars of Forbes ranking 10 in the Forbes , followed people Giovanni_Ferrero with 21.9 billion U.S. dollars .

Example 620:
titleEntities: {'Subject': ['Louis Cardinals'], 'Date': ['2001', '2018']}
title: St. Louis Cardinals revenue 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['356', '319', '310', '300', '294', '283', '239', '233', '207', '195', '195', '194', '184', '165', '151', '131', '128', '123']

gold: The statistic depicts the revenue of the St. Louis Cardinals from 2001 to 2018 . In 2018 , the revenue of the Major League Baseball franchise amounted to 356 million U.S. dollars.The St. Louis Cardinals are owned by William DeWittJr. , who bought the franchise for 150 million U.S. dollars in 1996 .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitle[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitle[0] templateTitleSubject[0] are owned by William DeWittJr. , who bought the franchise for 150 templateScale templateYLabel[2] templateYLabel[3] in 1996 .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] are owned by John Stanton and Chris Larson , who bought the franchise for 1.2 templateScale templateYLabel[2] templateYLabel[3] and 100 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[2] and 1992 respectively .
generated: The statistic depicts the Revenue of the Louis Cardinals from 2001 to 2018 .  In 2018 , the Revenue of the Major League Baseball franchise amounted to 356 million U.S. dollars.The Louis Cardinals are owned by John Stanton and Chris Larson , who bought the franchise for 1.2 million U.S. dollars and 100 million U.S. dollars in 2016 and 1992 respectively .

Example 621:
titleEntities: {'Subject': ['Marriott International'], 'Date': ['2007', '2018']}
title: Net income of Marriott International worldwide 2007 to 2018
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['1273', '1907', '1372', '780', '859', '753', '626', '571', '198', '458', '-346', '359', '697']

gold: Global hotel giant Marriot International accounted for a net income of approximately 1.27 billion U.S. dollars in 2019 , down from the previous year 's total of 1.91 billion U.S. dollars . A global player in the hotel industry With nearly seven thousand hotels around the world , Marriot International was one of the largest hotel chains worldwide in 2019 . It also ranked ahead of other large hotel chains such as Hilton Worldwide , Hyatt Hotel , and Host Hotels & Resorts in terms of revenue in 2018 .
gold_template: Global hotel giant Marriot templateTitleSubject[0] accounted for a templateYLabel[0] templateYLabel[1] of approximately templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] , down from the previous templateXLabel[0] 's total of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . A global player in the hotel industry With nearly seven thousand hotels around the world , Marriot templateTitleSubject[0] was one of the largest hotel chains templateTitle[4] in templateXValue[max] . It also ranked ahead of other large hotel chains such as Hilton templateTitle[4] , Hyatt Hotel , and Host Hotels & Resorts in terms of revenue in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net income of the U.S. dollars in Marriott International from 2007 to 2019 .  In 2019 , the Net income in Marriott International amounted to approximately 1273 U.S. dollars .

Example 622:
titleEntities: {'Subject': ['UEFA EURO'], 'Date': ['1960', '2016']}
title: Champions and finalists of UEFA EURO championship 1960 to 2016
Data:
                 0      1       2     3      4         5               6       7            8        9          10       11              12
Country    Germany  Spain  France  USSR  Italy  Portugal  Czechoslovakia  Greece  Netherlands  Denmark  Yugoslavia  Belgium  Czech_Republic
Winner           3      3       2     1      1         1               1       1            1        1           0        0               0
Runner-up        3      1       1     3      2         1               0       0            0        0           2        1               1 

gold: This statistic shows the winners and runners-up of the UEFA European Championships from 1960 to 2016 . Germany and Spain are the only countries to have won the title three times , whilst national teams from France have won the tournament twice .
gold_template: This statistic shows the winners and runners-up of the templateTitleSubject[0] European Championships from templateTitleDate[min] to templateTitleDate[max] . templateValue[0][0] and templateValue[0][1] are the only countries to have won the title templateValue[1][0] times , whilst national teams from templateValue[0][2] have won the tournament twice .

generated_template: This statistic shows the templateTitle[0] of templateTitle[3] the templateValue[0][0] United Kingdom ( templateTitleSubject[0] ) templateTitle[4] templateTitleDate[min] and templateTitleDate[max] . templateValue[0][0] to the source , templateValue[1][0] templateScale of templateTitle[3] were employed in templateValue[0][0] .
generated: This statistic shows the Champions of EURO the Germany United Kingdom ( UEFA EURO ) championship 1960 and 2016 .  Germany to the source , 3 billion of EURO were employed in Germany .

Example 623:
titleEntities: {'Subject': ['Poland'], 'Date': ['2019']}
title: Number of retail chains in Poland 2019 , by sector
X_Axis['Retail', 'Categories']: ['Fashion_&_Clothing', 'Furniture_&_Decoration', 'Footwear_&_Leather', 'Food', 'Personal_Care', 'Consumer_Electronics', 'Home_Ware', 'Car_Parts_&_Accessories', 'DIY_&_Gardening', 'Sport_&_Leisure', 'Toys_&_Games', 'Petrol', 'Jewelry_&_Watches', 'Telecom', 'Books_&_Magazines', 'Baby_Ware', 'Optical', 'Pet_Care']
Y_Axis['Number', 'of', 'retail', 'chains']: ['57', '28', '28', '25', '23', '23', '20', '14', '13', '13', '12', '12', '12', '11', '10', '9', '8', '5']

gold: Despite the government 's ban on Sunday trading , the situation of retail chains is not as dramatic as expected . In 2019 , 242 retail chains were operating in Poland , the largest share of which belonged to the clothing and footwear , furniture , and food sectors . Retail sales in Poland Retail sales volume in Poland have fluctuated but remained at six percent since 2015 .
gold_template: Despite the government 's ban on Sunday trading , the situation of templateXLabel[0] templateYLabel[2] is not as dramatic as expected . In templateTitleDate[0] , 242 templateXLabel[0] templateYLabel[2] were operating in templateTitleSubject[0] , the largest share of which belonged to the templateXValue[0] and templateXValue[2] , templateXValue[1] , and templateXValue[3] sectors . templateXLabel[0] sales in templateTitleSubject[0] templateXLabel[0] sales volume in templateTitleSubject[0] have fluctuated but remained at six templateScale since 2015 .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateTitleDate[0] . The templateYLabel[0] of templateYLabel[1] templateYLabel[2] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: This statistic displays the Number of retail chains in Poland 2019 .  The Number of retail chains amounted to 57 chains in 2019 .

Example 624:
titleEntities: {'Subject': ['Schindler'], 'Date': ['2007', '2018']}
title: Schindler - revenue 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'billion', 'Swiss', 'francs']: ['10.9', '10.2', '9.7', '9.4', '9.3', '8.8', '8.3', '7.9', '8.2', '8.3', '14.0', '13.8']

gold: The statistic represents Schindler Group 's revenue between the fiscal year of 2007 and the fiscal year of 2018 . In the fiscal year of 2018 , the Swiss manufacturer of escalators and elevators generated about 10.9 billion Swiss francs ( or about 11 billion U.S. dollars ) in revenue .
gold_template: The statistic represents templateTitleSubject[0] Group 's templateYLabel[0] between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the templateYLabel[2] manufacturer of escalators and elevators generated about templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] ( or about templateYValue[0] templateScale U.S. dollars ) in templateYLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] of templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , there were templateYValue[idxmax(X)] templateTitle[1] templateTitle[2] of the templateTitleSubject[0] restaurants in the templateTitleSubject[1] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] .
generated: This statistic shows the average Revenue of 2007 in Schindler from 2007 to 2018 .  In 2016 , there were 10.9 revenue 2007 of the Schindler restaurants in the Schindler , up from 10.2 billion Swiss francs in the previous Year .

Example 625:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2007', '2018']}
title: U.S. household expenditure on slipcovers and decorative pillows 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Average', 'annual', 'expenditure', 'in', 'U.S.', 'dollars']: ['5.01', '4.52', '3.65', '4.32', '4.21', '3.16', '3.94', '3.52', '3.71', '3.49', '2.93', '3.39']

gold: This statistic shows the average annual expenditure on slipcovers and decorative pillows per consumer unit in the United States from 2007 to 2018 . In 2018 , the country 's average expenditure on slipcovers and decorative pillows amounted to 5.01 U.S. dollars per consumer unit . Reflecting the growing complexity of mattress designs , the pillow segment continues to evolve with new constructions , styles and features designed to deliver a better sleep experience .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] and templateTitle[4] templateTitle[5] per consumer unit in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the country 's templateYLabel[0] templateYLabel[2] on templateTitle[3] and templateTitle[4] templateTitle[5] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] per consumer unit . Reflecting the templatePositiveTrend complexity of mattress designs , the pillow segment continues to evolve with new constructions , styles and features designed to deliver a better sleep experience .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] templateTitle[4] per consumer unit in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the country 's templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] templateTitle[4] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] per consumer unit .
generated: This statistic shows the Average annual expenditure on slipcovers decorative per consumer unit in the U.S. from 2007 to 2018 .  In 2018 , the country 's Average annual expenditure on slipcovers decorative amounted to 5.01 U.S. dollars per consumer unit .

Example 626:
titleEntities: {'Subject': ['Global'], 'Date': ['2018']}
title: Global leading countries in wine export 2018 , based on volume
X_Axis['Country']: ['Spain', 'Italy', 'France', 'Chile', 'Australia', 'South_Africa', 'Germany', 'United_States', 'Portugal', 'Argentina', 'New_Zealand']
Y_Axis['Export', 'volume', 'in', 'million', 'hectoliters']: ['21.0', '19.7', '14.1', '9.3', '8.6', '4.2', '3.8', '3.5', '3.0', '2.8', '2.6']

gold: Spain edged ahead of Italy to become the world 's top exporter of wine in 2018 . The country shipped 21 million hectoliters of the beverage in that year , compared to Italy 's 19.7 million and France 's 14.1 million . 2018 was also a peak year for wine production worldwide .
gold_template: templateXValue[0] edged ahead of templateXValue[1] to become the world 's top exporter of templateTitle[3] in templateTitleDate[0] . The templateXLabel[0] shipped templateYValue[max] templateScale templateYLabel[3] of the beverage in that year , compared to templateXValue[1] 's templateYValue[1] templateScale and templateXValue[2] 's templateYValue[2] templateScale . templateTitleDate[0] was also a peak year for templateTitle[3] production worldwide .

generated_template: This statistic shows the estimated templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In that year , the templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the estimated Export volume of the countries wine Global in 2018 , volume Country .  In that year , the Spain had the highest Export volume of 21.0 million hectoliters .

Example 627:
titleEntities: {'Subject': ['Rate'], 'Date': ['2006', '2013']}
title: Rate of recidivism among sentenced prisoner 2006 - 2013 , by number of sentences
Data:
                            0     1     2     3     4     5     6     7
Year                     2013  2012  2011  2010  2009  2008  2007  2006
One sentence               38    36    41    41    39    45    47    49
Two sentences              60    57    59    60    57    60    63    63
Three to five sentences    68    69    68    66    64    69    68    72
Six or more sentences      73    74    75    73    75    75    79    78 

gold: This statistic shows the rate of recidivism of sentenced prisoners in Finland from 2006 to 2013 , broken down by the number of prison sentences . 73 percent of sentenced prisoners with six or more previous prison sentences released in 2013 returned to prison within a five-year period .
gold_template: This statistic shows the templateTitleSubject[0] of templateTitle[1] of templateTitle[3] prisoners in Finland from templateValue[0][last] to templateValue[0][0] , broken down templateTitle[7] the templateTitle[8] of prison templateTitle[9] . templateValue[4][0] templateScale of templateTitle[3] prisoners with six or templateLabel[4][1] previous prison templateTitle[9] released in templateValue[0][0] returned to prison within a five-year period .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in the United Kingdom ( templateTitleSubject[0] ) from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . The templateTitle[0] of templateTitle[1] in templateTitleSubject[0] stood at 20 to templateValue[3][0] templateScale in templateValue[0][0] .
generated: This statistic shows the Rate of recidivism in the United Kingdom ( Rate ) from 2006 to 2013 , by number .  The Rate of recidivism in Rate stood at 20 to 68 % in 2013 .

Example 628:
titleEntities: {'Subject': ['Skype', 'United States'], 'Date': ['2018']}
title: Skype usage reach in the United States 2018 , by age group
X_Axis['Year']: ['18-24', '25-34', '35-44', '45-54', '55-64', '65-75', '75+']
Y_Axis['Reach']: ['51', '52', '42', '33', '25', '26', '16']

gold: This statistic shows the share of internet users in the United States who were using Skype as of January 2018 , sorted by age group . We Are Flint found that 51 percent of internet users aged from 18 to 24 years old used the telecommunications application software .
gold_template: This statistic shows the share of internet users in the templateTitleSubject[1] who were using templateTitleSubject[0] as of 2018 , sorted templateTitle[6] templateTitle[7] templateTitle[8] . We Are Flint found that templateYValue[0] templateScale of internet users aged from 18 to 24 years old used the telecommunications application software .

generated_template: This statistic shows the share of internet users in the templateTitleSubject[1] who were using Google Plus as of 2018 , sorted templateTitle[6] templateTitle[7] . We Are Flint found that templateYValue[max] templateScale of internet users aged from 25 to 34 years old used the social networking site .
generated: This statistic shows the share of internet users in the United States who were using Google Plus as of 2018 , sorted by age .  We Are Flint found that 52 % of internet users aged from 25 to 34 years old used the social networking site .

Example 629:
titleEntities: {'Subject': ['Jamaica'], 'Date': ['2019']}
title: Unemployment rate in Jamaica 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['9.52', '9.45', '11.66', '13.19', '13.51', '13.74', '15.25', '13.93', '12.7', '12.37', '11.36', '10.33', '9.75', '10.33', '11.26', '11.44', '11.74', '14.19', '14.93', '15.54', '15.74']

gold: In 2019 , the unemployment rate in Jamaica was at approximately 9.52 percent , a slight decrease from previous years . Jamaica 's economy is in good shape Although on a steady downward trend after peaking at over 15 percent in 2013 , Jamaica 's unemployment rate is still quite high but not alarmingly high in global comparison . More than half of the island 's population live in urban areas and cities already , and the numbers are rising .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale , a slight templateNegativeTrend from previous years . templateTitleSubject[0] 's economy is in good shape Although on a steady downward trend after peaking at over templateYValue[6] templateScale in templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is still quite high but not alarmingly high in global comparison . More than half of the island 's population live in urban areas and cities already , and the numbers are templatePositiveTrend .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Jamaica from 1999 to 2019 .  In 2019 , the Unemployment rate in Jamaica was at approximately 9.52 % .

Example 630:
titleEntities: {'Subject': ['Mattel'], 'Date': ['2012', '2019']}
title: Revenue of Mattel 's Barbie brand worldwide from 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Gross', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['1159.77', '1088.95', '954.89', '971.8', '905.9', '1009.5', '1202.8', '1275.3']

gold: This statistic shows the gross sales of Mattel 's Barbie brand worldwide from 2012 to 2019 . In 2019 , Mattel 's Barbie brand generated gross sales amounting to about 1.16 billion U.S. dollars , up from about 1.09 billion U.S. dollars the year before . Additional information on Mattel 's Barbie Since the launch of Mattel 's Barbie fashion doll in 1959 , Barbie has become a cultural icon in America and around the world .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] generated templateYLabel[0] templateYLabel[1] amounting to about templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] , up templateTitle[6] about templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] the templateXLabel[0] before . Additional information on templateTitleSubject[0] templateTitle[2] templateTitle[3] Since the launch of templateTitleSubject[0] templateTitle[2] templateTitle[3] fashion doll in 1959 , templateTitle[3] has become a cultural icon in America and around the world .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[4] was templateYValue[3] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the Gross sales of the Mattel worldwide from 2012 to 2019 .  In 2016 , the Gross sales of the Mattel brand was 971.8 million U.S. dollars .

Example 631:
titleEntities: {'Subject': ['UFC'], 'Date': []}
title: UFC : leading fighter earning totals as of May 2019
X_Axis['Month']: ['Conor_McGregor', 'Alistair_Overeem', 'Anderson_Silva', 'Michael_Bisping', 'Georges_St-Pierre', 'Mark_Hunt', 'Jon_Jones', 'Donald_Cerrone', 'Vitor_Belfort', 'Daniel_Cormier', 'Junior_dos_Santos', 'Brock_Lesnar', 'Andrei_Arlovski', 'Rashad_Evans', 'Lyoto_Machida', "Mauricio_'Shogun'_Rua", 'Dan_Henderson', 'Chuck_Liddell', 'Ronda_Rousey', 'Tito_Ortiz', 'Nate_Diaz', 'Frankie_Edgar', 'Tyron_Woodley', 'Fabricio_Werdum', 'Jose_Aldo', 'Stipe_Miocic', 'Chris_Weidman', 'Robbie_Lawler', 'Demetrious_Johnson', 'Frank_Mir']
Y_Axis['Total', 'earnings', 'in', 'million', 'U.S.', 'dollars']: ['12.02', '8.7', '8.11', '7.14', '7.04', '6.3', '5.95', '5.5', '5.46', '5.29', '5.14', '5.08', '4.77', '4.74', '4.59', '4.54', '4.4', '4.38', '4.28', '4.14', '4.1', '4.06', '3.97', '3.88', '3.81', '3.65', '3.65', '3.6', '3.54', '3.51']

gold: As of June 2019 Conor McGregor , aka The Notorious , is still the Ultimate Fighting Championship ( UFC ) fighter with the highest career earnings directly from UFC fights with approximately 12 million U.S. dollars . This does not include any undisclosed pay-per-view bonuses or any other bonuses . It also does not include any payments prior to UFC 46 in 2004 .
gold_template: As of 2019 templateXValue[0] , aka The Notorious , is still the Ultimate Fighting Championship ( templateTitleSubject[0] ) templateTitle[2] with the highest career templateYLabel[1] directly from templateTitleSubject[0] fights with approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . This does not include any undisclosed pay-per-view bonuses or any other bonuses . It also does not include any payments prior to templateTitleSubject[0] 46 in 2004 .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitle[3] templateTitle[4] templateXValue[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the templateTitle[0] templateTitle[1] templateXValue[1] with a templateYLabel[0] of templateYValue[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the UFC leading fighter of the earning totals Conor_McGregor from Frank_Mir to Conor_McGregor .  In Conor_McGregor , the UFC leading Alistair_Overeem with a Total of 8.7 million U.S. .

Example 632:
titleEntities: {'Subject': ['Jacksonville Jaguars'], 'Date': ['2018']}
title: Revenue of the Jacksonville Jaguars 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['424', '391', '377', '344', '315', '263', '260', '238', '236', '220', '217', '204', '189', '173', '169', '153', '142', '137']

gold: The statistic depicts the revenue of the Jacksonville Jaguars , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Jacksonville Jaguars was 424 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] are owned by John Stanton and Chris Larson , who bought the franchise for 1.2 templateScale templateYLabel[2] templateYLabel[3] and 100 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[2] and 1992 respectively .
generated: The statistic depicts the Revenue of the Jacksonville Jaguars from 2001 to 2018 .  In 2018 , the Revenue of the Major League Baseball franchise amounted to 424 million U.S. dollars.The Jacksonville Jaguars are owned by John Stanton and Chris Larson , who bought the franchise for 1.2 million U.S. dollars and 100 million U.S. dollars in 2016 and 1992 respectively .

Example 633:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2014', '2014']}
title: FIFA World Cup 2014 Brazil total costs
X_Axis['Breakdown', 'of', 'Project']: ['Stadium_construction_&_renovation', 'Urban_mobility_projects', 'Upgrades_at_airports', 'Public_security_&_military_defence_forces', 'Port_infrastructure', 'Telecommunications_networks_&_services', 'Complementary_work_for_Confederations_Cup', 'Tourism_infrastructure_projects']
Y_Axis['Costs', 'in', 'million', 'U.S.', 'dollars']: ['3600', '3400', '2600', '797', '249', '171', '88', '76']

gold: The statistic shows the estimated total costs of the FIFA World Cup 2014 in Brazil , including the costs for stadium construction and renovation , urban mobility projects , upgrades at airports , public security and military defence forces , port infrastructure , telecommunications networks and services , complementary work for the Confederations Cup and tourism infrastructure projects . The costs for stadium construction and renovation are estimated at around 3.6 billion U.S. dollars . 2014 World Cup costs - additional information The 2014 World Cup in Brazil cost an estimated $ 11.6 billion .
gold_template: The statistic shows the estimated templateTitle[5] templateYLabel[0] of the templateTitle[0] templateTitle[1] templateXValue[6] templateTitleDate[0] in templateTitleSubject[0] , including the templateYLabel[0] templateXValue[6] templateXValue[0] and templateXValue[0] , templateXValue[1] projects , templateXValue[2] at templateXValue[2] , templateXValue[3] and templateXValue[3] forces , templateXValue[4] , templateXValue[5] and templateXValue[5] , templateXValue[6] for the templateXValue[6] and templateXValue[last] templateXValue[4] templateXValue[1] . The templateYLabel[0] templateXValue[6] templateXValue[0] and templateXValue[0] are estimated at around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateTitleDate[0] templateTitle[1] templateXValue[6] templateYLabel[0] - additional information The templateTitleDate[0] templateTitle[1] templateXValue[6] in templateTitleSubject[0] cost an estimated $ 11.6 templateScale .

generated_template: This statistic gives information on the templateTitle[2] of templateTitleSubject[0] app worldwide as of 2019 , sorted templateTitle[5] templateTitle[6] . According to the source , it was found that templateYValue[1] templateScale of people hold a templateXLabel[0] .
generated: This statistic gives information on the Cup of Brazil app worldwide as of 2019 , sorted total costs .  According to the source , it was found that 3400 million of people hold a Breakdown .

Example 634:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Number of students in upper secondary education in Denmark 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'students']: ['148551', '150608', '149788', '148144', '147760', '148051', '144791', '140259', '132619', '122837', '118217']

gold: The statistic shows the number of students in upper secondary education in Denmark from 2008 to 2018 . The number increased from about 118 thousand upper secondary education students in 2008 to about 149 thousand students in 2018 .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templatePositiveTrend from about templateYValue[min] thousand templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] in templateXValue[idxmin(Y)] to about templateYValue[0] thousand templateYLabel[1] in templateXValue[max] .

generated_template: This statistic shows the templateScale of templateTitle[1] templateYLabel[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is forecasted that there were a total of templateYValue[idxmax(X)] templateScale templateYLabel[1] , up from templateYValue[min] templateScale in the previous templateXLabel[0] .
generated: This statistic shows the percentage of students from 2008 to 2018 .  In 2018 , it is forecasted that there were a total of 148551 million students , up from 118217 million in the previous Year .

Example 635:
titleEntities: {'Subject': ['Distribution'], 'Date': ['2018']}
title: Distribution of consumer transactions worldwide 2018 , by payment channel
X_Axis['Response']: ['In-store', 'Other_online', 'Buy_buttons', 'Other_mobile_transfers', 'P2P_transfer', 'Mobile_messenger_apps', 'QR_codes', 'Other_in-app_payments', 'Smart_home_device', 'Wearables_/_contactless', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['41', '14', '9', '8', '7', '7', '5', '4', '2', '2', '2']

gold: This statistic presents the most popular payment methods for everyday transactions according to internet users worldwide as of June 2018 . When asked to think about they payment methods for their ten most recent transactions , it was found that seven percent were made via P2P transfer . In-store still accounted for the single largest share of everyday transactions with 41 percent .
gold_template: This statistic presents the most popular templateTitle[6] methods for everyday templateTitle[2] according to internet users templateTitle[3] as of 2018 . When asked to think about they templateTitle[6] methods for their ten most recent templateTitle[2] , it was found that templateYValue[4] templateScale were made via templateXValue[4] . templateXValue[0] still accounted for the single largest templateYLabel[0] of everyday templateTitle[2] with templateYValue[max] templateScale .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] adult Americans on the templateTitle[0] . During the survey , it was found that templateYValue[max] templateScale of the templateYLabel[1] stated that they had templateXValue[8] templateXValue[2] hacking .
generated: This statistic shows the results of a survey among Distribution adult Americans on the Distribution .  During the survey , it was found that 41 % of the respondents stated that they had Smart_home_device Buy_buttons hacking .

Example 636:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Mid-size luxury vehicles : sales by model in the United States 2019
Data:
                   0         1                            2             3        4            5             6             7            8             9                   10       11               12        13            14         15         16
Model  Tesla_Model_3  Lexus_ES  Mercedes-Benz_E_/_CLS-Class  BMW_5-Series  Audi_A6  Lincoln_MKZ  Cadillac_XTS  Cadillac_CT6  Genesis_G80  Cadillac_CTS  Lincoln_Continental  Audi_A7  Volvo_90-Series  Lexus_GS  Infiniti_Q70  Jaguar_XF  Acura_RLX
2019          161100     51336                        39858         38709    17807        17726         11306          7952         7095          6966                 6587     4955             4052      3378          2552       1236       1019
2018          140317     48482                        46424         43937    10561        19852         17729          9669         7446         11220                 8758     3852             9892      6604          4479       2208       1931 

gold: This statistic represents the best-selling mid-size luxury car models in the United States in 2019 . That year , around 161,000 Tesla Model 3 vehicles were sold to customers in the United States . Model 3 sales increased by around 14.8 percent in 2019 , compared with the year before .
gold_template: This statistic represents the best-selling templateTitle[0] templateTitle[1] car models in the templateTitleSubject[0] in templateTitleDate[0] . That year , around 161,000 templateValue[0][0] 3 templateTitle[2] were sold to customers in the templateTitleSubject[0] . templateValue[0][0] sales templatePositiveTrend templateTitle[4] around 14.8 templateScale in templateTitleDate[0] , compared with the year before .

generated_template: As of 2019 , templateValue[1][0] templateScale of employees had the templateTitle[0] employees in the templateTitleSubject[0] . How are templateValue[2][0] templateScale of people who saying that , making it templateXValue[last] templateTitle[0] templateValue[0][0] . The templateValue[0][0] has also the ranking of people , with any templateScale of people .
generated: As of 2019 , 161100 % of employees had the Mid-size employees in the United States .  How are 140317 % of people who saying that , making it Mid-size Tesla_Model_3 .  The Tesla_Model_3 has also the ranking of people , with any percentage of people .

Example 637:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Wage and salary of U.S. employees , by industry 2018
X_Axis['Industry']: ['Management_of_companies_and_enterprises', 'Information', 'Utilities', 'Finance_and_insurance', 'Mining', 'Professional_scientific_and_technical_services', 'Wholesale_trade', 'Manufacturing', 'Government', 'Construction', 'Real_estate_and_rental_and_leasing', 'Wages_and_salaries_per_full-time_equivalent_employee', 'Private_industries', 'Transportation_and_warehousing', 'Health_care_and_social_assistance', 'Arts_entertainment_and_recreation', 'Educational_services', 'Administrative_and_waste_management_services', 'Other_services_except_government', 'Agriculture_forestry_fishing_and_hunting', 'Retail_trade', 'Accommodation_and_food_services']
Y_Axis['Wage', 'and', 'salary', 'in', 'U.S.', 'dollars']: ['130777', '123534', '113665', '112335', '106279', '102676', '81527', '71281', '68638', '65487', '65349', '64678', '63305', '59458', '56061', '51204', '48404', '45221', '45069', '38828', '38223', '30403']

gold: This statistic shows the annual wage and salary per full-time equivalent employee in the United States in 2018 , by industry . In 2018 , the average wage and salary per employee in the mining industry was at 106,279 U.S. dollars .
gold_template: This statistic shows the annual templateYLabel[0] and templateYLabel[1] templateXValue[11] equivalent templateXValue[11] in the templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the average templateYLabel[0] and templateYLabel[1] templateXValue[11] in the templateXValue[4] templateXLabel[0] was at templateYValue[4] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateXValue[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateYLabel[3] templateTitle[4] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[0] of the largest templateYLabel[0] templateYLabel[1] , with a total of templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the Management_of_companies_and_enterprises salary of the Wage salary dollars by in 2018 , Industry .  In 2018 , the Management_of_companies_and_enterprises of the largest Wage salary , with a total of 130777 million dollars .

Example 638:
titleEntities: {'Subject': ['Global'], 'Date': ['2020']}
title: Global pork exports 2020 , by country
X_Axis['Country']: ['EU', 'United_States', 'Canada', 'Brazil', 'Mexico', 'China', 'Others']
Y_Axis['Export', 'volume', 'in', 'thousand', 'metric', 'tons']: ['3900', '3221', '1400', '1000', '250', '100', '476']

gold: This statistic depicts the forecast export volume of pork worldwide in 2020 , by leading country . The estimated pork exports of the United States were amounted to approximately 3.22 million metric tons in 2020 .
gold_template: This statistic depicts the forecast templateYLabel[0] templateYLabel[1] of templateTitle[1] worldwide in templateTitleDate[0] , templateTitle[4] leading templateXLabel[0] . The estimated templateTitle[1] templateTitle[2] of the templateXValue[1] were amounted to approximately templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .

generated_template: This statistic depicts the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] worldwide in templateTitleDate[0] , templateTitle[5] leading templateXLabel[0] , in thousand templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[1] of the templateXValue[1] amounted to approximately templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in that year .
generated: This statistic depicts the Export volume of exports 2020 worldwide in 2020 , country leading , in thousand metric tons .  The exports 2020 pork of the United_States amounted to approximately 3221 thousand metric tons in that year .

Example 639:
titleEntities: {'Subject': ['Brazil'], 'Date': []}
title: Brazil : most valuable soccer players at the 2018 FIFA World Cup
X_Axis['Month']: ['Neymar', 'Philippe_Coutinho', 'Roberto_Firmino', 'Gabriel_Jesus', 'Casemiro', 'Marcelo', 'Alisson', 'Douglas_Costa', 'Marquinhos', 'Ederson']
Y_Axis['Market', 'value', 'in', 'million', 'euros']: ['180', '100', '80', '80', '60', '60', '60', '55', '55', '50']

gold: The statistic presents a ranking of Brazilian soccer players participating in the 2018 FIFA World Cup in Russia , based on market value . As of June 2018 , Neymar was the most valuable player , with a market value of 180 million euros . Philippe Coutinho ranked second , with a value of 100 million euros .
gold_template: The statistic presents a ranking of Brazilian templateTitle[3] templateTitle[4] participating in the templateTitleDate[0] templateTitle[6] templateTitle[7] templateTitle[8] in Russia , based on templateYLabel[0] templateYLabel[1] . As of 2018 , templateXValue[0] was the templateTitle[1] templateTitle[2] player , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] . templateXValue[1] ranked second , with a templateYLabel[1] of templateYValue[1] templateScale templateYLabel[3] .

generated_template: The statistic presents the templateYLabel[0] of the templateTitleSubject[0] of templateTitle[4] in templateTitleDate[0] . templateXValue[0] had the highest templateYLabel[0] of about templateYValue[max] templateScale templateYLabel[3] .
generated: The statistic presents the Market of the Brazil of players in .  Neymar had the highest Market of about 180 million euros .

Example 640:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000']}
title: Estimated U.S. retail per capita sales from 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'sales', 'in', 'U.S.', 'dollars']: ['15522', '15018', '14721', '14561', '14099', '13702', '13166', '12343', '11776', '12941', '13263', '12975', '12484', '11861', '11247', '10877', '10746', '10573']

gold: The timeline shows the estimated retail per capita sales in the United States from 2000 to 2017 . In 2017 , retail sales amounted to about 15,522 U.S. dollars per capita .
gold_template: The timeline shows the templateTitle[0] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[1] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateYLabel[2] amounted to about templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[7] of the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of the employees was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Per capita of the 2000 of the Estimated from 2000 to 2017 .  In 2017 , the capita of the employees was 15522 sales U.S. .

Example 641:
titleEntities: {'Subject': ['IT'], 'Date': ['2017', '2019']}
title: IT skill shortages facing IT leaders worldwide 2017 to 2019
Data:
                                0                        1               2                        3                  4            5                   6                       7                        8
IT category  Big_data_/_analytics  Artificial_intelligence  Cyber_security  Enterprise_architecture  Business_analysis  Development  Project_management  Technical_architecture  Security_and_resilience
2017                           42                        0               0                       34                 34           25                  26                      32                       28
2018                           46                        0               0                       35                 31           24                  25                      36                       35
2019                           44                       39              39                       34                 31            0                   0                       0                        0 

gold: The statistic reveals the IT functions with the greatest skill shortages , according to chief information officers ( CIOs ) worldwide from 2017 to 2019 . As of 2019 , 44 percent of respondents felt that big data and analytics functions were facing a skills shortage .
gold_template: The statistic reveals the IT functions with the greatest templateTitle[0] templateTitle[1] , according to chief information officers ( CIOs ) templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] . As of templateTitleDate[max] , templateValue[3][max] templateScale of respondents felt that templateValue[0][0] and templateValue[0][0] functions were templateTitle[2] a skills shortage .

generated_template: The statistic illustrates the number of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[min] and templateTitleDate[max] . In templateTitleDate[max] , templateValue[0][0] had a templateTitle[0] templateTitle[1] templateTitle[2] of templateValue[3][1] templateScale U.S. dollars .
generated: The statistic illustrates the number of skill shortages facing leaders worldwide in 2017 and 2019 .  In 2019 , Big_data_/_analytics had a skill shortages facing of 39 billion U.S. dollars .

Example 642:
titleEntities: {'Subject': ['Death'], 'Date': ['2016']}
title: Death toll in great earthquakes as of 2016
X_Axis['Earthquake']: ['Tangshan_(China_1976)', 'Haiti_(2010)', 'Nanchang_(China_1927)', 'Haiyuan_(China_1920)', 'Sumatra_(Indonesia_2004)', 'Kanto_(Japan_1923)', 'Ashgabat_(Turkmenistan_1948)', 'Szechuan_(China_2008)', 'Messina_(Italy_1908)', 'Muzaffarabad_(Pakistan_2005)']
Y_Axis['Death', 'toll']: ['242000', '222570', '200000', '180000', '165708', '143000', '110000', '87476', '75000', '73338']

gold: The statistic shows the earthquakes with the highest death toll worldwide from 1900 to 2016 . Total 242,000 people were killed in the earthquake in Tangshan / China in 1976 .
gold_template: The statistic shows the templateTitle[3] with the highest templateYLabel[0] templateYLabel[1] worldwide from 1900 to templateTitleDate[0] . Total templateYValue[max] people were killed in the templateXLabel[0] in templateXValue[0] / China in 1976 .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateXValue[3] templateTitle[4] on templateTitleSubject[0] templateTitle[7] as of 2019 , sorted templateTitle[4] templateYLabel[0] . During the survey , it was found that goods templateXValue[0] had the largest templateXValue[0] with over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] templateTitle[4] .
generated: This statistic gives information on the Death toll great Haiyuan_(China_1920) 2016 on Death 2016 as of 2019 , sorted 2016 Death .  During the survey , it was found that goods Tangshan_(China_1976) had the largest Tangshan_(China_1976) with over 242000 million toll 2016 .

Example 643:
titleEntities: {'Subject': ['Cleveland Cavaliers', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Cleveland Cavaliers ( NBA ) 2003 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1510', '1275', '1325', '1200', '1100', '915', '515', '434', '329', '355', '476', '477', '455', '380', '356', '298', '258', '222']

gold: This graph depicts the value of the Cleveland Cavaliers franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the franchise had an estimated value of 1.51 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] franchise of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] franchise of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the value of the Cleveland Cavaliers franchise of the National Basketball Association from 2003 to 2020 .  In 2020 , the Franchise had an estimated value of 1510 million U.S. dollars .

Example 644:
titleEntities: {'Subject': ['France'], 'Date': ['2018']}
title: Number of expressways gas stations , by provider in France 2018
X_Axis['Company']: ['Total', 'Shell', 'Avia', 'BP', 'Eni', 'Esso', 'Carrefour', 'Leclerc', 'Dyneff']
Y_Axis['Number', 'of', 'gas', 'stations']: ['121', '69', '72', '59', '46', '46', '10', '12', '8']

gold: This statistic shows the number of gas stations at motorways and expressways in France in 2018 , by brand . In 2018 , Esso possessed about 46 gas stations on French motorways and expressways in comparison to 121 gas stations for Total .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] at motorways and templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] brand . In templateTitleDate[0] , templateXValue[5] possessed about templateYValue[4] templateYLabel[1] templateYLabel[2] on French motorways and templateTitle[1] in comparison to templateYValue[max] templateYLabel[1] templateYLabel[2] for templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) as of templateTitleDate[0] . At that time , templateXValue[0] was the largest templateXValue[0] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number gas of the Number expressways gas stations by in the United Kingdom ( France ) as of 2018 .  At that time , Total was the largest Total with a Number gas of 121 stations .

Example 645:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2000', '2019']}
title: United Kingdom HMRC tax receipts 2000 - 2019 : corporation tax
Data:
                0        1        2        3        4        5        6        7        8        9       10       11       12       13       14       15       16       17       18
Year      2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09  2007/08  2006/07  2005/06  2004/05  2003/04  2002/03  2001/02  2000/01
Total       57202    54394    49534    44410    43005    40327    40482    43130    43040    31630    34101    41308    38166    35048    30200    25402    25826    28526    30092
Offshore     1948     1757      295      560     2026     3556     4412     8840     6864     4998     9826     5728     6709     7307     3831     3057     3662     3515     2329 

gold: This statistic shows the total United Kingdom ( UK ) HMRC corporation tax receipts from fiscal year 2000/01 to fiscal year 2018/19 . Offshore receipts peaked in 2008/09 with revenue of 9.8 billion British pounds ( GBP ) .
gold_template: This statistic shows the templateLabel[1][0] templateTitleSubject[0] ( UK ) templateTitle[2] templateTitle[7] templateTitle[3] templateTitle[4] from fiscal templateLabel[0][0] templateValue[0][last] to fiscal templateLabel[0][0] templateValue[0][0] . templateLabel[2][0] templateTitle[4] peaked in templateValue[0][10] with revenue of 9.8 templateScale British pounds ( GBP ) .

generated_template: This statistic shows the number of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] templateTitle[3] templateTitle[4] in the templateTitle[5] amounted to templateValue[2][0] templateScale .
generated: This statistic shows the number of United Kingdom HMRC in the United Kingdom from 2000/01 to 2018/19 .  In 2018/19 , the United Kingdom of the United Kingdom tax receipts in the 2000 amounted to 1948 % .

Example 646:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2024']}
title: Inflation rate in Luxembourg 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['1.93', '1.88', '1.95', '1.87', '1.74', '1.73', '2.02', '2.11', '0.04', '0.06', '0.7', '1.7', '2.89', '3.73', '2.8', '0.01', '4.09', '2.66', '2.96', '3.76', '3.24', '2.53', '2.06', '2.4', '3.78', '1.02', '0.97', '1.37', '1.56', '1.9', '2.2', '3.6', '3.2', '3.1', '3.7', '3.4', '1.4', '-0.1', '0.3', '4.09', '5.64']

gold: This statistic shows the average inflation rate in Luxembourg from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Luxembourg amounted to about 2.02 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Luxembourg from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Luxembourg amounted to about 2.02 % compared to the previous Year .

Example 647:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2018', '2019']}
title: Mexico : housing prices 2018 to 2019
X_Axis['Quarter']: ['Q2_2019', 'Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018']
Y_Axis['Price', 'in', 'thousand', 'Mexican', 'pesos']: ['1070.65', '1071.24', '804.75', '800.98', '815.93', '810.2']

gold: This statistic shows the average housing price in Mexico from the first quarter of 2018 to the second quarter of 2019 . In the second quarter of 2019 , Mexicans paid on average 1.07 million Mexican pesos to acquire residential property . Compared to the same period of the previous year , the nominal price increased by more than 30 percent .
gold_template: This statistic shows the average templateTitle[1] templateYLabel[0] in templateTitleSubject[0] from the first templateXLabel[0] of templateXValue[2] to the second templateXLabel[0] of templateXValue[0] . In the second templateXLabel[0] of templateXValue[0] , Mexicans paid on average templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] to acquire residential property . Compared to the same period of the previous year , the nominal templateYLabel[0] templatePositiveTrend by more than 30 templateScale .

generated_template: This statistic shows the templateXValue[0] distribution of the templateTitle[0] of templateXValue[last] templateTitleSubject[0] from templateXValue[0] to templateXValue[0] . According to the source , the templateTitle[0] of templateTitle[1] templateXValue[last] . In templateXValue[0] , the templateTitle[0] of templateXValue[last] and templateTitleSubject[0] was templateYValue[min] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Q2_2019 distribution of the Mexico of Q1_2018 Mexico from Q2_2019 to .  According to the source , the Mexico of housing Q1_2018 .  In Q2_2019 , the Mexico of Q1_2018 and Mexico was 800.98 Mexican pesos .

Example 648:
titleEntities: {'Subject': ['Royal Bank Canada'], 'Date': ['1995', '2019']}
title: Common share price of Royal Bank of Canada 1995 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['Share', 'price', 'in', 'Canadian', 'dollars']: ['102.75', '93.44', '102.65', '90.87', '74.15', '80.24', '71.41', '59.88', '51.98', '52.32', '56.4', '36.1', '50.74', '55.5', '90.81', '64.25', '61.8', '57.85', '51.83', '50.85', '63.5', '76.55', '75.6', '48.1', '31.13']

gold: This statistic presents the common share prices of Royal Bank of Canada from 1995 to 2019 . In 2019 , the common share price of the leading Canadian bank amounted to 102.75 Canadian dollars .
gold_template: This statistic presents the templateTitle[0] templateYLabel[0] prices of templateTitleSubject[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of the leading templateYLabel[2] templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Share price of the dollars of Royal Bank Canada from 1995 to 2019 .  In 2019 , the average Share price of Royal Bank Canada was 102.75 Canadian dollars .

Example 649:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2014']}
title: Type of tea most consumed in Great Britain 2014
X_Axis['Type', 'of', 'tea']: ['English_breakfast', 'Green', 'Earl_Grey', 'Fruit', 'Peppermint', 'Herbal', 'Camomile', 'Decaffeinated', 'Assam', 'Darjeeling', 'Chai', 'Other', 'Not_applicable_-_I_never_drink_tea']
Y_Axis['Share', 'of', 'respondents']: ['52', '23', '22', '19', '16', '15', '11', '10', '9', '8', '7', '16', '18']

gold: This statistic shows the results of a survey that asked British adults which types of tea , if any , they ever drink in Great Britain ( GB ) in 2014 . English breakfast tea ranked highest with a 52 percent share of respondents , followed by Green tea ( 23 percent ) and Earl Grey ( 22 percent ) . A majority of British consumers drink tea on a daily basis , with earlier times in the day the peak period for tea drinking .
gold_template: This statistic shows the results of a survey that asked British adults which types of templateXValue[last] , if any , they ever templateXValue[last] in templateTitleSubject[0] ( GB ) in templateTitleDate[0] . templateXValue[0] tea ranked highest with a templateYValue[max] templateScale templateYLabel[0] of templateYLabel[1] , followed by templateXValue[1] templateXValue[last] ( templateYValue[1] templateScale ) and templateXValue[2] ( templateYValue[2] templateScale ) . A majority of British consumers templateXValue[last] on a daily basis , with earlier times in the day the peak period for templateXValue[last] drinking .

generated_template: As of 2019 , templateXValue[0] templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] in the templateTitleSubject[0] , with templateYValue[max] templateScale of templateYLabel[1] saying they followed by templateXValue[1] with templateYValue[1] templateScale of templateYLabel[1] having made it . templateXValue[4] was followed by templateXValue[2] with templateYValue[2] templateScale of templateYLabel[1] having templateXValue[0] .
generated: As of 2019 , English_breakfast was the Type tea most Type in the Great Britain , with 52 % of respondents saying they followed by Green with 23 % of respondents having made it .  Peppermint was followed by Earl_Grey with 22 % of respondents having English_breakfast .

Example 650:
titleEntities: {'Subject': ['NAND'], 'Date': ['2020']}
title: Global NAND memory bit market share 2020 , by application
Data:
               0                        1            2          3         4      5
Race  Smartphone  SSD_(client_enterprise)  Memory_card  Tablet_PC  Consumer  Other
2019          35                       28           15         12         7      3
2020          36                       30           13         11         7      3 

gold: According to recent data , in 2020 it is forecast that 36 percent of total NAND memory capacity will go towards smartphones . NAND flash memory was originally developed by Toshiba in the early 1980s . It allows greater storage density and lower cost per bit than the alternative NOR flash , but is more useful as a secondary storage device than as primary storage , as it does not support a random-access external address bus .
gold_template: According to recent data , in templateTitleDate[0] it is forecast that templateValue[2][max] templateScale of total templateTitleSubject[0] templateValue[0][2] capacity will go towards smartphones . templateTitleSubject[0] flash templateValue[0][2] was originally developed templateTitle[7] Toshiba in the early 1980s . It allows greater storage density and lower cost per templateTitle[3] than the alternative NOR flash , but is more useful as a secondary storage device than as primary storage , as it does not support a random-access external address bus .

generated_template: According to recent data , in templateTitleDate[0] it is forecast that templateValue[2][max] templateScale of total templateTitleSubject[0] templateValue[0][2] capacity will go towards smartphones . templateTitleSubject[0] flash templateValue[0][2] was originally developed templateTitle[7] Toshiba in the early 1980s . It allows greater storage density and lower cost per templateTitle[3] than the alternative NOR flash , but is more useful as a secondary storage device than as primary storage , as it does not support a random-access external address bus .
generated: According to recent data , in 2020 it is forecast that 36 % of total NAND Memory_card capacity will go towards smartphones .  NAND flash Memory_card was originally developed by Toshiba in the early 1980s .  It allows greater storage density and lower cost per bit than the alternative NOR flash , but is more useful as a secondary storage device than as primary storage , as it does not support a random-access external address bus .

Example 651:
titleEntities: {'Subject': ['Online'], 'Date': ['2017']}
title: Online search query market share worldwide 2017 , by search engine
Data:
              0         1          2            3           4           5          6            7            8           9                 10
Response  Total  One_word  Two_words  Three_words  Four_words  Five_words  Six_words  Seven_words  Eight_words  Nine_words  Ten_or_more_words
Google    15.77     38.88      11.17         6.75        5.12        4.58        8.3         7.65         7.56         6.4               5.24
Yahoo      5.24      3.17       7.52         5.22        5.28        5.18       6.96         9.28         7.84        8.37               9.42
Bing      66.77     25.47      76.39        84.01       86.59       87.04      79.38        73.29        71.99       75.86              65.97
AOL        0.26      0.22       0.68          0.1        0.09        0.21       0.36         0.65         0.84        0.49                  0
Ask         3.4      2.68       2.75         3.25        2.66        2.95       4.73         8.96        11.76        8.87              19.37
Other      8.57     29.58       1.49         0.66        0.26        0.04       0.27         0.16            0           0                  0 

gold: This statistic shows the global search engine market share as of August 2017 , by search query size . During the survey period , it was found that Bing accounted for 25.47 percent of single-word search queries worldwide .
gold_template: This statistic shows the global templateTitle[1] templateTitle[9] templateTitle[3] templateTitle[4] as of 2017 , templateTitle[7] templateTitle[1] templateTitle[2] size . During the survey period , it was found that templateLabel[3][0] accounted for templateValue[3][1] templateScale of single-word templateTitle[1] queries templateTitle[5] .

generated_template: This statistic shows the estimated templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the templateTitle[4] of templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] . According to the source , templateValue[1][0] templateScale of respondents said that they would be generated templateValue[2][0] templateScale in the templateLabel[2][0] templateLabel[2][1] templateTitle[4] .
generated: This statistic shows the estimated Online search query market the share of Online 2017 in .  According to the source , 15.77 % of respondents said that they would be generated 5.24 % in the Yahoo share .

Example 652:
titleEntities: {'Subject': ['China'], 'Date': ['2018']}
title: Urbanization in China 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['59.15', '57.96', '56.74', '55.5', '54.26', '53.01', '51.77', '50.51', '49.23', '47.88', '46.54']

gold: This statistic shows the degree of urbanization in China from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 59.15 percent of China 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in China from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 59.15 % of China 's total population lived in urban areas and cities .

Example 653:
titleEntities: {'Subject': ['Toronto Blue Jays'], 'Date': ['2019']}
title: Toronto Blue Jays all-time home run leaders 2019
X_Axis['Players']: ['Carlos_Delgado', 'Jose_Bautista', 'Edwin_Encarnacion', 'Vernon_Wells', 'Joe_Carter', 'George_Bell', 'Jesse_Barfield', 'Lloyd_Moseby', 'Adam_Lind', 'Ernie_Whitt']
Y_Axis['Number', 'of', 'home', 'runs']: ['336', '288', '239', '223', '203', '202', '179', '149', '146', '131']

gold: This statistic shows the Toronto Blue Jays all-time home run leaders as of October 10 , 2019 . Carlos Delgado has hit the most home runs in Toronto Blue Jays franchise history with 336 home runs .
gold_template: This statistic shows the templateTitleSubject[0] Jays templateTitle[3] templateYLabel[1] templateTitle[5] templateTitle[6] as of October 10 , templateTitleDate[0] . templateXValue[0] has hit the most templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] Jays franchise history with templateYValue[max] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateTitleSubject[0] all-time templateYLabel[1] templateTitle[4] templateTitle[5] as of October 16 , templateTitleDate[0] . templateXValue[0] has hit the most templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] franchise history with templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Toronto Blue Jays all-time home run as of October 16 , 2019 .  Carlos_Delgado has hit the most home runs in Toronto Blue Jays franchise history with 336 home runs .

Example 654:
titleEntities: {'Subject': ['Personal'], 'Date': ['2014']}
title: Personal goals of health/fitness club members 2014
X_Axis['Reasons', 'to', 'Reasons', 'for', 'joining']: ['To_stay_healthy', 'To_feel_better_about_myself', 'To_stay_in_shape', 'To_look_better', 'To_lose_weight', 'To_get_in_shape', 'To_maintain_strength', 'To_stay_flexible', 'To_build_muscle', 'To_maintain_weight', 'For_a_specific_health_concern', 'To_impress_others', 'To_gain_weight']
Y_Axis['Percentage', 'of', 'respondents']: ['64', '52', '46', '45', '45', '44', '43', '40', '39', '27', '15', '5', '3']

gold: The statistic shows the results of a survey concerned with the reasons why people are members in health clubs in the U.S. 45 percent of the respondents said that one of the reasons for their health club membership was that they want to lose weight .
gold_template: The statistic shows the results of a survey concerned with the templateXLabel[0] why people are templateTitle[4] in templateXValue[10] clubs in the U.S. templateYValue[3] templateScale of the templateYLabel[1] said that one of the templateXLabel[0] templateXValue[10] their templateXValue[10] templateTitle[3] membership was that they want to templateXValue[4] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] of templateTitle[2] using the templateTitle[4] as of 2019 . During the survey period , it was found that templateYValue[max] templateScale of the templateYLabel[1] used templateXValue[0] on the social networking site .
generated: This statistic gives information on the Personal goals of health/fitness using the members as of 2019 .  During the survey period , it was found that 64 percentage of the respondents used To_stay_healthy on the social networking site .

Example 655:
titleEntities: {'Subject': ['Nokia'], 'Date': ['1999', '2019']}
title: Nokia 's net sales 1999 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Net', 'sales', 'in', 'billion', 'euros']: ['23.32', '22.56', '23.15', '23.64', '12.5', '11.76', '12.71', '30.18', '38.66', '42.45', '40.98', '50.71', '51.06', '41.12', '34.19', '29.37', '29.53', '30.02', '31.19', '30.38', '19.77']

gold: In 2018 , Nokia had 22.5 billion euros in net sales , which is a small decrease from the year before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in 2014 , Nokia has focused on its network infrastructure business .
gold_template: In templateXValue[1] , templateTitleSubject[0] had 22.5 templateScale templateYLabel[3] in templateYLabel[0] templateYLabel[1] , which is a small templateNegativeTrend from the templateXLabel[0] before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in templateXValue[5] , templateTitleSubject[0] has focused on its network infrastructure business .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the carmaker templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Net sales of the carmaker Nokia from 1999 to 2019 .  In 2019 , the Nokia of stood at 23.32 billion euros .

Example 656:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016', '2018']}
title: Top U.S. states based on milk production 2016 to 2018
Data:
                0          1      2         3      4         5             6          7           8           9
State  California  Wisconsin  Idaho  New_York  Texas  Michigan  Pennsylvania  Minnesota  New_Mexico  Washington
2016        40469      30123  14665     14765  10773     10876         10820       9666        7711        6650
2017        39798      30320  14627     14912  12054     11231         10938       9864        8212        6526
2018        40413      30579  15149     14882  12852     11168         10665       9868        8285        6736 

gold: This statistic shows the ten U.S. states with the highest amount of milk production from 2016 to 2018 . California , was the leading producer , where 40.4 billion pounds of milk were produced in 2018 . Milk production Dairy farming is an agricultural business which is engaged in the long-term milk production within the dairy industry .
gold_template: This statistic shows the ten templateTitleSubject[0] templateTitle[2] with the highest amount of templateTitle[4] templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . templateValue[0][0] , was the leading producer , where 40.4 templateScale pounds of templateTitle[4] were produced in templateTitleDate[max] . templateTitle[4] templateTitle[5] Dairy farming is an agricultural business which is engaged in the long-term templateTitle[4] templateTitle[5] within the dairy industry .

generated_template: This statistic shows the templateTitleSubject[0] of templateTitle[1] templateTitle[2] and templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] , by templateLabel[0][0] . In templateTitleDate[max] , templateValue[0][0] had the highest templateTitle[4] templateTitle[5] templateTitle[6] in templateValue[0][0] at templateValue[1][0] templateScale .
generated: This statistic shows the U.S. of states and milk from 2016 to 2018 , by State .  In 2018 , California had the highest milk production 2016 in California at 40469 % .

Example 657:
titleEntities: {'Subject': ['Johnson Johnson'], 'Date': ['2012', '2024']}
title: Revenue of Johnson & Johnson 's pharmaceuticals segment worldwide 2012 to 2024
Data:
                                    0     1      2     3      4      5      6      7      8      9     10     11     12
Year                             2012  2013   2014  2015   2016   2017   2018   2019   2020   2021   2022   2023   2024
Immunology                       7.87  9.19  10.19  10.4  11.97  12.24  12.34  12.49  12.09  11.69  11.14  10.24   9.34
Cardiovascular/Metabolism/Other  4.94  4.95   5.58  6.42    6.4   6.29   7.19   7.27    7.1   7.09   6.93    6.8   6.79
Oncology                         2.63  3.77   4.46   4.7   5.81   7.26   8.96  988.0  10.73  11.78  12.71  13.91  14.86
Central nervous system           6.72  6.67   6.49  6.26   6.08   5.99   5.74   4.93   4.84   4.94   5.05   4.68   5.03
Anti-infectives                  3.19  3.55    5.6  3.66   3.21   3.15   3.18   3.79   3.95   4.38   5.02   5.45   5.94 

gold: This statistic shows the revenue share of Johnson & Johnson 's pharmaceuticals segment worldwide from 2012 to 2024 , by product category . Johnson & Johnson is a multi-national company , specialized on pharmaceuticals , medical devices and consumer goods . The company is headquartered in New Brunswick , New Jersey .
gold_template: This statistic shows the templateTitle[0] share of templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] from templateValue[0][0] to templateValue[0][last] , by product category . templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] is a multi-national company , specialized on templateTitle[5] , medical devices and consumer goods . The company is headquartered in New Brunswick , New Jersey .

generated_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] employees templateTitle[4] from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . The average templateTitle[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] are expected to grow by templateValue[3][0] templateScale templateYLabel[3] in the previous templateLabel[0][0] .
generated: This statistic shows the Revenue of Johnson Johnson employees 's from 2024 to 2012 , worldwide 2012 .  The average Revenue of Johnson Johnson & Johnson 's are expected to grow by 2.63 % in the previous Year .

Example 658:
titleEntities: {'Subject': ['Arizona'], 'Date': ['2000', '2018']}
title: Arizona - real GDP 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Real', 'GDP', 'in', 'billion', 'U.S.', 'dollars']: ['309.07', '297.21', '288.68', '279.43', '273.41', '270.15', '268.29', '263.21', '257.48', '255.08', '277.48', '284.91', '277.29', '263.06', '243.25', '233.34', '219.31', '212.66', '207.79']

gold: This statistic shows the development of Arizona 's real GDP from 2000 to 2018 . In 2018 , the GDP of Arizona amounted to 309.07 billion U.S. dollars .
gold_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of the templateYLabel[0] templateYLabel[1] of the templateYLabel[2] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the development of the Real GDP of the billion of Arizona from 2000 to 2018 .  In 2018 , the Real GDP of Arizona was at 309.07 billion U.S. dollars .

Example 659:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2008', '2018']}
title: Age distribution in the Netherlands 2008 to 2018
Data:
                0      1      2      3      4      5      6      7      8      9     10
Year         2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
Ages 0-14   16.11  16.35  16.58  16.76  16.94  17.08   17.2  17.33  17.52  17.69  17.89
Ages 15-64   64.7  64.85  65.05  65.32  65.62  65.99  66.41  66.78  67.04  67.24  67.33
Ages 65+     19.2  18.79  18.37  17.92  17.44  16.93  16.39  15.89  15.44  15.07  14.77 

gold: This statistic depicts the age distribution of the Netherlands from 2008 to 2018 . In 2018 , about 16.11 percent of the population in the Netherlands fell into the 0 - 14 year category , 64.7 percent into the 15 - 64 age group and 19.2 percent were over 65 years of age . See the Netherlands ' population figures here .
gold_template: This statistic depicts the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][0] templateScale of the population in the templateTitleSubject[0] templateNegativeTrend into the 0 - 14 templateLabel[0][0] category , templateValue[2][0] templateScale into the templateValue[3][8] - 64 templateTitle[0] group and templateValue[3][0] templateScale were over templateValue[2][0] years of templateTitle[0] . See the templateTitleSubject[0] ' population figures here .

generated_template: This statistic depicts the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][0] templateScale of templateTitleSubject[0] 's population were between 0 and 14 years of templateTitle[0] , around templateValue[2][0] templateScale between the templateLabel[1][0] of 15 and templateValue[2][8] , and about templateValue[3][0] templateScale were templateValue[2][4] years or older .
generated: This statistic depicts the Age distribution of Netherlands from 2008 to 2018 .  In 2018 , about 16.11 % of Netherlands 's population were between 0 and 14 years of Age , around 64.7 % between the Ages of 15 and 67.04 , and about 19.2 % were 65.62 years or older .

Example 660:
titleEntities: {'Subject': ['London'], 'Date': ['2010', '2019']}
title: Number of knife crimes in London 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Number', 'of', 'offences', '(in', '1,000s)']: ['14.84', '14.77', '12.11', '9.74', '9.69', '10.08', '11.38', '14.17', '13.33']

gold: In 2018/19 the number of knife crime offences recorded in London reached almost 15 thousand , an increase of more than five thousand compared with 2015/16 . This rise reversed the trend of decreasing knife crime offences recorded between 2011/12 and 2014/15 , which saw a reduction of knife crime offences from just over 14 thousand to under 9.7 thousand . A wider trend The increase in knife crime witnessed in London has occurred alongside a general increase in overall knife crime throughout England and Wales .
gold_template: In templateXValue[0] the templateYLabel[0] of templateTitle[1] crime templateYLabel[1] recorded in templateTitleSubject[0] reached almost templateYValue[max] thousand , an templatePositiveTrend of more than five thousand compared with templateXValue[3] . This rise reversed the trend of templateNegativeTrend templateTitle[1] crime templateYLabel[1] recorded between templateXValue[7] and templateXValue[4] , which saw a reduction of templateTitle[1] crime templateYLabel[1] from just over templateYValue[7] thousand to under templateYValue[3] thousand . A wider trend The templatePositiveTrend in templateTitle[1] crime witnessed in templateTitleSubject[0] has occurred alongside a general templatePositiveTrend in overall templateTitle[1] crime throughout England and Wales .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , the templateTitle[0] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateYLabel[4] was templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Number offences of the London from the 2010/11 season to the 2018/19 season .  In the 2018/19 season , the Number offences of the London 1,000s) was 14.84 (in 1,000s) .

Example 661:
titleEntities: {'Subject': ['Grisham'], 'Date': ['2010', '2018']}
title: John Grisham - earnings 2010 to 2018
X_Axis['Month']: ['June_2017_to_June_2018', 'June_2016_to_June_2017', 'June_2015_to_June_2016', 'June_2014_to_June_2015', 'June_2013_to_June_2014', 'June_2012_to_June_2013', 'May_2011_to_May_2012', 'May_2010_to_April_2011']
Y_Axis['Earnings', 'in', 'million', 'U.S.', 'dollars']: ['21', '14', '18', '14', '17', '18', '26', '18']

gold: The statistic presents data on the annual earnings of John Grisham from May 2010 to June 2018 . The author earned 21 million U.S. dollars in the period June 2017 to June 2018 , up from 14 million a year earlier .
gold_template: The statistic presents data on the annual templateYLabel[0] of templateTitle[0] templateTitleSubject[0] from templateXValue[6] templateXValue[last] to templateXValue[0] . The author earned templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in the period templateXValue[0] to templateXValue[0] , up from templateYValue[min] templateScale a year earlier .

generated_template: The statistic presents data on the annual templateYLabel[0] of Joanne templateTitleSubject[0] ( templateTitle[0] templateTitleSubject[0] ) from templateXValue[6] templateXValue[last] to templateXValue[0] . The author earned templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in the period templateXValue[0] to templateXValue[0] , down from templateYValue[max] templateScale a year earlier .
generated: The statistic presents data on the annual Earnings of Joanne Grisham ( John Grisham ) from May_2011_to_May_2012 May_2010_to_April_2011 to June_2017_to_June_2018 .  The author earned 21 million U.S. dollars in the period June_2017_to_June_2018 to , down from 26 million a year earlier .

Example 662:
titleEntities: {'Subject': ['Paris'], 'Date': ['2010', '2018']}
title: International visitor spending in Paris 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['International', 'visitor', 'spending', 'in', 'billion', 'U.S.', 'dollars']: ['14.06', '13.05', '12.03', '13.41', '16.42', '19.5', '17.25', '14.95', '13.1']

gold: In 2018 , international visitor spending in Paris amounted to 14.06 billion U.S. dollars , up from 13.05 billion the previous year .
gold_template: In templateXValue[max] , templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[4] templateYLabel[5] , up from templateYValue[1] templateScale the previous templateXLabel[0] .

generated_template: In templateXValue[max] , the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] amounted to templateYValue[0] templateScale templateYLabel[4] templateYLabel[5] . According to the source , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] is defined as the total market value of all goods and services . It is considered to be a very important indicator of the economic strength of a country .
generated: In 2018 , the average International of visitor spending in the Paris amounted to 14.06 billion U.S. dollars .  According to the source , the International of visitor spending is defined as the total market value of all goods and services .  It is considered to be a very important indicator of the economic strength of a country .

Example 663:
titleEntities: {'Subject': ['New York'], 'Date': ['2018']}
title: Real GDP of New York , by industry 2018
X_Axis['Industry']: ['Finance_insurance_real_estate_rental_and_leasing', 'Professional_and_business_services', 'Government_and_government_enterprises', 'Information', 'Educational_services_health_care_and_social_assistance', 'Wholesale_trade', 'Retail_trade', 'Manufacturing', 'Arts_entertainment_recreation_accommodation_and_food_services', 'Construction', 'Transportation_and_warehousing', 'Utilities', 'Agriculture_forestry_fishing_and_hunting', 'Mining_quarrying_and_oil_and_gas_extraction']
Y_Axis['Real', 'value', 'added', 'in', 'billion', 'chained', '(2012)', 'U.S.', 'dollars']: ['418.87', '200.09', '151.94', '143.64', '141.49', '70.45', '69.22', '65.79', '61.7', '39.87', '28.29', '20.17', '3.3', '1.2']

gold: This graph shows the real value added to the Gross Domestic Product ( GDP ) of New York in 2018 , by industry . In 2018 , the mining industry added 1.2 billion chained 2012 U.S. dollars of value to the state GDP . Total real GDP amounted to about 1.44 trillion chained 2012 U.S. dollars .
gold_template: This graph shows the templateXValue[0] templateYLabel[1] templateYLabel[2] to the Gross Domestic Product ( templateTitle[1] ) of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[last] templateXLabel[0] templateYLabel[2] templateYValue[min] templateScale templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the state templateTitle[1] . Total templateXValue[0] templateTitle[1] amounted to about 1.44 templateScale templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] .

generated_template: This graph shows the templateXValue[0] templateYLabel[1] templateYLabel[2] to the Gross Domestic Product ( templateTitle[1] ) of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[3] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[last] templateXLabel[0] templateYLabel[2] templateYValue[min] templateScale templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the state templateTitle[1] .
generated: This graph shows the Finance_insurance_real_estate_rental_and_leasing value added to the Gross Domestic Product ( GDP ) of New York in 2018 , York Industry .  In 2018 , the Mining_quarrying_and_oil_and_gas_extraction Industry added 1.2 billion chained 2012 U.S. dollars of value to the state GDP .

Example 664:
titleEntities: {'Subject': ['West African'], 'Date': ['2014']}
title: Deaths per day in West African countries with 2014 Ebola outbreak by disease
X_Axis['Diseases']: ['Ebola', 'Lassa_fever', 'Tuberculosis', 'Diarrhoea', 'Malaria', 'HIV/AIDS']
Y_Axis['Number', 'of', 'deaths', 'per', 'day']: ['4', '14', '110', '404', '552', '685']

gold: This statistic shows the number of deaths per day by selected diseases in West African countries that are suffering from the Ebola outbreak in 2014 . Malaria causes some 552 deaths per day in these countries , while Ebola causes around four deaths per day ( as of August 2014 ) . Ebola compared to other diseases Ebola first emerged in 1976 in Sudan and the Democratic Republic of Congo .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[10] selected templateXLabel[0] in templateTitleSubject[0] countries that are suffering from the templateXValue[0] templateTitle[9] in templateTitleDate[0] . templateXValue[4] causes some templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] in these templateTitle[5] , while templateXValue[0] causes around templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( as of 2014 ) . templateXValue[0] compared to other templateXLabel[0] templateXValue[0] first emerged in 1976 in Sudan and the Democratic Republic of Congo .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) as of templateTitleDate[0] . According to the source , templateXValue[0] was the templateTitle[0] that year , with a total of templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[3] .
generated: This statistic shows the Deaths per day West in the United Kingdom ( West African ) as of 2014 .  According to the source , Ebola was the Deaths that year , with a total of 685 Number deaths day .

Example 665:
titleEntities: {'Subject': ['National Football League'], 'Date': ['2019', '2019']}
title: Average attendance in the National Football League 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Average', 'attendance']: ['66151', '67040', '67405', '69487', '68216', '68331', '68397', '67632', '67419', '66957', '67506', '68245']

gold: The average per game attendance during the 2019 NFL regular season was 66,151 , the league 's lowest figure across the entire decade . The Dallas Cowboys had the highest average attendance of all the 32 NFL teams , averaging 91,619 for their eight regular-season home games in 2019 . Cowboys in a league of their own Since moving to AT & T Stadium in 2009 , the Dallas Cowboys have led the NFL in average regular-season home attendance for ten consecutive seasons .
gold_template: The templateYLabel[0] per game templateYLabel[1] during the templateXValue[max] NFL regular season was templateYValue[idxmax(X)] , the templateTitleSubject[0] 's lowest figure across the entire decade . The Dallas Cowboys had the highest templateYLabel[0] templateYLabel[1] of all the 32 NFL teams , averaging 91,619 for their eight regular-season home games in templateXValue[idxmin(Y)] . Cowboys in a templateTitleSubject[0] of their own Since moving to AT & T Stadium in templateXValue[10] , the Dallas Cowboys have led the NFL in templateYLabel[0] regular-season home templateYLabel[1] for ten consecutive seasons .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitleSubject[0] was templateYValue[0] templateYValue[idxmax(X)]
generated: This statistic shows the Average attendance National Football attendance of the National Football League from 2008 to 2019 .  In 2019 , the Average attendance at Football games of the National Football League was 66151 . 

Example 666:
titleEntities: {'Subject': ['Puerto Rico'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in current U.S. dollars in Puerto Rico 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['101.52', '101.04', '100.62', '100.34', '100.17', '99.91', '101.13', '104.25', '104.34', '103.38', '102.45', '102.45', '101.57', '100.35', '98.38', '96.39', '93.64', '89.52', '87.28', '83.92', '80.32', '75.83', '72.55', '69.67', '61.7', '57.84', '54.09', '48.19', '45.34', '42.65', '39.69', '36.92', '34.63', '32.29', '30.6', '28.27', '26.18', '23.88', '21.97', '20.29', '19.16']

gold: The statistic shows gross domestic product ( GDP ) in current U.S. dollars in Puerto Rico from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] templateYLabel[4] templateYLabel[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Puerto Rico from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 667:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2018']}
title: Brazil : voting intention for 2018 presidential elections , by candidate
Data:
                                          0                     1                    2                       3                 4                   5                   6                         7                        8                     9                 10                        11                      12                                 13           14              15            16          17               18           19                    20           21                  22          23            24                25                  26                 27                               28          29
Month        Luiz_Inácio_Lula_da_Silva_(PT)  Jair_Bolsonaro_(PSL)  Marina_Silva_(REDE)  Geraldo_Alckmin_(PSDB)  Ciro_Gomes_(PDT)  Alvaro_Dias_(PODE)  João_Amoedo_(NOVO)  Henrique_Meirelles_(MDB)  Guilherme_Boulos_(PSOL)  Cabo_Daciolo_(PATRI)  Vera_Lúcia_(PSTU)  João_Goulart_Filho_(PPL)  José_Maria_Eymael_(DC)  Blank_or_voided_ballots_/_No_vote  Do_not_know  Jair_Bolsonaro  Marina_Silva  Ciro_Gomes  Geraldo_Alckmin  Alvaro_Dias  Fernando_Haddad_(PT)  João_Amoedo  Henrique_Meirelles  Vera_Lúcia  Cabo_Daciolo  Guilherme_Boulos  João_Goulart_Filho  José_Maria_Eymael  Blank_or_voided_ballots/No_vote  Don't_know
Situation A                              39                    19                    8                       6                 5                   3                   2                         1                        1                     1                  1                         0                       0                                 11            3               0             0           0                0            0                     0            0                   0           0             0                 0                   0                  0                                0           0
Situation B                               0                     0                    0                       0                 0                   0                   0                         0                        0                     0                  0                         0                       0                                  0            0              22            16          10                9            4                     4            2                   2           1             1                 1                   1                  0                               22           6 

gold: The statistic shows the results of a survey carried out in August 2018 to measure the voting intention for the leading candidates to the first round of presidential elections in Brazil . Respondents were presented with two sets of candidates reflecting different potential election situations . As of August 2018 , the voting intention for presidential candidate Marina Silva amounted to 8 percent in the situation A and 16 percent in the situation B .
gold_template: The statistic shows the results of a survey carried out in 2018 to measure the templateTitle[1] templateTitle[2] templateTitle[3] the leading candidates to the first round of templateTitle[5] templateTitle[6] in templateTitleSubject[0] . Respondents were presented with templateValue[1][6] sets of candidates reflecting different potential election situations . As of 2018 , the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[5] templateTitle[8] templateValue[0][2] templateValue[0][0] amounted to templateValue[1][2] templateScale in the templateLabel[1][0] A and templateValue[2][16] templateScale in the templateLabel[1][0] templateLabel[2][1] .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateValue[0][0] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . According to the survey results , templateValue[1][0] templateScale of the templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] that year .
generated: This statistic shows the Brazil of the voting intention for 2018 Luiz_Inácio_Lula_da_Silva_(PT) in 2018 , by candidate .  According to the survey results , 39 % of the Situation A that year .

Example 668:
titleEntities: {'Subject': ['Europe'], 'Date': ['2016']}
title: Population of Europe 2016 , by country and gender
Data:
                0         1               2         3         4         5         6         7            8        9       10        11              12       13       14       15       16           17        18       19       20       21        22       23       24       25       26         27       28       29        30                                     31      32       33      34          35          36      37       38             39
Country   Germany    France  United_Kingdom     Italy     Spain   Ukraine    Poland   Romania  Netherlands  Belgium   Greece  Portugal  Czech_Republic  Hungary  Belarus   Sweden  Austria  Switzerland  Bulgaria   Serbia  Denmark  Finland  Slovakia  Ireland  Croatia  Georgia  Armenia  Lithuania  Albania   Latvia  Slovenia  Former_Yugoslav_Republic_of_Macedonia  Kosovo  Estonia  Cyprus  Montenegro  Luxembourg   Malta  Iceland  Liechtenstein
Male     40514123  32340016        32224529  29456321  22807464  19717881  18377040   9649811      8417135  5569264  5224210   4901509         5186330  4688519  4420842  4930966  4265369      4121471   3477177  3446258  2837887  2701490   2646082  2335733  2022797  1779500  1429042    1329607  1461326   904299   1023333                                1037601  883294   616708  412692      307734      289193  217569   167270          18660
Female   41661561  34419934        33158027  31209230  23632635  22872998  19590169  10110503      8561985  5741853  5559538   5439821         5367513  5141966  5077522  4920051  4424707      4205655   3676607  3630114  2869364  2785818   2780170  2388987  2167872  1940900  1569535    1558951  1424700  1064658   1040855                                1033677  888310   699236  435627      314484      287056  216834   165259          18962 

gold: This graph displays the population of Europe in 2015 , by gender and country . Germany was the largest population of both genders in Europe , with 41.4 million females and 39.8 million males . The majority of countries listed have a higher population of women .
gold_template: This graph displays the templateTitle[0] of templateTitleSubject[0] in 2015 , templateTitle[3] templateTitle[5] and templateLabel[0][0] . templateValue[0][0] was the largest templateTitle[0] of both genders in templateTitleSubject[0] , with 41.4 templateScale females and 39.8 templateScale males . The majority of countries listed have a higher templateTitle[0] of women .

generated_template: This statistic provides a comparison of the average amount of time templateTitle[4] on household templateTitle[5] templateTitle[8] templateTitle[9] in templateTitleSubject[0] member templateTitle[7] as well as templateValue[0][19] , templateValue[0][27] and templateValue[0][28] . As of templateTitleDate[0] , Irish men templateTitle[4] templateValue[1][5] templateScale of the templateValue[0][0] had the highest templateTitle[4] with templateValue[2][0] templateScale of the templateValue[0][0] .
generated: This statistic provides a comparison of the average amount of time country on household gender in Europe member gender as well Serbia , Lithuania and Albania .  As of 2016 , Irish men country 19717881 % of the Germany had the highest country with 41661561 % of the Germany .

Example 669:
titleEntities: {'Subject': ['TV U.S.'], 'Date': ['2015']}
title: Leading primetime TV programming genres viewed in the U.S. 2015
X_Axis['Response']: ['Sitcoms_or_comedy', 'Movies', 'Drama', 'Reality', 'Action/adventure', 'History_or_science', 'News/public_affairs', 'Sports', '\'How_to"_or_home-related', 'Weather', 'Talk_or_interview', "Children's", 'Other_type_not_listed', 'Music_videos', 'Soap_operas']
Y_Axis['Share', 'of', 'respondents']: ['48', '43', '42', '34', '32', '27', '26', '22', '17', '13', '10', '7', '7', '6', '4']

gold: This graph shows the leading genres of primetime TV programming viewed regularly in the United States as of May 2015 . During a survey , 48 percent of respondents stated they regularly watched sitcoms or comedy shows on TV during primetime . Movies , part of the billion-dollar film industry , are the second most common genre on primetime TV programming .
gold_template: This graph shows the templateTitle[0] templateTitle[4] of templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitle[5] regularly in the templateTitle[6] as of 2015 . During a survey , templateYValue[max] templateScale of templateYLabel[1] stated they regularly watched templateXValue[0] or templateXValue[0] shows on templateTitleSubject[0] during templateTitle[1] . templateXValue[1] , part of the billion-dollar film industry , are the second most common genre on templateTitle[1] templateTitleSubject[0] templateTitle[3] .

generated_template: This statistic presents the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded templateXValue[3] by Harris Interactive in 2014 , templateYValue[max] templateScale of templateYLabel[1] stated that they used templateTitle[0] on the social network .
generated: This statistic presents the results of a survey among TV U.S. adult primetime .  The survey was fielded Reality by Harris Interactive in 2014 , 48 % of respondents stated that they used Leading on the social network .

Example 670:
titleEntities: {'Subject': ['Iraq'], 'Date': ['2024']}
title: Total population of Iraq 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['44.47', '43.35', '42.25', '41.18', '40.13', '39.12', '38.12', '37.14', '36.17', '35.21', '35.0']

gold: This statistic shows the total population of Iraq from 2014 to 2024 . In 2018 , the estimated total population of Iraq amounted to approximately 38.12 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[0] . See the figures for the templateTitle[1] of Italy for comparison .
generated: This statistic shows the Total population of Iraq from 2014 to 2018 , with projections up until 2024 .  In 2018 , the Total population of Iraq amounted to about 38.12 millions Inhabitants .  See the figures for the population of Italy for comparison .

Example 671:
titleEntities: {'Subject': ['Becton', 'Dickinson', 'Company'], 'Date': ['2011', '2018']}
title: Becton , Dickinson , and Company 's employees from 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'employees']: ['76032', '41900', '50900', '49500', '30600', '30000', '29600', '29400']

gold: The statistic shows the number of employees of Becton , Dickinson , and Company for the fiscal years 2011 to 2018 . The number of employees at Becton , Dickinson , and Company reached a high in 2016 with 50,900 employed at the company that year .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] , templateTitleSubject[1] , and templateTitleSubject[2] for the fiscal years templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] , templateTitleSubject[1] , and templateTitleSubject[2] reached a high in templateXValue[2] with templateYValue[2] employed at the templateTitleSubject[2] that templateXLabel[0] .

generated_template: The statistic illustrates the templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] templateTitle[4] templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[2] , the company employed some templateYValue[2] templateScale people employed in the world . templateTitleSubject[0] is a United States-based multinational oil and gas corporation , headquartered in Chicago , Illinois .
generated: The statistic illustrates the Number of employees the Becton employees from 2011 to 2018 .  In 2016 , the company employed some 50900 million people employed in the world .  Becton is a United States-based multinational oil and gas corporation , headquartered in Chicago , Illinois .

Example 672:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Birth rate in Italy 2018 , by region
X_Axis['Month']: ['Trentino-South_Tyrol', 'Campania', 'Sicily', 'Calabria', 'Lombardy', 'Emilia-Romagna', 'Lazio', 'Apulia', 'Aosta_Valley', 'Veneto', 'Abruzzo', 'Piedmont', 'Tuscany', 'Marche', 'Umbria', 'Basilicata', 'Friuli-Venezia_Giulia', 'Molise', 'Liguria', 'Sardinia']
Y_Axis['Birth', 'rate', 'per', 'thousand', 'inhabitants']: ['9.0', '8.3', '8.1', '7.8', '7.5', '7.3', '7.2', '7.2', '7.2', '7.2', '6.8', '6.7', '6.7', '6.7', '6.6', '6.6', '6.4', '6.2', '5.8', '5.7']

gold: In 2018 , Trentino-South Tyrol was the region in Italy with the highest birth rate nationwide , with nine births per every 1,000 inhabitants . The following three positions of the ranking were occupied by Southern regions : Campania , Sicily , and Calabria . Indeed , South-Italy was the macro-region with the largest birth-rate in Italy .
gold_template: In templateTitleDate[0] , templateXValue[0] was the templateTitle[5] in templateTitleSubject[0] with the highest templateYLabel[0] templateYLabel[1] nationwide , with templateYValue[max] births templateYLabel[2] every 1,000 templateYLabel[4] . The following three positions of the ranking were occupied templateTitle[4] Southern regions : templateXValue[1] , templateXValue[2] , and templateXValue[3] . Indeed , South-Italy was the macro-region with the largest birth-rate in templateTitleSubject[0] .

generated_template: In templateTitleDate[0] , the highest templateYLabel[0] templateYLabel[1] in the templateYLabel[2] in the country was templateXValue[0] . In the templateTitle[5] , the highest templateYLabel[0] templateYLabel[1] in the country was registered in templateTitleDate[0] , with templateYValue[max] templateScale . The highest templateYLabel[0] templateYLabel[1] in the templateTitle[5] has constantly constantly templateNegativeTrend .
generated: In 2018 , the highest Birth rate in the per in the country was Trentino-South_Tyrol .  In the region , the highest Birth rate in the country was registered in 2018 , with 9.0 thousand .  The highest Birth rate in the region has constantly decreased .

Example 673:
titleEntities: {'Subject': ['Kenya'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Kenya 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['158.84', '144.69', '131.37', '119.55', '109.13', '98.61', '87.93', '78.69', '69.19', '64.24', '61.55', '55.13', '50.42', '41.67', '40.0', '37.02', '35.9', '31.96', '25.83', '21.0', '18.06', '16.8', '14.76', '14.54', '14.14', '14.35', '15.74', '13.74', '13.57', '11.94', '9.42', '7.87', '11.33', '11.5', '12.18', '11.71', '11.81', '11.39', '10.39', '8.75', '8.79']

gold: The statistic shows gross domestic product ( GDP ) in Kenya from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Kenya from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 674:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['1900', '1930']}
title: United Kingdom : live births 1900 to 1930
X_Axis['Years', '1900-1930']: ["'30", "'29", "'28", "'27", "'26", "'25", "'24", "'23", "'22", "'21", "'20", "'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00"]
Y_Axis['Number', 'of', 'live', 'births']: ['769239', '761963', '783052', '777520', '825174', '842405', '865329', '900130', '924740', '1001725', '1126849', '826202', '787427', '790736', '922085', '956877', '1032734', '1032286', '1025828', '1033395', '1051240', '1073781', '1102345', '1077851', '1098475', '1092108', '1109542', '1113086', '1103483', '1092781', '1089487']

gold: This statistic shows the total number of live births in the United Kingdom ( UK ) from 1900 to 1930 . The largest single year on year increase came from 1919 to 1920 , not long after the end of the First World War , when births increased by 300,647 . Despite the lower level of population , from 1900 to 1914 the number of live births was consistently above 1 million .
gold_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] ( UK ) from templateTitleDate[min] to templateTitleDate[max] . The largest single year on year templatePositiveTrend came from 1919 to 1920 , not long after the end of the First World War , when templateYLabel[2] templatePositiveTrend by 300,647 . Despite the lower level of population , from templateTitleDate[min] to 1914 the templateYLabel[0] of templateYLabel[1] templateYLabel[2] was consistently above templateYValue[0] templateScale .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[4] in the templateTitle[0] from templateTitleDate[min] to 2019 , with projections up until templateTitleDate[max] . The templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitle[4] stood at templateYValue[7] templateScale templateYLabel[3] templateYLabel[4] that year .
generated: The statistic shows the Number of the Kingdom live 1900 in the United from 1900 to 2019 , with projections up until 1930 .  The United Kingdom live of the 1900 stood at 900130 million births that year .

Example 675:
titleEntities: {'Subject': ['India'], 'Date': ['2020']}
title: Opinion on novel coronavirus COVID-19 as a health issue India 2020
X_Axis['Response']: ['Staying_alert_and_taking_precautions', "Don't_believe_it_can_impact_us_or_Indians_at_large", "Can't_say", "Don't_know_much_about_coronavirus", "Don't_believe_it_can_impact_us"]
Y_Axis['Share', 'of', 'respondents']: ['72', '16', '6', '3', '3']

gold: According to a survey conducted to understand the opinion among Indians on the issue of novel coronavirus COVID-19 , about 72 percent of respondents stated that they are staying alert and taking precautions . On the contrary , 16 percent said that they did not believe that the virus can impact them or Indians at large . As of February 2020 , India had three confirmed cases of COVID-19 .
gold_template: According to a survey conducted to understand the templateTitle[0] among templateXValue[1] on the templateTitle[5] of templateTitle[1] templateXValue[3] templateTitle[3] , templateXValue[3] templateYValue[max] templateScale of templateYLabel[1] stated that they are templateXValue[0] and templateXValue[0] . On the contrary , templateYValue[1] templateScale said that they did not templateXValue[1] that the virus can templateXValue[1] them or templateXValue[1] at templateXValue[1] . As of 2020 , templateTitleSubject[0] had templateYValue[min] confirmed cases of templateTitle[3] .

generated_template: This statistic presents the templateScale of online consumers in the templateTitle[0] who are templateTitleSubject[0] templateTitle[2] templateXValue[last] as of 2017 . During the survey period , it was found that templateYValue[1] templateScale of templateYLabel[1] stated that they used templateXValue[1] templateXValue[0] on the social networking site .
generated: This statistic presents the percentage of online consumers in the Opinion who are India coronavirus Don't_believe_it_can_impact_us as of 2017 .  During the survey period , it was found that 16 % of respondents stated that they used Don't_believe_it_can_impact_us_or_Indians_at_large Staying_alert_and_taking_precautions on the social networking site .

Example 676:
titleEntities: {'Subject': ['Leading'], 'Date': ['2017']}
title: Leading smartphone and tablet activities 2017 , by device
Data:
                                        0       1                         2             3                4                                  5                                   6                                7                          8               9             10                                    11                   12                       13                                                14                       15                                16
Response    Watching_movies/videos_online  E-mail  Using_social_media_sites  Reading_news  Online_shopping  Downloading_software/applications  Listening_to_streaming_music/radio  Instant_messaging/video_calling  Uploading/sharing_content  Online_banking  Online_gaming  Using_online_payment_systems/wallets  Online_data_storage  Visiting_adult_websites  Visiting_online_dating_websites/services_or_apps  Online_gambling/betting  Trading_securities/shares_online
Tablet                                 23      22                        19            19               18                                 16                                  12                               12                         11              11             11                                    10                   10                        6                                                 6                        5                                 4
Smartphone                             52      59                        54            48               43                                 42                                  38                               43                         39              36             28                                    30                   29                       17                                                18                       10                                 9 

gold: This statistic gives information on the most popular activities on mobile internet devices . During an global online survey in the second half of 2017 , 19 percent of respondents stated that they accessed social media sites via tablet device .
gold_template: This statistic gives information on the most popular templateTitle[3] on mobile internet devices . During an global templateValue[0][0] survey in the second half of templateTitleDate[0] , templateValue[1][2] templateScale of respondents stated that they accessed templateValue[0][2] sites via templateTitle[2] templateTitle[6] .

generated_template: This statistic illustrates the templateTitleSubject[0] templateTitle[8] templateLabel[0][0] for selected as of the first half of templateTitleDate[0] , sorted templateTitle[7] templateTitle[8] . During the survey period , it was found that templateValue[1][0] templateScale of the templateValue[0][0] stated that they used Gmail as online search of the templateValue[0][0] while templateValue[2][0] templateScale of respondents stated that they shopped templateValue[0][1] would approve of the templateValue[0][2] news news news news news news news news news news .
generated: This statistic illustrates the Leading device Response for selected as of the first half of 2017 , sorted device .  During the survey period , it was found that 23 % of the Watching_movies/videos_online stated that they used Gmail as online search of the Watching_movies/videos_online while 52 % of respondents stated that they shopped E-mail would approve of the Using_social_media_sites news .

Example 677:
titleEntities: {'Subject': ['Yemen'], 'Date': ['2011', '2019']}
title: U.S. drone strikes in Yemen 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'drone', 'strikes']: ['8', '36', '127', '37', '21', '17', '22', '37', '11']

gold: The statistic shows the number of U.S. drone strikes in Yemen from 2011 to November 5 , 2019 . In 2018 , the U.S. carried out a total of 36 drone strikes in Yemen .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to 5 , templateXValue[max] . In templateXValue[1] , the templateTitle[0] carried out a total of templateYValue[1] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . It is estimated that the templateYLabel[1] of the templateTitle[1] templateTitle[2] into two numerically equal groups ; that is , there were templateYValue[idxmax(X)] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number of U.S. drone in Yemen from 2011 to 2019 .  It is estimated that the drone of the drone strikes into two numerically equal groups ; that is , there were 8 drone 1,000 strikes .

Example 678:
titleEntities: {'Subject': ['UK'], 'Date': ['2019']}
title: Annual median salary in financial , insurance and real estate services in the UK 2019
X_Axis['Service', 'In', 'Uk']: ['Financial_services_activities', 'Financial_&_insurance_average_all_categories', 'Insurance_reinsurance_pension_funding', 'Activities_auxiliary_to_financial', 'Real_estate']
Y_Axis['Median', 'annual', 'salary', 'in', 'GBP']: ['42066', '38921', '33822', '36973', '25924']

gold: This statistic illustrates the median annual gross salary for employees of the the financial and insurance sectors , as well as in real estate services in the United Kingdom ( UK ) in 2019 , in British pounds . In that year , the highest median annual gross salary was from the financial services activities sector , amounting with roughly 42.1 thousand British pounds . Among all sectors in this statistic , real estate services presented the lowest annual gross salary , with roughly 25.9 thousand British pounds .
gold_template: This statistic illustrates the templateYLabel[0] templateYLabel[1] gross templateYLabel[2] for employees of the templateXValue[0] and templateXValue[1] sectors , as well as in templateXValue[last] services in the United Kingdom ( templateXLabel[1] ) in templateTitleDate[0] , in British pounds . In that year , the highest templateYLabel[0] templateYLabel[1] gross templateYLabel[2] was from the templateXValue[0] activities sector , amounting with roughly templateYValue[max] thousand British pounds . Among templateXValue[1] sectors in this statistic , templateXValue[last] services presented the lowest templateYLabel[1] gross templateYLabel[2] , with roughly templateYValue[min] thousand British pounds .

generated_template: This statistic shows the results of a survey among the templateTitle[0] of people in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . It shows that the average templateYLabel[0] of people templateYLabel[2] templateYLabel[3] that year .
generated: This statistic shows the results of a survey among the Annual of people in the UK ( ) in 2019 .  It shows that the average Median of people salary GBP that year .

Example 679:
titleEntities: {'Subject': ['Spotify'], 'Date': ['2017', '2018']}
title: Churn rate of Spotify worldwide 2017 to 2018
X_Axis['Year']: ['2018', '2017']
Y_Axis['Churn', 'rate']: ['19.8', '18.1']

gold: The cumulative subscriber churn rate of Spotify worldwide amounted to 19.8 percent in 2018 , up from 18.1 percent in the previous year . The source noted that this is partially due to promotional trials - whilst such trials draw in new subscribers , many of whom tend to convert , Spotify counts paid trialists in its subscriber numbers , meaning that those who do not convert then become churned subscribers .
gold_template: The cumulative subscriber templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] amounted to templateYValue[max] templateScale in templateXValue[idxmax(Y)] , up from templateYValue[min] templateScale in the previous templateXLabel[0] . The source noted that this is partially due to promotional trials - whilst such trials draw in new subscribers , many of whom tend to convert , templateTitleSubject[0] counts paid trialists in its subscriber numbers , meaning that those who do not convert then become churned subscribers .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Churn rate of the Spotify worldwide 2017 2018 in Spotify from 2017 to 2018 .  In 2018 , the Churn rate in Spotify was 19.8 rate .

Example 680:
titleEntities: {'Subject': ['China'], 'Date': ['2012', '2024']}
title: China 's share of global gross domestic product ( GDP ) 2012 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Share', 'of', 'global', 'GDP']: ['21.39', '21.01', '20.61', '20.18', '19.72', '19.24', '18.69', '18.16', '17.64', '17.07', '16.5', '15.91', '15.26']

gold: The graph shows China 's share in global gross domestic product adjusted for purchasing-power-parity until 2018 , with a forecast until 2024 . In 2018 , China 's share was about 18.69 percent . China 's global GDP share – additional information Due to the introduction of capitalist market principles in 1978 , China 's economic market began to show immense change and growth .
gold_template: The graph shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] in templateYLabel[1] templateTitle[4] templateTitle[5] templateTitle[6] adjusted for purchasing-power-parity until templateXValue[6] , with a forecast until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] was about templateYValue[6] templateScale . templateTitleSubject[0] templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[0] – additional information Due to the introduction of capitalist market principles in 1978 , templateTitleSubject[0] templateTitle[1] economic market began to show immense change and growth .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] of the templateYLabel[1] templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] of the templateYLabel[1] templateYLabel[2] ( in relation to PPP dollars ) amounted to about templateYValue[6] templateScale .
generated: The statistic shows China 's Share of the global gross domestic product ( GDP ) adjusted for Purchasing Power Parity ( PPP ) from 2012 to 2024 .  In 2018 , China 's Share of the global GDP ( in relation to PPP dollars ) amounted to about 18.69 % .

Example 681:
titleEntities: {'Subject': ['Cotton'], 'Date': ['2000', '2019']}
title: Cotton yield per harvested acre in the U.S. 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Yield', 'per', 'acre', 'in', 'pounds']: ['817', '864', '905', '855', '766', '838', '821', '892', '790', '812', '776', '813', '879', '814', '831', '855', '730', '665', '705', '632']

gold: This statistic shows the total cotton yield per harvested acre in the U.S. from 2000 to 2019 . In 2019 , the total cotton yield per harvested acre amounted to approximately 817 pounds .
gold_template: This statistic shows the total templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateTitle[3] templateYLabel[2] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the total templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateTitle[3] templateYLabel[2] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company had approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] of templateYLabel[3] .
generated: This statistic shows the Yield per of the harvested Cotton worldwide from 2000 to 2019 .  In 2019 , the company had approximately 817 acre pounds of .

Example 682:
titleEntities: {'Subject': ['U.S. Real Gross Domestic Product GDP'], 'Date': ['1990', '2019']}
title: Per capita U.S. Real Gross Domestic Product ( GDP ) 1990 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90"]
Y_Axis['Per', 'capita', 'Real', 'GDP', 'in', 'chained', '(2012)', 'U.S.', 'dollars']: ['58055', '57006', '55679', '54733', '54231', '53077', '52142', '51548', '50770', '50355', '49501', '51240', '51794', '51330', '50381', '49125', '47756', '46858', '46497', '46498', '45146', '43593', '42211', '40900', '39875', '39295', '38234', '37696', '36900', '37435']

gold: This statistic shows the per capita Real Gross Domestic Product ( GDP ) of the United States from 1990 to 2019 . In 2019 , per capita Real GDP amounted to 58,055 chained ( 2012 ) U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitleSubject[0] Product ( templateYLabel[3] ) of the templateTitle[2] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] amounted to templateYValue[max] templateYLabel[4] ( 2012 ) templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateYLabel[3] templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[4] was templateYValue[last] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Per capita of the Per GDP Gross from 1990 to 2019 .  In 2019 , the Per capita of the Gross was 37435 Real GDP .

Example 683:
titleEntities: {'Subject': ['EPL'], 'Date': ['2019']}
title: Most followed EPL team in the U.S. 2019
X_Axis['Response']: ['Manchester_United', 'Liverpool', 'Manchester_City', 'Arsenal', 'Chelsea', 'Crystal_Palace', 'Newcastle_United', 'Tottenham_Hostpur', 'Aston_Villa', 'Brighton_&_Hove_Albion', 'Burnley', 'Everton', 'Leicester_City', 'Norwich_City', 'Sheffield_United', 'Southampton', 'Watford', 'West_Ham_United', 'Wolverhamption_Wanderers', 'Bournemouth']
Y_Axis['Share', 'of', 'respondents']: ['32', '14', '9', '6', '6', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '0']

gold: Despite being one of the most popular sports in the world , the level of interest in soccer in the United States still remains relatively low . The English Premier League is nevertheless keenly followed by some fans in the states and 32 percent of respondents stated that record league title winners , Manchester United , were their favorite team to follow .
gold_template: Despite being templateYValue[8] of the templateTitle[0] popular sports in the world , the level of interest in soccer in the templateXValue[0] States still remains relatively low . The English Premier League is nevertheless keenly templateTitle[1] by some fans in the states and templateYValue[max] templateScale of templateYLabel[1] stated that record league title winners , templateXValue[0] , were their favorite templateTitle[3] to follow .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded templateXValue[3] by Harris Interactive in 2014 , asking the templateYLabel[1] where they usually templateTitle[3] their shampoo and/or templateTitle[6] . During the survey , templateYValue[max] templateScale of templateYLabel[1] stated that they use templateXValue[2] at templateXValue[3] templateYValue[3] .
generated: This statistic shows the results of a survey among EPL adult followed .  The survey was fielded Arsenal by Harris Interactive in 2014 , asking the respondents where they usually team their shampoo and/or 2019 .  During the survey , 32 % of respondents stated that they use Manchester_City at Arsenal 6 .

Example 684:
titleEntities: {'Subject': ['Canada'], 'Date': ['2016']}
title: Number of hospitals in Canada by province 2016
X_Axis['Month']: ['Ontario', 'Quebec', 'British_Columbia', 'Alberta', 'Saskatchewan', 'Manitoba', 'New_Brunswick', 'Nova_Scotia', 'Newfoundland_and_Labrador', 'Northwest_Territories', 'Nunavut', 'Prince_Edward_Island', 'Yukon']
Y_Axis['Number', 'of', 'establishments']: ['492', '267', '206', '169', '77', '73', '54', '46', '39', '19', '19', '4', '1']

gold: Among Canadian provinces , Ontario had the largest number of hospitals with around 500 establishments , as of 2016 . Canada has a publicly funded health care system based on a system of taxation , fees and private funding . Current reports estimate that health care expenditures account for over 11 percent of Canada 's gross domestic product ( GDP ) .
gold_template: Among Canadian provinces , templateXValue[0] had the largest templateYLabel[0] of templateTitle[1] with around 500 templateYLabel[1] , as of templateTitleDate[0] . templateTitleSubject[0] has a publicly funded health care system based on a system of taxation , fees and private funding . Current reports estimate that health care expenditures account for over 11 templateScale of templateTitleSubject[0] 's gross domestic product ( GDP ) .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitleSubject[0] UK from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the highest templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] stood at templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number establishments of the Number hospitals Canada UK from Yukon to Ontario .  In Ontario , the highest Number establishments of Canada stood at 492 establishments .

Example 685:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Canada retail e-commerce sales growth 2018 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018']
Y_Axis['Year-over-year', 'change']: ['3.1', '4.7', '6.9', '9.1', '10.1', '9.1']

gold: This statistic gives information on retail e-commerce sales growth in Canada from 2018 to 2023 . In 2018 , retail e-commerce sales grew by 9.1 percent compared to the previous year . In 2022 , retail e-commerce sales are expected to grow 4.7 percent from the previous year .
gold_template: This statistic gives information on templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateTitle[1] templateTitle[2] templateTitle[3] templatePositiveTrend by templateYValue[idxmin(X)] templateScale compared to the previous templateXLabel[0] . In templateXValue[1] , templateTitle[1] templateTitle[2] templateTitle[3] are expected to grow templateYValue[1] templateScale from the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the source , the templateYLabel[1] of the total amount to around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .
generated: This statistic shows the Year-over-year change of the e-commerce sales growth 2018 in Canada from 2018 to 2023 .  According to the source , the change of the total amount to around 10.1 % change in 2019 .

Example 686:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019', '2030']}
title: U.S. - forecast for higher education outlays 2019 to 2030
X_Axis['Fiscal', 'Year']: ["'30", "'29", "'28", "'27", "'26", "'25", "'24", "'23", "'22", "'21", "'20", "'19"]
Y_Axis['Higher', 'education', 'outlay', 'in', 'billion', 'U.S.', 'dollars']: ['5', '5', '5', '5', '5', '5', '5', '5', '5', '4', '2', '33']

gold: The statistic shows a the amount of higher education outlays in the United States in 2019 with a forecast from 2020 to 2030 . Mandatory outlays for higher education are projected to be around five billion U.S. dollars in 2030 .
gold_template: The statistic shows a the amount of templateYLabel[0] templateYLabel[1] templateTitle[5] in the templateTitle[0] in templateTitleDate[min] with a templateTitle[1] from 2020 to templateTitleDate[max] . Mandatory templateTitle[5] templateTitle[2] templateYLabel[0] templateYLabel[1] are projected to be around templateYValue[0] templateScale templateYLabel[4] templateYLabel[5] in templateTitleDate[max] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In 2018 , the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] templateTitle[4] amounted to templateYValue[max] thousand templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Higher of for higher education outlays in U.S. from 2019 to 2030 .  In 2018 , the U.S. Higher of U.S. education amounted to 33 thousand outlay billion .

Example 687:
titleEntities: {'Subject': ['Instagram'], 'Date': ['2016', '2020']}
title: Instagram : number of brand sponsored influencer posts 2016 to 2020
X_Axis['Year']: ['2016', '2017', '2018', '2019', '2020']
Y_Axis['Annual', 'posts', 'in', 'millions']: ['1.26', '2.58', '3.7', '4.95', '6.12']

gold: This statistic presents the number of brand sponsored influencer posts on Instagram from 2016 to 2020 . In 2018 , there were 3.7 million brand sponsored influencer posts on the social platform . The volume of sponsored content on Instagram is projected to amount to 4.95 million posts in 2019 .
gold_template: This statistic presents the templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] on templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , there were templateYValue[2] templateScale templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] on the social platform . The volume of templateTitle[3] content on templateTitleSubject[0] is projected to amount to templateYValue[3] templateScale templateYLabel[1] in templateXValue[3] .

generated_template: The statistic shows the total templateYLabel[0] of the templateTitle[1] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitleSubject[0] templateTitle[1] templateYLabel[0] amounted to templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the total Annual of the number Instagram worldwide from 2016 to 2020 .  In 2016 , the Instagram number Annual amounted to 1.26 millions .

Example 688:
titleEntities: {'Subject': ['UEFA Champions League'], 'Date': ['2019']}
title: UEFA Champions League most games played by club 2019
X_Axis['Uefa', 'Club']: ['Real_Madrid_CF', 'FC_Bayern_München', 'FC_Barcelona', 'Manchester_United_FC', 'Juventus', 'SL_Benfica', 'AC_Milan', 'FC_Porto', 'FC_Dynamo_Kyiv', 'AFC_Ajax', 'Liverpool_FC', 'Celtic_FC', 'Arsenal_FC', 'RSC_Anderlecht', 'PSV_Eindhoven', 'FC_Internazionale_Milano', 'Galatasaray_AŞ', 'Rangers_FC', 'Chelsea_FC', 'Olympiacos_FC', 'Panathinaikos_FC']
Y_Axis['Games', 'played']: ['431', '341', '310', '279', '271', '252', '249', '243', '231', '217', '209', '206', '201', '200', '175', '172', '169', '161', '160', '160', '157']

gold: Real Madrid CF is the club to have played most played games in the UEFA competition from 1955 to 2019 , with 431 appearances . Within the German football league system , the leader was FC Bayern München with 341 appearances , whereas among the English teams , Manchester United FC was the leader with 279 games played . Since its founding in June 1954 , the number of national associations in UEFA grew from 25 to 55 .
gold_template: templateXValue[0] CF is the templateXLabel[1] to have templateYLabel[1] templateTitle[3] templateYLabel[1] templateYLabel[0] in the templateXLabel[0] competition from 1955 to templateTitleDate[0] , with templateYValue[max] appearances . Within the German football templateTitleSubject[0] system , the leader was templateXValue[1] München with templateYValue[1] appearances , whereas among the English teams , templateXValue[3] FC was the leader with templateYValue[3] templateYLabel[0] templateYLabel[1] . Since its founding in 1954 , the number of national associations in templateXLabel[0] templatePositiveTrend from 25 to 55 .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] in templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of templateYLabel[3] templateYLabel[4] amounted to approximately templateYValue[1] templateScale in templateXValue[1] .
generated: The statistic shows the UEFA Champions League Champions Games in the most in 2019 .  The Games played of amounted to approximately 341 million in FC_Bayern_München .

Example 689:
titleEntities: {'Subject': ['UFC'], 'Date': ['2019', '2019']}
title: UFC : events with the highest live attendance as of May 2019
X_Axis['Ufc', 'Fights']: ['Rousey_vs_Holm_(UFC_193)', 'St._Pierre_vs_Shields_(UFC_129)', 'Werdum_vs_Miocic_(UFC_198)', 'Gustafsson_vs_Johnson_(UFC_on_FOX_14)', 'St._Pierre_vs_Koscheck_2_(UFC_124)', 'Hunt_vs_Oleinik_(UFC_Fight_Night_136)', 'Redemption_(UFC_97)', 'Serra_vs_St._Pierre_2_(UFC_83)', 'Velasquez_vs_Werdum_(UFC_188)', 'Werdum_vs_Hunt_(UFC_180)', 'Edgar_vs_Henderson_(UFC_144)', 'Alvarez_vs_McGregor_(UFC_205)', 'St_Pierre_vs_Diaz_(UFC_158)', 'Khabib_vs_McGregor_(UFC_229)', 'Hendricks_vs_Lawler_(UFC_171)', 'Uprising_(UFC_68)', 'Holloway_vs_Ortega_(UFC_231)', 'Miocic_vs_Overeem_(UFC_203)']
Y_Axis['Attendance']: ['56214', '55724', '45207', '30000', '23152', '22603', '21451', '21390', '21036', '21000', '21000', '20427', '20145', '20034', '19324', '19049', '19039', '18785']

gold: As of June 2019 , out of all Ultimate Fighting Championship ( UFC ) events so far UFC 193 – Rousey vs. Holm on November 14th 2015 in Melbourne , Australia had the highest gate attendance with a total of 56,214 attendees , followed closely by UFC 129 – St. Pierre vs Shields on April 30th 2011 in Toronto Canada with a total attendance of 55,724 . Conor McGregor highest overall direct fight earnings When looking at the highest total earnings among fighters , directly resulting from UFC fights , Conor McGregor , who officially retired on March 26th 2019 from Mixed Martial Arts ( MMA ) , is still at first place with approximately 12 million U.S. dollars . However it is safe to say that this only a small fraction compared to his entire earnings , including Pay-Per-View bonuses , endorsement deals and not to mention his boxing match against Floyd Mayweather , for which alone he made more than 75 million U.S. dollars .
gold_template: As of 2019 , out of all Ultimate Fighting Championship ( templateXLabel[0] ) templateTitle[1] so far templateXLabel[0] 193 – templateXValue[0] vs. templateXValue[0] on 14th 2015 in Melbourne , Australia had the templateTitle[3] gate templateYLabel[0] templateTitle[2] a total of templateYValue[max] attendees , followed closely by templateXLabel[0] 129 – templateXValue[1] vs templateXValue[1] on 30th 2011 in Toronto Canada templateTitle[2] a total templateYLabel[0] of templateYValue[1] . Conor templateXValue[11] templateTitle[3] overall direct templateXValue[5] earnings When looking at the templateTitle[3] total earnings among fighters , directly resulting from templateXLabel[0] templateXLabel[1] , Conor templateXValue[11] , who officially retired on 26th templateTitleDate[0] from Mixed Martial Arts ( MMA ) , is still at first place templateTitle[2] approximately 12 templateScale U.S. dollars . However it is safe to say that this only a small fraction compared to his entire earnings , including Pay-Per-View bonuses , endorsement deals and not to mention his boxing match against Floyd Mayweather , for which alone he made more than 75 templateScale U.S. dollars .

generated_template: According to a templateTitleDate[0] survey , the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[4] stood at templateYValue[7] templateScale of goods that year . According to the source , a ranking of According to the templateTitle[0] templateTitle[1] templateTitle[2] that ranking valued at just under two templateScale of all templateXValue[last] . What is the templateTitle[0] templateTitle[1] templateTitle[2] ? templateYLabel[0] , According to be employment .
generated: According to a 2019 survey , the UFC events highest in the attendance stood at 21390 % of goods that year .  According to the source , a ranking of According to the UFC events highest that ranking valued at just under two percent of all Miocic_vs_Overeem_(UFC_203) .  What is the UFC events highest ? Attendance , According to be employment .

Example 690:
titleEntities: {'Subject': ['YouTube'], 'Date': ['2018']}
title: Average YouTube video length 2018 , by category
X_Axis['Youtube', 'Category']: ['Gaming', 'Film_and_animation', 'Entertainment', 'Total', 'Other', 'People_and_blogs', 'Music']
Y_Axis['Average', 'length', 'in', 'minutes']: ['24.7', '19.2', '12.9', '11.7', '9.5', '8.4', '6.8']

gold: This statistic presents the average length of YouTube videos as of December 2018 , sorted by category . According to the report , the average video length is at 11.7 minutes . Music content generally had the shortest video length among all categories on the platform , with an average length of 6.8 minutes per video .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateXLabel[0] videos as of 2018 , sorted templateTitle[5] templateXLabel[1] . According to the report , the templateYLabel[0] templateTitle[2] templateYLabel[1] is at templateYValue[3] templateYLabel[2] . templateXValue[last] content generally had the shortest templateTitle[2] templateYLabel[1] among all categories on the platform , with an templateYLabel[0] templateYLabel[1] of templateYValue[min] templateYLabel[2] per templateTitle[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[3] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[1] . templateXValue[0] ranked first with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Average YouTube of the length in YouTube 2018 , category .  Gaming ranked first with a Average length of 24.7 minutes .

Example 691:
titleEntities: {'Subject': ['India'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in India 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'rate', 'compared', 'to', 'previous', 'year']: ['7.33', '7.44', '7.44', '7.44', '7.03', '6.12', '6.81', '7.17', '8.17', '8', '7.41']

gold: The statistic shows the growth of the real gross domestic product ( GDP ) in India from 2014 to 2018 , with projections up until 2024 . GDP refers to the total market value of all goods and services that are produced within a country per year . It is an important indicator of the economic strength of a country .
gold_template: The statistic shows the templateYLabel[1] of the real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .

generated_template: The statistic shows the templateYLabel[1] templateYLabel[2] of the real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the growth rate of the real Gross domestic product ( GDP ) in the India from 2014 to 2018 , with projections up until 2024 .  GDP refers to the total market value of all goods and services that are produced within a country per Year .  It is an important indicator of the economic strength of a country .

Example 692:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2010', '2018']}
title: Migration balance in Belgium 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Migration', 'balance']: ['50180', '44536', '42239', '47682', '39954', '34843', '44365', '62157', '79446']

gold: In 2018 , the migration balance in Belgium was roughly 50,000 , meaning that the number of immigrants moving to Belgium outnumbered the number of people leaving the country by about 50,000 . This was an increase in comparison to the previous year , but significantly lower than for example in 2010 and 2011 , when the migration balance was 79,446 and 62,157 respectively . It was also considerably lower than in neighboring country the Netherlands , which in 2018 had a positive migration balance of over 86,000 .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was roughly 50,000 , meaning that the number of immigrants moving to templateTitleSubject[0] outnumbered the number of people leaving the country by about 50,000 . This was an templatePositiveTrend in comparison to the previous templateXLabel[0] , but significantly lower than for example in templateXValue[min] and templateXValue[7] , when the templateYLabel[0] templateYLabel[1] was templateYValue[idxmin(X)] and templateYValue[7] respectively . It was also considerably lower than in neighboring country the Netherlands , which in templateXValue[max] had a positive templateYLabel[0] templateYLabel[1] of over 86,000 .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[4] from templateXValue[min] to templateXValue[max] . According to the source , it is expected that the templateYLabel[2] will reach some templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: The statistic shows the Migration of balance Belgium 2018 from 2010 to 2018 .  According to the source , it is expected that the balance will reach some 50180 million balance in 2018 .

Example 693:
titleEntities: {'Subject': ['Leading'], 'Date': ['2018']}
title: Leading 50 retailers worldwide based on retail revenue 2018
X_Axis['Company', '(Country', 'of', 'origin)']: ['Wal-Mart_Stores_Inc_(United_States)', 'Costco_Wholesale_Corporation_(United_States)', 'Amazon.com_Inc._(United_States)', 'Schwarz_Group_(Germany)', 'The_Kroger_Co._(United_States)', 'Walgreens_Boots_Alliance_Inc_(United_States)', 'The_Home_Depot_Inc._(United_States)', 'Aldi_Einkauf_GmbH_&_Co._oHG_(Germany)', 'CVS_Health_Corporation_(United_States)', 'Tesco_PLC_(United_Kingdom)', 'Target_Corporation_(United_States)', 'Ahold_Delhaize_(Netherlands)', 'Aeon_Co._Ltd._(Japan)', "Lowe's_Companies_Inc._(United_States)", 'JD.com_Inc_(China)', 'Edeka_Group_(Germany)', 'Albertsons_Companies_Inc._(United_States)', 'Auchan_Holding_SA_(France)', 'Seven_&_i_Holdings_Co._Ltd._(Japan)', 'Rewe_Group_(Germany)', 'Centres_Distributeurs_E._Leclerc_(France)', 'Casino_Guichard-Perrachon_S.A._(France)', 'Best_Buy_Co._Inc._(United_States)', 'Woolworths_Limited_(Australia)', 'The_IKEA_Group_(INGKA_Holding_B.V.)_(Netherlands)', 'The_TJX_Companies_Inc._(United_States)', 'LVMH_Moët_Hennessy-Louis_Vuitton_S.A._(France)', 'J_Sainsbury_plc_(United_Kingdom)', 'ITM_Développement_International_(Intermarché)_(France)', 'Publix_Super_Markets_Inc._(United_States)', 'Loblaw_Companies_Limited_(Canada)', 'Suning.com_Co._Ltd._(China)', 'Inditex_S.A._(Spain)', 'Metro_Ag_(Germany)', 'Coles_Group_Limited', 'Mercadona_S.A._(Spain)', 'Dollar_General_Corporation_(United_States)', 'Ceconomy_AG_(Germany)', 'Migros-Genossenschafts_Bund_(Switzerland)_', 'H.E._Butt_Grocery_Company_(United_States)', "Macy's_Inc._(United_States)", 'X5_Retail_Group_N.V._(Russia)', 'H_&_M_Hennes_&_Mauritz_AB_(Sweden)', 'Système_U_Centrale_Nationale_(France)', 'Wm_Morrison_Supermarkets_PLC_(United_Kingdom)', 'Coop_Group_(Switzerland)', 'Groupe_Adeo_SA_(France)', 'Dollar_Tree_Inc._(United_States)', 'A.S._Watson_Group_(Hong_Kong_SAR)', 'Jerónimo_Martins_SGPS_S.A._(Portugal)']
Y_Axis['Retail', 'revenue', 'in', 'billion', 'U.S.', 'dollars']: ['514.41', '141.58', '140.21', '121.58', '117.53', '110.67', '108.2', '106.18', '83.99', '82.8', '74.43', '74.08', '71.45', '71.31', '62.88', '62.05', '60.54', '59.3', '59.1', '56.44', '44.48', '43.18', '42.88', '41.69', '41.47', '38.97', '37.85', '37.52', '36.63', '36.4', '35.36', '35.16', '30.67', '28.72', '27.29', '26.26', '25.63', '25.48', '25.13', '25.0', '24.97', '24.39', '24.32', '23.57', '23.51', '23.47', '23.16', '22.82', '21.56', '20.45']

gold: The retail industry encompasses the journey of a good or service . This typically starts with the manufacture of a product and ends with said product being purchased by a consumer from a retailer . As a result of globalization and various trade agreements between markets and countries , many retailers are capable of doing business on a global scale .
gold_template: The templateXValue[41] industry encompasses the journey of a good or service . This typically starts with the manufacture of a product and ends with said product being purchased by a consumer from a retailer . As a result of globalization and various trade agreements between templateXValue[29] and countries , many templateTitle[2] are capable of doing business on a global scale .

generated_template: This statistic depicts the templateTitleSubject[0] 20 templateTitle[1] templateTitle[2] in templateTitleDate[0] , templateTitle[4] on templateXValue[15] templateYLabel[1] . In that year , Amazon was the world 's templateTitleSubject[0] retailer with about templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] worth of templateYLabel[1] . Amazon is headquartered in the country , one of the templateTitleSubject[0] e-commerce markets templateTitle[2] .
generated: This statistic depicts the Leading 20 50 retailers in 2018 , based on Edeka_Group_(Germany) revenue .  In that year , Amazon was the world 's Leading retailer with about 514.41 billion U.S. dollars worth of revenue .  Amazon is headquartered in the country , one of the Leading e-commerce markets retailers .

Example 694:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2012']}
title: Number of U.S. patents held by tech companies in 2012
X_Axis['Company']: ['Samsung', 'Canon', 'Sony', 'Hewlett-Packard', 'Xerox', 'Intel', 'Motorola', 'Microsoft', 'Ricoh', 'Lucent', 'Nokia', 'Cisco', 'Apple', 'Oracle', 'Dell', 'Google', 'Verizon', 'Yahoo', 'AOL', 'Amazon']
Y_Axis['Number', 'of', 'U.S.', 'patents']: ['47855', '46322', '36508', '23904', '23603', '21153', '21027', '19800', '14363', '11713', '9615', '7208', '4649', '3371', '2489', '1124', '1110', '1029', '533', '448']

gold: This statistic shows the number of U.S. patents held by selected tech companies . As of March 2012 , Samsung held 47,855 U.S. patents . Apple , one of Samsung 's main competitors , held 4,649 patents .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] selected templateTitle[5] templateTitle[6] . As of 2012 , templateXValue[0] templateTitle[3] templateYValue[max] templateYLabel[1] templateYLabel[2] . templateXValue[12] , one of templateXValue[0] 's main competitors , templateTitle[3] templateYValue[12] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateXValue[4] templateTitle[4] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . The ranking is the templateYLabel[1] of templateXValue[0] , with a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the Number of the U.S. patents Xerox by in 2012 , .  The ranking is the U.S. of Samsung , with a Number of 47855 million patents .

Example 695:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2018']}
title: Distribution of GDP across economic sectors in the United Kingdom 2018
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
Agriculture   0.63   0.65    0.6   0.62   0.67   0.67   0.66   0.64   0.62   0.69   0.64
Industry     17.51  17.56  17.58  18.14  18.36  18.82  18.63  18.66  18.87  18.74  20.04
Services     71.04  70.93  70.92  70.41  70.14  69.76  70.08  69.96  70.55  71.61  69.79 

gold: In 2018 , agriculture contributed around 0.63 percent to the United Kingdom 's GDP , 17.51 percent came from the manufacturing industry , and 71.04 percent from the services sector . The UK is not a farmer 's market The vast majority of the UK 's GDP is generated by the services sector , and tourism in particular keeps the economy going . In 2017 , almost 214 billion British Pounds were contributed to the GDP through travel and tourism – about 277 billion U.S. dollars – and the forecasts see an upwards trend .
gold_template: In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitleSubject[0] 's templateTitle[1] , templateValue[2][0] templateScale came from the manufacturing templateLabel[2][0] , and templateValue[3][0] templateScale from the templateLabel[3][0] sector . The UK is not a farmer 's market The vast majority of the UK 's templateTitle[1] is generated by the templateLabel[3][0] sector , and tourism in particular keeps the economy going . In templateValue[0][1] , almost 214 templateScale British Pounds were contributed to the templateTitle[1] through travel and tourism – about 277 templateScale U.S. dollars – and the forecasts see an upwards trend .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the service sector .
generated: This statistic shows the Distribution of the GDP across economic ( sectors ) United Kingdom 2018 in United Kingdom from 2008 to 2018 .  In 2018 , Agriculture contributed around 0.63 % to the sectors of United Kingdom , 17.51 % came from the Industry and 71.04 % from the service sector .

Example 696:
titleEntities: {'Subject': ['Amadeus'], 'Date': ['2010', '2019']}
title: Amadeus : distribution market share 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Market', 'share']: ['44', '43.7', '43.9', '43.2', '42.5', '40.7', '40.1', '38.6', '37.7', '36.7']

gold: This statistic shows the Amadeus distribution platform market share of global air travel distribution systems from 2010 to 2019 . In 2019 , the Amadeus held 44 percent of the global air travel distribution systems market . In 2019 , the revenue of Amadeus was 6.25 billion U.S. dollars .
gold_template: This statistic shows the templateTitleSubject[0] templateTitle[1] platform templateYLabel[0] templateYLabel[1] of global air travel templateTitle[1] systems from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] held templateYValue[idxmax(X)] templateScale of the global air travel templateTitle[1] systems templateYLabel[0] . In templateXValue[idxmax(Y)] , the revenue of templateTitleSubject[0] was 6.25 templateScale U.S. dollars .

generated_template: This statistic shows the global templateYLabel[0] templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] is estimated to be templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the global Market share of the distribution market share 2010 from to 2019 .  In 2019 , the global Market share of Amadeus share 2010 is estimated to be 44 % .

Example 697:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2017', '2018']}
title: Market share of leading smartphone brands in the Netherlands 2017 to 2018
Data:
            0      1       2     3   4          5      6
Race  Samsung  Apple  Huawei  Sony  LG  Microsoft  Other
2017       41     32       6     5   4          3      9
2018       38     34       7     0   0          0      0 

gold: In 2018 , Samsung was the biggest smartphone brand in the Netherlands with a market share of close to 40 percent . This is a decrease compared to the previous year . Second in the Netherlands was Apple .
gold_template: In templateTitleDate[max] , templateValue[0][0] was the biggest templateTitle[3] brand in the templateTitleSubject[0] with a templateTitle[0] templateTitle[1] of close to 40 templateScale . This is a templateNegativeTrend compared to the previous year . Second in the templateTitleSubject[0] was templateValue[0][1] .

generated_template: The statistic illustrates the templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[0] in templateTitleDate[min] and templateTitleDate[max] , templateTitle[6] templateTitle[7] . In templateTitleDate[max] , templateValue[0][0] 's templateTitle[0] of templateValue[0][1] amounted to templateValue[1][max] templateScale .
generated: The statistic illustrates the share leading smartphone in the Market in 2017 and 2018 , 2017 2018 .  In 2018 , Samsung 's Market of Apple amounted to 41 % .

Example 698:
titleEntities: {'Subject': ['Swiss'], 'Date': ['2016']}
title: Most valuable Swiss watch brands worldwide 2016
X_Axis['Brand']: ['Rolex', 'Omega', 'Patek_Philippe', 'Chopard', 'Longines', 'Breguet', 'Audemars_Piguet', 'Tissot', 'Vacheron_Constantin', 'Tag_Heuer', 'Swatch', 'IWC', 'Jaeger_Lecoultre', 'Hublot', 'Breitling', 'Rado']
Y_Axis['Brand', 'value', 'in', 'million', 'CHF']: ['7387', '3677', '1885', '1098', '1089', '1004', '961', '847', '809', '764', '772', '779', '673', '401', '359', '335']

gold: This statistic presents the brand value of the most valuable Swiss watch brands in 2016 . In that year , Rolex was the most valuable Swiss watch brand at 7.25 billion Swiss francs ( CHF ) . Rolex is one of the worlds most powerful brands , and it has a long history within the watch making business .
gold_template: This statistic presents the templateXLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateXLabel[0] at 7.25 templateScale templateTitleSubject[0] francs ( templateYLabel[3] ) . templateXValue[0] is one of the worlds templateTitle[0] powerful templateTitle[4] , and it has a long history within the templateTitle[3] making business .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . According to the survey , templateXValue[0] was the templateTitle[0] templateTitle[1] templateXValue[0] with a score of templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the Most valuable the Swiss watch brands of Swiss in 2016 , .  According to the survey , Rolex was the Most valuable Rolex with a score of 7387 million CHF .

Example 699:
titleEntities: {'Subject': ['Romania'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Romania 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['17685.87', '16542.17', '15455.17', '14398.64', '13414.49', '12482.97', '12269.84', '10761.71', '9538.93', '8950.46', '10004.88', '9537.82', '8518.92', '9081.72', '8190.57', '8517.6', '10385.85', '8262.41', '5811.44', '4662.67', '3541.52', '2768.12', '2118.57', '1817.05', '1669.98', '1611.13', '1897.42', '1581.75', '1575.5', '1573.13', '1325.48', '1154.67', '853.11', '1249.01', '1652.37', '2329.57', '2609.66', '2534.07', '2278.87', '2115.46', '1721.25']

gold: The statistic shows gross domestic product ( GDP ) per capita in Romania from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateYLabel[0] of the total templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . According to the total market value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows the GDP of the total capita in the Romania from 1984 to 2017 , with projections up until 2024 .  According to the total market value of all goods and services produced in a country in a Year .  It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

Example 700:
titleEntities: {'Subject': ['Florida'], 'Date': ['2000', '2018']}
title: Personal income in Florida - income per capita 2000 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00"]
Y_Axis['Income', 'in', 'U.S.', 'dollars']: ['50070', '47869', '45684', '45287', '43109', '40659', '41055', '40047', '38474', '36611', '39247', '39602', '38448', '36011', '33694', '31704', '30727', '30344', '29428']

gold: This statistic shows the per capita personal income in Florida from 2000 to 2018 . In 2018 , the per capita personal income in Florida was 50,070 U.S. dollars . Personal income is the income that is received by persons from all sources .
gold_template: This statistic shows the templateTitle[4] templateTitle[5] templateTitle[0] templateYLabel[0] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[4] templateTitle[5] templateTitle[0] templateYLabel[0] in templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] . templateTitle[0] templateYLabel[0] is the templateYLabel[0] that is received by persons from all sources .

generated_template: This statistic displays the templateTitle[0] templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateTitle[1] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[0] templateTitle[1] was templateYValue[min] templateScale of templateYLabel[2] templateYLabel[3] . templateTitle[0] of the templateTitleSubject[0] As shown .
generated: This statistic displays the Personal Income of dollars in the income from 2000 to 2018 .  In 2018 , the Personal income was 29428 millions of dollars .  Personal of the Florida As shown .

Example 701:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1970']}
title: U.S. sulfur dioxide emissions 1970 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970']
Y_Axis['Volume', 'of', 'emissions', 'in', 'thousand', 'tons']: ['2815', '2960', '3685', '4674', '4873', '5079', '6479', '7732', '9089', '10324', '11699', '13123', '14546', '16347', '18619', '23077', '23307', '25926', '28044', '31218']

gold: Sulfur dioxide ( SO2 ) emissions in the United States have decreased significantly over the last decades . SO2 emissions have fallen to 2.82 million tons in 2017 , in comparison to 31.22 million tons in 1970 . This reduction in SO2 is largely due to the implementation of the Acid Rain Program under the Clean Air Act through a cap and trade program for fossil-fuel powered plants .
gold_template: templateTitle[1] templateTitle[2] ( SO2 ) templateYLabel[1] in the templateTitle[0] have templateNegativeTrend significantly over the last decades . SO2 templateYLabel[1] have fallen to templateYValue[min] templateScale templateYLabel[3] in templateXValue[idxmin(Y)] , in comparison to templateYValue[max] templateScale templateYLabel[3] in templateXValue[idxmax(Y)] . This reduction in SO2 is largely due to the implementation of the Acid Rain Program under the Clean Air Act through a cap and trade program for fossil-fuel powered plants .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of the templateTitle[0] of templateYLabel[3] templateTitle[4] templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] of templateTitleSubject[0] stood at templateYValue[4] templateYLabel[2] templateYLabel[3] in templateXValue[4] .
generated: This statistic shows the Volume of thousand tons of the U.S. of tons 1970 to 2017 .  According to the report , the U.S. of stood at 4873 thousand tons in 2013 .

Example 702:
titleEntities: {'Subject': ['Hispanic'], 'Date': ['2000', '2018']}
title: Hispanic population of the U.S. 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Population', 'in', 'millions']: ['59.87', '58.95', '57.47', '56.59', '55.39', '54.24', '53.09', '51.95', '50.76', '49.33', '47.79', '46.2', '44.61', '43.02', '41.5', '40.05', '38.62', '37.14', '35.66']

gold: The graph shows the Hispanic population in the United States from 2000 to 2018 . In 2018 , about 59.87 million people of Hispanic origin were living in the United States .
gold_template: The graph shows the templateTitleSubject[0] templateYLabel[0] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale people of templateTitleSubject[0] origin were living in the templateTitle[2] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale people were living in the templateTitle[2] in the templateTitleSubject[1] .
generated: The statistic shows the Hispanic population in the Hispanic from 2000 to 2018 .  In 2018 , approximately 59.87 millions people were living in the U.S. in the Hispanic .

Example 703:
titleEntities: {'Subject': ['Russia'], 'Date': ['2018', '2018']}
title: 2018 FIFA World Cup Russia ticket prices by category
Data:
                             0                        1                       2                          3                       4                         5           6
Ticket Type  Opening_match_(1)  Group_matches_(2_to_48)  Round_of_16_(49_to_56)  Quarter-finals_(57_to_60)  Semi-finals_(61_to_62)  3rd/4th_place_match_(63)  Final_(64)
Cat 1                      550                      210                     245                        365                     750                       365        1100
Cat 2                      390                      165                     185                        255                     480                       255         710
Cat 3                      220                      105                     115                        175                     285                       175         455
Cat 4                       50                       20                      35                         60                      70                        60         110 

gold: The FIFA World Cup is one of the biggest sporting events in the world , so it is no wonder that tickets for the matches are hard to come by . A category one ( highest level ) ticket for the opening match of the 2018 World Cup in Russia would have set you back 550 U.S. dollars , whilst the top range tickets for the final cost a whopping 1,100 U.S. dollars . France emerge victorious in front of a big crowd The final of the 2018 World Cup was contested between Croatia and France , with the latter emerging victorious after an entertaining 4 - 2 victory .
gold_template: The templateTitle[1] templateTitle[2] templateTitle[3] is templateLabel[1][1] of the biggest sporting events in the templateTitle[2] , so it is no wonder that tickets for the templateValue[0][1] are hard to come templateTitle[7] . A templateTitle[8] templateLabel[1][1] ( highest level ) templateLabel[0][0] for the templateValue[0][0] of the templateTitleDate[0] templateTitle[2] templateTitle[3] in templateTitleSubject[0] would have set you back templateValue[1][0] U.S. dollars , whilst the top range tickets for the templateValue[0][6] cost a whopping templateValue[1][last] U.S. dollars . France emerge victorious in front of a big crowd The templateValue[0][6] of the templateTitleDate[0] templateTitle[2] templateTitle[3] was contested between Croatia and France , with the latter emerging victorious after an entertaining templateLabel[4][1] - templateLabel[2][1] victory .

generated_template: The statistic shows the distribution of templateTitleSubject[0] employees templateTitle[4] the first half of templateTitleDate[0] , templateTitle[7] templateTitle[8] . The average templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitleSubject[0] people templateTitle[4] in templateTitleDate[0] was templateValue[2][0] U.S. dollars .
generated: The statistic shows the distribution of Russia employees the first half of 2018 , by category .  The average 2018 FIFA World of the Russia people in 2018 was 390 U.S. dollars .

Example 704:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Number of pet stores in Canada by region 2018
X_Axis['Month']: ['Ontario', 'Quebec', 'British_Columbia', 'Alberta', 'Manitoba', 'Nova_Scotia', 'Saskatchewan', 'New_Brunswick', 'Newfoundland_and_Labrador', 'Prince_Edward_Island', 'Yukon', 'Northwest_Territories']
Y_Axis['Number', 'of', 'stores']: ['762', '469', '348', '278', '63', '55', '49', '39', '22', '7', '3', '3']

gold: As of December 2019 , Ontario , Canada 's most populous province , was home to the most pet and pet supplies stores across the country . At that time , there were 762 such stores in Ontario . Quebec and British Columbia , the second and third most populous provinces , came next in the ranking with 469 and 348 stores respectively .
gold_template: As of 2019 , templateXValue[0] , templateTitleSubject[0] 's most populous province , was home to the most templateTitle[1] and templateTitle[1] supplies templateYLabel[1] across the country . At that time , there were templateYValue[max] such templateYLabel[1] in templateXValue[0] . templateXValue[1] and templateXValue[2] , the second and third most populous provinces , came next in the ranking with templateYValue[1] and templateYValue[2] templateYLabel[1] respectively .

generated_template: This graph shows the total templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] in the templateTitleSubject[1] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , there were a total of templateYValue[max] templateTitleSubject[0] templateYLabel[1] in the templateTitleSubject[1] . templateTitleSubject[0] is one of the most widespread templateTitle[1] in the world .
generated: This graph shows the total Number of Canada stores in the Canada from Northwest_Territories to Ontario .  In Ontario , there were a total of 762 Canada stores in the Canada .  Canada is one of the most widespread pet in the world .

Example 705:
titleEntities: {'Subject': ['Forecast'], 'Date': ['2009']}
title: Forecast of lanthanum oxide price globally 2009 to 2025
X_Axis['Year']: ['2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022', '2023', '2024', '2025']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'metric', 'ton']: ['4604', '23820', '98757', '25547', '7866', '5955', '6955', '7254', '7810', '7904', '7033', '6742', '6447', '6558', '6676', '6800', '6932']

gold: This statistic displays the average price of lanthanum oxide globally from 2009 to 2025 . It is expected that the price of lanthanum oxide will reach some 7,810 U.S. dollars per metric ton in 2017 . There are 17 rare earth elements and although , they may be fairly abundant in the Earth 's crust , often they occur at sparse intervals are are less economically exploitable .
gold_template: This statistic displays the average templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[4] from templateXValue[min] to templateXValue[max] . It is expected that the templateYLabel[0] of templateTitle[1] templateTitle[2] will reach some templateYValue[8] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[8] . There are 17 rare earth elements and although , they may be fairly abundant in the Earth 's crust , often they occur at sparse intervals are less economically exploitable .

generated_template: It is expected that by templateXValue[max] , the templateYLabel[0] of templateTitle[2] templateTitle[3] will reach around templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . It is expected that the templateYLabel[0] of templateTitle[4] templateTitle[3] will become more than 70 templateScale of the templateYLabel[0] templateYLabel[3] templateYLabel[4] templateYLabel[5] . The templateYLabel[0] templateYLabel[2] is expected to templatePositiveTrend in the following years .
generated: It is expected that by 2025 , the Price of oxide price will reach around 4604 million U.S. dollars per metric ton .  It is expected that the Price of globally price will become more than 70 % of the Price per metric ton .  The Price dollars is expected to increase in the following years .

Example 706:
titleEntities: {'Subject': ['Michelin'], 'Date': ['2018']}
title: Michelin - worldwide number of employees by region 2018
X_Axis['Region']: ['Europe', 'North_America', 'Asia_(excl._India)', 'South_America', 'Africa_India_Middle-East']
Y_Axis['Number', 'of', 'employees']: ['70599', '21541', '15259', '8166', '1848']

gold: The statistic shows the worldwide number of Michelin 's employees in 2018 , by region . Michelin had 21,541 employees in North America in 2018 . In that same year , some 17.5 percent of their employees worldwide were women .
gold_template: The statistic shows the templateTitle[1] templateYLabel[0] of templateTitleSubject[0] 's templateYLabel[1] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . templateTitleSubject[0] had templateYValue[1] templateYLabel[1] in templateXValue[1] in templateTitleDate[0] . In that same year , some 17.5 templateScale of their templateYLabel[1] templateTitle[1] were women .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateXValue[3] , the company employed about templateYValue[2] templateScale of the total .
generated: This statistic shows the Number of employees the worldwide number Michelin in 2018 , by Region .  In South_America , the company employed about 15259 % of the total .

Example 707:
titleEntities: {'Subject': ['Costs'], 'Date': ['2018']}
title: Costs of external consequences of cyber attacks on businesses 2018
X_Axis['Category']: ['Business_disruption', 'Information_loss', 'Equipment_damages', 'Revenue_loss']
Y_Axis['Cost', 'in', 'million', 'U.S.', 'dollars']: ['4.0', '5.9', '2.6', '0.5']

gold: This statistic provides information on the average annual of costs for external consequences of targeted cyber attacks on companies in global markets in 2018 . During the survey period it was found that business disruption accumulated an average 5.9 million U.S. dollars in annual damages from a targeted cyber attack on a business .
gold_template: This statistic provides templateXValue[1] on the average annual of templateTitleSubject[0] for templateTitle[1] templateTitle[2] of targeted templateTitle[3] templateTitle[4] on companies in global markets in templateTitleDate[0] . During the survey period it was found that templateXValue[0] accumulated an average templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in annual templateXValue[2] from a targeted templateTitle[3] attack on a templateXValue[0] .

generated_template: This statistic represents a ranking of the templateTitle[0] templateXLabel[1] templateTitle[2] in the templateTitle[3] templateTitle[4] in templateTitleDate[0] . During the survey , it was found that templateXValue[0] accounted for templateYValue[max] templateScale of templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[4] .
generated: This statistic represents a ranking of the Costs Category consequences in the cyber attacks in 2018 .  During the survey , it was found that Business_disruption accounted for 5.9 million of U.S. dollars of Costs attacks .

Example 708:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2008', '2015']}
title: Number of TV ads seen daily per person in the United Kingdom ( UK ) 2008 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Advertisements', 'seen']: ['45', '45', '47', '47', '47', '46', '43', '42']

gold: This statistic displays a timeline of the number of TV ads seen per individual per day in the United Kingdom from 2008 to 2015 . In 2012 , individuals saw on average 47 television commercials or advertisements per day .
gold_template: This statistic displays a timeline of the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[5] individual templateTitle[5] day in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , individuals saw on average templateYValue[max] television commercials or templateYLabel[0] templateTitle[5] day .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Advertisements seen of the seen worldwide from 2008 to 2015 .  In 2015 , the seen of United Kingdom amounted to 45 seen .

Example 709:
titleEntities: {'Subject': ['Cambodia'], 'Date': ['2019']}
title: Unemployment rate in Cambodia 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['1.02', '1.05', '1.06', '1.13', '1.2', '1.23', '1.28', '1.28', '1.32', '1.38', '1.28', '1.15', '1.17', '1.28', '1.47', '1.56', '1.57', '1.63', '1.64', '2.45', '2.55']

gold: This statistic shows the unemployment rate in Cambodia from 1999 to 2019 . In 2019 , the unemployment rate in Cambodia was 1.02 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Cambodia from 1999 to 2019 .  In 2019 , the Unemployment rate in Cambodia was at approximately 1.02 % .

Example 710:
titleEntities: {'Subject': ['Smartwatch'], 'Date': ['2014', '2015']}
title: Smartwatch global unit sales forecast 2014 to 2015
Data:
                               0     1
Year                        2015  2014
Western Europe               5.4   0.8
Asia Pacific                 6.7   1.0
North America                7.4   1.2
Central and Eastern Europe   1.3   0.2
China                        3.6   0.5
South America                0.6   0.1
Middle East and Africa       1.1   0.2 

gold: The statistic shows the unit sales of smartwatches worldwide from 2014 to 2015 , by region . For 2015 , smartwatch sales are projected to total 7.4 million units in North America .
gold_template: The statistic shows the templateTitle[2] templateTitle[3] of smartwatches worldwide from templateValue[0][last] to templateValue[0][0] , by region . For templateValue[0][0] , templateTitleSubject[0] templateTitle[3] are projected to total templateValue[3][0] templateScale units in templateLabel[3][0] templateLabel[3][1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[0] templateTitle[5] from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . In templateValue[0][0] , the estimated templateTitle[0] templateTitle[1] templateTitle[2] of all goods '' amounted to approximately templateValue[1][0] templateScale U.S. dollars in templateValue[0][0] .
generated: This statistic shows the Smartwatch global unit sales forecast Smartwatch 2014 from to 2015 , .  In 2015 , the estimated Smartwatch global unit of all goods '' amounted to approximately 5.4 billion U.S. dollars in 2015 .

Example 711:
titleEntities: {'Subject': ['Honduras'], 'Date': ['2019']}
title: Employment by economic sector in Honduras 2019
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2019   2018   2017   2016   2015   2014   2013   2012   2011   2010   2009
Agriculture  31.84  31.91  31.99  27.35  28.72  28.83  34.52  37.01  36.62  37.81  37.07
Industry     20.32   20.5  20.68  21.58  21.81  22.02  19.04  19.93  19.36  18.64  20.34
Services     47.84  47.59  47.33  51.07  49.47  49.14  46.45  43.07  44.02  43.55  42.59 

gold: The statistic shows the distribution of employment in Honduras by economic sector from 2009 to 2019 . In 2019 , 31.84 percent of the employees in Honduras were active in the agricultural sector , 20.32 percent in industry and 47.84 percent in the service sector .
gold_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the service sector .
generated: This statistic shows the Employment of the by economic sector ( Honduras ) 2019 in Honduras from 2009 to 2019 .  In 2019 , Agriculture contributed around 31.84 % to the Honduras of , 20.32 % came from the Industry and 47.84 % from the service sector .

Example 712:
titleEntities: {'Subject': ['Algeria'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Algeria 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['3768.01', '4047.72', '4055.39', '4032.71', '4039.1', '3980.12', '4080.91', '4012.13', '3918.94', '4153.32', '5466.33', '5477.06', '5574.51', '5453.89', '4480.72', '3886.06', '4943.5', '3986.56', '3508.96', '3141.03', '2636.31', '2128.39', '1806.86', '1768.58', '1794.7', '1630.07', '1633.09', '1658.73', '1643.27', '1499.14', '1542.97', '1894.95', '1873.42', '1819.98', '2473.51', '2127.87', '2143.74', '2705.11', '2698.92', '2753.7', '2432.72']

gold: The statistic shows gross domestic product ( GDP ) per capita in Algeria from 1984 to 2017 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateYLabel[0] of the total templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . According to the total market value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows the GDP of the total capita in the Algeria from 1984 to 2017 , with projections up until 2024 .  According to the total market value of all goods and services produced in a country in a Year .  It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

Example 713:
titleEntities: {'Subject': ['Number'], 'Date': ['2006', '2015']}
title: Number of vehicles in use worldwide 2006 to 2015
Data:
                          0       1       2       3       4       5       6       7       8       9
Year                   2015    2014    2013    2012    2011    2010    2009    2008    2007    2006
Commercial vehicles  335190  327825  315845  306721  288618  280127  271957  264970  258538  247488
Passenger cars       947080  907062  869083  834916  808401  775573  747899  727405  701690  679154 

gold: This statistic shows the number of passenger cars and commercial vehicles in use worldwide from 2006 to 2015 . In 2015 , around 947 million passenger cars and 335 million commercial vehicles were in operation worldwide .
gold_template: This statistic shows the templateTitleSubject[0] of templateLabel[2][0] templateLabel[2][1] and templateLabel[1][0] templateTitle[1] in templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , around 947 templateScale templateLabel[2][0] templateLabel[2][1] and 335 templateScale templateLabel[1][0] templateTitle[1] were in operation templateTitle[3] .

generated_template: The statistic illustrates the templateTitle[0] of templateTitle[1] templateTitle[2] templateNegativeTrend in the templateTitle[4] from templateValue[0][last] to templateValue[0][0] . According to the source , templateValue[1][0] templateScale of all goods users in templateValue[0][0] .
generated: The statistic illustrates the Number of vehicles use decreased in the 2006 from to 2015 .  According to the source , 335190 % of all goods users in 2015 .

Example 714:
titleEntities: {'Subject': ['Starwood Hotels Resorts'], 'Date': ['2005', '2015']}
title: Revenue of Starwood Hotels & Resorts 2005 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['5.76', '5.98', '6.12', '6.32', '5.62', '5.07', '4.7', '5.75', '6.0', '5.84', '5.86']

gold: This statistic shows the revenue of Starwood Hotels & Resorts worldwide from 2005 to 2015 . The hotel company generated approximately 5.76 billion U.S. dollars in revenue in 2015 . Starwood Hotels & Resorts was founded in 1969 and now has more than 1,200 properties worldwide .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] & templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . The hotel company generated approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] in templateXValue[max] . templateTitleSubject[0] & templateTitleSubject[0] was founded in 1969 and now has more than 1,200 properties worldwide .

generated_template: The statistic shows the templateTitle[1] templateYLabel[0] generated by the templateYLabel[2] advertising giant templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated a total of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Starwood Revenue generated by the U.S. advertising giant Starwood Hotels Resorts from 2005 to 2015 .  In 2015 , Starwood Hotels Resorts generated a total of 5.76 billion U.S. dollars .

Example 715:
titleEntities: {'Subject': ['Qdoba'], 'Date': ['2011', '2018']}
title: Qdoba company-owned restaurant sales from 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['192.62', '436.56', '415.5', '374.34', '338.45', '293.27', '239.49', '168.8']

gold: This statistic shows the sales made by Qdoba company-owned restaurants from 2011 to 2018 . Qdoba , owned by Jack in the Box Inc. , generated restaurant sales of approximately 192.62 million U.S. dollars in 2018 .
gold_template: This statistic shows the templateYLabel[0] made by templateTitleSubject[0] templateTitle[1] restaurants templateTitle[4] templateXValue[min] to templateXValue[max] . templateTitleSubject[0] , owned by Jack in the Box Inc. , generated templateTitle[2] templateYLabel[0] of approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[2] templateYLabel[0] of the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[2] templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the U.S. Sales of the from 2011 to 2018 .  In 2016 , the U.S. Sales of the Qdoba amounted to 415.5 million U.S. dollars .

Example 716:
titleEntities: {'Subject': ['Los Angeles Chargers'], 'Date': ['2006', '2019']}
title: Average ticket price of the Los Angeles Chargers 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['165.77', '199.1', '84.55', '84.55', '84.55', '84.55', '80.3', '80.3', '81.39', '81.39', '81.39', '73.64', '62.82']

gold: This graph depicts the average ticket price for Los Angeles Chargers games in the National Football League from 2006 to 2019 . In 2019 , the average ticket price was at 165.77 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] Chargers games in the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Los Angeles Chargers games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 165.77 U.S. dollars .

Example 717:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2017', '2018']}
title: Snapchat penetration rate in the Netherlands 2017 - 2018 , by age group
Data:
                0            1            2            3                   4
Year  15-19_years  20-39_years  40-64_years  65-79_years  80_years_and_older
2016           56           11            1            0                   1
2017           67           25            4            1                   0
2018           72           32            7            1                   1 

gold: This statistic shows the share of respondents using Snapchat in the Netherlands from 2016 to 2018 , by age group . As of 2018 , 72 percent of the respondents aged 15 to 19 years old indicated they used Snapchat . Because of the growing audiences , online social networks can hardly be overlooked as a marketing tool for Dutch businesses .
gold_template: This statistic shows the share of respondents using templateTitle[0] in the templateTitleSubject[0] from templateLabel[1][0] to templateTitleDate[max] , templateTitle[6] templateTitle[7] templateTitle[8] . As of templateTitleDate[max] , templateValue[3][max] templateScale of the respondents aged 15 to 19 templateValue[0][0] old indicated they used templateTitle[0] . Because of the templatePositiveTrend audiences , online social networks can hardly be overlooked as a marketing tool for Dutch businesses .

generated_template: templateTitleSubject[0] usage among ages 40 to 64 templateValue[0][0] old in the templateTitleSubject[1] templatePositiveTrend almost threefold in templateTitleDate[min] and templateTitleDate[max] . The templateValue[0][0] , templateValue[1][0] templateScale of individuals aged 65 years old when just under four years old in templateTitleDate[max] .
generated: Netherlands usage among ages 40 to 64 15-19_years old in the Netherlands increased almost threefold in 2017 and 2018 .  The 15-19_years , 56 % of individuals aged 65 years old when just under four years old in 2018 .

Example 718:
titleEntities: {'Subject': ['France'], 'Date': ['2008', '2018']}
title: Grocery market share in France 2008 to 2018
Data:
                0     1     2
Year         2008  2013  2018
"Systeme A"  20.3  22.3  21.8
Carrefour    22.9  19.9  19.5
Leclerc      14.2  17.7  19.3
Intermarche  15.2  13.6    15
Casino       11.6  10.2   9.9
Other        15.8  16.3  14.6 

gold: This statistic shows the market share of grocery store brands in France for the years 2008 , 2013 and a forecast for 2018 . The leading three supermarkets based on market share in 2013 were `` Systeme A '' ( Auchan and System U partnership ) , Carrefour and Leclerc . Leclerc 's share of the market is forecast to increase again in 2018 , to 19.3 percent .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] of templateTitle[0] store brands in templateTitleSubject[0] for the years templateValue[0][0] , templateValue[0][1] and a forecast for templateValue[0][last] . The leading three supermarkets based on templateTitle[1] templateTitle[2] in templateValue[0][1] were `` Systeme A '' ( Auchan and System U partnership ) , templateLabel[2][0] and templateLabel[3][0] . templateLabel[3][0] 's templateTitle[2] of the templateTitle[1] is forecast to templatePositiveTrend again in templateValue[0][last] , to templateValue[3][last] templateScale .

generated_template: Between templateValue[0][last] and templateValue[0][0] , the average templateTitle[0] templateTitle[1] in the United Kingdom ( templateTitleSubject[0] ) templatePositiveTrend to templateValue[2][last] templateScale in templateValue[0][0] . The templateTitle[3] of those aged between the ages of people in the previous templateLabel[0][0] of 18 years old in both a templateTitle[3] of positive change in templateValue[0][0] compared to the previous templateLabel[0][0] .
generated: Between 2018 and 2008 , the average Grocery market in the United Kingdom ( France ) increased to 19.5 % in 2008 .  The France of those aged between the ages of people in the previous Year of 18 years old in both a France of positive change in 2008 compared to the previous Year .

Example 719:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: Number of U.S. households with broadband internet access 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Number', 'of', 'U.S.', 'households', 'with', 'broadband', 'internet', 'access', '(in', 'millions)']: ['110.57', '108.19', '105.71', '102.21', '97.81', '96.03', '92.51', '88.32', '84.52', '79.99', '77.13', '71.7', '60.24', '51.16', '37.35', '27.74', '19.88', '12.79', '7.07']

gold: This timeline shows the number of households in the United States with permanent internet access via broadband from 2000 to 2018 . In the most recently reported period , close to 110.57 million U.S. households had fixed broadband internet access , up from close to 80 million in 2009 . The United States are one of the biggest online markets worldwide .
gold_template: This timeline shows the templateYLabel[0] of templateYLabel[2] in the templateTitle[1] templateYLabel[3] permanent templateYLabel[5] templateYLabel[6] via templateYLabel[4] from templateXValue[min] to templateXValue[max] . In the most recently reported period , close to templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] had fixed templateYLabel[4] templateYLabel[5] templateYLabel[6] , up from close to templateYValue[9] templateScale in templateXValue[9] . The templateTitle[1] are one of the biggest online markets worldwide .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the data from the templateTitle[0] of the templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Number of U.S. the worldwide from 2000 to 2018 .  In 2018 , the data from the Number of the U.S. stood at 110.57 households broadband .

Example 720:
titleEntities: {'Subject': ['Uber U.S.'], 'Date': ['2017', '2019']}
title: Driver satisfaction with Uber U.S. 2017 to 2019
Data:
                          0                  1                           2               3               4
Response  Strongly_disagree  Somewhat_disagree  Neither_agree_nor_disagree  Somewhat_agree  Strongly_agree
2017                   10.8               25.4                        14.4            39.7             9.7
2018                    8.2               17.3                        16.3            43.4            14.8
2019                   13.9               22.5                        19.1            34.1            10.5 

gold: This statistic shows the driver satisfaction with Uber in the United States from 2017 to 2019 . During the 2019 survey , 34.1 percent of the respondents agreed that they are somewhat satisfied with Uber .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitleSubject[0] in the templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] . During the templateTitleDate[max] survey , templateValue[3][max] templateScale of the respondents agreed that they are templateValue[0][1] satisfied templateTitle[2] templateTitleSubject[0] .

generated_template: This statistic shows the templateScale of online consumers in the templateTitle[0] who are using templateTitle[5] as of 2019 . During the survey period , it was found that templateValue[2][max] templateScale of all templateValue[0][0] accessed the templateTitle[0] .
generated: This statistic shows the percentage of online consumers in the Driver who are using 2019 as of 2019 .  During the survey period , it was found that 43.4 % of all Strongly_disagree accessed the Driver .

Example 721:
titleEntities: {'Subject': ['BMW'], 'Date': ['2006', '2016']}
title: BMW - motor vehicles produced worldwide 2006 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Production', 'in', 'thousand', 'units']: ['2359.76', '2279.5', '2165.57', '2006.37', '2065.48', '1738.16', '1481.25', '1258.42', '1439.92', '1541.5', '1366.84']

gold: This timeline represents the motor vehicle production of BMW worldwide from 2006 through 2016 . Globally , the manufacturer of automobiles and motorcycles produced a little over 2.3 million vehicles in 2016 .
gold_template: This timeline represents the templateTitle[1] vehicle templateYLabel[0] of templateTitleSubject[0] templateTitle[4] from templateYValue[3] through templateXValue[max] . Globally , the manufacturer of automobiles and motorcycles templateTitle[3] a little over templateYValue[1] templateScale templateTitle[2] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[1] of the total templateYLabel[0] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the source , the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitleSubject[0] has templateNegativeTrend by roughly templateYValue[0] templateScale in templateXValue[max] .
generated: This statistic shows the thousand of the total Production in the BMW from 2006 to 2016 .  According to the source , the BMW motor vehicles of the BMW has decreased by roughly 2359.76 thousand in 2016 .

Example 722:
titleEntities: {'Subject': ['Portugal'], 'Date': ['2012', '2028']}
title: International tourism spending in Portugal 2012 to 2028
X_Axis['Year']: ['2028', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Spending', 'in', 'billion', 'euros']: ['26.4', '19.4', '18.1', '15.6', '14.5', '13.9', '12.8', '12.2']

gold: This statistic shows the total annual spend of international tourists ( visitor exports ) in Portugal from 2012 to 2018 and a forecast for 2028 . In 2017 international visitors in Portugal spent a total of 18.1 billion euros . This is expected to increase in 2018 to 19.4 billion euros .
gold_template: This statistic shows the total annual spend of templateTitle[0] tourists ( visitor exports ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[1] and a forecast for templateXValue[max] . In templateXValue[2] templateTitle[0] visitors in templateTitleSubject[0] spent a total of templateYValue[2] templateScale templateYLabel[2] . This is expected to templatePositiveTrend in templateXValue[1] to templateYValue[1] templateScale templateYLabel[2] .

generated_template: In templateXValue[max] , the total annual templateYLabel[0] of templateTitle[0] tourists ( 'visitor exports ' _ ) in templateTitleSubject[0] templatePositiveTrend from templateYValue[min] templateScale templateYLabel[2] in templateXValue[idxmin(Y)] to templateYValue[max] templateScale templateYLabel[2] in templateXValue[idxmax(Y)] . This figure has templatePositiveTrend in the last five years .
generated: In 2028 , the total annual Spending of International tourists ( 'visitor exports ' _ ) in Portugal increased from 12.2 billion euros in 2012 to 26.4 billion euros in 2028 .  This figure has increased in the last five years .

Example 723:
titleEntities: {'Subject': ['Fastest'], 'Date': ['2019', '2019']}
title: Fastest supercomputers - number of computer cores 2019
X_Axis['Location']: ['Summit_United_States', 'Sierra__United_States', 'Sunway_TaihuLight__China', 'Tianhe-2A_China', 'Frontera_United_States', 'Piz_Daint__Switzerland', 'Trinity__United_States', 'AI_Bridging_Cloud_Infrastructure_(ABCI)_Japan', 'SuperMUC-NG_Germany', 'Lassen_United_States']
Y_Axis['Number', 'of', 'cores']: ['2414592', '1572480', '10649600', '4981760', '448448', '387872', '979072', '391680', '305856', '288288']

gold: This ranking lists the number of computer cores of the 10 fastest supercomputers . The fastest supercomputer as of June 2019 , Summit , operated by the Oak Ridge National Laboratory in Tennessee , China , had 2.4 million computer cores .
gold_template: This ranking lists the templateYLabel[0] of templateTitle[3] templateYLabel[1] of the 10 templateTitleSubject[0] templateTitle[1] . The templateTitleSubject[0] supercomputer as of 2019 , templateXValue[0] , operated by the Oak Ridge National Laboratory in Tennessee , templateXValue[2] , had templateYValue[0] templateScale templateTitle[3] templateYLabel[1] .

generated_template: As of templateTitleDate[0] , the statistic shows that ranking of any templateTitle[0] employees within the templateTitleSubject[0] . According to the survey results , it was that the templateTitle[0] of templateXValue[7] will be templateYValue[7] templateScale templateYLabel[2] templateYLabel[3] .
generated: As of 2019 , the statistic shows that ranking of any Fastest employees within the Fastest .  According to the survey results , it was that the Fastest of AI_Bridging_Cloud_Infrastructure_(ABCI)_Japan will be 391680 million cores .

Example 724:
titleEntities: {'Subject': ['Africa'], 'Date': ['2018', '2022']}
title: Outbound visitor growth in Africa 2018 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018']
Y_Axis['Year-over-year', 'visitor', 'growth']: ['3.9', '4.9', '3.3', '4.8', '6.1']

gold: This statistic shows outbound visitor growth in Africa from 2018 to 2022 . In 2018 , outbound visitor numbers from Africa grew by 6.1 percent over the previous year . The visitor number was forecast to grow by approximately four percent in 2022 .
gold_template: This statistic shows templateTitle[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateTitle[0] templateYLabel[1] numbers from templateTitleSubject[0] templatePositiveTrend by templateYValue[idxmin(X)] templateScale over the previous templateXLabel[0] . The templateYLabel[1] number was forecast to grow by approximately templateYValue[0] templateScale in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[1] of the real templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYLabel[1] numbers for the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] is expected to grow by templateYValue[idxmin(X)] templateScale .
generated: This statistic shows the visitor of the real Outbound visitor growth Africa ( Year-over-year ) in Africa from 2018 to 2022 .  In 2018 , visitor numbers for the visitor growth Africa 2018 is expected to grow by 6.1 % .

Example 725:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Total number of registered automobiles in the U.S. by state 2017
X_Axis['State']: ['California', 'Texas', 'Florida', 'Illinois', 'Ohio', 'New_York', 'Pennsylvania', 'Georgia', 'North_Carolina', 'Michigan', 'Virginia', 'Washington', 'New_Jersey', 'Arizona', 'Tennessee', 'Indiana', 'Massachusetts', 'Minnesota', 'Missouri', 'Wisconsin', 'Alabama', 'Maryland', 'South_Carolina', 'Colorado', 'Kentucky', 'Oregon', 'Louisiana', 'Oklahoma', 'Connecticut', 'Iowa', 'Nevada', 'Kansas', 'Arkansas', 'Utah', 'Mississippi', 'Nebraska', 'New_Mexico', 'West_Virginia', 'Idaho', 'Hawaii', 'New_Hampshire', 'Montana', 'Rhode_Island', 'Delaware', 'Maine', 'South_Dakota', 'North_Dakota', 'Vermont', 'Dist._of_Col.', 'Wyoming', 'Alaska']
Y_Axis['Number', 'of', 'registered', 'automobiles']: ['14860967', '8176879', '7778493', '4655844', '4605459', '4563035', '4463267', '3536055', '3317918', '3257473', '3251339', '2925765', '2798699', '2391624', '2325004', '2261440', '2208468', '2146187', '2141050', '2092584', '2065463', '2006904', '1808401', '1785058', '1691085', '1542461', '1389436', '1337624', '1302339', '1268996', '1047508', '987259', '926789', '925052', '822864', '677040', '609494', '562984', '550006', '512386', '505381', '441392', '420313', '419199', '387233', '353180', '276545', '221936', '208141', '193523', '173487']

gold: California had the most automobile registrations in 2017 : More than 14.86 million such vehicles were registered in the most populous U.S. federal state . California also leads in terms of annual car sales with almost one million cars sold in 2017 . Additionally , California residents also purchased about 715,000 crossover and sport-utility vehicles ( CUVs and SUVs ) , some 221,270 pickups , and almost 80,000 vans and minivans in 2017 .
gold_template: templateXValue[0] had the most automobile registrations in templateTitleDate[0] : More than templateYValue[max] templateScale such vehicles were templateYLabel[1] in the most populous templateTitleSubject[0] federal templateXLabel[0] . templateXValue[0] also leads in terms of annual car sales with almost templateYValue[26] templateScale cars sold in templateTitleDate[0] . Additionally , templateXValue[0] residents also purchased about 715,000 crossover and sport-utility vehicles ( CUVs and SUVs ) , some 221,270 pickups , and almost 80,000 vans and minivans in templateTitleDate[0] .

generated_template: This statistic represents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[0] in templateTitleDate[0] , templateTitle[3] templateXLabel[0] . In that year , there were over templateYValue[1] thousand templateYLabel[1] living in templateTitleSubject[0] .
generated: This statistic represents the Number of registered automobiles in the Total in 2017 , automobiles State .  In that year , there were over 8176879 thousand registered living in U.S. .

Example 726:
titleEntities: {'Subject': ['Q4 Q3'], 'Date': ['2019']}
title: Zynga 's quarterly revenue from Q4 2012- Q3 2019
X_Axis['Quarter']: ["Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['345.29', '306.5', '265.4', '248.69', '233.24', '217.04', '208.23', '233.28', '224.59', '209.23', '194.3', '190.54', '182.42', '181.74', '186.72', '185.77', '195.74', '199.92', '183.29', '192.55', '176.61', '153.23', '168.02', '176.36', '202.58', '230.74', '263.59', '311.17']

gold: This statistic contains data on the global revenue of Zynga from the fourth quarter of 2012 to the third quarter of 2019 . In the third quarter of 2019 , the social gaming company generated a total revenue of 345.29 million U.S. dollars .
gold_template: This statistic contains data on the global templateYLabel[0] of templateTitle[0] templateTitle[4] the fourth templateXLabel[0] of 2012 to the third templateXLabel[0] of templateTitleDate[max] . In the third templateXLabel[0] of templateTitleDate[max] , the social gaming company generated a total templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic presents the ranking of the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitle[4] from the second templateXLabel[0] of templateTitleDate[min] to the third templateXLabel[0] of templateTitleDate[max] . During that templateXLabel[0] , the company 's templateYLabel[0] amounted to over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the preceding templateXLabel[0] .
generated: The statistic presents the ranking of the Zynga 's quarterly Revenue in the from second Quarter of 2019 to the third Quarter of 2019 .  During that Quarter , the company 's Revenue amounted to over 345.29 million U.S. dollars , up from 306.5 million U.S. dollars in the preceding Quarter .

Example 727:
titleEntities: {'Subject': ['Canada'], 'Date': ['2027']}
title: Projected increase of the national defense budget in Canada fiscal years 2018 to 2027
X_Axis['Year']: ['2026-2027', '2025-2026', '2024-2025', '2023-2024', '2022-2023', '2021-2022', '2020-2021', '2019-2020', '2018-2019', '2017-2018']
Y_Axis['Annual', 'funding', 'increase', 'in', 'million', 'Canadian', 'dollars']: ['2300', '2020', '1751', '1495', '1250', '1016', '793', '580', '377', '184']

gold: This statistic shows the projected increase over baseline spending for the national defense budget of Canada between the fiscal years 2018 to 2027 . In fiscal year 2027 , the defense budget for Canada is projected to be 2.3 billion Canadian dollars greater than 2026 budget .
gold_template: This statistic shows the templateTitle[0] templateYLabel[2] over baseline spending for the templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleSubject[0] between the templateTitle[6] templateTitle[7] templateTitleDate[min] to templateTitleDate[max] . In templateTitle[6] templateXLabel[0] templateTitleDate[max] , the templateTitle[3] templateTitle[4] for templateTitleSubject[0] is templateTitle[0] to be templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] greater than 2026 templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in the templateTitle[0] from templateTitleDate[min] to templateTitleDate[max] . The average templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Annual funding of the national in the Projected from 2027 to .  The average Annual funding of the Canada increase amounted to 2300 increase million .

Example 728:
titleEntities: {'Subject': ['MoviePass'], 'Date': ['2019']}
title: MoviePass subscribers in the U.S. 2019
X_Axis['Month']: ['April_2019', 'June_2018', 'March_2018', 'January_2018', 'September_2017', 'August_2017', 'December_2016']
Y_Axis['Number', 'of', 'subscribers', 'in', 'thousands']: ['225', '3000', '2000', '1500', '500', '150', '20']

gold: As of April 2019 , it was estimated that MoviePass had just 225 thousand subscribers in the United States , a significant drop from the three million recorded in June 2018 . MoviePass is no stranger to media attention , but after the recent claims from a source that they had obtained internal data about MoviePass 's floundering subscriber base , the company is once again in the spotlight . What is MoviePass ? MoviePass is a subscription-based movie ticketing service , designed to allow subscribers to buy a select amount of movie tickets each month for a single fee .
gold_template: As of templateXValue[0] , it was estimated that templateTitleSubject[0] had just templateYValue[0] thousand templateYLabel[1] in the templateTitle[2] , a significant drop from the templateYValue[max] templateScale recorded in templateXValue[1] . templateTitleSubject[0] is no stranger to media attention , but after the recent claims from a source that they had obtained internal data about templateTitleSubject[0] 's floundering subscriber base , the company is once again in the spotlight . What is templateTitleSubject[0] ? templateTitleSubject[0] is a subscription-based movie ticketing service , designed to allow templateYLabel[1] to buy a select amount of movie tickets each templateXLabel[0] for a single fee .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] the templateTitle[2] templateTitle[4] templateTitle[5] as of 2018 , templateTitle[7] templateYLabel[0] of templateXValue[0] . According to the source , there were templateYValue[max] templateScale templateTitle[1] templateTitle[2] templateYLabel[1] on the platform , followed templateTitle[6] templateXValue[1] with templateYValue[1] templateScale templateYLabel[1] .
generated: This statistic shows the Number of MoviePass the U.S. 2019 as of 2018 , 2019 Number of April_2019 .  According to the source , there were 3000 thousands subscribers U.S. on the platform , followed 2019 June_2018 with 3000 thousands subscribers .

Example 729:
titleEntities: {'Subject': ['Company'], 'Date': ['2007', '2019']}
title: Coca-Cola Company 's net operating revenues worldwide 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenues', 'in', 'billion', 'U.S.', 'dollars']: ['37.27', '34.3', '36.21', '41.86', '44.29', '46.0', '46.85', '48.02', '46.54', '35.12', '30.99', '31.94', '28.86']

gold: This timeline depicts the Coca-Cola Company 's net operating revenues worldwide from 2007 to 2019 . In 2019 , the Coca-Cola Company 's net operating revenues worldwide amounted to around 37.27 billion U.S. dollars . The Coca-Cola Company is a producer , retailer and marketer of non-alcoholic beverages and is well-known for its soft drink Coca-Cola .
gold_template: This timeline depicts the templateTitle[0] templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] templateTitle[6] amounted to around templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] . The templateTitle[0] templateTitleSubject[0] is a producer , retailer and marketer of non-alcoholic beverages and is well-known for its soft drink templateTitle[0] .

generated_template: The statistic shows the templateYLabel[0] of the development of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] of the templateTitle[1] were employed in templateTitleSubject[0] .
generated: The statistic shows the Revenues of the development of Company from 2007 to 2019 .  In 2019 , approximately 37.27 billion U.S. of the Company were employed in Company .

Example 730:
titleEntities: {'Subject': ['Ownership'], 'Date': ['2012']}
title: Ownership and personal use of laptop/netbook computers 2012 , by country
X_Axis['Country']: ['Italy', 'United_Kingdom', 'Germany', 'Japan', 'Australia', 'Spain', 'France', 'United_States']
Y_Axis['Percentage', 'of', 'population']: ['80', '75', '73', '72', '71', '71', '69', '68']

gold: This statistic shows the percentage of laptop/netbook computer ownership and usage among the populations of selected countries as of September 2012 . In the U.S. laptop/netbook ownership and usage was found to be at 68 percent of the population .
gold_template: This statistic shows the templateScale of templateTitle[3] computer templateTitleSubject[0] and usage among the populations of selected countries as of 2012 . In the U.S. templateTitle[3] templateTitleSubject[0] and usage was found to be at templateYValue[min] templateScale of the templateYLabel[1] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[3] templateTitle[4] . In templateTitleDate[0] , about templateYValue[idxmax(X)] templateScale of the templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the personal Percentage in Ownership 2012 , laptop/netbook computers .  In 2012 , about 80 percentage of the population lived below the personal line .

Example 731:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000']}
title: Estimated U.S. clothing and clothing accessories per capita sales 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'sales', 'in', 'U.S.', 'dollars']: ['795', '804', '797', '786', '774', '763', '734', '689', '667', '709', '734', '714', '680', '650', '616', '599', '587', '594']

gold: The timeline shows the estimated clothing and clothing accessories per capita sales in the United States from 2000 to 2017 . In 2017 , clothing and clothing accessories sales amounted to 795 U.S. dollars per capita .
gold_template: The timeline shows the templateTitle[0] templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[2] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[2] templateYLabel[3] of the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Per capita of the sales U.S. of the Estimated from 2000 to 2017 .  In 2017 , the Per capita of U.S. was 795 sales U.S. .

Example 732:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: United States - lasting of love in a relationship in 2019
X_Axis['Year']: ['Less_than_two_years', '2_to_5_years', '6_to_10_years', '11_to_20_years', 'A_lifetime']
Y_Axis['Share', 'of', 'respondents']: ['2', '6', '3', '2', '77']

gold: This statistic shows the results of a survey conducted in the United States in 2019 on how long the respondents thought the feeling of love could last in a relationship . According to 77 percent of respondents , the feeling of love in a relationship can last a lifetime .
gold_template: This statistic shows the results of a survey conducted in the templateTitleSubject[0] in templateTitleDate[0] on how long the templateYLabel[1] thought the feeling of templateTitle[3] could last in a templateTitle[4] . According to templateYValue[max] templateScale of templateYLabel[1] , the feeling of templateTitle[3] in a templateTitle[4] can last a templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitle[4] templateTitle[5] in templateTitleDate[0] . During the survey , it was found that templateYValue[1] templateScale of the templateYLabel[1] had purchased templateXValue[1] followers on the social network .
generated: This statistic gives information on the United States lasting of the relationship 2019 in .  During the survey , it was found that 6 % of the respondents had purchased 2_to_5_years followers on the social network .

Example 733:
titleEntities: {'Subject': ['France'], 'Date': ['2017', '2022']}
title: France : public debt 2017 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'GDP']: ['91.4', '94.2', '96.1', '97.1', '96.8', '96.8']

gold: In 2018 , public debt in France represented 96.8 percent of the country 's GDP . According to the source , public debt in France is supposed to decrease in the upcoming years , while staying at a high level . The French Ministry of Economy has stated that the French public debt will normally reach 91.4 percent in 2022 , compared to 96.8 percent in 2017 .
gold_template: In templateXValue[4] , templateTitle[1] templateTitle[2] in templateTitleSubject[0] represented templateYValue[last] templateScale of the country 's templateYLabel[1] . According to the source , templateTitle[1] templateTitle[2] in templateTitleSubject[0] is supposed to templateNegativeTrend in the upcoming years , while staying at a high level . The French Ministry of Economy has stated that the French templateTitle[1] templateTitle[2] will normally reach templateYValue[min] templateScale in templateXValue[idxmin(Y)] , compared to templateYValue[last] templateScale in templateXValue[min] .

generated_template: In templateXValue[max] , the templateYLabel[1] of the gross domestic product ( templateYLabel[0] ) of templateTitleSubject[0] stood at around templateYValue[idxmax(X)] templateScale . This is a slight templatePositiveTrend from the previous templateXLabel[0] , and in the country 's templateYLabel[0] of the templateYLabel[1] were on the rise .
generated: In 2022 , the GDP of the gross domestic product ( Share ) of France stood at around 91.4 % .  This is a slight increase from the previous Year , and in the country 's Share of the GDP were on the rise .

Example 734:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1998', '2018']}
title: U.S. mining industry total employment 1998 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998']
Y_Axis['Number', 'of', 'employees', 'in', 'thousands']: ['672', '622', '611', '751', '842', '810', '794', '727', '649', '640', '717', '658', '615', '558', '523', '502', '509', '537', '521', '531', '570']

gold: This statistic shows the number of people employed in the United States mining industry from 1998 to 2018 . In 2018 , there were some 672,000 people employed by the U.S. mining industry . The U.S. mining industry has been active since colonial times , and continues to be an important industry .
gold_template: This statistic shows the templateYLabel[0] of people employed in the templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were some templateYValue[0] people employed by the templateTitleSubject[0] templateTitle[1] templateTitle[2] . The templateTitleSubject[0] templateTitle[1] templateTitle[2] has been active since colonial times , and continues to be an important templateTitle[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] people employed in the templateTitleSubject[0] . Employment in the templateTitle[4] .
generated: This statistic shows the Number of employees in the U.S. total employment from 1998 to 2018 .  In 2018 , there were 672 people employed in the U.S. Employment in the employment .

Example 735:
titleEntities: {'Subject': ['Ibex Spain'], 'Date': ['2019']}
title: Forecast of dividend yield of companies of Ibex 35 Spain 2019
X_Axis['Company']: ['Mediaset', 'Ence', 'Repsol', 'Endesa', 'Enagas', 'Sabadell', 'IAG_(Iberia)', 'Mapfre', 'Caixabank', 'Acerinox', 'Telefónica', 'Santander', 'BBVA', 'Bankia', 'REE', 'Naturgy', 'ACS', 'Bankinter', 'AENA', 'Iberdrola', 'Merlin_Prop', 'Inditex', 'Técnicas_Reunidas', 'Accciona', 'Viscofan', 'Ferrovial', 'CIE_Automotive', 'Immobiliaria_Colonial', 'Melia_Hotels_INTL', 'Amadeus', 'Grifols', 'Indra', 'Siemens_Gamesa', 'Cellnex_Telecom', 'Arcelormittal']
Y_Axis['Percentage', 'dividend', 'yield']: ['8.46', '6.7', '6.54', '6.27', '6.25', '5.9', '5.85', '5.84', '5.79', '5.77', '5.55', '5.47', '5.37', '5.34', '5.33', '5.13', '5.07', '4.73', '4.42', '4.38', '4.3', '4.17', '3.82', '3.72', '3.32', '3.17', '2.8', '2.22', '2.09', '1.82', '1.63', '1.31', '0.7', '0.36', '0']

gold: The Ibex 35 is Spain 's main stock market index , featuring 35 of the companies with the most liquid assets that are traded in the Madrid Stock Exchange or Bolsa de Madrid in Spanish . Out of the 35 entities , the Madrid-based television network Mediaset España had the highest dividend yield during the time in consideration at 8.46 percent . The energy and cellulose-specialized company Ence came in second on the list , at 6.7 percent of dividend yield in 2019 .
gold_template: The templateTitleSubject[0] templateTitle[5] is templateTitleSubject[0] 's main stock market index , featuring templateTitle[5] of the templateTitle[3] with the most liquid assets that are traded in the Madrid Stock Exchange or Bolsa de Madrid in Spanish . Out of the templateTitle[5] entities , the Madrid-based television network templateXValue[0] España had the highest templateYLabel[1] templateYLabel[2] during the time in consideration at templateYValue[max] templateScale . The energy and cellulose-specialized templateXLabel[0] templateXValue[1] came in second on the list , at templateYValue[1] templateScale of templateYLabel[1] templateYLabel[2] in templateTitleDate[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of templateTitleSubject[0] in templateTitleDate[0] . At that time , templateXValue[0] was the templateTitle[0] templateTitle[1] templateXLabel[0] templateXLabel[1] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Forecast dividend yield companies Ibex Percentage of Ibex Spain in 2019 .  At that time , Mediaset was the Forecast dividend Company with a Percentage dividend of 8.46 yield .

Example 736:
titleEntities: {'Subject': ['Banco Santander'], 'Date': ['2019']}
title: Banco Santander global operations : geographic diversification of profit 2019
X_Axis['Region']: ['North_America', 'South_America', 'Europe']
Y_Axis['Share', 'of', 'attributable', 'profit']: ['16', '37', '47']

gold: This statistic shows the geographic diversification of Banco Santander operations as of the end of 2019 . Regions presented are ranked by their contribution to the total profit contribution of the Santander group . Europe attributed to almost half of all underlying attributable profit for Banco Santander .
gold_template: This statistic shows the templateTitle[4] templateTitle[5] of templateTitleSubject[0] operations as of the end of templateTitleDate[0] . Regions presented are ranked by their contribution to the total templateYLabel[2] contribution of the templateTitleSubject[0] group . templateXValue[last] attributed to almost half of all underlying templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] .

generated_template: This statistic shows the total templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In that year , the templateTitle[0] templateTitle[1] of templateXValue[last] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the total Banco of the Santander global operations geographic in 2019 , Region .  In that year , the Banco Santander of Europe attributable of Banco Santander amounted to approximately 47 profit .

Example 737:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2024']}
title: Total population of Malaysia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['34.92', '34.5', '34.07', '33.65', '33.22', '32.8', '32.39', '32.02', '31.63', '31.19', '30.71']

gold: The population in Malaysia grew to 32.4 million people in 2018 . This is in line with a steady positive trend that has been happening since at least 2014 and is forecast to continue until at least 2024 , as well as with the growth rates in other ASEAN countries . Malaysian demographics As the fertility rate slowly declines , the population growth rate should slowly decline as well .
gold_template: The templateTitle[1] in templateTitleSubject[0] templatePositiveTrend to templateYValue[6] templateScale people in templateXValue[6] . This is in line with a steady positive trend that has been happening since at least templateXValue[min] and is forecast to continue until at least templateXValue[max] , as well as with the growth rates in other ASEAN countries . Malaysian demographics As the fertility rate slowly declines , the templateTitle[1] growth rate should slowly decline as well .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was around templateYValue[6] templateScale templateYLabel[0] .
generated: This statistic shows the Total population of Malaysia from 2014 to 2018 , with projections up until 2024 .  In 2018 , the Total population of Malaysia was around 32.39 millions Inhabitants .

Example 738:
titleEntities: {'Subject': ['Cars'], 'Date': []}
title: Cars of the future - expectations
Data:
                              0                  1                               2                                             3                             4                                         5                                         6
Race             Maximal_safety  Economic_mobility  Highest_comfortwhen_travelling  Sustainability_andenvironmental_friendliness  That_it_is_mainly_functional  Driving_enjoyment_-Driving_should_be_fun  Reflecting_my_socialstatus/my_self-image
I mostly agree             17.3               25.1                            34.1                                          27.6                          37.1                                        35                                      21.2
I totally agree            75.3               63.7                            51.8                                          58.1                          41.6                                        40                                      13.7 

gold: The statistic shows people 's expectations of cars in the future . 75.3 percent of respondents totally agree that cars of the future should be as save as possible .
gold_template: The statistic shows people 's templateTitle[2] of templateTitleSubject[0] in the templateTitle[1] . templateValue[2][0] templateScale of respondents templateLabel[2][1] templateLabel[1][2] templateValue[0][4] templateTitleSubject[0] of the templateTitle[1] templateValue[0][5] be as save as possible .

generated_template: This statistic shows the results of a survey among the adult templateValue[0][0] in the country on templateTitleDate[0] . The survey shows that templateValue[1][0] templateScale of the respondents with a templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] templateLabel[1][2] .
generated: This statistic shows the results of a survey among the adult Maximal_safety in the country on .  The survey shows that 17.3 % of the respondents with a I mostly agree .

Example 739:
titleEntities: {'Subject': ['Americans'], 'Date': ['2003', '2015']}
title: Share of Americans with at least one tattoo - by age 2003 to 2015
Data:
                0         1         2         3         4             5
Country  18_to_24  25_to_29  30_to_39  40_to_49  50_to_64  65_and_older
2003           13        36        28        14        10             7
2008            9        32        25        12         8             9
2012           22        30        38        27        11             5
2015           35        42        55        33        13            11 

gold: This statistic shows the share of Americans with at least one tattoo in selected years from 2003 through 2015 , by age group . In 2012 , 22 percent of respondents aged 18 to 24 years had one or more tattoos on their body .
gold_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] templateTitle[2] at templateTitle[3] one templateTitle[5] in selected years from templateTitleDate[min] through templateTitleDate[max] , templateTitle[6] templateTitle[7] group . In templateLabel[3][0] , templateValue[3][0] templateScale of respondents aged templateValue[0][0] to templateValue[0][0] years had one or more tattoos on their body .

generated_template: This statistic provides information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[min] and templateTitleDate[max] . In templateTitleDate[max] , templateValue[0][0] was the world 's templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] with with templateValue[1][max] templateScale of respondents stated that year .
generated: This statistic provides information on the Americans least one tattoo by in 2003 and 2015 .  In 2015 , 18_to_24 was the world 's Americans least one with 36 % of respondents stated that year .

Example 740:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Leading baby wipes vendors in the U.S. 2016 , based on sales
X_Axis['Company']: ['Private_label', 'Kimberly_Clark_Corp.', 'Procter_&_Gamble', 'Seventh_Generation', 'The_Honest_Co.', 'Johnson_&_Johnson', 'Nice-Pak_Products', 'Paper_Partners', 'Kas_Direct', 'Irish_Breeze']
Y_Axis['Million', 'U.S.', 'dollars']: ['494.4', '416.2', '276.6', '9.6', '8.6', '7.1', '6.3', '6.1', '5.7', '4.4']

gold: The statistic shows the leading baby wipes vendors in the United States in 2016 , based on sales . In that year , Kimberly Clark was the second largest U.S. baby wipes vendor with sales of 416.2 million U.S. dollars . Total sales of U.S. baby wipes vendors amounted to about 1.25 billion U.S. dollars in 2016 .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] , templateTitle[6] on templateTitle[7] . In that year , templateXValue[1] was the second largest templateYLabel[1] templateTitle[1] templateTitle[2] vendor with templateTitle[7] of templateYValue[1] templateScale templateYLabel[1] templateYLabel[2] . Total templateTitle[7] of templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] amounted to about 1.25 templateScale templateYLabel[1] templateYLabel[2] in templateTitleDate[0] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateYLabel[0] . As of the measured period , templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] the templateTitle[2] templateXValue[0] , accounting for over templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] . The second place of templateXValue[0] was templateXValue[last] , with templateYValue[min] templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic gives information on the Leading baby wipes of vendors U.S. Million .  As of the measured period , Private_label was the Leading baby wipes the Private_label , accounting for over 494.4 million U.S. dollars .  The second place of Private_label was Irish_Breeze , with 4.4 million U.S. dollars .

Example 741:
titleEntities: {'Subject': ['BTS', 'South Korea'], 'Date': ['2013']}
title: BTS album total sales in South Korea 2013 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013']
Y_Axis['Number', 'of', 'sold', 'albums', 'in', 'thousand', 'copies']: ['2723', '1459', '563', '218', '58']

gold: The South Korean K-pop boyband BTS , also known as the Bangtan Boys sold around 2.7 million copies of their albums up until 2017 . The boyband is known as the biggest boyband in South Korea and has fans all over the globe . The BTS phenomenon BTS , also known as ‘ Bangtan Boys ' ( or Bangtan Sonyeondan in Korean ) , formed in 2010 and had their debut in 2013 .
gold_template: The templateTitleSubject[1] Korean K-pop boyband templateTitleSubject[0] , also known as the Bangtan Boys templateYLabel[1] around templateYValue[max] templateScale templateYLabel[4] of their templateYLabel[2] up until templateXValue[idxmax(Y)] . The boyband is known as the biggest boyband in templateTitleSubject[1] and has fans all over the globe . The templateTitleSubject[0] phenomenon templateTitleSubject[0] , also known as ‘ Bangtan Boys ' ( or Bangtan Sonyeondan in Korean ) , formed in 2010 and had their debut in templateXValue[min] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[3] of the templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of the templateYLabel[1] of the templateTitleSubject[0] in templateTitleSubject[0] amounted to approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: This statistic shows the Number of thousand the sold of BTS from 2013 to 2017 .  The Number of the sold of the BTS in amounted to approximately 2723 albums thousand in 2017 .

Example 742:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Leading 20 U.S. brands 2018 , by brand value
X_Axis['Brand']: ['Amazon', 'Apple', 'Google', 'Microsoft', 'AT&T', 'Facebook', 'Verizon', 'Walmart', 'The_Home_Depot', 'Walt_Disney', 'Wells_Fargo', 'Starbucks', 'YouTube', 'Bank_of_America', 'Citi', 'Chase', 'Coca-Cola', 'Marlboro', 'IBM', 'Nike']
Y_Axis['Brand', 'value', 'in', 'million', 'U.S.', 'dollars']: ['187905', '153634', '142755', '119595', '87005', '83202', '71154', '67867', '47056', '45750', '39948', '39268', '37847', '36687', '36407', '36265', '36188', '33569', '32854', '32421']

gold: Amazon.com is an international e-commerce company offering online retail , computing services , consumer electronics and digital content , as well as other local services such as daily deals and groceries . As of 2018 , Amazon was ranked as the most valuable U.S. brand , with a brand value of around 188 billion U.S. dollars . Apple , Google , and Microsoft were the next most valuable brands in the United States that year .
gold_template: Amazon.com is an international e-commerce company offering online retail , computing services , consumer electronics and digital content , as well as other local services such as daily deals and groceries . As of templateTitleDate[0] , templateXValue[0] was ranked as the most valuable templateYLabel[3] templateXLabel[0] , with a templateXLabel[0] templateYLabel[1] of around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . templateXValue[1] , templateXValue[2] , and templateXValue[3] were the next most valuable templateTitle[3] in the templateTitle[2] that year .

generated_template: This statistic shows a ranking of the templateTitle[0] templateTitle[1] templateXValue[0] templateTitle[3] templateTitle[4] in templateTitleDate[0] , based on templateYLabel[0] . According to the source , templateXValue[0] was the leading templateXValue[0] with a templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] , followed templateTitle[4] templateXValue[1] with templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows a ranking of the Leading 20 Amazon brands 2018 in , based on Brand .  According to the source , Amazon was the leading Amazon with a value of 187905 million U.S. , followed 2018 Apple with 153634 million U.S. dollars .

Example 743:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2020', '2020']}
title: Most popular product brands on Facebook 2020
X_Axis['Platform']: ['Facebook', 'Samsung', 'Coca-Cola', 'YouTube', "McDonald's", 'Candy_Crush_Saga', 'Criminal_Case', 'Texas_HoldEm_Poker', 'Instagram', 'KFC']
Y_Axis['Facebook', 'fans', 'in', 'millions']: ['214.62', '160.05', '107.11', '84.35', '79.86', '71.54', '65.88', '63.66', '60.34', '55.0']

gold: Coca-Cola is one of the most recognizable brands worldwide , which in this case translates to over 107 million Facebook followers . Due its global reach , advertisers and marketers have recognized Facebook 's potential as a marketing tool to reach new and existing customers and as a result have come up with new and innovative ways of connecting with their target audiences .
gold_template: templateXValue[2] is one of the templateTitle[0] recognizable templateTitle[3] worldwide , which in this templateXValue[6] translates to over templateYValue[2] templateScale templateXValue[0] followers . Due its global reach , advertisers and marketers have recognized templateXValue[0] 's potential as a marketing tool to reach new and existing customers and as a result have come up with new and innovative ways of connecting with their target audiences .

generated_template: This statistic shows the most popular templateXValue[5] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , the photo sharing app templateXValue[0] had the largest templateYLabel[0] of almost templateYValue[max] templateScale templateYLabel[1] , followed by templateXValue[1] with templateYValue[1] templateScale templateYLabel[1] .
generated: This statistic shows the most popular Candy_Crush_Saga brands Facebook in 2020 .  In that year , the photo sharing app Facebook had the largest Facebook of almost 214.62 millions fans , followed by Samsung with 160.05 millions fans .

Example 744:
titleEntities: {'Subject': ['U.S. August'], 'Date': ['2014', '2014']}
title: Reasons for eating fast food in the U.S. as of August 2014
X_Axis['Response']: ['Convenient', 'Cheap', 'I_just_like_it', 'Lack_of_other_options', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['67.2', '31.7', '30.6', '13.2', '5.6']

gold: This statistic shows the reasons for eating fast food in the United States as of August 2014 . During the survey , 13.2 percent of respondents said that they ate fast food because they lacked other options .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] as of templateTitleSubject[0] templateTitleDate[0] . During the survey , templateYValue[3] templateScale of templateYLabel[1] said that they ate templateTitle[3] templateTitle[4] because they lacked templateXValue[3] .

generated_template: This statistic presents the distribution of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] as of 2019 . During the survey , it was found that templateYValue[max] templateScale of the templateYLabel[1] stated that they used templateXValue[0] on a daily basis .
generated: This statistic presents the distribution of for eating fast food in the U.S. as of 2019 .  During the survey , it was found that 67.2 % of the respondents stated that they used Convenient on a daily basis .

Example 745:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Number of professionals at the leading accounting firms in the U.S. 2019
X_Axis['Accounting', 'firm']: ['Deloitte', 'PwC', 'Ernst_&_Young', 'KPMG', 'RSM_US', 'Grant_Thornton', 'BDO_USA', 'CliftonLarsonAllen', 'Crowe_Horwath', 'CBIZ_/_Mayer_Hoffman_McCann', 'Moss_Adams', 'CohnReznick', 'BKD', 'Baker_Tilly_Virchow_Krause', 'Marcum', 'Plante_Moran', 'Dixon_Hughes_Goodman', 'EisnerAmper', 'Wipfli', 'Carr_Riggs_&_Ingram', 'Eide_Bailly', 'Citrin_Cooperman_&_Co.', 'Armanino', 'Withum', 'Mazars_USA']
Y_Axis['Number', 'of', 'professionals']: ['73855', '35350', '33600', '26447', '7252', '6616', '4958', '4056', '3402', '2470', '2066', '1908', '1824', '2095', '1219', '1796', '1385', '979', '1229', '1346', '1386', '675', '900', '720', '601']

gold: This statistic shows the number of professionals at the leading accounting firms in the United States as of March 2019 . The largest accounting firm in the U.S. , Deloitte , employed 73,855 professionals at the end of their fiscal year in June 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] at the templateTitle[2] templateXLabel[0] templateTitle[4] in the templateTitle[5] as of 2019 . The largest templateXLabel[0] templateXLabel[1] in the templateTitleSubject[0] , templateXValue[0] , employed templateYValue[max] templateYLabel[1] at the end of their fiscal year in 2018 .

generated_template: This statistic shows the results of a survey among the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] in templateTitleDate[0] . The templateTitle[0] of the templateXValue[2] templateXValue[13] , with over templateYValue[2] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the results of a survey among the Number professionals leading in the U.S. in 2019 .  The Number of the Ernst_&_Young Baker_Tilly_Virchow_Krause , with over 33600 thousand professionals .

Example 746:
titleEntities: {'Subject': ['New York Knicks', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the New York Knicks ( NBA ) 2003 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['4600', '4000', '3600', '3300', '3000', '2500', '1400', '1100', '780', '655', '586', '613', '608', '592', '543', '494', '401', '398']

gold: This graph depicts the franchise value of the New York Knicks of the National Basketball Association from 2003 to 2020 . In 2020 , the New York Knicks franchise had an estimated value of 4.6 billion U.S. dollars . New York Knicks - additional information The basketball team New York Knicks is part of the Eastern Conference of the National Basketball Association ( NBA ) .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Knicks of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] Knicks templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] Knicks - additional information The basketball team templateTitleSubject[0] Knicks is part of the Eastern Conference of the National Basketball Association ( templateTitleSubject[1] ) .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] franchise had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Franchise value of the New York Knicks of the National Basketball Association from 2003 to 2020 .  In 2020 , the New York Knicks franchise had an estimated value of 4600 million U.S. dollars .

Example 747:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2014']}
title: Average price of wearable technology products in the United Kingdom ( UK ) 2014
X_Axis['Wearable', 'Technology', 'Products']: ['Action_cameras_headsets_and_glasses', 'Smart_watches', 'Total_market', 'Wrist_sport_computers', 'Health_and_fitness_trackers']
Y_Axis['Average', 'price', 'in', 'GBP']: ['220', '163', '120', '82', '73']

gold: This statistic shows the average price of wearable technology products in the United Kingdom ( UK ) during the period from January to September 2014 , by product . Smart watches cost an average 163 British pounds in the UK , while wrist sport computers cost an average 82 British pounds .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateXLabel[0] templateXLabel[1] templateXLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) during the period from to 2014 , by product . templateXValue[1] cost an templateYLabel[0] templateYValue[1] British pounds in the templateTitleSubject[1] , while templateXValue[3] computers cost an templateYLabel[0] templateYValue[3] British pounds .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[7] templateXLabel[1] . The templateXValue[0] of templateXValue[0] amounted to over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateTitleDate[0] .
generated: This statistic shows the Average price wearable technology of the United Kingdom ( United Kingdom ) in 2014 , UK Technology .  The Action_cameras_headsets_and_glasses of amounted to over 220 million GBP in 2014 .

Example 748:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010']}
title: Amount spent on smartphone accessories by U.S. consumers 2010
Data:
                          0              1                  2
Response     Android_owners  iPhone_owners  BlackBerry_owners
Under $50                60             33                 47
$50 - $100               19             33                 31
$100 - $200               8             19                 15
$200 and up               6              9                  1 

gold: This statistic shows the results of a survey conducted in 2010 in the United States asking smartphone users how much they have spent on accessories for their cell phone . The survey revealed that 60 percent of Android owners spent under 50 U.S. dollars on accessories for their mobile phone .
gold_template: This statistic shows the results of a survey conducted in templateTitleDate[0] in the templateTitle[5] asking templateTitle[2] users how much they have templateTitle[1] on templateTitle[3] for their cell phone . The survey revealed that templateValue[1][0] templateScale of templateValue[0][0] spent templateLabel[1][0] 50 templateTitleSubject[0] dollars on templateTitle[3] for their mobile phone .

generated_template: This statistic shows the results of a survey among the leading templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] . The survey shows that templateValue[1][0] templateScale of the respondents of the people in templateTitleDate[0] .
generated: This statistic shows the results of a survey among the leading spent smartphone accessories in the United Kingdom ( U.S. ) in 2010 .  The survey shows that 60 % of the respondents of the people in 2010 .

Example 749:
titleEntities: {'Subject': ['USD'], 'Date': ['2017', '2019']}
title: Monthly exchange rate of USD to GBP 2017 to 2019
X_Axis['Month']: ['Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17']
Y_Axis['USD', 'GBP', 'exchange', 'rate']: ['0.82', '0.82', '0.79', '0.79', '0.77', '0.77', '0.75', '0.76', '0.79', '0.78', '0.79', '0.76', '0.78', '0.76', '0.76', '0.75', '0.73', '0.71', '0.73', '0.71', '0.74', '0.74', '0.76', '0.74', '0.77']

gold: The statistic presents the monthly exchange rate of U.S. dollar to British Pound from August 2017 to August 2019 . The USD GBP exchange rate amounted to approximately 0.82 on August 30 , 2019 , which means that one U.S. dollar could buy around 0.82 British Pound on that day . USD GBP exchange rate – additional information Over the past two years , the monthly exchange rate of the U.S. dollar to the British Pound has experienced pronounced fluctuations .
gold_template: The statistic presents the templateTitle[0] templateYLabel[2] templateYLabel[3] of U.S. dollar to British Pound from 2017 to 2019 . The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] amounted to approximately templateYValue[max] on 30 , templateTitleDate[max] , which means that templateYValue[max] U.S. dollar could buy around templateYValue[max] British Pound on that day . templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] – additional information Over the past two years , the templateTitle[0] templateYLabel[2] templateYLabel[3] of the U.S. dollar to the British Pound has experienced pronounced fluctuations .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] of the templateTitle[1] templateTitleSubject[0] templateTitle[4] templateTitleSubject[0] from templateTitleDate[min] to 2019 , in templateScale templateYLabel[3] . In 2019 , the templateTitle[2] templateTitle[3] templateTitleSubject[0] was templateYValue[max] thousand templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the average USD GBP of the exchange USD GBP from 2017 to 2019 , in percentage rate .  In 2019 , the rate USD was 0.82 thousand exchange rate .

Example 750:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Music genres preferred by consumers in the U.S. 2018
X_Axis['Response']: ['Rock', 'Pop', 'Country', 'R&B_and_Soul', 'Hip_Hop', 'Easy_Listening', 'Electronic/dance', 'Jazz', 'Blues', 'Classical_and_Opera', 'Heavy_metal', 'Reggae', 'Folk', 'Latin', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['56.8', '56.1', '49.9', '38.9', '37.4', '32.7', '20.6', '20.2', '20', '17.4', '16.9', '16.1', '14.1', '11', '9.3']

gold: According to a study carried out by Deezer in May 2018 , the most popular genre among Americans was rock music , with 56.8 percent of respondents stating that they were currently listening to music within this genre as of the date of survey . Pop and country music were the second and third most popular genres respectively , and 20.2 percent of respondents said they preferred jazz . The appeal of rock and pop music The broad appeal of rock and pop music can in part be attributed to how both genres often blend seamlessly into one another and influence other music styles .
gold_template: According to a study carried out templateTitle[3] Deezer in 2018 , the most popular genre among Americans was templateXValue[0] templateTitle[0] , with templateYValue[max] templateScale of templateYLabel[1] stating that they were currently templateXValue[5] to templateTitle[0] within this genre as of the date of survey . templateXValue[1] and templateXValue[2] templateTitle[0] were the second and third most popular templateTitle[1] respectively , and templateYValue[7] templateScale of templateYLabel[1] said they templateTitle[2] templateXValue[7] . The appeal of templateXValue[0] and templateXValue[1] templateTitle[0] The broad appeal of templateXValue[0] and templateXValue[1] templateTitle[0] can in part be attributed to how both templateTitle[1] often blend seamlessly into one another and influence templateXValue[last] templateTitle[0] styles .

generated_template: This statistic shows the results of a survey about the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] in templateTitleDate[0] . During the survey , it was found that templateYValue[1] templateScale of the templateYLabel[1] most popular templateXValue[5] would be templateXValue[5] templateXValue[0] .
generated: This statistic shows the results of a survey about the Music genres preferred by in the U.S. in 2018 .  During the survey , it was found that 56.1 % of the respondents most popular Easy_Listening would be Easy_Listening Rock .

Example 751:
titleEntities: {'Subject': ['NFL'], 'Date': ['2020', '2020']}
title: Number of conference championship game appearances of NFL teams 2020
X_Axis['American', 'Football', 'Teams']: ['Pittsburgh_Steelers', 'San_Francisco_49ers', 'New_England_Patriots', 'Dallas_Cowboys', 'L.A._/_Oakland_Raiders', 'Denver_Broncos', 'L.A._/_St._Louis_Rams', 'Minnesota_Vikings', 'Green_Bay_Packers', 'Miami_Dolphins', 'Baltimore_/_Indianapolis_Colts', 'Philadelphia_Eagles', 'Washington_Redskins', 'New_York_Giants', 'Chicago_Bears', 'Buffalo_Bills']
Y_Axis['Number', 'of', 'appearances']: ['16', '16', '15', '14', '11', '10', '10', '9', '8', '7', '7', '7', '6', '5', '5', '5']

gold: The Pittsburgh Steelers have played in the joint most conference championship games since the merger of the American Football League ( AFL ) and the National Football League ( NFL ) in 1970 . The Steelers have progressed to win the Super Bowl on six occasions , level with the New England Patriots for the most wins of all NFL teams . The NFL 's conference call The merger between the AFL and NFL before the 1970 season saw the formation of two new conferences : the American Football Conference ( AFC ) and the National Football Conference ( NFC ) .
gold_template: The templateXValue[0] have played in the joint most templateTitle[1] templateTitle[2] games since the merger of the templateXLabel[0] templateXLabel[1] League ( AFL ) and the National templateXLabel[1] League ( templateTitleSubject[0] ) in 1970 . The templateXValue[0] have progressed to win the Super Bowl on templateYValue[12] occasions , level with the templateXValue[2] Patriots for the most wins of all templateTitleSubject[0] templateXLabel[2] . The templateTitleSubject[0] 's templateTitle[1] call The merger between the AFL and templateTitleSubject[0] before the 1970 season saw the formation of two templateXValue[2] conferences : the templateXLabel[0] templateXLabel[1] templateTitle[1] ( AFC ) and the National templateXLabel[1] templateTitle[1] ( NFC ) .

generated_template: The statistic shows the results of a survey among the leading templateTitle[7] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[min] to templateTitleDate[max] . The templateXValue[0] was the most common templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] with templateYValue[max] templateScale of templateYLabel[1] , followed by templateXValue[1] at templateYValue[1] templateScale .
generated: The statistic shows the results of a survey among the leading 2020 in the United Kingdom ( NFL ) from 2020 to .  The Pittsburgh_Steelers was the most common Number conference championship in the NFL with 16 % of appearances , followed by San_Francisco_49ers at 16 % .

Example 752:
titleEntities: {'Subject': ['Volkswagen'], 'Date': ['2013', '2018']}
title: Annual closing prices - Volkswagen shares 2013 to 2018
Data:
                      0       1       2       3       4       5
Year               2018    2017    2016    2015    2014    2013
Common stock      139.1   168.7  136.75   142.3   180.1   196.9
Preferred stock  138.92  166.45  133.35  133.75  184.65  204.15 

gold: In 2018 , Volkswagen shares closed at almost 140 euros : Volkswagen 's preference shares were valued at just under 139 euros , while ordinary shares closed at a little over 139 euros . In 2018 , Volkswagen 's shares were worth significantly less than five years earlier . Much of this drop in value is attributed to the company 's involvement in the 2015 diesel emissions scandal .
gold_template: In templateValue[0][0] , templateTitleSubject[0] templateTitle[4] closed at almost 140 euros : templateTitleSubject[0] 's preference templateTitle[4] were valued at just under templateValue[1][0] euros , while ordinary templateTitle[4] closed at a little over templateValue[1][0] euros . In templateValue[0][0] , templateTitleSubject[0] 's templateTitle[4] were worth significantly less than five years earlier . Much of this drop in value is attributed to the company 's involvement in the templateValue[0][3] diesel emissions scandal .

generated_template: This statistic shows the templateTitle[0] of the employees of templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , broken down templateTitle[8] templateTitle[9] . In templateValue[0][0] , the templateTitle[0] of templateTitleSubject[0] employees amounted to templateValue[2][0] templateScale templateYLabel[3] .
generated: This statistic shows the Annual of the employees of Volkswagen from 2013 to 2018 , broken down 2018 .  In 2018 , the Annual of Volkswagen employees amounted to 138.92 million .

Example 753:
titleEntities: {'Subject': ['Chicago White Sox'], 'Date': ['2001', '2018']}
title: Chicago White Sox revenue 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['272', '266', '269', '240', '227', '210', '216', '214', '210', '194', '196', '193', '173', '157', '131', '124', '106', '101']

gold: The statistic depicts the revenue of the Chicago White Sox from 2001 to 2018 . In 2018 , the revenue of the Major League Baseball franchise amounted to 272 million U.S. dollars.The Chicago White Sox are owned by Jerry Reinsdorf , who bought the franchise for 20 million U.S. dollars in 1981 .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Sox from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] Sox are owned by Jerry Reinsdorf , who bought the franchise for 20 templateScale templateYLabel[2] templateYLabel[3] in 1981 .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] are owned by John Stanton and Chris Larson , who bought the franchise for 1.2 templateScale templateYLabel[2] templateYLabel[3] and 100 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[2] and 1992 respectively .
generated: The statistic depicts the Revenue of the Chicago White Sox from 2001 to 2018 .  In 2018 , the Revenue of the Major League Baseball franchise amounted to 272 million U.S. dollars.The Chicago White Sox are owned by John Stanton and Chris Larson , who bought the franchise for 1.2 million U.S. dollars and 100 million U.S. dollars in 2016 and 1992 respectively .

Example 754:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1945', '2018']}
title: Number of choking-deaths in the U.S. 1945 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950', '1945']
Y_Axis['Number', 'of', 'deaths']: ['5084', '5216', '4829', '5051', '4816', '4864', '4634', '4708', '4570', '4370', '4366', '4344', '4332', '4386', '4470', '4272', '4128', '4185', '4313', '3185', '3303', '3551', '3249', '3106', '2753', '1836', '2397', '1608', '1350', '897']

gold: In 2018 , there were around 5,084 choking deaths in the United States . Death from choking is more common among the elderly with food most often responsible for such incidents . The use of abdominal thrusts , of the Heimlich Maneuver , is suggested to dislodge objects and prevent suffocation .
gold_template: In templateXValue[max] , there were around templateYValue[idxmax(X)] choking templateYLabel[1] in the templateTitle[2] . Death from choking is more common among the elderly with food most often responsible for such incidents . The use of abdominal thrusts , of the Heimlich Maneuver , is suggested to dislodge objects and prevent suffocation .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[1] templateTitle[2] in the templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[idxmax(X)] templateTitle[1] templateTitle[2] in the templateTitle[0] .
generated: The statistic shows the Number of deaths choking-deaths U.S. in the Number between 1945 and 2018 .  In 2018 , there were a total of 5084 choking-deaths U.S. in the Number .

Example 755:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2015', '2016']}
title: Money spent while attending music festivals in the United Kingdom 2015 to 2016
Data:
                                0     1
Year                         2016  2015
Less than 10 British pounds   1.9   2.5
10-50 British pounds         18.6  18.2
50-100 British pounds        26.2  25.2
100-150 British pounds       22.4  20.9
150-200 British pounds       16.1  18.1
250-300 British pounds        8.3   8.7
300-350 British pounds          3   2.9
Over 350 British pounds       3.5   3.5 

gold: This statistic displays the amount of money spent in while attending festivals in the United Kingdom from 2015 to 2016 . It shows that in 2016 , roughly 26 percent of respondents spent between 50 and 100 British pounds at the festival .
gold_template: This statistic displays the amount of templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] templateTitle[5] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . It shows that in templateValue[0][0] , roughly templateValue[3][max] templateScale of respondents templateTitle[1] between 50 and 100 templateLabel[1][3] templateLabel[1][4] at the festival .

generated_template: This statistic shows the templateTitle[0] of the templateTitleSubject[0] of templateTitleSubject[0] templateTitle[4] from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . The templateTitle[0] of the templateTitleSubject[0] employees in templateValue[0][0] was expected to reach approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] in templateXValue[idxmax(Y)] .
generated: This statistic shows the Money of the United Kingdom of music from 2015 to 2016 , Kingdom 2015 .  The Money of the United Kingdom employees in 2016 was expected to reach approximately 2015 million in 2015 .

Example 756:
titleEntities: {'Subject': ['Best'], 'Date': ['2018']}
title: Best ad campaigns worldwide 2018
X_Axis['Campaign/Brand', '(Agency)']: ["Let's_make_viral_products_instead!_/Carvaan_(the_Womb_Mumbai)", 'Healthy_Hands_Chalk_Sticks_/_Savlon_(Ogilvy_Mumbai)', "India's_Newest_Status_Symbol_/_Harpic_(McCann_New_Delhi)", "How_L'Oréal_Paris_True_Match_climbed_to_No.1_by_making_everyone_feel_‘Worth_It’_/_True_Match_((McCann_London_Paris_Melbourne)", 'The_Child_Replacement_Programme_/_Pedigree_(Colenso_BBDO_Auckland_/_Starcom_Auckland)', 'Made_Possible_by_Melbourne_/_University_of_Melbourne_(McCann_Melbourne)', 'The_Impact_of_Fearless_Girl_/_State_Street_Global_Advisors_(McCann_New_York)', 'Beauty_and_brains:_How_we_supercharged_the_Audi_premium_2015-2018_/_Audi_(BBH_London_/_Salmon_London_/_MediaCom_London_/_PHD_London)', 'The_Hearing_Test_In_Disguise/_Cochlear_(CHE_Proximity_Sydney)', 'Original_is_Never_Finished_/_adidas_Originals_(Johannes_Leonardo_New_York)']
Y_Axis['Number', 'of', 'points']: ['75.2', '72.8', '63.3', '58.9', '56.6', '53.1', '50.1', '49.3', '45.9', '44.0']

gold: In an annual assessment of advertising campaigns , the top three spots were taken by promotional activities in India . The Womb Mumbai agency was responsible for creating and executing the most effective campaign for the product Carvaan – a digital music player designed to specifically reach older generations . The campaign was titled ‘ Let 's make viral products instead ! ' and by delivering meaningful and relevant messages was successful in increasing sales and net profit for the manufacturer .
gold_template: In an annual assessment of advertising templateTitle[2] , the top three spots were taken templateXValue[3] promotional activities in India . The templateXValue[0] Mumbai agency was responsible for creating and executing the most effective campaign for the product Carvaan – a digital music player designed to specifically reach older generations . The campaign was titled ‘ Let 's templateXValue[0] products instead ! ' and templateXValue[3] delivering meaningful and relevant messages was successful in templatePositiveTrend sales and net profit for the manufacturer .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] employees in the United Kingdom ( templateTitleSubject[0] ) as of templateTitleDate[0] . The source defines templateXValue[0] , with a templateXValue[0] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Best ad campaigns employees in the United Kingdom ( Best ) as of 2018 .  The source defines Let's_make_viral_products_instead!_/Carvaan_(the_Womb_Mumbai) , with a Let's_make_viral_products_instead!_/Carvaan_(the_Womb_Mumbai) of 75.2 percentage points .

Example 757:
titleEntities: {'Subject': ['Canada'], 'Date': ['2024']}
title: Unemployment rate in Canada 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Unemployment', 'rate']: ['6.5', '6.39', '6.25', '6.12', '6', '5.79', '5.83', '6.33', '6.99', '6.9', '6.93']

gold: The statistic shows the unemployment rate in Canada from 2014 to 2018 , with projections up until 2024 . In 2018 , the unemployment rate in Canada was at around 5.83 percent . Canada 's economy Three-quarter of Canada 's workforce is employed in the services sector , with the other two sectors , agriculture and industry , accounting for the rest of Canada 's employment .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at around templateYValue[6] templateScale . templateTitleSubject[0] 's economy Three-quarter of templateTitleSubject[0] 's workforce is employed in the services sector , with the other two sectors , agriculture and industry , accounting for the rest of templateTitleSubject[0] 's employment .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[6] templateScale .
generated: This statistic shows the Unemployment rate in Canada from 2014 to 2018 , with projections up until 2024 .  In 2018 , the Unemployment rate in Canada was at approximately 5.83 % .

Example 758:
titleEntities: {'Subject': ['US'], 'Date': ['2024']}
title: National debt in the US in relation to gross domestic product ( GDP ) 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'relation', 'to', 'GDP']: ['115.77', '114.19', '112.25', '109.99', '108.02', '106.22', '104.26', '105.99', '106.82', '104.65', '104.41']

gold: The statistic shows the national debt of the United States from 2014 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . In 2018 , the national debt of the United States was at around 104.26 percent of the gross domestic product . See the US GDP for further information .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the country from templateXValue[min] to templateXValue[6] in templateYLabel[2] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of the country was at around templateYValue[min] templateScale of the templateTitle[4] templateTitle[5] templateTitle[6] . See the templateTitleSubject[0] templateYLabel[3] for further information .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[1] of the templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale of the templateYLabel[3] .
generated: The statistic shows the National debt of the US from 2014 to 2018 , with projections up until 2024 .  In 2018 , the debt of the US amounted to approximately 104.26 % of the GDP .

Example 759:
titleEntities: {'Subject': ['Germany'], 'Date': ['1997', '2019']}
title: Daily time spent watching television in Germany 1997 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997']
Y_Axis['Daily', 'viewing', 'time', 'in', 'minutes']: ['211', '217', '221', '223', '223', '221', '221', '222', '225', '223', '212', '207', '208', '212', '211', '210', '203', '201', '192', '190', '185', '188', '183']

gold: This statistic shows data on the development of the daily television consumption in Germany from 1997 to 2019 . In 2019 , the average German television spent 211 minutes watching TV every day . Compared to 1997 , this was an increase of 28 minutes .
gold_template: This statistic shows data on the development of the templateYLabel[0] templateTitle[4] consumption in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average German templateTitle[4] templateTitle[2] templateYValue[idxmax(X)] templateYLabel[3] templateTitle[3] TV every day . Compared to templateXValue[min] , this was an templatePositiveTrend of 28 templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Daily viewing of the spent in Germany from 1997 to 2019 .  In 2019 , the average Daily viewing of Germany was 211 time minutes .

Example 760:
titleEntities: {'Subject': ['Dow Jones Composite Index'], 'Date': ['2019']}
title: Annual performance of the Dow Jones Composite Index 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Index', 'value', 'in', 'points']: ['9386.48', '7710.44', '8310.35', '6902.45', '5978.34', '6473.6', '5641.55', '4442.07', '4232.17', '4033.19', '3566.68', '3086.07', '4394.05', '4120.96', '3638.06', '3395.82', '3000.75', '2374.96', '2892.23', '3317.61']

gold: The Dow Jones Composite Index finished the year 2019 at 9,386.48 points . This is the highest point of the index in the past two decades . What is Dow Jones Composite Index ? The Dow Jones Composite Index is one of the indices from the Dow Jones index family .
gold_template: The templateTitleSubject[0] Composite templateYLabel[0] finished the templateXLabel[0] templateXValue[max] at templateYValue[idxmax(X)] templateYLabel[2] . This is the highest point of the templateYLabel[0] in the past two decades . What is templateTitleSubject[0] Composite templateYLabel[0] ? The templateTitleSubject[0] Composite templateYLabel[0] is one of the indices from the templateTitleSubject[0] index family .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , there were a total of templateYValue[2] templateScale templateYLabel[2] in the templateTitle[0] .
generated: The statistic shows the Index of the performance Dow points in the Annual from 2000 to 2019 .  According to the report , there were a total of 8310.35 percentage points in the Annual .

Example 761:
titleEntities: {'Subject': ['United States'], 'Date': ['1996', '2018']}
title: Number of registered voters in the United States 1996 to 2018
X_Axis['Year']: ['2018', '2016', '2014', '2012', '2010', '2008', '2004', '2000', '1996']
Y_Axis['Number', 'of', 'registered', 'voters', 'in', 'millions']: ['153.07', '157.6', '142.17', '153.16', '137.26', '146.31', '142.07', '129.55', '127.66']

gold: In 2018 , there were 153.07 million people registered to vote in the United States . Although this was an increase from 1996 , when 127.66 million people were registered to vote , it 's lower than the peak of 157.6 million registered voters in 2016 . Voting requirements While voting laws differ from state to state , the basic requirements are the same across the entire country .
gold_template: In templateXValue[max] , there were templateYValue[idxmax(X)] templateScale people templateYLabel[1] to vote in the templateTitleSubject[0] . Although this was an templatePositiveTrend from templateXValue[min] , when templateYValue[idxmin(X)] templateScale people were templateYLabel[1] to vote , it 's lower than the peak of templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[idxmax(Y)] . Voting requirements While voting laws differ from state to state , the basic requirements are the same across the entire country .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[1] of the templateYLabel[2] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of a templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Number of the registered of the voters States from 1996 to 2018 .  In 2018 , the average Number registered of a voters United States was 153.07 voters millions .

Example 762:
titleEntities: {'Subject': ['Worldwide'], 'Date': ['2013', '2015']}
title: Worldwide shipments of smart watches 2013 to 2015
X_Axis['Year']: ['2015', '2014', '2013']
Y_Axis['Shipments', 'in', 'millions']: ['24.92', '7.44', '1.23']

gold: The statistic shows the projected worldwide smart watches shipments from 2013 to 2015 . Shipments of smart watches are forecast to total around 25 million units in 2015 .
gold_template: The statistic shows the projected templateTitleSubject[0] templateTitle[2] templateTitle[3] templateYLabel[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] of templateTitle[2] templateTitle[3] are forecast to total around templateYValue[max] templateScale units in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[4] .
generated: This statistic shows the Shipments millions of the watches 2013 of Worldwide from 2013 to 2015 .  In 2015 , the Shipments millions of Worldwide was 24.92 millions .

Example 763:
titleEntities: {'Subject': ['Costa Rica'], 'Date': ['2018']}
title: Distribution of gross domestic product ( GDP ) across economic sectors Costa Rica 2018
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
Agriculture   4.58   5.01   5.13   4.96   5.12   5.04   5.41   5.84   6.59   7.09   7.02
Industry     19.47  18.99  19.14  19.42  20.17  20.76  21.93  22.32  23.24  23.65  23.69
Services     68.35  68.07  67.51  67.39  66.38  65.69  64.14  63.11   61.8  60.84  59.18 

gold: This statistic shows the distribution of the gross domestic product ( GDP ) across economic sectors in Costa Rica from 2008 to 2018 . In 2018 , agriculture contributed around 4.58 percent to the GDP of Costa Rica , 19.47 percent came from the industry and 68.35 percent from the services sector .
gold_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the templateLabel[3][0] sector .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the service sector .
generated: This statistic shows the Distribution of the gross domestic product ( GDP ) across economic sectors in Costa Rica from 2008 to 2018 .  In 2018 , Agriculture contributed around 4.58 % to the GDP of Costa Rica , 19.47 % came from the Industry and 68.35 % from the service sector .

Example 764:
titleEntities: {'Subject': ['DOTA'], 'Date': ['2019']}
title: DOTA 2 The International prize pool 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Prize', 'pool', 'in', 'million', 'U.S.', 'dollars']: ['34.33', '25.53', '24.69', '20.77', '18.43', '10.93', '2.87', '1.6', '1.6']

gold: The timeline presents the Defense of the Ancients 2 The International championships prize pool from 2012 to 2019 . The International 9 , DOTA 's 2019 championship , which took place in Shanghai , had a prize pool of 34.33 million U.S. dollars , up from 25.53 million a year earlier .
gold_template: The timeline presents the Defense of the Ancients templateYValue[min] The templateTitle[2] championships templateYLabel[0] templateYLabel[1] from templateXValue[idxmin(Y)] to templateXValue[max] . The templateTitle[2] 9 , templateTitleSubject[0] 's templateXValue[max] championship , which took place in Shanghai , had a templateYLabel[0] templateYLabel[1] of templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateScale a templateXLabel[0] earlier .

generated_template: The timeline presents the templateTitle[1] templateYLabel[0] templateYLabel[1] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in the previous templateXLabel[0] .
generated: The timeline presents the 2 Prize pool in the DOTA from 2011 to 2019 .  In 2019 , the DOTA 2 Prize pool of the DOTA amounted to 34.33 million U.S. dollars , up from 25.53 million U.S. dollars in the previous Year .

Example 765:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2019']}
title: Mexico : age distribution of internet users 2019
X_Axis['Year']: ['6-11_years', '12-17_years', '18-24_years', '25-34_years', '35-44_years', '45-54_years', 'Over_55_years']
Y_Axis['Share', 'of', 'respondents']: ['12', '14', '18', '22', '14', '12', '8']

gold: This statistic gives information on the age distribution of internet users in Mexico as of April 2019 . During this period of time , it was found that 22 percent of internet users in the Latin American country were aged between 25 and 34 years .
gold_template: This statistic gives information on the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] as of 2019 . During this period of time , it was found that templateYValue[max] templateScale of templateTitle[3] templateTitle[4] in the Latin American country were aged between 25 and 34 templateXValue[0] .

generated_template: This statistic displays the templateYLabel[0] of individuals in the templateTitle[1] templateTitle[2] in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . According to the source , templateYValue[max] templateScale of templateYLabel[1] reported accessing the templateTitle[0] smartphone users in the templateTitle[4] , templateYValue[4] templateScale of templateYLabel[1] reported .
generated: This statistic displays the Share of individuals in the age distribution in the United Kingdom ( Mexico ) from Over_55_years to 45-54_years .  According to the source , 22 % of respondents reported accessing the Mexico smartphone users in the users , 14 % of respondents reported .

Example 766:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2017']}
title: Participants in paintball in the U.S. from 2006 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'participants', 'in', 'millions']: ['3.41', '3.71', '3.39', '3.44', '3.6', '3.5', '3.56', '3.66', '4.55', '4.86', '5.48', '4.55']

gold: This statistic shows the number of participants in paintball in the United States from 2006 to 2017 . In 2017 , the number of participants ( aged six years and older ) in paintball amounted to approximately 3.41 million . Participation in paintball - additional information The number of participants in paintball in the United States increased significantly from 4.55 million in 2006 up to 5.48 million in 2007 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[idxmax(X)] templateScale . Participation in templateTitle[1] - additional information The templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the templateTitle[2] templatePositiveTrend significantly templateTitle[3] templateYValue[last] templateYValue[idxmin(X)] in templateXValue[min] up to templateYValue[max] templateScale in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Number of participants in paintball the U.S. from 2006 to 2017 .  In 2017 , the Number of participants ( aged six years and older ) in paintball amounted to approximately 3.41 millions .

Example 767:
titleEntities: {'Subject': ['Tennessee Titans'], 'Date': ['2019']}
title: Average regular season home attendance of the Tennessee Titans 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Average', 'attendance']: ['64509', '64520', '65651', '64659', '62304', '69143', '69143', '69143', '69143', '69143', '69143', '69143']

gold: This graph depicts the average regular season home attendance of the Tennessee Titans from 2008 to 2019 . In 2019 , the average attendance at home games of the Tennessee Titans was 64,509 .
gold_template: This graph depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitleSubject[0] was templateYValue[0] templateYValue[idxmax(X)]

generated_template: This graph depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitleSubject[0] was templateYValue[0] templateYValue[idxmax(X)]
generated: This graph depicts the Average regular season home attendance of the Tennessee Titans from 2008 to 2019 .  In 2019 , the Average attendance at home games of the Tennessee Titans was 64509 . 

Example 768:
titleEntities: {'Subject': ['LVMH Group'], 'Date': ['2008', '2019']}
title: Total number of employees of the LVMH Group worldwide 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Total', 'number', 'of', 'employees']: ['156000', '136633', '128637', '116786', '125346', '121289', '114635', '106348', '97559', '83542', '77302', '77087']

gold: This timeline shows the total number of employees of the LVMH Group worldwide from 2008 to 2019 . In 2019 , the total number of employees of the LVMH Group worldwide was 156,000 .
gold_template: This timeline shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateYLabel[2] of the templateTitleSubject[0] worldwide was templateYValue[max] templateYValue[idxmax(X)]

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[2] templateYLabel[3] templateTitle[4] of goods and services from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Total of the employees Group of goods and services from 2008 to 2019 .  In 2019 , the Total of the LVMH Group amounted to 156000 employees .

Example 769:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1995', '2019']}
title: Net interest margin of banks in the U.S. 1995 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['Net', 'interest', 'margin']: ['3.35', '3.31', '3.14', '3.03', '2.98', '3.1', '3.2', '3.41', '3.55', '3.76', '3.36', '3.27', '3.35', '3.45', '3.56', '3.62', '3.75', '4.07', '3.85', '3.93', '4.04', '4.04', '4.3', '4.3', '4.23']

gold: Net interest margin is a measure of the difference between the interest income generated by banks or other financial institutions and the amount of interest paid out to their lenders relative to the amount of their ( interest-earning ) assets . It is similar to the gross margin of non-financial companies . In 2019 , the average net interest margin of the U.S. banks amounted to 3.35 percent .
gold_template: templateYLabel[0] templateYLabel[1] templateYLabel[2] is a measure of the difference between the templateYLabel[1] income generated by templateTitle[3] or other financial institutions and the amount of templateYLabel[1] paid out to their lenders relative to the amount of their ( interest-earning ) assets . It is similar to the gross templateYLabel[2] of non-financial companies . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitle[3] amounted to templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Net interest of the margin of U.S. from 1995 to 2019 .  In 2019 , the average Net interest of U.S. was 3.35 margin .

Example 770:
titleEntities: {'Subject': ['Spotify'], 'Date': ['2011', '2018']}
title: Number of Spotify employees 2011 to 2018
X_Axis['Year']: ['2018', '2016', '2015', '2014', '2013', '2011']
Y_Axis['Number', 'of', 'employees']: ['4165', '2162', '1581', '1365', '958', '311']

gold: This statistic gives information on the number of Spotify employees from 2011 to 2018 . In the most recently reported period , the total number of employees at the music streaming service was 4,165 . In 2011 , Spotify employed 311 workers .
gold_template: This statistic gives information on the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In the most recently reported period , the total templateYLabel[0] of templateYLabel[1] at the music streaming service was templateYValue[idxmax(X)] . In templateXValue[min] , templateTitleSubject[0] employed templateYValue[idxmin(X)] workers .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . The company had a total of templateYValue[idxmax(X)] templateYLabel[1] globally .
generated: This statistic shows the Number of employees Spotify worldwide from 2011 to 2018 .  The company had a total of 4165 employees globally .

Example 771:
titleEntities: {'Subject': ['Greece'], 'Date': ['2016']}
title: Population of Greece 2016 , by gender
X_Axis['Gender']: ['Females', 'Males', 'Total']
Y_Axis['Total', 'population']: ['5559538', '5224210', '10783748']

gold: This statistic displays the population of Greece by gender in 2016 . In this year there were a total of 10.78 million inhabitants in Greece . There were almost 5.56 million female inhabitants , whereas there were less than 5.3 million male inhabitants .
gold_template: This statistic displays the templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateXLabel[0] in templateTitleDate[0] . In this year there were a templateXValue[last] of templateYValue[max] templateScale inhabitants in templateTitleSubject[0] . There were almost templateYValue[0] templateScale female inhabitants , whereas there were less than 5.3 templateScale male inhabitants .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[1] who were using templateTitle[5] as of 2019 , sorted templateTitle[7] templateTitle[8] . During that period of time , it was found that templateYValue[last] templateScale of templateXValue[last] were templateXValue[last] templateXValue[0] .
generated: This statistic shows the Total of adults in the Greece who were using gender as of 2019 , sorted gender .  During that period of time , it was found that 10783748 % of Total were Females .

Example 772:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: U.S. teens : most frequently accessed social networks 2019
X_Axis['Platform']: ['Instagram', 'Facebook', 'Snapchat', 'YouTube', 'Twitter', 'WhatsApp', 'TikTok', 'Other', "Don't_know"]
Y_Axis['Share', 'of', 'respondents', 'who', 'use', 'network', 'most', 'often']: ['25', '24', '22', '16', '5', '4', '2', '1', '1']

gold: During an September 2019 survey , it was found that 25 percent of teens in the United States used Instagram the most out of all social networks . This is hardly surprising , as U.S. teens rated Instagram among their favorite social network for the past few years . In spring 2019 , 35 percent of teenagers ranked the photo-sharing app as their most important social network .
gold_template: During an 2019 survey , it was found that templateYValue[max] templateScale of templateTitle[1] in the templateTitle[0] used templateXValue[0] the templateYLabel[5] out of all templateTitle[5] templateTitle[6] . This is hardly surprising , as templateTitleSubject[0] templateTitle[1] rated templateXValue[0] among their favorite templateTitle[5] templateYLabel[4] for the past few years . In spring templateTitleDate[0] , 35 templateScale of teenagers ranked the photo-sharing app as their templateYLabel[5] important templateTitle[5] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] with the highest templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] in templateTitleDate[0] . During the survey period , it was found that templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] templateYLabel[3] had a templateXValue[0] templateYValue[1] templateScale .
generated: This statistic shows the U.S. teens most frequently with the highest Share respondents in U.S. 2019 .  During the survey period , it was found that 25 % of respondents who use had a Instagram 24 % .

Example 773:
titleEntities: {'Subject': ['Asia'], 'Date': ['2019']}
title: Life expectancy in Asia in 2019
Data:
                 0             1             2             3               4           5
Region   East_Asia  Western_Asia  Asia_(total)  Central_Asia  Southeast_Asia  South_Asia
Males           75            72            71            70              69          68
Females         80            77            75            76              75          71 

gold: This statistic shows the average life expectancy in Asia for those born in 2019 , by gender and region . In East Asia , the average life expectancy was 75 years for males and 80 years for females in 2019 . Life expectancy Life expectancy is , in the statistical sense , the number of years to be lived .
gold_template: This statistic shows the average templateTitle[0] templateTitle[1] in templateValue[0][0] for those born in templateTitleDate[0] , by gender and templateLabel[0][0] . In templateValue[0][0] , the average templateTitle[0] templateTitle[1] was templateValue[1][0] years for templateLabel[1][0] and templateValue[2][0] years for templateLabel[2][0] in templateTitleDate[0] . templateTitle[0] templateTitle[1] templateTitle[0] templateTitle[1] is , in the statistical sense , the number of years to be lived .

generated_template: This statistic shows the average templateTitle[0] templateTitle[1] in templateValue[0][0] for those born in templateTitleDate[0] , by gender and templateLabel[0][0] . The average templateTitle[0] templateTitle[1] in the whole continent was templateValue[1][2] years for templateLabel[1][0] and templateValue[2][2] years for templateLabel[2][0] . The average templateTitle[0] templateTitle[1] globally was 70 years for templateLabel[1][0] and 75 years for templateLabel[2][0] in templateTitleDate[0] .
generated: This statistic shows the average Life expectancy in East_Asia for those born in 2019 , by gender and Region .  The average Life expectancy in the whole continent was 71 years for Males and 75 years for Females .  The average Life expectancy globally was 70 years for Males and 75 years for Females in 2019 .

Example 774:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Percentage of U.S. population who are veterans in 2018 , by age and gender
Data:
                     0               1               2               3                  4
Year    18_to_34_years  35_to_54_years  55_to_64_years  65_to_74_years  75_years_and_over
Male              3.53            8.57           13.73           31.89              45.51
Female            0.77            1.54            1.75            1.22               1.06 

gold: This graph displays the percentage of Americans who were veterans in 2018 , distinguished by age and gender . In 2018 , 47.51 percent of the male Americans , aged 75 years and over , were veterans .
gold_template: This graph displays the templateScale of Americans templateTitle[3] were templateTitle[5] in templateTitleDate[0] , distinguished templateTitle[7] templateTitle[8] and templateTitle[9] . In templateTitleDate[0] , 47.51 templateScale of the templateLabel[1][0] Americans , aged templateValue[0][4] templateValue[0][0] and templateValue[0][4] , were templateTitle[5] .

generated_template: The statistic provides a comparison of the average amount of templateTitle[0] templateTitle[3] templateTitle[4] and templateTitle[5] templateTitle[8] templateTitle[9] in the templateTitleSubject[0] member templateTitle[7] in templateTitleDate[0] , templateTitle[7] templateTitle[8] templateTitle[9] . In templateValue[0][0] , the templateTitle[0] of the templateTitle[2] templateTitle[3] templateTitle[4] had amounted to templateValue[2][0] and templateValue[1][0] per 100,000 inhabitants .
generated: The statistic provides a comparison of the average amount of Percentage who are and veterans age gender in the U.S. member by in 2018 , by age gender .  In 18_to_34_years , the Percentage of the population who are had amounted to 0.77 and 3.53 per 100,000 inhabitants .

Example 775:
titleEntities: {'Subject': ['Leading'], 'Date': ['2018']}
title: Leading airlines worldwide - based on total number of passengers 2018
X_Axis['Airlines']: ['American', 'Delta', 'Southwest', 'United_Continental', 'Lufthansa', 'Ryanair', 'China_Southern', 'China_Eastern', 'IAG', 'Air_China']
Y_Axis['Number', 'of', 'passengers', 'in', 'millions']: ['203.7', '192.5', '163.6', '158.3', '142.3', '142.1', '140.0', '122.5', '113.0', '110.0']

gold: This statistic depicts the leading airline groups worldwide in 2018 , based on passenger traffic . In that year , the ranking was dominated by U.S.-based airlines : American Airlines topped the list , with 203.7 million passengers . Air passengers The rising middle classes in emerging economies are expected to help global air traffic grow at an average annual growth rate of four percent till 2034 .
gold_template: This statistic depicts the templateTitleSubject[0] airline groups templateTitle[2] in templateTitleDate[0] , templateTitle[3] on passenger traffic . In that year , the ranking was dominated by U.S.-based templateXLabel[0] : templateXValue[0] templateXLabel[0] topped the list , with templateYValue[max] templateScale templateYLabel[1] . templateXValue[last] templateYLabel[1] The templatePositiveTrend middle classes in emerging economies are expected to help global templateXValue[last] traffic grow at an average annual growth rate of four templateScale till 2034 .

generated_template: The statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . According to the source , templateXValue[0] had the largest templateYLabel[0] of templateYValue[max] templateScale templateYLabel[1] , followed by templateXValue[1] with templateYValue[1] templateScale .
generated: The statistic presents the Leading airlines worldwide in Leading 2018 , number Airlines .  According to the source , American had the largest Number of 203.7 millions passengers , followed by Delta with 192.5 millions .

Example 776:
titleEntities: {'Subject': ['Germany'], 'Date': ['2024']}
title: Inflation rate in Germany 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2.13', '2.04', '1.92', '1.7', '1.67', '1.49', '1.93', '1.71', '0.38', '0.68', '0.77', '1.6', '2.15', '2.48', '1.12', '0.25', '2.75', '2.29', '1.79', '1.93', '1.75', '1.09', '1.31', '1.9', '1.4', '0.68', '0.59', '1.52', '1.27', '1.73', '2.72', '4.48', '5.05', '3.47', '2.69', '2.78', '1.27', '0.24', '-0.13', '2.08', '2.4']

gold: The inflation rate in Germany was 1.93 percent in 2018 , expected to reach 2.13 percent by 2024 . The current rate meets the European Central Bank 's target rate , which is `` below , but close to , 2 percent . '' Many central bankers favor inflation between 2 and 3 percent , but Germans in particular would rather risk deflation than too much inflation .
gold_template: The templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[6] templateScale in templateXValue[6] , expected to reach templateYValue[0] templateScale by templateXValue[max] . The current templateYLabel[1] meets the European Central Bank 's target templateYLabel[1] , which is `` below , but close to , templateYValue[0] templateScale . '' Many central bankers favor templateYLabel[0] between templateYValue[0] and templateYValue[16] templateScale , but Germans in particular would rather risk deflation than too much templateYLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Germany from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Germany amounted to about 1.93 % compared to the previous Year .

Example 777:
titleEntities: {'Subject': ['Cuba'], 'Date': ['2015']}
title: Popularity of Fidel Castro in Cuba 2015
X_Axis['Response']: ['Very/somewhat_positive', 'Very/somewhat_negative', "Don't_know_/_no_answer"]
Y_Axis['Share', 'of', 'respondents']: ['44', '50', '6']

gold: This statistic shows the results of a 2015 survey among adult Cubans living in Cuba on their opinion of Fidel Castro , their former President . 50 percent of respondents stated they have a very or somewhat negative opinion of Fidel Castro .
gold_template: This statistic shows the results of a templateTitleDate[0] survey among adult Cubans living in templateTitleSubject[0] on their opinion of templateTitle[1] templateTitle[2] , their former President . templateYValue[max] templateScale of templateYLabel[1] stated they have a very or somewhat templateXValue[1] opinion of templateTitle[1] templateTitle[2] .

generated_template: This statistic presents the templateScale of online consumers in the templateTitle[0] who are templateTitleSubject[0] templateTitle[2] members . During the most recent survey period in 2016 , it was found that templateYValue[min] templateScale of responding Millennials had an templateTitleSubject[0] templateTitle[2] templateXValue[0] .
generated: This statistic presents the percentage of online consumers in the Popularity who are Cuba Castro members .  During the most recent survey period in 2016 , it was found that 6 % of responding Millennials had an Cuba Castro Very/somewhat_positive .

Example 778:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2007', '2018']}
title: U.S. household expenditure on soaps and detergents 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Average', 'annual', 'expenditure', 'in', 'U.S.', 'dollars']: ['92.05', '89.43', '82.13', '84.24', '79.89', '82.68', '84.71', '78.49', '81.94', '85.66', '81.1', '75.68']

gold: This statistic shows the average annual expenditure on soaps and detergents per consumer unit in the United States from 2007 to 2018 . In 2018 , the country 's average expenditure on soaps and detergents amounted to 92.05 U.S. dollars per consumer unit .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] and templateTitle[4] per consumer unit in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the country 's templateYLabel[0] templateYLabel[2] on templateTitle[3] and templateTitle[4] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] per consumer unit .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] templateTitle[4] per consumer unit in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the country 's templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] templateTitle[4] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] per consumer unit .
generated: The statistic shows the Average annual expenditure on soaps detergents per consumer unit in the U.S. from 2007 to 2018 .  In 2018 , the country 's Average annual expenditure on soaps detergents amounted to 92.05 U.S. dollars per consumer unit .

Example 779:
titleEntities: {'Subject': ['Barcelona'], 'Date': ['2010']}
title: Quarterly average daily rate of hotels in Barcelona 2010 to 2017
Data:
           0    1    2    3
Quarter   Q4   Q3   Q2   Q1
2010     120  129  130  116
2011     136  161  147  121
2012     150  155  171  153
2013     125  127  135  121
2014     144  163  170  151
2015     169  182  170  145
2016     175  180  182  151
2017       0    0    0  163 

gold: This statistic shows the quarterly average daily rate of hotels in Barcelona from 2010 to 2017 . In the first quarter of 2017 , the average daily rate of hotels in Barcelona in Spain was 163 euros , down from 175 euros in the previous quarter .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In the first templateLabel[0][0] of templateTitleDate[max] , the templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in Spain was templateValue[5][1] euros , down from templateValue[7][0] euros in the previous templateLabel[0][0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In the first templateLabel[0][0] of templateTitleDate[max] , the templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] in Denmark was templateValue[8][max] U.S. dollars .
generated: This statistic shows the Quarterly average daily rate of hotels in Barcelona from 2010 to .  In the first Quarter of 2010 , the average daily rate of hotels in Barcelona Denmark was 163 U.S. dollars .

Example 780:
titleEntities: {'Subject': ['New York Rangers', 'NHL'], 'Date': ['2014/15', '2014/15']}
title: New York Rangers ( NHL ) average ticket price 2014/15
X_Axis['Year']: ['14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['77.87', '74.89', '72.04', '66.2', '58.57', '58.57', '58.57', '54.96', '50.33', '45.83']

gold: This graph depicts the average ticket price of New York Rangers games within the National Hockey League from 2005/06 to 2014/15 . In the 2005/06 season , the average ticket price was 45.83 U.S. dollars . The Rangers play their home games at the Madison Square Garden in New York City .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] Rangers games within the National Hockey League from 2005/06 to templateTitle[7] . In the 2005/06 season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] play their home games at the Madison Square Garden in templateTitleSubject[0] City .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games within the National Hockey League from 2005/06 to templateTitle[6] . In the 2005/06 , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for New York Rangers games within the National Hockey League from 2005/06 to price .  In the 2005/06 , the Average ticket price was 45.83 U.S. dollars .

Example 781:
titleEntities: {'Subject': ['Slovakia'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Slovakia 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['5.49', '5.29', '4.94', '4.27', '3.69', '4.0', '3.73', '3.54', '3.36', '3.34', '4.03', '3.74', '3.55']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Slovakia from 2006 to 2018 . In 2018 there were around 5.49 million arrivals at accommodation establishments in Slovakia .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] there were around templateYValue[idxmax(X)] templateScale templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in travel templateTitle[3] ( including both international and domestic tourists ) amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Number of tourist arrivals at accommodation establishments in Slovakia from 2006 to 2018 .  In 2018 , the Number of arrivals in travel accommodation ( including both international and domestic tourists ) amounted to approximately 5.49 millions .

Example 782:
titleEntities: {'Subject': ['Revenue'], 'Date': ['2018']}
title: Revenue per employee of leading tech companies 2018
X_Axis['Company']: ['Netflix', 'Apple', 'Facebook', 'Alphabet', 'Microsoft', 'Twitter', 'Alibaba', 'Amazon']
Y_Axis['Revenue', 'per', 'employee', 'in', 'U.S.', 'dollars']: ['2224555', '2012083', '1569056', '1385214', '873910', '776112', '550737', '359671']

gold: This statistic shows the revenue per employee of selected tech companies in 2018 . Netflix was the most effective company , generating over 2.22 million U.S. dollars of revenue per employee . Apple ranked in second place with an annual revenue of 2.01 million U.S. dollars per employee .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of selected templateTitle[4] templateTitle[5] in templateTitleDate[0] . templateXValue[0] was the most effective templateXLabel[0] , generating over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] of templateYLabel[0] templateYLabel[1] templateYLabel[2] . templateXValue[1] ranked in second place with an annual templateYLabel[0] of templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateXValue[4] templateTitle[4] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . According to the survey findings , templateValue[1][0] templateScale of the templateYLabel[2] templateYLabel[3] in templateXValue[4] was templateXValue[0] , while templateXValue[2] with a templateYLabel[0] of templateYValue[2] templateScale .
generated: This statistic shows the Revenue of the per employee Microsoft tech in 2018 , .  According to the survey findings , percentage of the employee U.S. in Microsoft was Netflix , while Facebook with a Revenue of 1569056 % .

Example 783:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2017']}
title: Vietnam social media user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['52', '52', '51', '50', '49', '48', '46']

gold: This statistic presents the social networking reach in Vietnam from 2017 to 2023 . In 2017 , 46 percent of the population in the country accessed social media . In 2019 , this share is projected to reach 49 percent .
gold_template: This statistic presents the templateTitle[1] networking reach in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the templateYLabel[1] in the country accessed templateTitle[1] templateTitle[2] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] templateScale .

generated_template: This statistic presents the templateTitle[0] networking reach in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the templateYLabel[1] in the country accessed templateTitle[0] templateTitle[1] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] templateScale .
generated: This statistic presents the Vietnam networking reach in Vietnam from 2017 to 2023 .  In 2017 , 46 % of the population in the country accessed Vietnam social .  In 2019 , this Share is projected to reach 49 % .

Example 784:
titleEntities: {'Subject': ['United States'], 'Date': ['2017']}
title: Average costs for a wedding in the United States in 2017 , by item
X_Axis['Category']: ['Overall_Wedding_(with_ring', 'Venue_(reception_hall)', 'Engagement_ring', 'Reception_band', 'Photographer', 'Florist/décor', 'Ceremony_site', 'Wedding/event_planner', 'Videographer', 'Wedding_Dress', 'Rehearsal_dinner', 'Reception_DJ', 'Transportation', 'Ceremony_musicians', 'Wedding_cake', 'Invitations', 'Groom’s_attire_and_accessories', 'Officiant', 'Favors', 'Catering_(price_per_person)']
Y_Axis['Costs', 'in', 'U.S.', 'dollars']: ['33391', '15163', '5764', '4019', '2630', '2379', '2311', '1988', '1912', '1509', '1285', '1231', '830', '761', '540', '408', '286', '284', '252', '70']

gold: This statistic shows the average costs for selected wedding items in the United States as of 2017 , sorted by item . According to the source , average costs for a wedding in the United States amounted to about 33,391 U.S. dollars in 2017 .
gold_template: This statistic shows the templateTitle[0] templateYLabel[0] templateTitle[2] selected templateXValue[0] items in the templateTitleSubject[0] as of templateTitleDate[0] , sorted templateTitle[7] templateTitle[8] . According to the source , templateTitle[0] templateYLabel[0] templateTitle[2] a templateXValue[0] in the templateTitleSubject[0] amounted to about templateYValue[max] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] .

generated_template: This statistic presents the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] as of 2019 . During that period , it was found that templateXValue[0] accounted for over templateYValue[max] templateScale of all templateYLabel[2] .
generated: This statistic presents the Costs of Average costs U.S. in the United States as of 2019 .  During that period , it was found that Overall_Wedding_(with_ring accounted for over 33391 % of all dollars .

Example 785:
titleEntities: {'Subject': ['eBay'], 'Date': ['2014', '2019']}
title: eBay : gross merchandise volume 2014 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14"]
Y_Axis['Gross', 'merchandise', 'volume', 'in', 'billion', 'U.S.', 'dollars']: ['23.3', '21.72', '22.6', '22.59', '24.64', '22.72', '23.63', '23.59', '24.43', '21.68', '21.4', '20.9', '22.28', '20.05', '20.79', '20.45', '21.86', '19.6', '20.06', '20.15', '21.79', '20.03', '20.44']

gold: This statistic gives information on eBay 's total gross merchandise volume from the second quarter of 2014 to the fourth quarter of 2019 . In the most recently reported period , the auction site 's gross merchandise volume amounted to 23.3 billion U.S. dollars .
gold_template: This statistic gives information on templateTitleSubject[0] 's total templateYLabel[0] templateYLabel[1] templateYLabel[2] from the second templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the most recently reported period , the auction site 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to templateYValue[0] templateScale templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the second templateXLabel[0] of templateTitleDate[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateYLabel[3] .
generated: This statistic shows the eBay gross merchandise volume 2014 eBay from the first Quarter of 2014 to the fourth Quarter of 2019 .  In the second Quarter of 2019 , the eBay gross merchandise of eBay billion .

Example 786:
titleEntities: {'Subject': ['Norway'], 'Date': ['2019']}
title: Employment by economic sector in Norway 2019
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2019   2018   2017   2016   2015   2014   2013   2012   2011   2010   2009
Agriculture   2.02   2.04   2.06   2.09   2.01   2.25   2.21   2.23   2.37   2.55    2.7
Industry     19.09  19.24  19.41  19.47  20.12  20.44  20.32  20.23  20.25   19.7  20.22
Services     78.89  78.72  78.53  78.44  77.87  77.31  77.48  77.54  77.38  77.76  77.08 

gold: The statistic shows the distribution of employment in Norway by economic sector from 2009 to 2019 . In 2019 , 2.02 percent of the employees in Norway were active in the agricultural sector , 19.09 percent in industry and 78.89 percent in the service sector .
gold_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .

generated_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .
generated: The statistic shows the distribution of Employment in Norway by economic sector from 2009 to 2019 .  In 2019 , 2.02 % of the employees in Norway were active in the agricultural sector , 19.09 % in Industry and 78.89 % in the service sector .

Example 787:
titleEntities: {'Subject': ['New York Mets'], 'Date': ['2019']}
title: Franchise value of the New York Mets 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2300', '2100', '2000', '1650', '1350', '800', '811', '719', '747', '858', '912', '824', '736', '604', '505', '442', '498', '482']

gold: This graph depicts the value of the New York Mets franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 2.3 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Mets templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Terry and Kim Pegula .
generated: This graph depicts the Franchise value of the New York Mets of the National Football League from 2002 to 2019 .  In 2019 , the Franchise value came to 2300 million U.S. dollars .  The New York Mets are owned by Terry and Kim Pegula .

Example 788:
titleEntities: {'Subject': ['Los Angeles Clippers', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Los Angeles Clippers ( NBA ) 2003 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2600', '2200', '2150', '2000', '2000', '1600', '575', '430', '324', '305', '295', '297', '294', '285', '248', '224', '208', '205']

gold: This graph depicts the value of the Los Angeles Clippers franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the franchise had an estimated value of 2.6 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Clippers templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] franchise of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the value of the Los Angeles Clippers franchise of the National Basketball Association from 2003 to 2020 .  In 2020 , the Franchise had an estimated value of 2600 million U.S. dollars .

Example 789:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2018']}
title: Urbanization in Malaysia 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['76.04', '75.45', '74.84', '74.21', '73.58', '72.93', '72.28', '71.61', '70.91', '70.08', '69.23']

gold: This statistic shows the degree of urbanization in Malaysia from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 76.04 percent of Malaysia 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Malaysia from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 76.04 % of Malaysia 's total population lived in urban areas and cities .

Example 790:
titleEntities: {'Subject': ['Croatia'], 'Date': ['2018']}
title: Share of economic sectors in the GDP in Croatia 2018
Data:
                 0      1      2      3      4      5      6      7      8      9    10
Year          2018   2017   2016   2015   2014   2013   2012   2011   2010   2009  2008
Agriculture   2.85   2.94   3.11   3.01   2.99    3.5   3.34   3.68    3.7   4.04  3.98
Industry     20.35  20.84  21.26  21.36   21.6  21.38  21.99  22.32  22.17  23.59  23.6
Services     59.01  58.82  58.51  58.48  58.82  58.51   58.9  59.37  59.02  57.94  57.1 

gold: This statistic shows the share of economic sectors in the gross domestic product ( GDP ) in Croatia from 2008 to 2018 . In 2018 , the share of agriculture in Croatia 's gross domestic product was 2.85 percent , industry contributed approximately 20.35 percent and the services sector contributed about 59.01 percent .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] in the gross domestic product ( templateTitle[3] ) in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitle[0] of templateLabel[1][0] in templateTitleSubject[0] 's gross domestic product was templateValue[1][0] templateScale , templateLabel[2][0] contributed approximately templateValue[2][0] templateScale and the templateLabel[3][0] sector contributed about templateValue[3][0] templateScale .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the templateLabel[3][0] sector .
generated: This statistic shows the Share of the economic sectors GDP ( Croatia ) 2018 in Croatia from 2008 to 2018 .  In 2018 , Agriculture contributed around 2.85 % to the Croatia of , 20.35 % came from the Industry and 59.01 % from the Services sector .

Example 791:
titleEntities: {'Subject': ['England'], 'Date': ['2000', '2018']}
title: Obesity prevalence in England 2000 - 2018 , by gender
Data:
          0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18
Year   2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  2000
Men      26    27    26    27    24    26    24    24    26    22    24    24    24    22    23    22    22    21    21
Women    29    30    27    27    27    24    25    26    26    24    25    24    24    24    23    23    23    23    21 

gold: The prevalence of obesity among adults in England has been creeping upwards since 2000 . In that year , 21 percent of men and women in England were classified as obese . However , by 2018 this share had increased to 29 percent for women and 26 percent of men .
gold_template: The templateTitle[1] of templateTitle[0] among adults in templateTitleSubject[0] has been creeping upwards since templateValue[0][last] . In that templateLabel[0][0] , templateValue[1][last] templateScale of templateLabel[1][0] and templateLabel[2][0] in templateTitleSubject[0] were classified as obese . However , templateTitle[5] templateValue[0][0] this share had templatePositiveTrend to templateValue[2][0] templateScale for templateLabel[2][0] and templateValue[1][0] templateScale of templateLabel[1][0] .

generated_template: In templateValue[0][0] , the templateTitle[0] templateTitle[1] in the templateTitle[3] was templateValue[1][0] templateLabel[1][0] and templateValue[2][0] templateScale for templateLabel[2][0] in the templateTitleSubject[0] , while templateLabel[2][0] it was found that templateValue[2][0] templateScale of the templateTitle[1] at templateTitle[2] templateTitle[3] in templateValue[0][0] . Among templateLabel[1][0] was templateValue[1][0] templateScale of the templateTitle[0] , templateValue[2][0] templateScale of templateLabel[1][0] it was templateValue[1][0] templateScale as for templateLabel[2][0] in the templateTitle[1] in templateValue[0][0] .
generated: In 2018 , the Obesity prevalence in the 2000 was 26 Men and 29 % for Women in the England , while Women it was found that 29 % of the prevalence at England 2000 in 2018 .  Among Men was 26 percent of the Obesity , 29 percent of Men it was 26 percent as for Women in the prevalence in 2018 .

Example 792:
titleEntities: {'Subject': ['Global'], 'Date': ['2013', '2030']}
title: Global energy commodity price index 2013 to 2030
X_Axis['Year']: ['2030', '2025', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Price', 'index', 'in', 'real', '2010', 'U.S.', 'dollars']: ['87.2', '79.1', '74.7', '73.3', '72.0', '74.3', '87.0', '68.1', '55.1', '65.0', '111.7', '120.1']

gold: This statistic shows a commodity price index of energy from 2013 through 2018 , and gives projections for 2030 . In 2018 , the index stood at around 87 real US dollars . Real 2010 US dollars reflect buying power in 2010 .
gold_template: This statistic shows a templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitle[1] from templateXValue[min] through templateXValue[6] , and gives projections for templateXValue[max] . In templateXValue[6] , the templateYLabel[1] stood at around templateYValue[0] templateYLabel[2] US templateYLabel[5] . templateYLabel[2] templateYLabel[3] US templateYLabel[5] reflect buying power in templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . The average templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] is expected to reach templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .
generated: This statistic shows the Price index of the energy commodity price index from 2013 to 2030 .  The average Price index of the Global energy commodity price index is expected to reach 87.2 real 2010 in 2013 .

Example 793:
titleEntities: {'Subject': ['Most'], 'Date': ['2018']}
title: Most shazamed songs of all time worldwide 2018
X_Axis['Song', 'Titles']: ['Wake_Me_Up_by_Avicii', 'Somebody_That_I_Used_To_Know_by_Gotye_feat._Kimbra', 'Lean_On_by_Major_Lazer_Feat._MØ_&_DJ_Snake', 'Let_Her_Go_by_Passenger', 'Thinking_Out_Loud_by_Ed_Sheeran', 'Blurred_Lines_by_Robin_Thicke_T.I._and_Pharrell', 'Take_Me_To_Church_by_Hozier', 'Rather_Be_by_Clean_Bandit_feat._Jess_Glynne', 'Cheerleader_(Felix_Jaehn_Remix)_by_Omi', 'All_of_Me_by_John_Legend']
Y_Axis['Number', 'of', 'identifications', 'in', 'millions']: ['23.77', '21.63', '21.38', '21.29', '20.66', '20.4', '20.13', '19.82', '19.77', '17.58']

gold: This statistic shows the most popular songs on Shazam ever worldwide as of August 2018 , ranked by the number of identifications . As of that month , the music identification app had identified the song Wake Me Up by Avicii almost 23.8 million times , making it the most shazamed song in history .
gold_template: This statistic shows the templateTitleSubject[0] popular templateTitle[2] on Shazam ever templateTitle[5] as of 2018 , ranked templateXValue[0] the templateYLabel[0] of templateYLabel[1] . As of templateXValue[1] month , the music identification app had identified the templateXLabel[0] templateXValue[0] Up templateXValue[0] almost templateYValue[max] templateScale times , making it the templateTitleSubject[0] templateTitle[1] templateXLabel[0] in history .

generated_template: This statistic provides information on the average templateYLabel[0] of templateYLabel[3] templateTitle[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[7] in the United Kingdom ( templateTitleSubject[0] ) . The distribution of templateYLabel[1] stood at templateYValue[4] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: This statistic provides information on the average Number of millions time identifications millions 2018 in the United Kingdom ( Most ) .  The distribution of identifications stood at 20.66 millions in 2018 .

Example 794:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Sweden 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['669.68', '632.46', '598.91', '567.67', '540.79', '528.93', '556.07', '540.55', '515.74', '503.65', '580.25', '579.36', '544.48', '563.8', '488.91', '430.11', '514.59', '488.4', '420.54', '389.49', '382.15', '331.53', '264.35', '240.28', '260.16', '271.18', '267.23', '264.83', '288.46', '264.35', '226.34', '210.19', '279.38', '268.62', '255.94', '213.05', '202.61', '179.07', '147.3', '111.54', '106.62']

gold: The statistic shows the gross domestic product ( GDP ) in Sweden from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Sweden from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 795:
titleEntities: {'Subject': ['USB', 'Germany'], 'Date': ['2004', '2018']}
title: Sales volume of USB flash drives in Germany 2004 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Sales', 'volume', 'in', 'millions']: ['12.88', '13.51', '16.17', '15.94', '15.46', '13.5', '15.85', '13.82', '11.78', '12.99', '13.0', '8.18', '5.0', '3.2', '2.03']

gold: USB flash drives experienced fluctuating sales numbers in recent years , with almost 12.9 million units sold in 2018 . Meanwhile , revenue generated amounted to 155 million euros in the same year , a decrease on the one before . Storage media USB flash drives revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard drives and optical storage units like CD-R and CD-RW discs .
gold_template: templateTitleSubject[0] templateTitle[3] templateTitle[4] experienced fluctuating templateYLabel[0] numbers in recent years , with almost templateYValue[0] templateScale units sold in templateXValue[max] . Meanwhile , revenue generated amounted to 155 templateScale euros in the same templateXLabel[0] , a templateNegativeTrend on the one before . Storage media templateTitleSubject[0] templateTitle[3] templateTitle[4] revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard templateTitle[4] and optical storage units like CD-R and CD-RW discs .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of a templateYLabel[4] of of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Sales volume of the Sales volume millions in USB from 2004 to 2018 .  In 2018 , the Sales volume of a millions of USB was 12.88 millions .

Example 796:
titleEntities: {'Subject': ['LA Galaxy', 'MLS'], 'Date': ['2019']}
title: Player expenses ( payroll ) of LA Galaxy ( MLS ) 2019
X_Axis['Month']: ['Zlatan_Ibrahimovic', 'Jonathan_dos_Santos', 'Romain_Alessandrini', 'Jorgen_Skjelvik', 'Diego_Polenta', 'Giancarlo_Gonzalez_Castro', 'Favio_Alvarez', 'Joe_Corona', 'Perry_Kitchen', 'Uriel_Antuna', 'David_Bingham', 'Sebastian_Lletget', 'Joao_Pedro', 'Rolf_Feltscher', 'Chris_Pontius', 'Emmanuel_Boateng', 'Juninho', 'Daniel_Steres', 'Tomas_Hilliard-Arce', 'Dave_Romney', 'Hugo_Arellano', 'Julian_Araujo', 'Ethan_Zubak', 'Bradford_Jamieson', 'Efrain_Alvarez', 'Emil_Cuello', 'Matt_Lampson', 'Servando_Carrasco', 'Justin_Vom_Steeg', 'Diedie_Traore']
Y_Axis['Payroll', 'in', 'thousand', 'U.S.', 'dollars']: ['7200.0', '2000.0', '1940.0', '1066.67', '777.28', '719.58', '586.25', '550.0', '474.17', '420.0', '375.0', '312.67', '261.0', '242.65', '190.0', '185.0', '163.13', '155.5', '123.13', '118.95', '90.07', '86.25', '78.13', '76.4', '75.61', '70.25', '70.25', '70.25', '57.23', '56.25']

gold: The statistic shows the player expenses ( payroll ) of the LA Galaxy club of Major League Soccer by player in 2019 . Jonathan Dos Santos received a salary of two million U.S. dollars in that year .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[1] Santos received a salary of templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in that year .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] received a salary of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Player expenses ( Payroll ) of the LA Galaxy of Major League Soccer by Player in 2019 .  Zlatan_Ibrahimovic received a salary of 7200.0 thousand U.S. dollars .

Example 797:
titleEntities: {'Subject': ['United States'], 'Date': ['2014']}
title: Sales of streaming devices in the United States 2014
X_Axis['Streaming', 'Brand']: ['Apple_TV', 'Fire_TV', 'Roku', 'Chromecast', 'Tivo', 'Other']
Y_Axis['Unit', 'sales', 'in', 'millions']: ['8.8', '5.8', '5.0', '4.0', '1.2', '2.8']

gold: The statistic shows the unit sales of streaming devices in the United States in 2014 . 8.8 million units of the Apple TV streaming device are forecast to be sold in the United States in 2014 .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateXLabel[0] templateTitle[2] in the templateTitleSubject[0] in templateTitleDate[0] . templateYValue[max] templateScale units of the templateXValue[0] streaming device are forecast to be sold in the templateTitleSubject[0] in templateTitleDate[0] .

generated_template: This statistic illustrates the distribution of templateYLabel[1] of the United Kingdom ( templateTitleSubject[0] ) . The templateXValue[0] accounted for almost templateYValue[0] templateScale of the employees in the United Kingdom ( templateTitleSubject[1] ) .
generated: This statistic illustrates the distribution of sales the United Kingdom ( United States ) .  The Apple_TV accounted for almost 8.8 millions of the employees in the United Kingdom ( United States ) .

Example 798:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2015']}
title: Average U.S. teen Twitter follower count 2015
X_Axis['Year']: ['0_to_100', '101_to_200', '201_to_300', '301_and_more', "Don't_know"]
Y_Axis['Share', 'of', 'respondents']: ['29', '10', '5', '10', '44']

gold: This statistic presents the average network size of teenage Twitter users in the United States . During the March 2015 survey , it was found that 10 percent of respondents had 101 to 200 followers on the social network . The U.S. teen average was 95 followers .
gold_template: This statistic presents the templateTitle[0] network size of teenage templateTitleSubject[0] users in the templateTitle[1] . During the 2015 survey , it was found that templateYValue[1] templateScale of templateYLabel[1] had templateXValue[1] to templateXValue[1] followers on the social network . The templateTitle[1] templateTitle[2] templateTitle[0] was 95 followers .

generated_template: This statistic presents the templateTitle[0] network size of teenage templateTitleSubject[0] users in the templateTitle[1] . During the 2015 survey , it was found that templateYValue[1] templateScale of templateYLabel[1] had templateXValue[1] to templateXValue[1] followers on the social network . The templateTitle[1] templateTitle[2] templateTitle[0] was 150 followers .
generated: This statistic presents the Average network size of teenage Twitter users in the U.S. During the 2015 survey , it was found that 10 % of respondents had 101_to_200 to followers on the social network .  The U.S. teen Average was 150 followers .

Example 799:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2018', '2020']}
title: Rate of inflation in Belgium 2018 to 2020
X_Axis['Month']: ["Jan_'20", "Dec_'19", "Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18"]
Y_Axis['Year', 'on', 'year', 'percentage', 'change']: ['1.41', '0.76', '0.39', '0.48', '0.8', '1.26', '1.42', '1.73', '1.89', '2.08', '2.33', '2.17', '1.99', '2.34', '2.78', '2.75', '2.35', '2.24', '2.17', '2.08', '1.82', '1.52', '1.39', '1.48', '1.71']

gold: This statistic shows the percentage change in the previous year of the inflation rate in Belgium from January 2018 to January 2020 . In January 2020 , the inflation rate in Belgium increased by approximately 1.14 percent . November 2018 saw the highest inflation in this period , with an inflation rate of nearly 2.8 percent in comparison to the previous year .
gold_template: This statistic shows the templateScale templateYLabel[3] in the previous templateYLabel[0] of the templateTitle[1] templateTitle[0] in templateTitleSubject[0] from 2018 to 2020 . In 2020 , the templateTitle[1] templateTitle[0] in templateTitleSubject[0] templatePositiveTrend by approximately 1.14 templateScale . 2018 saw the highest templateTitle[1] in this period , with an templateTitle[1] templateTitle[0] of nearly templateYValue[max] templateScale in comparison to the previous templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] as of 2019 . The templateTitle[0] of templateTitleSubject[0] was templateXValue[0] , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Rate Belgium ( UK ) 2020 Year percentage change as of 2019 .  The Rate of Belgium was Jan_'20 , with a Year of 2.78 percentage change .

Example 800:
titleEntities: {'Subject': ['Highest'], 'Date': ['2018']}
title: Highest earning eSports countries in 2018 , by prize winnings
X_Axis['Country']: ['United_States', 'China', 'South_Korea', 'Denmark', 'Finland', 'Russia', 'Sweden', 'France', 'Canada', 'Germany']
Y_Axis['Prize', 'money', 'in', 'million', 'U.S.', 'dollars']: ['28.07', '16.02', '13.54', '10.4', '7.31', '6.86', '6.29', '6.04', '4.68', '4.16']

gold: The graph shows the highest earning eSports countries worldwide in 2018 , ranked by prize money won . According to the estimates , Chinese eSports gamers brought home over 16 million U.S. dollars of winnings from eSports competitions in 2018 . The U.S ranked first with 28 million U.S dollars in prize money won .
gold_template: The graph shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] worldwide in templateTitleDate[0] , ranked templateTitle[5] templateYLabel[0] templateYLabel[1] won . According to the estimates , Chinese templateTitle[2] gamers brought home over templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] of templateTitle[7] from templateTitle[2] competitions in templateTitleDate[0] . The U.S ranked first with templateYValue[max] templateScale U.S templateYLabel[4] in templateYLabel[0] templateYLabel[1] won .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] in selected countries in templateTitleDate[0] . In that year , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , followed templateTitle[5] templateXValue[1] with templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] on templateYLabel[0] templateYLabel[1] .
generated: This statistic shows the Prize money of Highest earning in selected countries in 2018 .  In that year , United_States had the highest Prize money of 28.07 million U.S. dollars , followed by China with 16.02 million U.S. dollars on Prize money .

Example 801:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Top media publishers in the U.S. 2017 , by online traffic
X_Axis['Media', 'Publisher']: ['msn.com', 'drudgereport.com', 'news.google.com', 'espn.com', 'cnn.com', 'finance.yahoo.com', 'foxnews.com', 'sports.yahoo.com', 'nytimes.com', 'washingtonpost.com', 'buzzfeed.com', 'businessinsider.com', 'huffingtonpost.com', 'bbc.com', 'usatoday.com', 'cnet.com', 'forbes.com', 'nbcnews.com', 'dailymail.co.uk', 'news.yahoo.com']
Y_Axis['Page', 'views', 'in', 'millions']: ['1810.3', '1256.2', '1236.8', '1179.1', '853.9', '838.8', '663.3', '618.3', '603.6', '497.6', '472.7', '335.3', '328.5', '262.6', '249.6', '249.2', '232.6', '226.7', '224.8', '185.1']

gold: The graph shows the ranking of the top media publishers in the United States in May 2017 , by total online traffic . During the measured period , MSN received the maximum views from desktop and mobile web users combined , surpassing 1.81 billion combined pageviews .
gold_template: The graph shows the ranking of the templateTitle[0] templateXLabel[0] templateTitle[2] in the templateTitle[3] in 2017 , templateTitle[5] total templateTitle[6] templateTitle[7] . During the measured period , MSN received the maximum templateYLabel[1] from desktop and mobile web users combined , surpassing templateYValue[max] templateScale combined pageviews .

generated_template: This statistic presents the distribution of templateTitle[0] employees in the templateTitleSubject[0] as of 2019 , based on templateXValue[0] . According to the source , it was found that templateYValue[7] templateScale of people .
generated: This statistic presents the distribution of Top employees in the U.S. as of 2019 , based on msn.com .  According to the source , it was found that 618.3 millions of people .

Example 802:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. honey imports by country of origin 2017
X_Axis['Country']: ['India', 'Vietnam', 'Argentina', 'Brazil', 'Ukraine', 'Canada', 'Mexico', 'Thailand', 'New__Zealand', 'Uruguay']
Y_Axis['Imports', 'in', 'metric', 'tons']: ['45170', '36288', '35378', '24031', '19363', '15786', '4783', '4490', '4201', '4025']

gold: This statistic gives a ranking of the ten countries from which the U.S. imported the largest amount of honey in 2017 . More than 15,700 metric tons of honey were imported from Canada that year .
gold_template: This statistic gives a ranking of the ten countries from which the templateTitleSubject[0] imported the largest amount of templateTitle[1] in templateTitleDate[0] . More than 15,700 templateYLabel[1] templateYLabel[2] of templateTitle[1] were imported from templateXValue[5] that year .

generated_template: This statistic shows the 20 templateTitleSubject[0] of the templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , the templateXValue[0] was the largest templateTitleSubject[0] exporter , with templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 U.S. of the metric imports by country in 2017 .  In that year , the India was the largest U.S. exporter , with 45170 tons .

Example 803:
titleEntities: {'Subject': ['United States'], 'Date': ['2000', '2019']}
title: Number of snow sports ( skier/snowboard ) visits in the United States 2000 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02', '00/01']
Y_Axis['Number', 'of', 'skier', 'visits', 'in', 'millions']: ['59.0', '53.3', '54.7', '52.8', '53.6', '56.49', '56.9', '50.97', '60.54', '59.79', '57.35', '60.5', '55.07', '58.9', '56.88', '57.07', '57.59', '54.41', '57.34']

gold: This graph depicts the estimated number of skier and snowboard visits in the U.S. during the winter seasons between 2000 and 2019 . During the 2018/2019 season , the U.S. had about 59 million snow sports visits in total .
gold_template: This graph depicts the estimated templateYLabel[0] of templateYLabel[1] and snowboard templateYLabel[2] in the templateTitle[6] during the winter seasons between templateTitleDate[min] and templateTitleDate[max] . During the 2018/2019 season , the templateTitle[6] had about templateYValue[0] templateScale templateTitle[1] templateTitle[2] templateYLabel[2] in total .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitleSubject[0] templateTitle[4] from the 2001/02 season to the 2018/19 season . In 2018/19 , the average templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] was templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number skier of the skier/snowboard United States visits from the 2001/02 season to the 2018/19 season .  In 2018/19 , the average Number skier of the United States was 60.54 visits millions .

Example 804:
titleEntities: {'Subject': ['CAGR'], 'Date': ['2015']}
title: Forecast CAGR global digital health market by major segment 2015 to 2020
X_Axis['Name']: ['Average', 'EHR/EMR', 'Telehealth', 'Wireless_health', 'Mobile_health']
Y_Axis['Compound', 'annual', 'growth', 'rate', '2015-2020']: ['21', '4', '15', '23', '41']

gold: This statistic displays a projection of the CAGR for the global digital health market from 2015 to 2020 , by segment . During this period , the mobile health market 's compound annual growth rate is expected to be around 41 percent . The digital health market is expected to reach over 200 billion U.S. dollars by 2020 driven particularly by the mobile health market .
gold_template: This statistic displays a projection of the templateTitleSubject[0] for the templateTitle[2] templateTitle[3] templateXValue[3] templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[6] templateTitle[8] . During this period , the templateXValue[last] templateXValue[3] templateTitle[5] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] is expected to be around templateYValue[max] templateScale . The templateTitle[3] templateXValue[3] templateTitle[5] is expected to reach over 200 templateScale U.S. dollars templateTitle[6] templateTitleDate[max] driven particularly templateTitle[6] the templateXValue[last] templateXValue[3] templateTitle[5] .

generated_template: This statistic shows the results of a survey among the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] . The survey results were templateXValue[0] with a templateXValue[last] of templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the results of a survey among the Forecast CAGR global digital health in CAGR 2015 .  The survey results were Average with a Mobile_health of 41 % of annual growth rate .

Example 805:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Share of U.S. travelers who have had items stolen or lost 2015
X_Axis['Response']: ['Smartphone', 'Credit_card', 'Debit_card', 'License', 'Passport']
Y_Axis['Share', 'of', 'respondents']: ['10', '9', '8', '6', '4']

gold: The statistic shows the share of U.S. survey respondents who have had items with sensitive information lost or stolen , while traveling . 10 percent of respondents have lost or had a smartphone stolen whilst traveling .
gold_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] survey templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] with sensitive information templateTitle[8] or templateTitle[7] , while traveling . templateYValue[max] templateScale of templateYLabel[1] templateTitle[4] templateTitle[8] or templateTitle[5] a templateXValue[0] templateTitle[7] whilst traveling .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded templateXValue[3] by Harris Interactive in 2014 , asking the templateYLabel[1] from templateTitleSubject[0] . Some templateYValue[2] templateScale of templateYLabel[1] stated that they had a templateXValue[0] .
generated: This statistic shows the results of a survey among U.S. adult .  The survey was fielded License by Harris Interactive in 2014 , asking the respondents from U.S. Some 8 % of respondents stated that they had a Smartphone .

Example 806:
titleEntities: {'Subject': ['Amazon'], 'Date': ['2004', '2019']}
title: Annual net sales of Amazon 2004 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Net', 'revenue', 'in', 'billion', 'U.S.', 'dollars']: ['280.52', '232.89', '177.87', '135.99', '107.01', '88.99', '74.45', '61.09', '48.08', '34.2', '24.51', '19.17', '14.84', '10.71', '8.49', '6.92']

gold: The time series shows the net revenue of Amazon.com 's e-commerce and service sales from 2004 to 2019 , in billion US dollars . In the last reported year , the multinational e-commerce company 's net revenue was 280.5 billion U.S. dollars , up from 177.86 billion US dollars in 2017 . Amazon.com , an American electronic commerce company originally founded in 1994 , is the world 's largest online retailer of books , clothing , electronics , music and many more goods .
gold_template: The time series shows the templateYLabel[0] templateYLabel[1] of Amazon.com 's e-commerce and service templateTitle[2] from templateXValue[min] to templateXValue[max] , in templateScale US templateYLabel[4] . In the last reported templateXLabel[0] , the multinational e-commerce company 's templateYLabel[0] templateYLabel[1] was templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , up from 177.86 templateScale US templateYLabel[4] in templateXValue[2] . Amazon.com , an American electronic commerce company originally founded in 1994 , is the world 's largest online retailer of books , clothing , electronics , music and many more goods .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] reported a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net revenue of the Amazon from 2004 to 2019 .  In 2019 , Amazon reported a Net revenue of 280.52 billion U.S. dollars .

Example 807:
titleEntities: {'Subject': ['Southwest Airlines'], 'Date': ['2011', '2019']}
title: Southwest Airlines - available seat miles 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['ASMs', 'in', 'billions']: ['157.25', '159.8', '153.81', '148.52', '140.5', '131.0', '130.34', '128.14', '120.58']

gold: Southwest Airlines grew its available seat miles ( ASMs ) from 120.58 billion in 2011 to 157.25 billion in 2019 . ASMs are calculated by the total number of seats multiplied by the total distance traveled . When describing the airline industry , people consider ASMs as a measure of capacity .
gold_template: templateTitleSubject[0] templatePositiveTrend its templateTitle[2] templateTitle[3] templateTitle[4] ( templateYLabel[0] ) from templateYValue[min] templateScale in templateXValue[idxmin(Y)] to templateYValue[0] templateScale in templateXValue[max] . templateYLabel[0] are calculated by the total number of seats multiplied by the total distance traveled . When describing the airline industry , people consider templateYLabel[0] as a measure of capacity .

generated_template: This statistic shows the templateYLabel[0] of people employed by the templateTitle[0] since templateXValue[min] and templateXValue[max] . As of templateXValue[max] , the templateScale of the employees employees were generated in templateTitleSubject[0] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the ASMs of people employed by the Southwest since 2011 and 2019 .  As of 2019 , the billions of the employees were generated in Southwest Airlines .  In 2019 , the Southwest of Southwest Airlines amounted to 157.25 billions .

Example 808:
titleEntities: {'Subject': ['Global'], 'Date': ['2017']}
title: Global leading fishing nations 2017
X_Axis['Country']: ['China', 'Indonesia', 'India', 'United_States', 'Russia', 'Peru', 'Vietnam', 'Japan', 'Norway', 'Myanmar']
Y_Axis['Capture', 'in', 'million', 'metric', 'tons']: ['15.37', '6.69', '5.43', '5.04', '4.87', '4.16', '3.28', '3.2', '2.37', '2.15']

gold: This statistic shows the world 's leading fishing nations in 2017 , based on capture production . China caught about 15.37 million metric tons of fish in that year . Worldwide fishing The act of fishing dates back to thousands of years before the Common Era .
gold_template: This statistic shows the world 's templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , based on templateYLabel[0] production . templateXValue[0] caught about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of fish in that year . Worldwide templateTitle[2] The act of templateTitle[2] dates back to thousands of years before the Common Era .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In that year , the templateXValue[0] was the templateTitle[2] templateTitle[3] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Capture million of fishing nations in 2017 , Country .  In that year , the China was the fishing nations with a Capture million of 15.37 metric tons .

Example 809:
titleEntities: {'Subject': ['Europe'], 'Date': []}
title: Second division football leagues in Europe ranked by revenue 2011/12
X_Axis['Football', 'League']: ['Championship_(England)', '2._Bundesliga_(Germany)', 'Serie_B_(Italy)', 'Ligue_2_(France)', 'Segunda_Division_(Spain)']
Y_Axis['Income', '(in', 'million', 'euros)']: ['590.3', '361.7', '275.5', '213.7', '201.1']

gold: The statistic shows a ranking of selected second division European football leagues based on revenue in the 2011/12 season . The second division of German professional soccer had a total income of 361.7 million euros .
gold_template: The statistic shows a ranking of selected templateTitle[0] templateXValue[last] European templateXLabel[0] templateTitle[3] based on templateTitle[7] in the templateTitle[8] season . The templateTitle[0] templateXValue[last] of German professional soccer had a total templateYLabel[0] of templateYValue[1] templateScale euros .

generated_template: The statistic shows the distribution of employees employees in the templateTitleSubject[0] in templateTitleDate[0] , broken down templateTitle[8] templateXLabel[0] . According to the source , it was found that templateXValue[0] had a total of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the distribution of employees in the Europe in , broken down 2011/12 Football .  According to the source , it was found that Championship_(England) had a total of 590.3 million euros) .

Example 810:
titleEntities: {'Subject': ['Most'], 'Date': ['2018']}
title: Most commonly reported types of cyber crime 2018
X_Axis['Cyber', 'Incidents']: ['Non-payment/Non-delivery', 'Extortion', 'Personal_data_breach', 'No_lead_value', 'Phishing/Vishing/Smishing/Pharming', 'BEC/EAC', 'Confidence_fraud/romance', 'Harassment/threats_of_violence', 'Advance_fee', 'Identity_theft', 'Spoofing']
Y_Axis['Number', 'of', 'complaints']: ['65116', '51146', '50642', '36936', '26379', '20373', '18493', '18415', '16362', '16128', '15569']

gold: This statistic gives information on the most common types of cyber crime as reported to the U.S. Internet Crime Complaint Center in 2018 . In the reported period , 16,128 cases of online identity theft were reported to the IC3 . Non-payment or non-delivery fraud ranked first with 65,116 complaints .
gold_template: This statistic gives information on the templateTitleSubject[0] common templateTitle[3] of templateXLabel[0] templateTitle[5] as templateTitle[2] to the U.S. Internet templateTitle[5] Complaint Center in templateTitleDate[0] . In the templateTitle[2] period , templateYValue[9] cases of online templateXValue[9] were templateTitle[2] to the IC3 . Non-payment or non-delivery fraud ranked first with templateYValue[max] templateYLabel[1] .

generated_template: This statistic presents the templateYLabel[0] of employees employees employees in the templateTitle[0] in templateTitleDate[0] , based on templateXValue[0] . According to the source , templateYValue[2] templateScale of templateYLabel[2] templateYLabel[3] .
generated: This statistic presents the Number of employees in the Most in 2018 , based on Non-payment/Non-delivery .  According to the source , 50642 % of complaints .

Example 811:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2014', '2017']}
title: Car dealership network sizes in the United Kingdom ( UK ) 2014 to 2017
Data:
                     0         1        2       3           4    5       6        7     8       9   10    11       12     13       14    15    16          17          18     19      20      21     22     23    24          25     26        27
Car Manufacturer  Ford  Vauxhall  Peugeot  Nissan  Volkswagen  Kia  Toyota  Citroen  Fiat  Suzuki  BMW  Mini  Hyundai  Mazda  Renault  Audi  Seat  Land_Rover  Mitsubishi  Volvo  Jaguar  Subaru  Skoda  Honda  Jeep  Alfa_Romeo  Lexus  Mercedes
July 2014          506       271      187     203         233  179     182      189   171     144  144   142      149    132      151   114   135         117         110    106      89      59    125    176    64          59     47        35
January 2017       475       234      179     197         196  187     177      176   157     163  146   143      158    130      151   124   131         119         106    121      85      73     79    154    96          62     47        38 

gold: As of January 2017 , Ford was the car maker with the largest dealership network in the United Kingdom ( UK ) . When compared to figures from July 2014 , Ford had consecutively higher numbers in car dealerships in their franchise network than any other manufacturer , numbering 475 in the beginning of 2017 . The first Ford dealership in the UK was established in 1910 , while imports of Ford vehicles from the US had started seven years earlier .
gold_template: As of templateLabel[2][0] templateTitleDate[max] , templateValue[0][0] was the templateLabel[0][0] maker with the largest templateTitle[1] templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) . When compared to figures from templateLabel[1][0] templateTitleDate[min] , templateValue[0][0] had consecutively higher numbers in templateLabel[0][0] dealerships in their franchise templateTitle[2] than any other templateLabel[0][1] , numbering templateValue[2][0] in the beginning of templateTitleDate[max] . The first templateValue[0][0] templateTitle[1] in the templateTitleSubject[1] was established in 1910 , while imports of templateValue[0][0] vehicles from the US had started seven years earlier .

generated_template: This statistic shows the 20 templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[7] templateTitle[8] . The templateTitle[0] templateTitle[1] of the templateValue[0][0] group in templateTitleDate[0] was templateValue[2][0] British pounds .
generated: This statistic shows the 20 Car dealership network sizes in the United Kingdom ( United Kingdom ) in 2014 , 2017 .  The Car dealership of the Ford group in 2014 was 475 British pounds .

Example 812:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013/14', '2018/19']}
title: Coffee consumption in the U.S. 2013/14 - 2018/19
X_Axis['Year']: ['2018/2019', '2017/2018', '2016/2017', '2015/2016', '2014/2015', '2013/2014']
Y_Axis['Consumption', 'in', 'million', '60-kilogram', 'bags']: ['26.5', '25.84', '25.02', '25.11', '23.58', '23.81']

gold: Coffee consumption in the United States amounted to nearly 26.5 million 60-kilogram bags in the 2018/2019 fiscal year . This is an increase from the total U.S. coffee consumption in the previous fiscal year , at 25.84 million 60-kilogram bags in the United States . Coffee production The coffee plant has its origins in Ethiopia and is now grown all over the world .
gold_template: templateTitle[0] templateYLabel[0] in the templateTitle[2] amounted to nearly templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] fiscal templateXLabel[0] . This is an templatePositiveTrend from the total templateTitleSubject[0] templateTitle[0] templateYLabel[0] in the previous fiscal templateXLabel[0] , at templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the templateTitle[2] . templateTitle[0] production The templateTitle[0] plant has its origins in Ethiopia and is now grown all over the world .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] . In templateXValue[0] , templateYValue[max] templateScale of templateTitleSubject[0] were spent worldwide .
generated: This statistic shows the Coffee Consumption of million 60-kilogram bags of consumption in U.S. 2013/14 .  In 2018/2019 , 26.5 million of U.S. were spent worldwide .

Example 813:
titleEntities: {'Subject': ['European'], 'Date': []}
title: European football clubs average attendance 2013/14
X_Axis['Club', 'Name']: ['Boussia_Dortmund', 'Manchester_United', 'Barcelona', 'Real_Madrid', 'Bayern_Munich', 'Schalke_04', 'Arsenal', 'Borussia_Mönchengladbach', 'Hertha_BSC', 'Hamburger_SV', 'Ajax_Amsterdam', 'VfB_Stuttgart', 'Newcastle_United', 'Manchester_City', 'Eintracht_Frankfurt', 'Celtic_FC', 'FC_Internazionale', 'Atletico_Madrid', 'FC_Köln', 'Feyenoord', 'Hannover_96', 'Paris_Saint_Germain', 'Liverpool', 'SL_Benfica', 'Rangers_FC']
Y_Axis['Average', 'attendance']: ['80295', '75205', '72115', '71565', '71000', '61750', '60015', '52240', '51890', '51825', '50905', '50500', '50395', '47075', '47055', '46810', '46245', '46245', '46235', '45755', '45635', '45420', '44670', '43615', '42935']

gold: The statistic shows the European football clubs with the highest average per game attendance in the 2013/14 season . Germany 's Borussia Dortmund had the highest average attendance throughout Europe , with an average of over 80,000 fans attending each of their home games .
gold_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] with the highest templateYLabel[0] per game templateYLabel[1] in the templateTitle[5] season . Germany 's templateXValue[7] templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] throughout Europe , with an templateYLabel[0] of over 80,000 fans attending each of their home games .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the templateYLabel[0] templateYLabel[1] at the templateYValue[3] templateScale .
generated: This statistic shows the Average football clubs average attendance in the European from Rangers_FC to Boussia_Dortmund .  In Boussia_Dortmund , the Average attendance at the 71565 % .

Example 814:
titleEntities: {'Subject': ['Papa John'], 'Date': ['2007', '2018']}
title: Number of Papa John 's restaurants worldwide 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'restaurants']: ['5303', '5199', '5097', '4893', '4663', '4428', '4163', '3883', '3646', '3469', '3380', '3208']

gold: In 2018 , pizza chain Papa John 's had a total of approximately 5,303 restaurants worldwide , up from 5,199 the previous year . This figure has been steadily growing for 12 years . Papa John 's place in the U.S. market Papa John 's ranked in fourth place in terms of U.S. sales when compared to competitors such as Domino 's Pizza , Pizza Hut and Little Caesars .
gold_template: In templateXValue[max] , pizza chain templateTitleSubject[0] 's had a total of approximately templateYValue[max] templateYValue[idxmax(X)] templateTitle[5] , up from templateYValue[1] the previous templateXLabel[0] . This figure has been steadily templatePositiveTrend for 12 years . templateTitleSubject[0] 's place in the U.S. market templateTitleSubject[0] 's ranked in fourth place in terms of U.S. sales when compared to competitors such as Domino templateTitle[3] Pizza , Pizza Hut and Little Caesars .

generated_template: This statistic shows the templateYLabel[0] of The templateTitleSubject[0] restaurants from templateXValue[min] to templateXValue[max] . In templateXValue[max] , U.S.-based restaurant chain The templateTitleSubject[0] had templateYValue[max] templateYValue[idxmax(X)] , up from templateYValue[1] the previous templateXLabel[0] .
generated: This statistic shows the Number of The Papa John restaurants from 2007 to 2018 .  In 2018 , U.S.-based restaurant chain The Papa John had 5303 , up from 5199 the previous Year .

Example 815:
titleEntities: {'Subject': ['Oakland Raiders', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Oakland Raiders ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2900', '2420', '2380', '2100', '1430', '970', '825', '785', '761', '758', '797', '861', '812', '736', '676', '624', '576', '421']

gold: This graph depicts the franchise value of the Oakland Raiders from the National Football League from 2002 to 2019 . In 2019 , the franchise value amounted to around 2.9 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] amounted to around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Terry and Kim Pegula .
generated: This graph depicts the Franchise value of the Oakland Raiders of the National Football League from 2002 to 2019 .  In 2019 , the Franchise value came to 2900 million U.S. dollars .  The Oakland Raiders are owned by Terry and Kim Pegula .

Example 816:
titleEntities: {'Subject': ['UK'], 'Date': ['2019', '2023']}
title: Output gap forecast comparison UK from 2019 to 2023
Data:
         0     1     2     3     4
Year  2023  2022  2021  2020  2019
OBR      0     0  -0.1  -0.2  -0.1
IMF      0     0     0     0     0 

gold: This statistic shows a comparison of output gap predictions in the United Kingdom ( UK ) from 2019 to 2023 . In 2019 , the Office for Budget Responsibility ( OBR ) estimated a negative output gap of 0.1 percent .
gold_template: This statistic shows a templateTitle[3] of templateTitle[0] templateTitle[1] predictions in the United Kingdom ( templateTitleSubject[0] ) templateTitle[5] templateValue[0][last] to templateValue[0][0] . In templateValue[0][last] , the Office for Budget Responsibility ( templateLabel[1][0] ) estimated a negative templateTitle[0] templateTitle[1] of 0.1 templateScale .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) from templateValue[0][last] to templateValue[0][0] . In templateValue[0][last] , the number of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] amounted to templateValue[1][last] templateScale U.S. dollars .
generated: This statistic shows the Output of gap forecast and comparison UK in the United Kingdom ( UK ) from 2019 to 2023 .  In 2019 , the number of Output gap forecast comparison UK in the UK amounted to -0.1 billion U.S. dollars .

Example 817:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1790', '2019']}
title: Change in the regional distribution of the U.S. population from 1790 to 2019
Data:
              0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19    20    21    22    23    24    25    26    27
Year       1790  1800  1810  1820  1830  1840  1850  1860  1870  1880  1890  1900  1910  1920  1930  1940  1950  1960  1970  1980  1990  2000  2010  2015  2016  2017  2018  2019
Northeast  50.1  49.6  48.2  45.2  43.1  39.6  37.2  33.7  31.9  28.9  27.6  27.6    28    28  27.9  27.2  26.1  24.9  24.1  21.7  20.4    19  17.9  17.5  17.4  17.2  17.2  17.1
Midwest       0     1     4   8.9  12.5  19.6  23.3  28.9  33.7  34.6  35.6  34.6  32.4  32.1  31.3  30.4  29.4  28.8  27.8    26    24  22.9  21.7  21.1    21    21  20.9  20.8
South      49.9  49.4  47.8  45.9  44.4  40.7  38.7  35.4  31.9  32.9  31.8  31.8  31.9  31.2  30.7  31.5  31.2  30.7  30.9  33.3  34.4  35.6  37.1  37.7  37.9    38  38.1  38.3
West          0     0     0     0     0     0   0.8     2   2.6   3.6     5     5   7.7   8.7    10  10.9  13.3  15.6  17.1  19.1  21.2  22.5  23.3  23.6  23.7  23.8  23.8  23.9 

gold: This statistic shows the change in the regional distribution of the U.S. population each decade from 1790 to 2019 . In 2019 , 17.1 percent of the population in the United States lived in the Northeast .
gold_template: This statistic shows the templateTitle[0] in the templateTitle[1] templateTitle[2] of the templateTitleSubject[0] templateTitle[4] each decade templateTitle[5] templateValue[0][0] to templateValue[0][last] . In templateValue[0][last] , templateValue[1][last] templateScale of the templateTitle[4] in the templateTitle[3] lived in the templateLabel[1][0] .

generated_template: This statistic shows the average templateTitle[0] templateTitle[1] rate of templateTitle[2] templateTitle[3] templateTitle[4] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[3][0] templateScale of templateTitle[0] templateTitle[1] were sold in the templateLabel[3][0] .
generated: This statistic shows the average Change regional rate of distribution U.S. population from 2019 to 1790 .  In 1790 , about 49.9 % of Change regional were sold in the South .

Example 818:
titleEntities: {'Subject': ['Canada'], 'Date': ['2015', '2019']}
title: Average retail price for regular , unleaded gasoline in Canada 2015 to 2019
Data:
           0      1      2      3      4      5      6      7      8      9     10     11
Month    Dec    Nov    Oct    Sep    Aug    Jul    Jun    May    Apr    Mar    Feb    Jan
2015    99.1  103.7  103.9  105.8  115.0  119.1  118.8  112.4  106.0  106.4  100.2   90.6
2016   104.9  101.0  106.7  102.8  101.9  102.1  108.6  104.1  100.0   91.9   86.7   92.6
2017   116.9  120.8  112.4  116.0  109.7  107.0  106.5  110.9  116.1  106.0  106.5  112.2
2018   106.4  113.8  125.9  130.4  131.8  133.9  132.9  135.9  131.6  123.1  119.7  120.6
2019   114.5  115.0  117.2  116.9  117.9  124.4  119.9  130.7  129.5  117.7  105.1  103.1 

gold: The average retail price for regular , unleaded gasoline at self-service stations in Canada was 114.5 Canadian cents per liter in December 2019 . Prices hitt a high of 1.3 dollars per liter in May 2019 . The average price in 2018 was around 1.26 dollars per liter , a substantial increase from an average of 1.07 dollars in 2015 .
gold_template: The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] , templateTitle[5] templateTitle[6] at self-service stations in templateTitleSubject[0] was templateValue[5][0] Canadian cents per liter in 2019 . Prices hitt a high of 1.3 dollars per liter in templateValue[0][7] templateTitleDate[max] . The templateTitle[0] templateTitle[2] in templateLabel[4][0] was around 1.26 dollars per liter , a substantial templatePositiveTrend from an templateTitle[0] of 1.07 dollars in templateTitleDate[min] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In 2019 , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] was templateValue[5][0] Canadian dollars .
generated: This statistic shows the Average retail price for regular unleaded in Canada from 2015 to 2019 .  In 2019 , the Average retail price for regular unleaded in Canada was 114.5 Canadian dollars .

Example 819:
titleEntities: {'Subject': ['Ukraine'], 'Date': ['2016']}
title: Largest cities in Ukraine 2016
X_Axis['Month']: ['Kyïv_(Kiev)', 'Charkiv', 'Odėsa', "Dnipropėtrovs'k", "Donėc'k", 'Zaporižžija', "L'viv", 'Kryvyj_Rih', 'Mykolaïv', "Mariupol'"]
Y_Axis['Residents', 'in', 'millions']: ['2.91', '1.45', '1.01', '0.98', '0.93', '0.76', '0.73', '0.64', '0.49', '0.45']

gold: This statistic shows the ten largest cities in Ukraine in 2016 . Around 2.91 million people lived in Kiev in 2016 , making it the largest city in Ukraine . Economic situation in the Ukraine Ukraine has a population of around 42 million inhabitants - close to 70 percent of which live in urban areas , with almost 3 million living in Ukraine 's largest city and capital , Kiev .
gold_template: This statistic shows the ten templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] . Around templateYValue[max] templateScale people lived in Kiev in templateTitleDate[0] , making it the templateTitle[0] city in templateTitleSubject[0] . Economic situation in the templateTitleSubject[0] has a population of around 42 templateScale inhabitants - close to 70 templateScale of which live in urban areas , with almost templateYValue[max] templateScale living in templateTitleSubject[0] 's templateTitle[0] city and capital , Kiev .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] with the highest templateYLabel[0] of templateTitleSubject[0] templateXValue[0] . templateXValue[0] had the templateTitle[0] templateTitle[1] templateTitle[2] of templateYValue[max] templateScale templateYLabel[2] of templateTitleSubject[0] .
generated: The statistic shows the Largest cities Ukraine with the highest Residents of Ukraine Kyïv_(Kiev) .  Kyïv_(Kiev) had the Largest cities Ukraine of 2.91 millions of Ukraine .

Example 820:
titleEntities: {'Subject': ['Myanmar'], 'Date': ['2024']}
title: Total population of Myanmar 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['54.65', '54.36', '54.05', '53.72', '53.38', '53.02', '52.83', '52.65', '52.25', '51.85', '51.42']

gold: This statistic shows the total population of Myanmar , also known as Burma , from 2014 to 2024 . All figures after 2015 are estimates . In 2015 , the total population of Myanmar was at approximately 51.85 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] , also known as Burma , from templateXValue[min] to templateXValue[max] . All figures after templateXValue[9] are estimates . In templateXValue[9] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was at approximately templateYValue[9] templateScale templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was around templateYValue[6] templateScale templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] After a subtle year-over-year templatePositiveTrend from 2004 to 2011 , the templateTitle[1] of templateTitleSubject[0] has experienced a slight drop from 2011 until templateXValue[min] .
generated: The statistic shows the Total population of Myanmar from 2014 to 2018 , with projections up until 2024 .  In 2018 , the Total population of Myanmar was around 52.83 millions Inhabitants .  population of Myanmar After a subtle year-over-year increase from 2004 to 2011 , the population of Myanmar has experienced a slight drop from 2011 until 2014 .

Example 821:
titleEntities: {'Subject': ['United States'], 'Date': ['2000']}
title: Revenue of water utilities - United States 2000 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2010', '2005', '2000']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['53.8', '52.0', '52.6', '43.5', '35.1', '29.9']

gold: This statistic represents the revenue of the water utility industry in the United States between 2000 and 2015 . In 2015 , this particular industry generated revenue of approximately 53.8 billion U.S. dollars .
gold_template: This statistic represents the templateYLabel[0] of the templateTitle[1] utility industry in the templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , this particular industry generated templateYLabel[0] of approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: In templateXValue[max] , the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] generated by templateYLabel[2] templateTitleSubject[0] in the fiscal templateXLabel[0] of templateXValue[max] . In the templateTitle[0] generated approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] for templateTitle[2] . templateTitleSubject[0] - additional information templateTitleSubject[0] is one of the four largest accounting and audit firms in the world .
generated: In 2015 , the water utilities United States generated by U.S. United States in the fiscal Year of 2015 .  In the Revenue generated approximately 53.8 billion U.S. dollars in Revenue for utilities .  United States - additional information United States is one of the four largest accounting and audit firms in the world .

Example 822:
titleEntities: {'Subject': ['NBA'], 'Date': ['2012', '2013']}
title: Global basketball shoe sales 2012 - 2013 , by NBA player
Data:
                      0             1             2            3             4
Quarter  Michael_Jordan  Lebron_James  Kevin_Durant  Kobe_Bryant  Derrick_Rose
2012               2000           300            35           50            40
2013               2500           300           175           50            40 

gold: This timeline depicts global basketball shoe sales in 2012 and 2013 , by NBA player . In 2013 , Michael Jordan and his shoe brand , Jordan , earned approximately 2.5 billion U.S. dollars .
gold_template: This timeline depicts templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[min] and templateTitleDate[max] , templateTitle[6] templateTitleSubject[0] templateTitle[8] . In templateTitleDate[max] , templateValue[0][0] and his templateTitle[2] brand , templateValue[0][0] , earned approximately 2.5 templateScale U.S. dollars .

generated_template: This statistic depicts the total templateTitle[0] templateTitle[1] and templateTitle[2] templateTitle[3] of templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . In the most recently reported period , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] templateTitle[5] in the country was templateValue[2][max] U.S. dollars .
generated: This statistic depicts the total Global basketball and shoe sales of NBA in 2012 and 2013 .  In the most recently reported period , the Global basketball shoe sales of NBA 2013 in the country was 2500 U.S. dollars .

Example 823:
titleEntities: {'Subject': ['Academy Awards'], 'Date': ['1928', '2015']}
title: Ethnicity distribution of acting winners at the Academy Awards 1928 to 2015
Data:
                              0           1
Award              Best_Actress  Best_Actor
White                      98.9        93.2
Other Ethnicities           1.1         6.8 

gold: The statistic presents distribution of winners from the best acting categories at the Academy Awards from 1928 to 2015 , by ethnicity . It was found 98.9 percent of all `` Best Actress '' winners have been white , the one percent of all winners from other ethnicities is represented by Halle Berry who won the `` Best Actress '' award in 2002 .
gold_template: The statistic presents templateTitle[1] of templateTitle[3] from the templateValue[0][0] templateTitle[2] categories at the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , by templateTitle[0] . It was found templateValue[1][0] templateScale of all `` templateValue[0][0] '' templateTitle[3] have been templateLabel[1][0] , the templateValue[2][0] templateScale of all templateTitle[3] from templateLabel[2][0] templateLabel[2][1] is represented by Halle Berry who won the `` templateValue[0][0] '' templateLabel[0][0] in 2002 .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] templateTitle[5] in templateTitleDate[min] and templateTitleDate[max] . The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] amounted to about templateValue[1][max] templateScale U.S. dollars in templateTitleDate[0] .
generated: This statistic shows the Ethnicity distribution acting winners of Academy Awards Awards in 1928 and 2015 .  The Ethnicity distribution acting winners in Academy Awards amounted to about 98.9 million U.S. dollars in 1928 .

Example 824:
titleEntities: {'Subject': ['Groupon'], 'Date': ['2019']}
title: Groupon : global sales force as of Q4 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10", "Q2_'10", "Q1_'10", "Q4_'09", "Q3_'09", "Q2_'09", "Q1_'09"]
Y_Axis['Size', 'of', 'global', 'sales', 'force']: ['2316', '2438', '2327', '2377', '2268', '2334', '2373', '2404', '2407', '2457', '2485', '2624', '2626', '2695', '2893', '3068', '3992', '4168', '4321', '4429', '4984', '4420', '4834', '4801', '4679', '4566', '4677', '5087', '5587', '5735', '5196', '4853', '4850', '3556', '2573', '1572', '1281', '128', '76', '44', '18', '2']

gold: The statistic above shows the size of Groupon 's sales force as of the fourth quarter of 2019 . By the last measured quarter , the number of inside and outside merchant sales representatives , as well as sales support staff , amounted to 2,316 , up from 2,268 sales employees of the corresponding quarter in the previous year .
gold_template: The statistic above shows the templateYLabel[0] of templateTitleSubject[0] 's templateYLabel[2] templateYLabel[3] as of the fourth templateXLabel[0] of templateTitleDate[0] . By the last measured templateXLabel[0] , the number of inside and outside merchant templateYLabel[2] representatives , as well as templateYLabel[2] support staff , amounted to templateYValue[0] , up from templateYValue[4] templateYLabel[2] employees of the corresponding templateXLabel[0] in the previous year .

generated_template: This statistic shows the change in the templateYLabel[1] of the templateYLabel[1] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the third templateXLabel[0] of templateTitleDate[max] . In the first templateXLabel[0] of templateTitleDate[max] , the templateTitle[0] of templateTitleSubject[0] templateYLabel[3] was templateYValue[min] templateYLabel[1] templateYLabel[3] .
generated: This statistic shows the change in the global of the global of Groupon from the first Quarter of 2019 to the third Quarter of 2019 .  In the first Quarter of 2019 , the Groupon of force was 2 global force .

Example 825:
titleEntities: {'Subject': ['Global'], 'Date': ['2017']}
title: Global industrial robot market : key companies by related revenue 2017
X_Axis['Companies']: ['ABB', 'Omron', 'Fanuc', 'Kawasaki_Robotics', 'KUKA', 'Yaskawa']
Y_Axis['Industrial', 'robot', 'revenue', 'in', 'million', 'euros']: ['6819', '3010', '1730', '1510', '1200', '1200']

gold: This statistic represents the leading companies in the global industrial robot market in 2017 , based on industrial robot revenue . In that year , Fanuc generated some 1.7 billion euros from industrial robot sales . Industrial robot market Although industrial robots have made inroads into a growing number of industries such as the food and beverages industry , the highly automated car manufacturing sector remains the largest area of application for electro-mechanical machines .
gold_template: This statistic represents the leading templateXLabel[0] in the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateTitle[3] in templateTitleDate[0] , based on templateYLabel[0] templateYLabel[1] templateYLabel[2] . In that year , templateXValue[2] generated some templateYValue[2] templateScale templateYLabel[4] from templateYLabel[0] templateYLabel[1] sales . templateYLabel[0] templateYLabel[1] templateTitle[3] Although templateYLabel[0] robots have made inroads into a templatePositiveTrend number of industries such as the food and beverages industry , the highly automated car manufacturing sector remains the largest area of application for electro-mechanical machines .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the largest templateXValue[0] templateYLabel[3] of the templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . According to the source , the templateTitle[0] templateXValue[0] generated approximately templateYValue[max] templateScale templateYLabel[3] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in that year .
generated: This statistic shows the Global industrial the largest ABB million of the robot in 2017 , key Companies .  According to the source , the Global ABB generated approximately 6819 million of robot revenue million in that year .

Example 826:
titleEntities: {'Subject': ['Olympic Summer Games'], 'Date': ['1996', '2016']}
title: Olympic Summer Games - revenue generated from domestic sponsorships 1996 to 2016
X_Axis['Year']: ['2016_Rio', '2012_London', '2008_Beijing', '2004_Athens', '2000_Sydney', '1996_Atlanta']
Y_Axis['Domestic', 'sponsorship', 'revenue', 'in', 'million', 'U.S.', 'dollars']: ['848', '1150', '1218', '302', '492', '426']

gold: The statistic shows the revenue generated from domestic sponsorships at the Olympic Summer Games from 1996 to 2016 . The 2016 Olympic Games in Rio de Janeiro generated about 848 million U.S. dollars in sponsorship revenues .
gold_template: The statistic shows the templateYLabel[2] templateTitle[4] templateTitle[5] templateYLabel[0] templateTitle[7] at the templateTitleSubject[0] Games templateTitle[5] templateXValue[last] to templateXValue[0] . The templateXValue[0] templateTitleSubject[0] in templateXValue[0] de Janeiro templateTitle[4] about templateYValue[0] templateScale templateYLabel[4] templateYLabel[5] in templateYLabel[1] revenues .

generated_template: This statistic shows the results of a survey among a templateTitle[0] of people in templateTitleSubject[0] in templateTitleDate[0] . It was found that the templateTitle[0] of people templateTitle[3] templateTitle[4] of the country will be templateXValue[0] , with a total of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the results of a survey among a Olympic of people in Olympic Summer Games 1996 .  It was found that the Olympic of people revenue generated of the country will be 2016_Rio , with a total of 1218 revenue million .

Example 827:
titleEntities: {'Subject': ['Greece'], 'Date': ['2024']}
title: Government revenue and spending in Greece 2024
Data:
               0      1      2      3      4      5      6      7      8      9     10
Year        2024   2023   2022   2021   2020   2019   2018   2017   2016   2015   2014
Revenue    96.93  96.14  94.39  92.87  91.31  90.66  88.06  87.14  87.28  84.77  82.39
Spending  100.51  99.12  96.81  95.05  93.27  91.25  86.29  85.23   86.3  89.68  89.67 

gold: This statistic shows the government revenue and spending in Greece from 2014 to 2018 , with projections up until 2024 . In 2018 , the government revenue in Greece amounted to around 88.06 billion euros , while government spending came to around 86.29 billion euros .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] and templateTitle[2] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][6] , with projections up until templateValue[0][0] . In templateValue[0][6] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to around templateValue[1][6] templateScale euros , while templateTitle[0] templateTitle[2] came to around templateValue[2][6] templateScale euros .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] and templateTitle[2] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][6] , with projections up until templateValue[0][0] . In templateValue[0][6] , templateTitle[0] templateTitle[1] in the templateTitleSubject[0] amounted to around templateValue[1][6] templateScale euros , while templateTitle[0] templateTitle[2] came to around templateValue[2][6] templateScale euros .
generated: This statistic shows the Government revenue and spending in the Greece from 2014 to 2018 , with projections up until 2024 .  In 2018 , Government revenue in the Greece amounted to around 88.06 million euros , while Government spending came to around 86.29 million euros .

Example 828:
titleEntities: {'Subject': ['Department Homeland Security'], 'Date': ['2020', '2020']}
title: Breakdown of the Department of Homeland Security budget by organization 2020
X_Axis['Organisation']: ['Federal_Emergency_Management_Agency', 'U.S._Customs_and_Border_Protection', 'U.S._Coast_Guard', 'U.S._Immigration_and_Customs_Enforcement', 'Transportation_Security_Administration', 'United_States_Citizenship_and_Immigration_Services', 'Cybersecurity_and_Infrastructure_Security_Agency', 'U.S._Secret_Service', 'Management_Directorate', 'Science_and_Technology_Directorate', 'Countering_Weapons_of_Mass_Destruction', 'Federal_Law_Enforcement_Training_Centers', 'Analysis_and_Operations', 'Office_of_Inspector_General']
Y_Axis['Budget', 'authority', 'in', 'million', 'U.S.', 'dollars']: ['28708.82', '20850.39', '11339.98', '9308.59', '7785.63', '4840.41', '3168.08', '2573.98', '1698.6', '582.12', '423.16', '350.94', '276.64', '170.19']

gold: This statistic gives a breakdown of how the budget of the Department of Homeland is allocated amongst the various organizations administered by the agency in FY 2020 . The second budget of President Trump in FY 2020 saw funding for U.S. Customs and Border Protection increase by around 4.16 billion dollars from the 2018 figure to 20.85 billion U.S. dollars .
gold_template: This statistic gives a templateTitle[0] of how the templateYLabel[0] of the templateTitleSubject[0] of templateTitleSubject[0] is allocated amongst the various organizations administered templateTitle[5] the templateXValue[0] in FY templateTitleDate[0] . The second templateYLabel[0] of President Trump in FY templateTitleDate[0] saw funding for templateXValue[1] and templateXValue[1] templatePositiveTrend templateTitle[5] around 4.16 templateScale templateYLabel[4] from the 2018 figure to templateYValue[1] templateScale templateXValue[1] templateYLabel[4] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] employees in the templateTitle[4] as of 2019 . According to the survey findings , it was that the templateXValue[0] education accounted for templateYValue[7] templateScale of people .
generated: This statistic gives information on the Breakdown Department Homeland employees in the budget as of 2019 .  According to the survey findings , it was that the Federal_Emergency_Management_Agency education accounted for 2573.98 million of people .

Example 829:
titleEntities: {'Subject': ['BT Group Adjusted'], 'Date': ['2008', '2019']}
title: BT Group : Adjusted revenues 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'GBP']: ['24082£', '23746£', '24082£', '18879£', '17840£', '18287£', '18339£', '19397£', '20076£', '20911£', '21431£', '20704£']

gold: British Telecommunications ( BT ) Group generated 24.08 billion British pounds in 2019 , slightly up on the year prior , and 6.2 billion British pounds higher than the low of 17.84 billion British pounds recorded in 2015 . BT Group revenue mix BT Group have had to respond to the impact of the changing telecommunications landscape on their revenue streams . The volume of calls made in the United Kingdom ( UK ) has more than halved since the beginning of 2012 , affecting revenue , and revenue streams such as business network access have also decreased .
gold_template: British Telecommunications ( templateTitleSubject[0] ) templateTitleSubject[0] generated 24.08 templateScale British pounds in templateXValue[max] , slightly up on the templateXLabel[0] prior , and 6.2 templateScale British pounds higher than the low of 17.84 templateScale British pounds recorded in templateXValue[4] . templateTitleSubject[0] revenue mix templateTitleSubject[0] have had to respond to the impact of the changing telecommunications landscape on their templateYLabel[0] streams . The volume of calls made in the United Kingdom ( UK ) has more than halved since the beginning of templateXValue[7] , affecting templateYLabel[0] , and templateYLabel[0] streams such as business network access have also templateNegativeTrend .

generated_template: This statistic shows the total templateYLabel[0] of the templateTitle[0] employees in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] of the templateTitleSubject[0] templateTitle[2] templateYLabel[0] templatePositiveTrend by templateYValue[idxmin(X)] templateScale , compared to the previous templateXLabel[0] .
generated: This statistic shows the total Revenue of the BT employees in the BT Group Adjusted from 2008 to 2019 , from 2008 to 2019 .  In 2008 , the BT of the BT Group Adjusted Adjusted Revenue increase by 20704£ million , compared to the previous Year .

Example 830:
titleEntities: {'Subject': ['Pork'], 'Date': ['2020']}
title: Pork imports of selected countries worldwide 2020 , by country
X_Axis['Country']: ['China', 'Japan', 'Mexico', 'Korea_South', 'United_States', 'Hong_Kong', 'Philippines', 'Canada', 'Others']
Y_Axis['Import', 'volume', 'in', 'thousand', 'metric', 'tones']: ['3700', '1490', '1235', '695', '401', '300', '300', '260', '1590']

gold: This statistic depicts import volume of pork worldwide in 2020 , by leading country . The pork imports of Japan are estimated to be about 1.49 million metric tons in 2020 .
gold_template: This statistic depicts templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[4] in templateTitleDate[0] , templateTitle[6] leading templateXLabel[0] . The templateTitleSubject[0] templateTitle[1] of templateXValue[1] are estimated to be about templateYValue[1] templateScale templateYLabel[3] tons in templateTitleDate[0] .

generated_template: This statistic shows the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In that year , the templateXValue[0] was ranked first with a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the 20 Pork imports the selected countries in 2020 , worldwide Country .  In that year , the China was ranked first with a Import of 3700 thousand metric .

Example 831:
titleEntities: {'Subject': ['Johnson Johnson'], 'Date': ['2005', '2019']}
title: Johnson & Johnson 's net income 2005 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['15119', '15297', '1300', '16540', '15409', '16323', '13831', '10853', '9672', '13334', '12266', '12949', '10576', '11053', '10060']

gold: Johnson & Johnson 's revenues have generally been increasing over the last several years . As of 2019 , Johnson & Johnson 's total net income was estimated to be about 15.12 billion U.S. dollars . This is a slight decrease from the previous year when net income was at about 15.3 billion U.S. dollars .
gold_template: templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] revenues have generally been templatePositiveTrend over the last several years . As of templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] total templateYLabel[0] templateYLabel[1] was estimated to be about templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] . This is a slight templateNegativeTrend from the previous templateXLabel[0] when templateYLabel[0] templateYLabel[1] was at about templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . According to the report , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] amounted to templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net income of the Johnson Johnson of worldwide from 2005 to 2019 .  According to the report , the Net income of the Johnson Johnson 's amounted to 15119 million U.S. dollars .

Example 832:
titleEntities: {'Subject': ['Missing'], 'Date': ['2012', '2019']}
title: Missing persons cases 2012 to 2019
X_Axis['Year']: ['H1_2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'cases']: ['236656', '229687', '211219', '189110', '166280', '149036', '133989', '120791']

gold: In June 2019 , the number of cases of missing people amounted to 236,656 . More specifically , the chart displays the total number of reports recorded by the Police between 1974 and June 2019 . In 1974 , a database recording the number of missing people cases was started .
gold_template: In 2019 , the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] people amounted to templateYValue[max] . More specifically , the chart displays the total templateYLabel[0] of reports recorded by the Police between 1974 and 2019 . In 1974 , a database recording the templateYLabel[0] of templateTitleSubject[0] people templateYLabel[1] was started .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , there were a total of templateYValue[2] templateScale templateYLabel[1] with the templateYLabel[3] of templateTitle[0] in templateTitleSubject[0] .
generated: This statistic shows the Number of Missing persons cases in the 2019 from 2012 to H1_2019 .  In 2017 , there were a total of 211219 million cases with the cases of Missing in .

Example 833:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014', '2016']}
title: Consumer spending on mobile games in the U.S. 2014 to 2016
X_Axis['Year']: ['2016', '2015', '2014']
Y_Axis['Average', 'expenditure', 'in', 'U.S.', 'dollars']: ['77.6', '70.44', '60.95']

gold: This statistic gives information on the overall amount spent on mobile games by mobile games in the United States from 2014 to 2016 . During the last reported period , it was found that the average overall mobile gaming expenditure per user amounted to 77.6 U.S. dollars .
gold_template: This statistic gives information on the overall amount spent on templateTitle[2] templateTitle[3] by templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . During the last reported period , it was found that the templateYLabel[0] overall templateTitle[2] gaming templateYLabel[1] per user amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateYLabel[0] templateTitle[2] in templateTitleSubject[0] amounted to templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Average of recreational expenditure in U.S. from 2014 to 2016 .  In 2015 , the Average mobile in U.S. amounted to 70.44 billion U.S. dollars .

Example 834:
titleEntities: {'Subject': ['South Africa', 'Facebook'], 'Date': ['2017', '2023']}
title: South Africa : number of Facebook users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['19.8', '19.0', '18.3', '17.6', '16.9', '16.2', '15.4']

gold: This statistic shows the number of Facebook users in South Africa from 2017 to 2023 . In 2023 , the number of Facebook users in South Africa is expected to reach 19.8 million , up from 16.2 million users in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[max] templateYValue[idxmax(X)] , up from templateYValue[5] templateScale templateYLabel[1] in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around templateYValue[idxmax(X)] templateScale templateTitle[2] templateTitle[3] templateYLabel[1] in the country , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] .
generated: This statistic shows the Number of Facebook users in South Africa from 2017 to 2023 .  In 2023 , it is estimated that there will be around 19.8 millions number Facebook users in the country , up from 15.4 millions in 2017 .

Example 835:
titleEntities: {'Subject': ['Women', 'Great Britain'], 'Date': ['2017']}
title: Women 's magazine websites ranked by visitors in Great Britain ( GB ) 2017
X_Axis['British', 'Retailers']: ['tesco.com', 'asda.com', 'bbcgoodfood.com', 'johnlewis.com', 'ok.co.uk', 'jamieoliver.com', 'waitrose.com', 'Cosmopolitan_UK', 'vogue.co.uk', 'goodhousekeeping.co.uk', 'slimmingworld.co.uk', 'hellomagazine.com', 'deliciousmagazine.co.uk', 'glamourmagazine.co.uk', 'idealhome.co.uk', 'vanityfair.com', 'marieclaire.co.uk', 'elleuk.com', 'celebsnow.co.uk', 'womanandhome.com', 'redonline.co.uk', 'youandyourwedding.co.uk', 'whatsontv.co.uk', 'takeabreak.co.uk', 'weightwatchers.co.uk']
Y_Axis['Visitors', 'in', 'thousands']: ['3498', '2067', '1901', '1576', '542', '456', '441', '298', '260', '239', '227', '223', '191', '158', '153', '136', '124', '103', '80', '78', '77', '62', '59', '39', '38']

gold: Among the websites of women 's magazines in the United Kingdom ( UK ) , as defined by the National Readership Survey , tesco.com was the most visited . Tesco.com , the website of the retailer Tesco with the print magazine titled Tesco - Food Family Living , attracted 3.498 million views monthly from October 2016 to September 2017 . Top selling print editions Between January and December of 2018 , Cosmopolitan was the leading women 's lifestyle and fashion magazine by copies sold , with more than 334 thousand copies making their way off the shelves .
gold_template: Among the templateTitle[3] of templateTitleSubject[0] templateTitle[1] magazines in the United Kingdom ( templateXValue[7] ) , as defined templateTitle[5] the National Readership Survey , templateXValue[0] was the most visited . templateXValue[0] , the website of the retailer Tesco with the print templateTitle[2] titled Tesco - Food Family Living , attracted 3.498 templateScale views monthly from October 2016 to 2017 . Top selling print editions Between and of 2018 , templateXValue[7] was the leading templateTitleSubject[0] templateTitle[1] lifestyle and fashion templateTitle[2] templateTitle[5] copies sold , with more than 334 thousand copies making their way off the shelves .

generated_template: The statistic depicts the templateTitle[2] of templateYLabel[1] at that templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , based on the templateXValue[0] . templateXValue[0] accounted for templateYValue[max] templateScale of the templateYLabel[1] of the total templateYLabel[0] .
generated: The statistic depicts the magazine of thousands at that ranked in Women 2017 , based on the tesco.com .  tesco.com accounted for 3498 thousands of the thousands of the total Visitors .

Example 836:
titleEntities: {'Subject': ['Instagram'], 'Date': ['2017']}
title: Dogs with the most Instagram followers worldwide 2017
X_Axis['Month']: ['Jiff_Pom_@Jiffpom', 'Doug_the_Pug_@itsdougthepug', 'Maruto_@Marutaro', 'Marnie_@Marniethedog', 'Tuna_@tunameltsmyheart', 'Loki_@Loki_the_wolfdog', 'Harlow_Indiana_and_Reese_of_@harlowandsage', 'Maddie_@thiswildidea', 'Manny_@manny_the_frenchie', 'Loki_and_Bear_@Lokistagram']
Y_Axis['Number', 'of', 'followers', 'in', 'millions']: ['6.6', '2.9', '2.6', '2.1', '1.9', '1.6', '1.5', '1.2', '1.0', '1.0']

gold: This statistic presents a ranking of the most popular dogs on Instagram as of October 2017 , sorted by the highest numbers of followers . Pomeranian Jiff Pom was the most-followed dog on the photo sharing app platform with 6.6 million followers .
gold_template: This statistic presents a ranking of the templateTitle[2] popular templateTitle[0] on templateTitleSubject[0] as of October templateTitleDate[0] , sorted by the highest numbers of templateYLabel[1] . Pomeranian templateXValue[0] was the most-followed dog on the photo sharing app platform templateTitle[1] templateYValue[max] templateScale templateYLabel[1] .

generated_template: This statistic gives information on the most-followed templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] as of 2019 , templateTitle[4] templateYLabel[0] . According to the source , templateXValue[0] was the most-followed templateTitle[2] almost templateYValue[max] templateScale templateYLabel[1] .
generated: This statistic gives information on the most-followed Dogs most Instagram of as 2019 , worldwide Number .  According to the source , Jiff_Pom_@Jiffpom was the most-followed Instagram almost 6.6 millions followers .

Example 837:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Crucial problems in U.S. politics as of 2018
X_Axis['Response']: ['Immigration', 'Race_relations/_racism', 'Donald_Trump', 'Parties/_gridlock/_divisions_in_country', 'Economy_(general)_health_care/costs/accessibility', 'Unemployment/_lack_of_jobs', 'Distribution_of_wealth_/_Inequality', 'Education/_schools/_affording_education', 'Foreign_policy_(general)', 'Crime/violence/judicial_system', 'Morality/_ethics/_family_values', 'Gun_control/_too_many_guns', 'Lack_of_humanity/_respect', 'Environment/_global_warming', 'Dissatisfaction_with_government/_politics/_corruption', 'Defense/_national_security']
Y_Axis['Share', 'of', 'respondents']: ['12', '8', '8', '7', '6', '4', '4', '4', '4', '3', '3', '3', '3', '2', '2', '2']

gold: In this 2018 survey , eight percent of respondents stated they think the biggest problem for the United States today is Donald Trump . In fact , most of the important problems mentioned are somehow related to the current POTUS – the most mentioned one being immigration and racism . A country divided Since Trump 's inauguration in January 2017 , political camps in the United States are deeply divided and global politics is in turmoil ; Trump 's job approval ratings are notoriously low , and in fact , they are lower than Reagan 's or Nixon 's averages ever were .
gold_template: In this templateTitleDate[0] survey , templateYValue[1] templateScale of templateYLabel[1] stated they think the biggest problem for the templateTitle[2] today is templateXValue[2] . In fact , most of the important templateTitle[1] mentioned are somehow related to the current POTUS – the most mentioned one being templateXValue[0] and templateXValue[1] . A templateXValue[3] divided Since templateXValue[2] 's inauguration in 2017 , political camps in the templateTitle[2] are deeply divided and templateXValue[13] templateTitle[3] is in turmoil ; templateXValue[2] 's job approval ratings are notoriously low , and in fact , they are lower than Reagan 's or Nixon 's averages ever were .

generated_template: As of templateTitleDate[0] , it was found that templateYValue[max] templateScale of the templateYLabel[1] claimed that templateYValue[1] templateScale of the templateXValue[2] a templateXValue[1] templateXValue[2] . templateXValue[1] 's templateXValue[2] with templateYValue[2] templateScale of templateYLabel[1] .
generated: As of 2018 , it was found that 12 % of the respondents claimed that 8 % of the Donald_Trump a Race_relations/_racism Donald_Trump .  Race_relations/_racism 's Donald_Trump with 8 % of respondents .

Example 838:
titleEntities: {'Subject': ['Gini', 'Taiwan'], 'Date': ['2008', '2018']}
title: Gini index of Taiwan 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis["Gini's", 'concentration', 'coefficient']: ['33.8', '33.7', '33.6', '33.8', '33.6', '33.6', '33.8', '34.2', '34.2', '34.5', '34.1']

gold: This statistic shows the Gini 's concentration coefficient in Taiwan from 2008 to 2018 . In 2018 , the Gini index in Taiwan was 33.8 points , around 0.7 percentage points lower than in 2009 . In the countries having relative equality in their distributions of income , the value of the Gini coefficient usually ranges between the scores of 20 and 35 .
gold_template: This statistic shows the templateTitleSubject[0] 's templateYLabel[1] templateYLabel[2] in templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateTitle[1] in templateTitleSubject[1] was templateYValue[idxmax(X)] points , around 0.7 templateScale points lower than in templateXValue[9] . In the countries having relative equality in their distributions of income , the value of the templateTitleSubject[0] templateYLabel[2] usually ranges between the scores of 20 and templateYValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Gini's concentration of the coefficient 2018 of Gini from 2008 to 2018 .  In 2018 , the concentration of Gini was approximately 33.8 coefficient .

Example 839:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2009', '2019']}
title: Facebook : annual revenue 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['70697', '55838', '40653', '27638', '17928', '12466', '7872', '5089', '3711', '1974', '777']

gold: In 2019 , Facebook 's revenue amounted to 70.7 billion US dollars , up from 55.8 billion U.S. dollars in the previous fiscal year . The social network 's main source of income is digital advertising . Facebook revenue and income – more information As a side effect from being the biggest social network worldwide , Facebook is the leading platform for social media advertising and marketing with 94 percent of global marketers utilizing Facebook in their marketing efforts .
gold_template: In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] amounted to templateYValue[max] templateScale US templateYLabel[3] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the previous fiscal templateXLabel[0] . The social network 's main source of income is digital advertising . templateTitleSubject[0] templateYLabel[0] and income – more information As a side effect from being the biggest social network worldwide , templateTitleSubject[0] is the leading platform for social media advertising and marketing with 94 templateScale of global marketers utilizing templateTitleSubject[0] in their marketing efforts .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] are owned by John Stanton and Chris Larson , who bought the franchise for 1.2 templateScale templateYLabel[2] templateYLabel[3] and 100 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[2] .
generated: The statistic depicts the Revenue of the Facebook from 2009 to 2019 .  In 2019 , the Revenue of the Major League Baseball franchise amounted to 70697 million U.S. dollars.The Facebook are owned by John Stanton and Chris Larson , who bought the franchise for 1.2 million U.S. dollars and 100 million U.S. dollars in 2017 .

Example 840:
titleEntities: {'Subject': ['MLS'], 'Date': ['2018']}
title: Average player salary in MLS by team 2018
X_Axis['Team']: ['Toronto_FC', 'LA_Galaxy', 'New_York_City_FC', 'Chicago_Fire', 'Los_Angeles_FC', 'Seattle_Sounders_FC', 'Portland_Timbers', 'Montreal_Impact', 'Colorado_Rapids', 'Atlanta_United', 'Sporting_Kansas_City', 'Orlando_City_SC', 'Philadelphia_Union', 'FC_Dallas', 'Real_Salt_Lake', 'New_England_Revolution', 'Minnesota_United_FC', 'San_Jose_Earthquakes', 'New_York_Red_Bulls', 'D.C._United', 'Columbus_Crew_SC', 'Vancouver_Whitecaps_FC', 'Houston_Dynamo']
Y_Axis['Salary', 'in', 'thousand', 'U.S.', 'dollars']: ['902.41', '648.15', '544.23', '492.86', '479.64', '421.48', '417.33', '383.55', '366.67', '364.94', '327.86', '309.66', '307.59', '300.97', '299.33', '272.8', '271.67', '268.62', '265.17', '259.23', '258.15', '257.19', '195.52']

gold: The statistic shows the average player salary in Major League Soccer by team in 2018 . The average player salary in Toronto FC amounted to about 902 thousand U.S. dollars .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in Major League Soccer templateTitle[4] templateXLabel[0] in templateTitleDate[0] . The templateTitle[0] templateTitle[1] templateYLabel[0] in templateXValue[0] amounted to about templateYValue[max] thousand templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows a ranking of the templateTitleSubject[0] League according to the templateYLabel[2] templateYLabel[0] templateTitle[4] , templateTitle[7] templateTitle[8] . In the last reported period , the templateXValue[0] accounted for templateYValue[max] templateScale of all templateYLabel[2] .
generated: The statistic shows a ranking of the MLS League according to the U.S. Salary by , 2018 .  In the last reported period , the Toronto_FC accounted for 902.41 thousand of all U.S. .

Example 841:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Annual compensation earned by U.S. physicians by specialty 2019
X_Axis['Occupation']: ['Orthopedics', 'Plastic_surgery', 'Otolaryngology', 'Cardiology', 'Dermatology', 'Radiology', 'Gastroenterology', 'Urology', 'Anesthesiology', 'Ophthalmology', 'General_surgery', 'Oncology', 'Emergency_medicine', 'Critical_care', 'Pulmonology', 'Pathology', 'Physical_medicine/rehabilitation', 'Nephrology', 'Obstetricians/gynecologists', 'Allergy/immunology', 'Neurology', 'Psychiatry', 'Rheumatology', 'Internal_medicine', 'Infectious_disease', 'Diabetes/endocrinology', 'Family_medicine', 'Pediatrics', 'Public_health/preventive_medicine']
Y_Axis['Annual', 'compensation', 'in', 'thousand', 'U.S.', 'dollars']: ['482', '471', '461', '430', '419', '419', '417', '408', '392', '366', '362', '359', '353', '349', '331', '308', '306', '305', '303', '275', '267', '260', '259', '243', '239', '236', '231', '225', '209']

gold: Orthopedic physicians earn an average annual compensation of around 482,000 U.S. dollars . This makes orthopedic physicians the most well-compensated physicians in the United States as of 2019 . An orthopaedic physician specializes in injuries and diseases involving bones , muscles , joints , nerves and other parts of the musculoskeletal system .
gold_template: Orthopedic templateTitle[5] earn an average templateYLabel[0] templateYLabel[1] of around templateYValue[max] templateYLabel[3] templateYLabel[4] . This makes orthopedic templateTitle[5] the most well-compensated templateTitle[5] in the templateTitle[4] as of templateTitleDate[0] . An orthopaedic physician specializes in injuries and diseases involving bones , muscles , joints , nerves and other parts of the musculoskeletal system .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] templateTitle[4] people in templateTitleDate[0] , templateTitle[7] templateTitle[8] of templateYLabel[1] . In that year , templateXValue[0] was ranked first , with a score of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Annual of compensation earned by U.S. people in 2019 , specialty 2019 of compensation .  In that year , Orthopedics was ranked first , with a score of 482 thousand U.S. .

Example 842:
titleEntities: {'Subject': ['Miami', 'Fort Lauderdale', 'Palm Beach'], 'Date': ['2010', '2018']}
title: Miami-Fort Lauderdale-West Palm Beach metro area population U.S. 2010 to 2018
X_Axis['Year']: ['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Number', 'of', 'residents']: ['5583547', '5693531', '5771731', '5849411', '5921030', '5998284', '6086935', '6149687', '6198782']

gold: This statistic shows the population of the Miami-Fort Lauderdale-West Palm Beach area in the United States from 2010 to 2018 . In 2018 , about 6.19 million people lived in the Miami-Fort Lauderdale-West Palm Beach metropolitan area .
gold_template: This statistic shows the templateTitle[6] of the templateTitle[0] templateTitle[1] templateTitleSubject[2] area in the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about 6.19 templateScale people lived in the templateTitle[0] templateTitle[1] templateTitleSubject[2] metropolitan templateTitle[5] .

generated_template: This statistic shows the templateTitle[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale people lived in templateTitleSubject[0] , making it the templateTitle[0] of the templateTitleSubject[0] .
generated: This statistic shows the Miami-Fort of the Miami from 2010 to 2018 .  In 2018 , about 6198782 million people lived in Miami , making it the Miami-Fort of the Miami .

Example 843:
titleEntities: {'Subject': ['UFC'], 'Date': []}
title: UFC : most knockdowns landed by fighters worldwide as of March 2020
X_Axis['Month']: ['Donald_Cerrone', 'Anderson_Silva', 'Jeremy_Stephens', 'Junior_Dos_Santos', 'Chuck_Liddell', 'Lyoto_Machida', 'Mauricio_Rua', 'Thiago_Alves', 'Melvin_Guillard', 'Anthony_Johnson']
Y_Axis['Number', 'of', 'knockdowns', 'landed']: ['20', '18', '18', '14', '14', '14', '14', '13', '13', '13']

gold: Donald Cerrona is leading the Ultimate Fighting Championship ( UFC ) in most knockdowns landed as of March 2020 , followed by Anderson Silva and Jeremy Stephens , both with 18 knockdowns during UFC fights . At the same point in time George St-Pierre holds the record for most takedowns in the UFC with a total of 90 and also the most strikes landed , with 2,591 landed strikes during UFC fights . 474 UFC fight in 2018 In 2018 , a total of 39 UFC events were hosted around the world featuring 474 fights .
gold_template: templateXValue[0] Cerrona is leading the Ultimate Fighting Championship ( templateTitleSubject[0] ) in templateTitle[1] templateYLabel[1] templateYLabel[2] as of templateTitle[7] templateTitleDate[0] , followed templateTitle[4] templateXValue[1] and templateXValue[2] , both with templateYValue[1] templateYLabel[1] during templateTitleSubject[0] fights . At the same point in time George St-Pierre holds the record for templateTitle[1] takedowns in the templateTitleSubject[0] with a total of 90 and also the templateTitle[1] strikes templateYLabel[2] , with 2,591 templateYLabel[2] strikes during templateTitleSubject[0] fights . 474 templateTitleSubject[0] fight in 2018 In 2018 , a total of 39 templateTitleSubject[0] events were hosted around the world featuring 474 fights .

generated_template: As of the third quarter of templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] of people on the templateTitleSubject[0] . According to the source , templateXValue[0] accounted for over templateYValue[max] templateScale of people of total templateYLabel[2] templateYLabel[3] , followed by templateXValue[1] with templateYValue[1] templateScale .
generated: As of the third quarter of , Donald_Cerrone had the highest Number of people on the UFC .  According to the source , Donald_Cerrone accounted for over 20 % of people total landed , followed by Anderson_Silva with 18 % .

Example 844:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019']}
title: Number of Dollar Tree stores in Canada by province 2019
X_Axis['Province']: ['Manitoba', 'Saskatchewan', 'Alberta', 'British_Columbia', 'Ontario']
Y_Axis['Number', 'of', 'stores']: ['13', '16', '37', '49', '110']

gold: As of February 2019 , Ontario was the Canadian province home to the most Dollar Tree stores in the whole of Canada , with 110 . British Columbia and Alberta ranked in second and third places , with 49 and 37 stores respectively . At that time , there were Dollar Tree stores located in five of Canada 's ten provinces .
gold_template: As of 2019 , templateXValue[last] was the Canadian templateXLabel[0] home to the most templateTitle[1] templateTitle[2] templateYLabel[1] in the whole of templateTitleSubject[0] , with templateYValue[max] . templateXValue[3] and templateXValue[2] ranked in second and third places , with templateYValue[3] and templateYValue[2] templateYLabel[1] respectively . At that time , there were templateTitle[1] templateTitle[2] templateYLabel[1] located in five of templateTitleSubject[0] 's ten provinces .

generated_template: There were templateYValue[max] templateTitleSubject[0] stores across templateTitleSubject[1] in templateTitleDate[0] . templateXValue[1] was templateTitleSubject[0] to templateYValue[1] , the most templateYLabel[1] templateTitle[6] far . templateXValue[2] and templateXValue[3] were ranked in second and third places , with templateYValue[2] and templateYValue[3] templateYLabel[1] located in each templateXLabel[0] respectively .
generated: There were 110 Canada stores across Canada in 2019 .  Saskatchewan was Canada to 16 , the most stores province far .  Alberta and British_Columbia were ranked in second and third places , with 37 and 49 stores located in each Province respectively .

Example 845:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Argentina 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['12177.22', '11835.19', '11138.8', '10462.47', '9730.91', '9887.79', '11658.22', '14588.01', '12772.87', '14895.32', '13208.83', '14488.83', '13889.79', '12787.81', '10412.95', '8337.81', '9146.79', '7315.73', '5976.08', '5163.55', '4314.4', '3648.06', '2898.29', '7851.66', '8386.59', '8452.88', '9005.38', '8918.83', '8385.15', '8053.12', '8125.91', '7558.5', '7420.32', '6233.39', '4709.66', '2779.87', '4386.53', '3790.75', '3739.41', '3149.68', '4241.38']

gold: The statistic shows the gross domestic product ( GDP ) per capita in Argentina in 1984 and 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateYLabel[0] of the total templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . According to the total market value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows the GDP of the total capita in the Argentina from 1984 to 2017 , with projections up until 2024 .  According to the total market value of all goods and services produced in a country in a Year .  It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

Example 846:
titleEntities: {'Subject': ['Nintendo DS'], 'Date': ['2019']}
title: Top selling Nintendo DS games worldwide in 2019
X_Axis['Month']: ['New_Super_Mario_Bros.', 'Nintendogs', 'Mario_Kart_DS', 'Brain_Age', 'Pokemon_Diamond_Version_/_Pearl_Version', 'Pokemon_Black_Version_/_White_Version', 'Brain_Age_2', 'Pokemon_HeartGold_Version_/_SoulSilver_Version', 'Animal_Crossing:_Wild_World', 'Super_Mario_64_DS']
Y_Axis['Sales', 'in', 'million', 'units']: ['30.8', '23.96', '23.6', '19.01', '17.67', '15.64', '14.88', '12.72', '11.75', '11.06']

gold: This statistic shows the top selling Nintendo DS titles worldwide . With 30.8 million units sold , New Super Mario Bros. was the top selling Nintendo DS game as of September 2019 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateXValue[2] titles templateTitle[5] . With templateYValue[max] templateScale templateYLabel[2] sold , templateXValue[0] Mario templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateXValue[2] game as of 2019 .

generated_template: How many people have been sold ? Almost templateYValue[max] templateScale people were sold in templateTitleDate[0] , making it the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] during that year . In the last ten years , templateXValue[0] character had almost templateYValue[max] templateScale of all people .
generated: How many people have been sold ? Almost 30.8 million people were sold in 2019 , making it the Top selling in the Nintendo DS during that year .  In the last ten years , New_Super_Mario_Bros. character had almost 30.8 million of all people .

Example 847:
titleEntities: {'Subject': ['Norway'], 'Date': ['2019']}
title: Leading telecommunication companies in Norway 2019 , by turnover
X_Axis['Telecom', 'Name']: ['Telenor_ASA', 'Telenor_Norge_AS', 'Telia_Norge_AS', 'Telenor_Global_Services_AS', 'Get_AS', 'Eltel_Networks_AS', 'Canal_Digital_Norge_AS', 'Broadnet_Topholding_AS', 'Broadnet_AS', 'Nextgentel_Holding_ASA', 'RiksTV_AS', 'Phonero_AS', 'Viken_Fiber_Holding_AS', 'Norkring_AS', 'TDC_AS', 'Telenor_Satellite_AS', 'Fugro_Norway_AS', 'Viasat_AS', 'Kongsberg_Satellite_Services_AS', 'Netel_AS']
Y_Axis['Turnover', 'in', 'million', 'NOK']: ['124756', '24669', '9091', '3128', '2867', '2445', '2157', '1579', '1424', '1359', '1220', '1109', '1106', '995', '946', '846', '702', '686', '618', '486']

gold: Telenor ASA ranked first as the leading telecom company in Norway , by turnover , as of May 2019 . Its turnover was nearly 125 billion Norwegian kroner . Telenor Norge AS and Telia Norge AS came second and third in the ranking , with revenues of roughly 25 billion and nine billion Norwegian kroner .
gold_template: templateXValue[0] ranked first as the templateTitle[0] templateXLabel[0] company in templateXValue[16] , templateTitle[5] templateYLabel[0] , as of 2019 . Its templateYLabel[0] was nearly templateYValue[max] templateScale Norwegian kroner . templateXValue[0] templateXValue[1] AS and templateXValue[2] templateXValue[1] AS came second and third in the ranking , with revenues of roughly templateYValue[1] templateScale and templateYValue[2] templateScale Norwegian kroner .

generated_template: This statistic shows the templateTitle[0] templateYValue[3] templateYLabel[2] templateYLabel[3] of the templateYLabel[1] templateTitle[1] templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . It was that time , templateXValue[0] , with over templateYValue[max] templateScale templateYLabel[3] of templateYLabel[1] , followed by templateXValue[1] with templateYValue[1] templateScale of the templateYLabel[1] .
generated: This statistic shows the Leading 3128 NOK of the million telecommunication companies in 2019 , Telecom .  It was that time , Telenor_ASA with over 124756 million NOK of million , followed by Telenor_Norge_AS with 24669 million of the million .

Example 848:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2011', '2019']}
title: Twitter : quarterly revenue 2011 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['1007.43', '823.72', '841.38', '786.89', '908.84', '758.11', '710.54', '664.87', '731.56', '589.63', '573.86', '548.25', '717.21', '615.93', '601.96', '594.52', '710.47', '569.24', '502.38', '435.94', '479.08', '361.27', '312.17', '250.49', '242.68', '168.58', '139.29', '114.34', '112.25', '82.33', '68.05', '54.31', '41.21', '26.42', '20.77', '17.91']

gold: In the fourth quarter of 2019 , Twitter 's revenue amounted to over 1 billion U.S. dollars , a 22 percent increase compared to the previous quarter . The majority of the social network 's revenues are generated via advertising , followed by data licensing and other revenues . Twitter company information Twitter ranks among the biggest social networks worldwide .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] 's templateYLabel[0] amounted to over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , a 22 templateScale templatePositiveTrend compared to the previous templateXLabel[0] . The majority of the social network 's revenues are generated via advertising , followed by data licensing and other revenues . templateTitleSubject[0] company information templateTitleSubject[0] ranks among the biggest social networks worldwide .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . As of the most recently reported templateXLabel[0] , templateTitleSubject[0] 's total templateYLabel[0] amounted to templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Twitter quarterly revenue in the Twitter from the first Quarter of 2011 to the fourth Quarter of 2019 .  As of the most recently reported Quarter , Twitter 's total Revenue amounted to 1007.43 million U.S. dollars .

Example 849:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Reactions to racist posts on social media in the U.S. 2017
X_Axis['Response']: ['Ignore_the_post', 'Unfollow/unfriend_the_poster', 'Comment_on/_reply_to_the_post', 'Discuss_the_content_with_the_poster_offline_(e.g._on_the_phone_in_person_etc.)', "Don't_know"]
Y_Axis['Share', 'of', 'respondents']: ['40', '31', '17', '6', '7']

gold: This statistic shows the most common reactions of online adults in the United States when encountering racist posts on social media . During the August 2017 survey , 40 percent of respondents stated they would ignore the content , whereas 31 percent would unfollow or unfriend the poster .
gold_template: This statistic shows the most common templateTitle[0] of online adults in the templateTitle[5] when encountering templateTitle[1] templateTitle[2] on templateTitle[3] templateTitle[4] . During the 2017 survey , templateYValue[max] templateScale of templateYLabel[1] stated they would templateXValue[0] the templateXValue[3] , whereas templateYValue[1] templateScale would unfollow or unfriend the templateXValue[1] .

generated_template: As of the third quarter of templateTitleDate[0] , it was found that templateYValue[max] templateScale of templateTitleSubject[0] users in the templateTitleSubject[1] accessed the social platform templateXValue[0] a templateXValue[0] . A further templateYValue[1] templateScale of templateYLabel[1] claimed that they used templateXValue[1] templateXValue[0] .
generated: As of the third quarter of 2017 , it was found that 40 % of U.S. users in the U.S. accessed the social platform Ignore_the_post a .  A further 31 % of respondents claimed that they used Unfollow/unfriend_the_poster Ignore_the_post .

Example 850:
titleEntities: {'Subject': ['Budget'], 'Date': ['2000']}
title: U.S. government - Budget surplus or deficit 2000 to 2025
X_Axis['Year']: ["'25", "'24", "'23", "'22", "'21", "'20", "'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00"]
Y_Axis['Surplus/deficit', 'in', 'trillion', 'U.S.', 'dollars']: ['-0.53', '-0.55', '-0.75', '-0.92', '-0.97', '-1.08', '-0.98', '-0.78', '-0.67', '-0.58', '-0.44', '-0.48', '-0.68', '-1.08', '-1.3', '-1.29', '-1.41', '-0.46', '-0.16', '-0.25', '-0.32', '-0.41', '-0.38', '-0.16', '0.13', '0.24']

gold: In 2019 , the U.S. government had a budget deficit of 0.53 trillion U.S. dollars . This is compared to 2000 , when the government had a budget surplus of 0.24 trillion U.S. dollars . U.S. Government budget The government budget is a financial statement that demonstrates the government 's suggested revenues and spending for the financial year .
gold_template: In 2019 , the templateYLabel[2] templateTitle[1] had a templateTitleSubject[0] templateTitle[4] of 0.53 templateScale templateYLabel[2] templateYLabel[3] . This is compared to templateTitleDate[min] , when the templateTitle[1] had a templateTitleSubject[0] templateTitle[3] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateYLabel[2] templateTitle[1] templateTitleSubject[0] The templateTitle[1] templateTitleSubject[0] is a financial statement that demonstrates the templateTitle[1] 's suggested revenues and spending for the financial templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitleSubject[0] from templateTitleDate[min] to 2019 , with projections up until templateXValue[max] . In 2019 , the templateTitle[0] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[2] was templateYValue[last] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Surplus/deficit trillion of the U.S. Budget from 2000 to 2019 , with projections up until '25 .  In 2019 , the U.S. Surplus/deficit trillion of the Budget was 0.24 U.S. dollars .

Example 851:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Survey on regretting tattoos in the U.S. 2015
X_Axis['Response']: ['Yes', 'No']
Y_Axis['Share', 'of', 'respondents']: ['23', '77']

gold: This statistic shows the results of a survey among adult Americans who have one or more tattoos . The survey was conducted in 2015 , asking the respondents whether they ever regret getting any of their tattoos , or not . 77 percent of respondents stated they do not regret getting any of their tattoos .
gold_template: This statistic shows the results of a templateTitle[0] among adult Americans who have one or more templateTitle[2] . The templateTitle[0] was conducted in templateTitleDate[0] , asking the templateYLabel[1] whether they ever regret getting any of their templateTitle[2] , or not . templateYValue[max] templateScale of templateYLabel[1] stated they do not regret getting any of their templateTitle[2] .

generated_template: This statistic shows the results of a survey in the country in templateTitleDate[0] on one-night-stands . templateYValue[max] templateScale of templateYLabel[1] in the country stated they templateTitle[3] templateTitle[4] a templateTitle[5] before .
generated: This statistic shows the results of a survey in the country in 2015 on one-night-stands .  77 % of respondents in the country stated they U.S. 2015 a before .

Example 852:
titleEntities: {'Subject': ['United States'], 'Date': ['1995', '2018']}
title: Natural gas consumption in the United States : 1995 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995']
Y_Axis['Consumption', 'in', 'trillion', 'cubic', 'feet']: ['29.95', '27.13', '27.44', '27.24', '26.59', '26.16', '25.54', '24.48', '24.09', '22.91', '23.28', '23.1', '21.7', '22.01', '23.33', '22.21']

gold: Consumption of natural gas in the United States reached a record high of 29.95 trillion cubic feet in 2018 and has risen by roughly a third since 1995 . Natural gas consumption increased consistently from 2009 to 2016 , after over a decade of fluctuation from 1995 to 2008 . High demand for natural gas Natural gas is a colorless , odorless fossil fuel that comes from underground rock formations .
gold_template: templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitleSubject[0] reached a record high of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] and has risen by roughly a third since templateXValue[min] . templateTitle[0] templateTitle[1] templateYLabel[0] templatePositiveTrend consistently from templateXValue[9] to templateXValue[2] , after over a decade of fluctuation from templateXValue[min] to templateXValue[10] . High demand for templateTitle[0] templateTitle[1] templateTitle[0] templateTitle[1] is a colorless , odorless fossil fuel that comes from underground rock formations .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] was approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Consumption trillion of the Natural gas United States United from 1995 to 2018 .  In 2018 , the Natural of United States United was approximately 29.95 cubic feet .

Example 853:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2024']}
title: Inflation rate in Argentina 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['16.98', '22.21', '26.87', '32.26', '51', '54.44', '34.28', '25.68', '10.62', '10.04', '9.78', '10.46', '6.27', '8.59', '8.83', '10.9', '9.64', '4.42']

gold: Inflation in Argentina was 34 percent in 2018 , expected to rise to nearly 44 percent the following year before declining to 6 percent by 2024 . A major factor in this is the legacy of poor fiscal discipline left by the economic depression from 1998 to 2002 . What causes inflation ? Inflation is a rise in price levels for all goods .
gold_template: templateYLabel[0] in templateTitleSubject[0] was templateYValue[6] templateScale in templateXValue[6] , expected to rise to nearly 44 templateScale the following templateXLabel[0] before declining to templateYValue[12] templateScale by templateXValue[max] . A major factor in this is the legacy of poor fiscal discipline left by the economic depression from 1998 to 2002 . What causes templateYLabel[0] ? templateYLabel[0] is a rise in price levels for all goods .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Argentina from 2004 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Argentina amounted to about 34.28 % compared to the previous Year .

Example 854:
titleEntities: {'Subject': ['Levi Strauss'], 'Date': ['2010', '2019']}
title: Global net income of Levi Strauss 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['394.98', '285.24', '284.56', '291.21', '209.89', '104.31', '228.14', '140.96', '135.11', '149.45', '150.71', '230.38', '461.29', '240.72', '151.88']

gold: In 2019 , Levi Strauss ' net income amounted to approximately 394.98 million U.S. dollars . The company is most well-known for denim jeans brand , Levi 's , however they produce many different types of apparel products . Levi Strauss & Co. Levi Strauss & Co. ( Levi 's ) is an apparel company that designs , markets and sells jeans , casual wear and other related accessories for men , women and children .
gold_template: In templateXValue[max] , templateTitleSubject[0] ' templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] . The company is most well-known for denim jeans brand , templateTitleSubject[0] 's , however they produce many different types of apparel products . templateTitleSubject[0] & Co. templateTitleSubject[0] & Co. ( templateTitleSubject[0] 's ) is an apparel company that designs , markets and sells jeans , casual wear and other related accessories for men , women and children .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the employees of the templateTitleSubject[0] , from templateXValue[min] to templateXValue[max] . According to the report , the templateYLabel[0] templateYLabel[1] made up just over 5 templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Net income of the employees of the Levi Strauss , from 2005 to 2019 .  According to the report , the Net income made up just over 5 million U.S. dollars .

Example 855:
titleEntities: {'Subject': ['Hispanic'], 'Date': ['2017']}
title: Hispanic high school graduation rate , by state U.S. 2017
X_Axis['State']: ['West_Virginia', 'Vermont', 'Maine', 'Alabama', 'Texas', 'Arkansas', 'Missouri', 'New_Jersey', 'Kentucky', 'Tennessee', 'Illinois', 'Iowa', 'Delaware', 'Nebraska', 'Florida', 'Kansas', 'Mississippi', 'North_Carolina', 'South_Carolina', 'California', 'Wisconsin', 'Wyoming', 'U.S._average', 'Montana', 'Hawaii', 'Nevada', 'Oklahoma', 'Connecticut', 'Utah', 'Alaska', 'Rhode_Island', 'North_Dakota', 'New_Hampshire', 'Indiana', 'Idaho', 'Arizona', 'Massachusetts', 'Maryland', 'Pennsylvania', 'Ohio', 'Georgia', 'Michigan', 'Virginia', 'Washington', 'Oregon', 'District_of_Columbia', 'New_York', 'Colorado', 'South_Dakota', 'New_Mexico', 'Louisiana', 'Minnesota']
Y_Axis['Graduation', 'rate', 'of', 'the', 'class', 'of', '2016']: ['92', '90', '89', '88', '87.7', '85.7', '84.4', '84.3', '84', '83.8', '83.5', '82.4', '82', '81.6', '81.3', '81.1', '81', '80.6', '80.5', '80.3', '80.3', '80', '80', '80', '80', '79.7', '79.3', '77.7', '77.3', '77', '76', '76', '76', '75.8', '74.8', '74.5', '74.4', '74', '73.9', '73.6', '73.6', '73.3', '73', '72.7', '72.5', '72', '71.2', '71.1', '71', '70.5', '67', '66.3']

gold: This statistic shows the high school graduation rate of Hispanic ( Latino ) students in the United States of the class of 2017 . Data is distinguished by state . In 2017 , West Virginia had the highest graduation rate for Hispanic students in the U.S. at 92 percent .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] ( Latino ) students in the templateTitle[7] of the templateYLabel[2] of templateTitleDate[0] . Data is distinguished templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] students in the templateXValue[22] at templateYValue[max] templateScale .

generated_template: This statistic shows the 20 templateTitle[0] of templateTitle[1] in the templateTitle[3] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In that year , the templateXValue[0] of templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of approximately templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Hispanic of high in the graduation in 2017 , rate State .  In that year , the West_Virginia of had the highest Graduation rate of approximately 92 class 2016 .

Example 856:
titleEntities: {'Subject': ['European Union'], 'Date': ['2018', '2018']}
title: Number of dogs in the European Union 2018 , by country
X_Axis['Country']: ['Germany', 'United_Kingdom', 'Poland', 'Italy', 'France', 'Spain', 'Romania', 'Portugal', 'Czechia', 'Netherlands', 'Belgium', 'Hungary', 'Slovakia', 'Sweden', 'Austria', 'Finland', 'Bulgaria', 'Greece', 'Denmark', 'Lithuania', 'Ireland', 'Slovenia', 'Latvia', 'Estonia']
Y_Axis['Number', 'of', 'dogs', 'in', 'thousands']: ['9400', '9000', '7600', '7002', '6950', '6270', '4000', '2100', '2000', '1520', '1315', '1180', '900', '880', '827', '810', '740', '660', '595', '550', '450', '290', '260', '210']

gold: This statistic presents the number of pet dogs in the European Union by country , as of 2018 . Germany ranked highest with a dog population of approximately 9.4 million in 2018 , followed by the United Kingdom ( UK ) with nine million . The number of dogs in Europe has seen a notable increase since 2010 , with the number of dogs significantly increasing by more than eleven million from 2010 to 2018 .
gold_template: This statistic presents the templateYLabel[0] of pet templateYLabel[1] in the templateTitleSubject[0] by templateXLabel[0] , as of templateTitleDate[0] . templateXValue[0] ranked highest with a dog population of approximately templateYValue[max] templateScale in templateTitleDate[0] , followed templateTitle[5] the templateXValue[1] ( UK ) with templateYValue[max] templateScale . The templateYLabel[0] of templateYLabel[1] in Europe has seen a notable templatePositiveTrend since 2010 , with the templateYLabel[0] of templateYLabel[1] significantly templatePositiveTrend templateTitle[5] more than eleven templateScale from 2010 to templateTitleDate[0] .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in selected countries templateTitle[5] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In this year , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic displays the Number dogs of European Union in selected countries by in 2018 , country .  In this year , Germany had the highest Number dogs of 9400 thousands .

Example 857:
titleEntities: {'Subject': ['Google'], 'Date': ['2014', '2019']}
title: Google : global corporate demography 2014 - 2019 , by gender
Data:
           0     1     2     3     4     5
Year    2014  2015  2016  2017  2018  2019
Female  30.6  30.6  30.6  30.8  30.9  31.6
Male    69.4  69.4  69.4  69.2  69.1  68.4 

gold: This statistic illustrates the distribution of Google employees worldwide from 2014 to 2019 , sorted by gender . In the most recently measured period , 31.6 percent of the total number of global Google employees were female . The majority of employees were male .
gold_template: This statistic illustrates the distribution of templateTitleSubject[0] employees worldwide from templateValue[0][0] to templateValue[0][last] , sorted templateTitle[6] templateTitle[7] . In the most recently measured period , templateValue[1][last] templateScale of the total number of templateTitle[1] templateTitleSubject[0] employees were templateLabel[1][0] . The majority of employees were templateLabel[2][0] .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] in the templateTitleSubject[0] and templateTitle[4] templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , there were a total of templateValue[1][0] templateScale templateLabel[1][0] respondents of the templateTitleSubject[0] owners .
generated: The statistic shows the Google of global in the Google and 2014 2019 to 2014 .  In 2014 , there were a total of 30.6 million Female respondents of the Google owners .

Example 858:
titleEntities: {'Subject': ['NFL', 'MLB', 'NBA NHL'], 'Date': ['2013']}
title: Total brand value major sports leagues ( NFL , MLB , NBA & NHL ) 2013
X_Axis['Sports', 'League']: ['National_Football_League', 'Major_League_Baseball', 'National_Basketball_Association', 'National_Hockey_League']
Y_Axis['Brand', 'value', '(in', 'million', 'U.S.', 'dollars)']: ['9131.8', '4408.8', '2734.6', '1560.4']

gold: The statistic compares the total brand value of North American sports leagues in 2013 . All teams of the National Football League had a combined brand value of 9.1 billion U.S. dollars . A brand is defined here as the trademark and associated intellectual property .
gold_template: The statistic compares the templateTitle[0] templateYLabel[0] templateYLabel[1] of North American templateXLabel[0] templateTitle[5] in templateTitleDate[0] . All teams of the templateXValue[0] League had a combined templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[4] dollars . A templateYLabel[0] is defined here as the trademark and associated intellectual property .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[1] . The templateYLabel[0] templateYLabel[1] of the templateXValue[0] amounted to about templateYValue[max] templateScale U.S. dollars . A templateYLabel[0] is defined here as the trademark and associated intellectual property .
generated: The statistic shows the Total brand value of the sports in NFL 2013 , MLB League .  The Brand value of the National_Football_League amounted to about 9131.8 million U.S. dollars .  A Brand is defined here as the trademark and associated intellectual property .

Example 859:
titleEntities: {'Subject': ['U.S.-'], 'Date': ['2019']}
title: U.S.- employment by industry 2019
X_Axis['Industry']: ['Education_and_health_services', 'Wholesale_and_retail_trade', 'Professional_and_business_services', 'Manufacturing', 'Leisure_and_hospitality', 'Construction', 'Financial_activities', 'Transportation_and_utilities', 'Other_services', 'Public_administration', 'Information', 'Agriculture_and_related', 'Mining_quarrying_and_oil_and_gas_extraction']
Y_Axis['Employment', 'in', 'thousands']: ['35894', '19742', '19606', '15741', '14643', '11373', '10765', '8991', '7617', '7225', '2766', '2425', '750']

gold: In 2019 , the education and health services industry employed the largest number of people in the United States . That year , about 35.89 million people were employed in the education and health services industry . Education and Health Services Industry Despite being one of the wealthiest nations in the world , the United States has started to fall behind in both education and the health care industry .
gold_template: In templateTitleDate[0] , the templateXValue[0] and templateXValue[0] industry employed the largest number of people in the country . That year , about templateYValue[max] templateScale people were employed in the templateXValue[0] and templateXValue[0] industry . templateXValue[0] and templateXValue[0] Industry Despite being one of the wealthiest nations in the world , the country has started to fall behind in both templateXValue[0] and the templateXValue[0] care templateXLabel[0] .

generated_template: This statistic shows the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[0] of templateXValue[1] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the estimated Employment thousands 2019 in U.S.- 2019 , Industry .  In 2019 , the Education_and_health_services of Wholesale_and_retail_trade with a Employment thousands of 35894 thousands .

Example 860:
titleEntities: {'Subject': ['Instagram', 'Sweden'], 'Date': ['2017']}
title: Leading influencers on Instagram in Sweden 2017 , by followers
X_Axis['Month']: ['Anna_Nyström_(annanystrom)', 'thisisamans.world', 'Kenza_Zouiten_(kenzas)', 'Christoffer_Collin_(wisslaren)', 'Angelica_Blick_(angelicablick)', 'Janni_Delér_(jannid)', 'Victoria_Törnegren_(victoriatornegren)', 'Mattias_Klum_(mattiasklumofficial)', 'Alexandra_Bring_(alexandrabring)', 'TrainForFitspo_(trainforfitspo)']
Y_Axis['Instagram', 'followers', 'in', 'millions']: ['4.9', '2.1', '1.8', '1.4', '1.3', '1.2', '0.77', '0.73', '0.65', '0.51']

gold: This statistic displays a ranking of the top ten Swedish influencers on Instagram ranked by the number of followers . As of August 2017 , Anna Nyström ( annanystrom ) ranked first with 4.9 million followers and Thisis MansWorld ( thisisamans.world ) came in second with 2.1 million followers .
gold_template: This statistic displays a ranking of the top ten Swedish templateTitle[1] on templateYLabel[0] ranked templateTitle[5] the number of templateYLabel[1] . As of 2017 , templateXValue[0] ( annanystrom ) ranked first with templateYValue[max] templateScale templateYLabel[1] and Thisis MansWorld ( templateXValue[1] ) came in second with templateYValue[1] templateScale templateYLabel[1] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateYLabel[0] . As of 2019 , templateXValue[0] had the templateTitle[0] templateYLabel[0] of all templateTitleSubject[0] templateTitle[4] . According to the templateTitle[2] , with a templateTitle[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic gives information on the Leading influencers Instagram of Sweden 2017 Instagram .  As of 2019 , Anna_Nyström_(annanystrom) had the Leading Instagram of all Instagram 2017 .  According to the Instagram , with a Leading of 4.9 millions .

Example 861:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2009', '2018']}
title: Ice cream : manufacturing sales volume in the United Kingdom ( UK ) 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Sales', 'volume', 'in', 'thousand', 'liters']: ['328055', '288292', '258484', '279630', '304972', '439485', '423158', '431035', '442961', '431696']

gold: During 2018 , the sales volume of ice cream manufactured in the UK amounted to approximately 328 million liters . From 2013 to 2014 , sales fell by over a hundred million liters and have not recovered since . Before this decline , sales were rather stable at over 420 million liters .
gold_template: During templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] manufactured in the templateTitleSubject[1] amounted to approximately templateYValue[0] templateScale templateYLabel[3] . From templateXValue[5] to templateXValue[4] , templateYLabel[0] templateNegativeTrend by over a hundred templateScale templateYLabel[3] and have not recovered since . Before this decline , templateYLabel[0] were rather stable at over 420 templateScale templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] of the templateYLabel[2] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of the templateYLabel[1] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Sales of the volume of the thousand volume from 2009 to 2018 .  In 2018 , the Ice of the volume of United Kingdom amounted to 328055 thousand liters .

Example 862:
titleEntities: {'Subject': ['Rwanda'], 'Date': ['2018']}
title: Urbanization in Rwanda 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['17.21', '17.13', '17.06', '17', '16.97', '16.95', '16.94', '16.94', '16.93', '16.93', '16.93']

gold: This statistic shows the degree of urbanization in Rwanda from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 17.21 percent of Rwanda 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Rwanda from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 17.21 % of Rwanda 's total population lived in urban areas and cities .

Example 863:
titleEntities: {'Subject': ['Lithuania'], 'Date': ['2024']}
title: Inflation rate in Lithuania 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2.18', '2.18', '2.18', '2.21', '2.18', '2.32', '2.53', '3.72', '0.68', '-0.68', '0.24', '1.16', '3.16', '4.12', '1.19', '4.16', '11.16', '5.75', '3.76', '2.67', '1.15']

gold: This statistic shows the average inflation rate in Lithuania from 2004 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Lithuania had amounted to about 2.53 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] had amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Lithuania from 2004 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Lithuania amounted to about 2.53 % compared to the previous Year .

Example 864:
titleEntities: {'Subject': ['Snapchat'], 'Date': ['2020']}
title: Snapchat : distribution of global audiences 2020 , by gender
X_Axis['Sex']: ['Female', 'Male']
Y_Axis['Share', 'of', 'active', 'users']: ['61', '38']

gold: This statistic gives information on the distribution of Snapchat users worldwide as of January 2020 , sorted by gender . During the survey period , 61 percent of Snapchat audiences were female and 38 percent were male .
gold_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[2] worldwide as of 2020 , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[max] templateScale of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[min] templateScale were templateXValue[last] .

generated_template: This statistic gives information on the templateScale of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] as of 2020 , sorted by templateTitle[1] . During the survey period , templateYValue[min] templateScale of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[max] templateScale were templateXValue[last] .
generated: This statistic gives information on the percentage of Snapchat active users as of 2020 , sorted by distribution .  During the survey period , 38 % of Snapchat audiences were Female and 61 % were Male .

Example 865:
titleEntities: {'Subject': ['Alibaba'], 'Date': ['2016']}
title: Alibaba : quarterly gross merchandise volume Q2 2012-Q2 2016
X_Axis['Quarter']: ["Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12"]
Y_Axis['Total', 'GMV', 'in', 'billion', 'yuan']: ['837.0', '741.94', '964.0', '713.0', '673.0', '600.0', '787.0', '556.0', '501.0', '430.0', '529.0', '374.0', '345.0', '294.0', '346.0', '228.0', '209.0']

gold: This statistic gives information on Alibaba 's total gross merchandise volume from the second quarter of 2012 to the second quarter of 2016 . As of the last reported quarter , the website 's total GMV amounted to 837 billion yuan with 75 percent of gross merchandise sales being generated via mobile .
gold_template: This statistic gives information on templateTitleSubject[0] 's templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] from the second templateXLabel[0] of 2012 to the second templateXLabel[0] of templateTitleDate[0] . As of the last reported templateXLabel[0] , the website 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateScale templateYLabel[3] with 75 templateScale of templateTitle[2] templateTitle[3] sales being generated via mobile .

generated_template: This statistic gives information on templateTitleSubject[0] 's templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] from the second templateXLabel[0] of 2011 to the second templateXLabel[0] of templateTitleDate[max] . As of the last reported templateXLabel[0] , the website 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateScale templateYLabel[3] .
generated: This statistic gives information on Alibaba 's Total gross merchandise volume from the second Quarter of 2011 to the second Quarter of 2016 .  As of the last reported Quarter , the website 's Total GMV amounted to 837.0 billion yuan .

Example 866:
titleEntities: {'Subject': ['Canada'], 'Date': []}
title: Enrollment of postsecondary students in Canada , by age and gender 2017/18
Data:
                         0               1               2               3               4                  5             6
Year    Less_than_20_years  20_to_24_years  25_to_29_years  30_to_34_years  35_to_39_years  40_years_and_over  Not_reported
Male                227739          412731          134769           58491           35760              57366          1572
Female              279924          498453          164106           82065           57324              98523           864 

gold: This statistic shows the total number of students enrolled in postsecondary institutions in Canada in academic year 2017/18 , distinguished by age group and gender . In 2017/18 , a total of 498,453 female students aged between 20 and 24 years were enrolled in postsecondary institutions in Canada .
gold_template: This statistic shows the total number of templateTitle[2] enrolled in templateTitle[1] institutions in templateTitleSubject[0] in academic templateLabel[0][0] templateTitle[7] , distinguished templateTitle[4] templateTitle[5] group and templateTitle[6] . In templateTitle[7] , a total of templateValue[2][1] templateLabel[2][0] templateTitle[2] aged between templateValue[0][0] and templateValue[0][1] templateValue[0][0] were enrolled in templateTitle[1] institutions in templateTitleSubject[0] .

generated_template: This statistic provides a comparison of the templateTitle[0] amount of time templateTitle[4] on templateTitle[5] housework by gender . As of templateTitleDate[0] , it was found that templateValue[2][0] templateScale of the population had completed templateValue[2][0] templateScale of women in templateValue[0][0] .
generated: This statistic provides a comparison of the Enrollment amount of time by on age housework by gender .  As of , it was found that 279924 % of the population had completed 279924 % of women in Less_than_20_years .

Example 867:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Most popular leisure activities among adults in the U.S. 2013
X_Axis['Response']: ['Watch_TV', 'Reading', 'Computer/internet', 'Spending_time_with_family_and_friends', 'Watching/going_to_the_movies', 'Exercise/working_out', 'Playing_video_games_and_computer/internet_games', 'Walking/running/jogging', 'Gardening', 'Concerts/listening_to/playing_music']
Y_Axis['Share', 'of', 'respondents']: ['42', '37', '19', '18', '11', '10', '10', '8', '7', '7']

gold: This statistic shows the most popular leisure activities among adults in the United States as of September 2013 . During the survey , 42 percent of the respondents named watching TV as their most preferred activity during leisure time .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2013 . During the survey , templateYValue[max] templateScale of the templateYLabel[1] named watching templateXValue[0] as their templateTitle[0] preferred activity during templateTitle[2] templateXValue[3] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] in the templateTitle[7] as of 2013 . During the survey , templateYValue[3] templateScale of templateTitleSubject[0] stated templateXValue[3] with templateXValue[3] and templateXValue[3] as a favorite templateTitle[2] activity .
generated: This statistic shows the Most popular leisure activities among U.S. in the 2013 as of 2013 .  During the survey , 18 % of U.S. stated Spending_time_with_family_and_friends with and Spending_time_with_family_and_friends as a favorite leisure activity .

Example 868:
titleEntities: {'Subject': ['Jewish'], 'Date': ['2010']}
title: Countries with the largest Jewish population in 2010
X_Axis['Country']: ['United_States', 'Israel', 'Canada', 'France', 'United_Kingdom', 'Germany', 'Russia', 'Argentina', 'Australia', 'Brazil', 'Mexico', 'South_Africa', 'Spain', 'Italy', 'Ukraine', 'Belgium', 'Netherlands', 'Chile', 'Moldova', 'Switzerland', 'Turkey', 'Austria', 'Hungary', 'India', 'Panama']
Y_Axis['Estimated', 'Jewish', 'population', 'in', 'thousand']: ['5690', '5610', '350', '310', '280', '230', '230', '200', '110', '110', '70', '70', '60', '50', '50', '30', '30', '20', '20', '20', '20', '10', '10', '10', '10']

gold: This statistic shows the top 25 countries in the world with the largest number of Jewish population in 2010 . In 2010 , there were living about 5.7 million Jews in the United States .
gold_template: This statistic shows the top 25 templateTitle[0] in the world templateTitle[1] the templateTitle[2] number of templateYLabel[1] templateYLabel[2] in templateTitleDate[0] . In templateTitleDate[0] , there were living about templateYValue[max] templateScale Jews in the templateXValue[0] .

generated_template: This statistic shows the 20 templateTitleSubject[0] ( UK ) templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . The templateTitle[0] templateTitle[1] templateTitle[3] templateTitleSubject[0] was templateXValue[0] , with a templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Jewish ( UK ) Jewish population 2010 in .  The Countries largest population Jewish was United_States , with a Estimated of 5690 population thousand .

Example 869:
titleEntities: {'Subject': ['New Orleans Saints'], 'Date': ['2019']}
title: Average ticket price of the New Orleans Saints 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['97.72', '90.14', '91.25', '84.87', '84.87', '74.99', '74.99', '74.99', '74.99', '62.22', '62.22', '54.86', '54.86']

gold: This graph depicts the average ticket price for New Orleans Saints games in the National Football League from 2006 to 2019 . In 2019 , the average ticket price was at 97.72 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] Saints games in the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for New Orleans Saints games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 97.72 U.S. dollars .

Example 870:
titleEntities: {'Subject': ['United States'], 'Date': ['2012']}
title: Number of vehicles imported into the United States by country 2012
X_Axis['Country']: ['Japan', 'Canada', 'Mexico', 'Korea', 'Germany']
Y_Axis['Imports', 'in', 'units']: ['2441298', '2105848', '1440116', '1032638', '796121']

gold: This statistic shows the number of passenger vehicles imported into the United States in 2012 , with a breakdown by country . U.S. imports of passenger vehicles from Korea stood at around 1.03 million units in 2012 .
gold_template: This statistic shows the templateTitle[0] of passenger templateTitle[1] templateTitle[2] templateTitle[3] the templateTitleSubject[0] in templateTitleDate[0] , with a breakdown templateTitle[6] templateXLabel[0] . templateTitle[5] templateYLabel[0] of passenger templateTitle[1] from templateXValue[3] stood at around templateYValue[3] templateScale templateYLabel[1] in templateTitleDate[0] .

generated_template: This statistic shows the results of a survey in the templateYLabel[1] of a templateTitleSubject[0] on templateTitle[4] in templateTitleDate[0] . According to the source , the templateTitle[0] of goods and services produced in templateXValue[3] . In templateTitleDate[0] , the templateXValue[0] had templateYValue[7] templateScale of the templateYLabel[1] templateYLabel[3] .
generated: This statistic shows the results of a survey in the units of a United States on United in 2012 .  According to the source , the Number of goods and services produced in Korea .  In 2012 , the Japan had 796121 % of the units .

Example 871:
titleEntities: {'Subject': ['Israel'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Israel 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['511.65', '483.88', '457.45', '433.17', '410.5', '387.72', '370.59', '353.25', '318.95', '299.81', '309.56', '292.92', '257.44', '261.72', '233.73', '207.45', '215.96', '178.74', '153.98', '142.41', '135.34', '126.81', '121.04', '130.67', '132.24', '117.01', '115.86', '114.62', '109.86', '100.24', '84.38', '73.92', '73.55', '65.82', '58.02', '49.17', '48.33', '39.01', '32.66', '26.53', '28.55']

gold: The statistic shows the gross domestic product ( GDP ) in Israel from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Israel from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 872:
titleEntities: {'Subject': ['Major League Baseball'], 'Date': ['2019', '2019']}
title: Highest salaries in Major League Baseball 2019
X_Axis['Baseball', 'Player']: ['Stephen_Strasburg', 'Max_Scherzer', 'Zack_Greinke', 'David_Price', 'Clayton_Kershaw', 'Miguel_Cabrera', 'Yoenis_Cespedes', 'Albert_Pujols', 'Justin_Verlander', 'Felix_Hernandez', 'Jon_Lester', 'Nolan_Arenado', 'Giancarlo_Stanton', 'Jake_Arrieta', 'Jordan_Zimmermann', 'Joey_Votto', 'Robinson_Cano', 'J.D._Martinez', 'Josh_Donaldson', 'Jason_Heyward']
Y_Axis['Salary', 'in', 'million', 'U.S.', 'dollars']: ['38.33', '37.41', '34.5', '31.0', '31.0', '30.0', '29.0', '28.0', '28.0', '27.86', '27.5', '26.0', '26.0', '25.0', '25.0', '25.0', '24.0', '23.75', '23.0', '22.5']

gold: American professional sports leagues are known for paying their players handsomely and Major League Baseball is no different . The highest earner in the MLB in 2019 was the starting pitcher for the Arizona Diamondbacks , Stephen Strasburg . The 30-year-old three-time All-Star took home an annual salary of 38.33 million U.S. dollars in the 2019 season .
gold_template: American professional sports leagues are known for paying their players handsomely and templateTitleSubject[0] Baseball is no different . The templateTitle[0] earner in the MLB in templateTitleDate[0] was the starting pitcher for the Arizona Diamondbacks , templateXValue[0] . The 30-year-old three-time All-Star took home an annual templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateTitleDate[0] season .

generated_template: The statistic shows the templateTitleSubject[0] players with the most templateYLabel[0] templateYLabel[1] templateXLabel[1] in templateTitleDate[0] . templateXValue[0] is the largest templateYLabel[0] leader of the templateTitleSubject[0] League with templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Major League Baseball players with the most Salary million Player in 2019 .  Stephen_Strasburg is the largest Salary leader of the Major League Baseball League with 38.33 million dollars .

Example 873:
titleEntities: {'Subject': ['Malware'], 'Date': ['2018']}
title: Malware types encountered during data compromise investigations 2018
X_Axis['Type', 'Of', 'Malware']: ['Downloader', 'Remote_administration_tool_(RAT)', 'Webshell', 'Memory_scraper/dumper', 'Injector', 'Dropper', 'Coin_miners', 'Formjacking', 'Ransomware']
Y_Axis['Share', 'of', 'encounters']: ['13', '10', '8', '8', '5', '4', '3', '2', '0.7']

gold: The ranking presents the most commonly encountered types of malware used in industrial cyber attacks worldwide in 2018 . During the survey period , downloaders were detected in 13 percent of malware attack investigations . A further three percent of detected malware were coin miners .
gold_template: The ranking presents the most commonly templateTitle[2] templateTitle[1] of templateXLabel[1] used in industrial cyber attacks worldwide in templateTitleDate[0] . templateTitle[3] the survey period , downloaders were detected in templateYValue[max] templateScale of templateXLabel[1] attack templateTitle[6] . A further templateYValue[6] templateScale of detected templateXLabel[1] were templateXValue[6] .

generated_template: The statistic shows the results of a survey among the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] . templateValue[1][0] templateScale of the templateYLabel[1] were held held templateTitle[4] templateXValue[0] , compared to the previous year .
generated: The statistic shows the results of a survey among the Malware types encountered in Malware 2018 .  2018 % of the encounters were held data Downloader , compared to the previous year .

Example 874:
titleEntities: {'Subject': ['United States'], 'Date': ['2017']}
title: Uber users in the United States 2017 , by device
X_Axis['Device']: ['Smartphone', 'Tablet', 'Desktop', 'Mobile-only']
Y_Axis['Number', 'of', 'visits']: ['17.7', '1.2', '6.8', '16.6']

gold: By far the most common method to access the ride-sharing platform Uber in the United States is via smartphone ; around 17.7 million U.S. adults had accessed Uber via smartphone as of April 2017 – significantly higher than the next most popular platform , desktop computers , with 6.8 million users . The U.S. ride sharing market Uber is the largest ride-sharing platform in the United States , accounting for just under 70 percent of the total market as of October 2018 . However Lyft , the next-largest ride sharing platform in the U.S. , has seen significant growth in ridership over the last five years , narrowing the gap between it and Uber .
gold_template: templateTitle[5] far the most common method to access the ride-sharing platform templateTitle[0] in the templateTitleSubject[0] is via templateXValue[0] ; around templateYValue[max] templateScale templateTitle[3] adults had accessed templateTitle[0] via templateXValue[0] as of 2017 – significantly higher than the next most popular platform , templateXValue[2] computers , with templateYValue[2] templateScale templateTitle[1] . The templateTitle[3] ride sharing market templateTitle[0] is the largest ride-sharing platform in the templateTitleSubject[0] , accounting for just under 70 templateScale of the total market as of October 2018 . However Lyft , the next-largest ride sharing platform in the templateTitle[3] , has seen significant growth in ridership over the last five years , narrowing the gap between it and templateTitle[0] .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[1] . According to the source , the templateXValue[0] accounted for approximately templateYValue[max] templateScale of all templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Uber of the users United States in United States 2017 , device .  According to the source , the Smartphone accounted for approximately 17.7 % of all visits .

Example 875:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010', '2015']}
title: Concussion rate in the U.S. 2010 to 2015 , by age
X_Axis['Year']: ['<1_year', '1-4_years', '5-9_years', '10-14_years', '15-17_years', '18-19_years', '20-24_years', '25-29_years', '30-34_years', '35-39_years', '40-44_years', '45-49_years', '50-54_years', '55-59_years', '60-64_years']
Y_Axis['Rate', 'per', '1,000', 'members']: ['1.0', '2.7', '3.5', '10.7', '18.8', '10.1', '4.5', '2.5', '2.0', '2.0', '1.9', '1.9', '1.8', '1.8', '1.8']

gold: The highest rate of concussions among U.S. Blue Cross Blue Shield ( BCBS ) members from 2010 to 2015 was among those aged 15 - 17 years . Among that age group the rate of concussion was 18.8 per 1,000 members . Unsurprisingly , the age group with the lowest rate of concussion was those aged less than 1 year .
gold_template: The highest templateYLabel[0] of concussions among templateTitleSubject[0] Blue Cross Blue Shield ( BCBS ) templateYLabel[3] from templateTitleDate[min] to templateTitleDate[max] was among those aged 15 - 17 templateXValue[1] . Among that templateTitle[6] group the templateYLabel[0] of templateTitle[0] was templateYValue[max] templateYLabel[1] 1,000 templateYLabel[3] . Unsurprisingly , the templateTitle[6] group with the lowest templateYLabel[0] of templateTitle[0] was those aged less than templateYValue[min] templateXValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in the templateTitle[2] from templateTitleDate[min] to templateTitleDate[max] , in templateYLabel[1] . In templateTitleDate[max] , the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] was at templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Concussion rate in the U.S. from 2010 to 2015 , in per .  In 2015 , the Concussion rate in the U.S. was at 18.8 1,000 members .

Example 876:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2012', '2019']}
title: Small domestic appliances : revenue in the United Kingdom ( UK ) 2012 to 2019
X_Axis['Quarter']: ['Q3_2019', 'Q2_2019', 'Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018', 'Q4_2017', 'Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015', 'Q3_2015', 'Q2_2015', 'Q1_2015', 'Q4_2014', 'Q2_2014', 'Q1_2014', 'Q4_2013', 'Q3_2013', 'Q2_2013', 'Q1_2013', 'Q4_2012', 'Q3_2012']
Y_Axis['Revenue', 'in', 'million', 'GBP']: ['531', '508', '565', '821', '537', '507', '566', '833', '547', '498', '537', '821', '528', '494', '561', '844', '521', '508', '581', '740', '473', '528', '707', '444', '457', '478', '660', '450']

gold: The statistic shows trends in small domestic appliance revenue in the United Kingdom ( UK ) from the third quarter of 2012 to the third quarter of 2019 . In the third quarter of 2019 , small domestic appliances generated a revenue of 531 million British pounds . Small domestic appliances include household appliances such as toasters , coffee makers , food processors , and other table-top or counter-top electric goods .
gold_template: The statistic shows trends in templateTitle[0] templateTitle[1] appliance templateYLabel[0] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from the third templateXLabel[0] of templateXValue[26] to the third templateXLabel[0] of templateXValue[0] . In the third templateXLabel[0] of templateXValue[0] , templateTitle[0] templateTitle[1] templateTitle[2] generated a templateYLabel[0] of templateYValue[0] templateScale British pounds . templateTitle[0] templateTitle[1] templateTitle[2] include household templateTitle[2] such as toasters , coffee makers , food processors , and other table-top or counter-top electric goods .

generated_template: In the third templateXLabel[0] of templateXValue[0] , the templateTitleSubject[0] ( IT ) sector of the Technical Consumer Goods industry generated a templateYLabel[0] of approximately 2.302 templateScale British pounds . This represents growth of five templateScale on templateXValue[0] templateXValue[3] . templateYLabel[0] is traditionally higher during the fourth templateXLabel[0] of each year , exceeding templateYValue[2] templateScale British pounds in templateXValue[3] , the first time that mark had been broken since templateXValue[3] templateXValue[27] .
generated: In the third Quarter of Q3_2019 , the United Kingdom ( IT ) sector of the Technical Consumer Goods industry generated a Revenue of approximately 2.302 million British pounds .  This represents growth of five million on Q3_2019 Q4_2018 .  Revenue is traditionally higher during the fourth Quarter of each year , exceeding 565 million British pounds in Q4_2018 , the first time that mark had been broken since Q4_2018 Q3_2012 .

Example 877:
titleEntities: {'Subject': ['China'], 'Date': ['2019', '2019']}
title: China : weekly box office revenue November 2019 , by movie
X_Axis['Hollywood', 'Films']: ['Frozen_2', 'Two_Tigers', 'Knives_Out', 'Miss_Forever', 'Somewhere_Winter', 'My_People_My_Country', 'Midway', "Leggenda_del_pianista_sull'oceano_La", 'Better_Days', 'Wings_Over_Everest']
Y_Axis['Box', 'office', 'revenue', 'in', 'million', 'U.S.', 'dollars']: ['37.3', '19.6', '13.5', '5.1', '4.6', '3.3', '3.2', '2.7', '1.7', '1.5']

gold: The graph shows the weekly revenues of the top 10 movies in China . From November 25 to December 1 , 2019 , Disney 's Frozen 2 ranked first after 10 screening days , earning about 37.3 million U.S. dollars . Cinema industry in China – additional information With an annual box office revenue of about 9.15 billion U.S. dollars , China remained as the second in terms of global cinema market size in 2018 , second only to the United States with box office revenues of then 11.08 billion U.S. dollars .
gold_template: The graph shows the templateTitle[1] revenues of the top 10 movies in templateTitleSubject[0] . From templateTitle[5] 25 to 1 , templateTitleDate[0] , Disney 's templateXValue[0] ranked first after 10 screening templateXValue[8] , earning about templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] . Cinema industry in templateTitleSubject[0] – additional information With an annual templateYLabel[0] templateYLabel[1] templateYLabel[2] of about 9.15 templateScale templateYLabel[4] templateYLabel[5] , templateTitleSubject[0] remained as the second in terms of global cinema market size in 2018 , second only to the country with templateYLabel[0] templateYLabel[1] revenues of then 11.08 templateScale templateYLabel[4] templateYLabel[5] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[4] as of 2019 . The templateXValue[0] had the highest templateYLabel[2] with over templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] . The total templateYLabel[0] of templateTitle[2] .
generated: This statistic gives information on the China weekly box in the revenue as of 2019 .  The Frozen_2 had the highest revenue with over 37.3 million U.S. dollars .  The total Box of .

Example 878:
titleEntities: {'Subject': ['Europe'], 'Date': ['2018']}
title: Ecstasy use in the past 12 months in Europe as of 2018  , by country
X_Axis['Country']: ['Netherlands', 'Ireland', 'United_Kingdom', 'Bulgaria', 'Estonia', 'Finland', 'Norway', 'Sweden', 'Hungary', 'Czechia', 'France', 'Slovakia', 'Croatia', 'Germany', 'Spain', 'Denmark', 'Poland', 'Austria', 'Lithuania', 'Italy', 'Latvia', 'Slovenia', 'Belgium', 'Luxembourg', 'Greece', 'Cyprus', 'Turkey', 'Portugal', 'Romania']
Y_Axis['Share', 'of', 'population']: ['3.3', '2.1', '1.7', '1.3', '1.2', '1.1', '1', '0.9', '0.9', '0.8', '0.6', '0.6', '0.6', '0.6', '0.6', '0.5', '0.4', '0.4', '0.4', '0.4', '0.3', '0.3', '0.3', '0.2', '0.2', '0.1', '0.1', '0.1', '0.1']

gold: The Netherlands had the highest prevalence of ecstasy use among adults in Europe as of 2018 , with approximately 3.3 percent of the population reported to using ecstasy . This was followed by Ireland with 2.1 percent of the population and then the United Kingdom at 1.7 percent . Only 0.1 percent of respondents in Romania , Portugal , Turkey and Cyprus reported using ecstasy in this period .
gold_template: The templateXValue[0] had the highest prevalence of templateTitle[0] templateTitle[1] among adults in templateTitleSubject[0] as of templateTitleDate[0] , with approximately templateYValue[max] templateScale of the templateYLabel[1] reported to using templateTitle[0] . This was followed templateTitle[7] templateXValue[1] with templateYValue[1] templateScale of the templateYLabel[1] and then the templateXValue[2] at templateYValue[2] templateScale . Only templateYValue[min] templateScale of respondents in templateXValue[last] , templateXValue[27] , templateXValue[26] and templateXValue[25] reported using templateTitle[0] in this period .

generated_template: As of templateTitleDate[0] , the templateXValue[0] had the highest prevalence of templateTitle[0] templateTitle[1] in templateTitleSubject[0] at templateYValue[max] templateScale of the templateYLabel[1] having used in the previous year . This was followed templateTitle[7] templateXValue[2] and the templateXValue[1] which both had templateYValue[1] templateScale of individuals using templateTitle[0] in this period . Breakdown of templateTitle[0] users in the UK When broken down templateTitle[7] age , the highest prevalence of templateTitle[0] users in the UK is found in the youngest age groups .
generated: As of 2018 , the Netherlands had the highest prevalence of Ecstasy use in Europe at 3.3 % of the population having used in the previous year .  This was followed by United_Kingdom and the Ireland which both had 2.1 % of individuals using Ecstasy in this period .  Breakdown of Ecstasy users in the UK When broken down by age , the highest prevalence of Ecstasy users in the UK is found in the youngest age groups .

Example 879:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Important features of music streaming services in the U.S. 2018
X_Axis['Response']: ['The_variety_of_music_available', 'Low_price_point', 'The_ability_to_listen_on_multiple_divices', 'Clean_user_interface', 'Good_algorithms_to_find_new_music', 'The_ability_to_combine_your_music_library_with_your_streaming_service_library', 'The_ability_to_stream_on_smart_home_devices', 'Curated_playlists', 'Artist_exclusives']
Y_Axis['Share', 'of', 'respondents']: ['81', '80', '68', '66', '58', '64', '57', '52', '46']

gold: This statistic presents data on the most important features of music streaming services among adults in the United States as of March 2018 . During a survey , 81 percent of respondents stated that the variety of music available was the most important feature of music streaming services .
gold_template: This statistic presents data on the most templateTitle[0] templateTitle[1] of templateXValue[0] templateXValue[5] templateTitle[4] among adults in the templateTitle[5] as of 2018 . During a survey , templateYValue[max] templateScale of templateYLabel[1] stated that the templateXValue[0] of templateXValue[0] was the most templateTitle[0] feature of templateXValue[0] templateXValue[5] templateTitle[4] .

generated_template: This statistic presents the templateScale of online consumers in the templateTitle[0] who are templateTitleSubject[0] templateTitle[2] templateXValue[last] as of 2019 . During the survey period , it was found that templateYValue[1] templateScale of templateYLabel[1] stated that they used templateXValue[1] a templateXValue[1] .
generated: This statistic presents the percentage of online consumers in the Important who are U.S. music Artist_exclusives as of 2019 .  During the survey period , it was found that 80 % of respondents stated that they used Low_price_point a .

Example 880:
titleEntities: {'Subject': ['Estée Lauder'], 'Date': ['2017']}
title: Net sales share of Estée Lauder worldwide 2017 , by distribution channel
X_Axis['Distribution', 'channel']: ['Department_stores', 'Travel_retail', 'Freestanding_retail_stores', 'Specialty-multi', 'Perfumeries', 'Salon_and_spa', 'Online', 'Other']
Y_Axis['Net', 'sales', 'share']: ['42', '14', '11', '11', '5', '3', '7', '7']

gold: This statistic depicts the net sales share of Estée Lauder worldwide in 2017 , by distribution channel . In that year , 42 percent of the Estée Lauder Companies net sales were generated by department stores . Estée Lauder Companies is headquartered in Midtown Manhattan , New York City and was founded in 1946 by Joseph Lauder and his wife Estée Lauder .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] worldwide in templateTitleDate[0] , templateTitle[7] templateXLabel[0] templateXLabel[1] . In that year , templateYValue[max] templateScale of the templateTitleSubject[0] Companies templateYLabel[0] templateYLabel[1] were generated templateTitle[7] templateXValue[0] . templateTitleSubject[0] Companies is headquartered in Midtown Manhattan , New York City and was founded in 1946 templateTitle[7] Joseph templateTitleSubject[0] and his wife templateTitleSubject[0] .

generated_template: This statistic illustrates the distribution of templateTitle[0] templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateYLabel[1] in templateTitleDate[0] , templateTitle[4] segments . templateXValue[0] accounted for templateYValue[max] templateScale of the company 's templateYLabel[0] that year .
generated: This statistic illustrates the distribution of Net Estée Lauder share sales in 2017 , Lauder segments .  Department_stores accounted for 42 % of the company 's Net that year .

Example 881:
titleEntities: {'Subject': ['Ghana'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Ghana 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['90.48', '84.7', '79.34', '74.63', '69.76', '67.08', '65.52', '58.98', '54.99', '48.6', '53.17', '63.28', '56.5', '53.65', '43.04', '34.25', '38.41', '33.94', '28.79', '24.52', '20.24', '17.49', '14.2', '12.23', '11.47', '17.76', '17.15', '15.79', '15.46', '14.54', '12.3', '13.38', '16.48', '16.67', '14.9', '12.9', '12.68', '11.93', '14.23', '15.38', '17.33']

gold: The statistic shows gross domestic product ( GDP ) in Ghana from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Ghana from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 882:
titleEntities: {'Subject': ['Home Depot', 'Lowe'], 'Date': ['2011', '2018']}
title: Number of customer transactions at the Home Depot and Lowe 's worldwide 2011 to 2018
Data:
               0     1     2     3     4     5     6     7
Year        2018  2017  2016  2015  2014  2013  2012  2011
Home Depot  1621  1579  1544  1501  1442  1391  1364  1318
Lowe's       941   953   945   878   857   828   804   810 

gold: This statistic depicts the number of customer transactions at the Home Depot and Lowe 's worldwide from 2011 to 2018 . In 2018 , the Home Depot carried out 1.62 billion customer transactions worldwide .
gold_template: This statistic depicts the templateTitle[0] of templateTitle[1] templateTitle[2] at the templateTitleSubject[0] and templateTitleSubject[1] templateTitle[6] templateTitle[7] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitleSubject[0] carried out 1.62 templateScale templateTitle[1] templateTitle[2] templateTitle[7] .

generated_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] employees templateTitle[4] from templateValue[0][last] to templateValue[0][0] . The templateTitle[0] of templateTitleSubject[0] employees amounted to approximately templateValue[1][0] templateScale templateYLabel[3] in templateValue[0][0] .
generated: This statistic shows the Number of Home Depot employees Depot from 2011 to 2018 .  The Number of Home Depot employees amounted to approximately 1621 million in 2018 .

Example 883:
titleEntities: {'Subject': ['Spotify'], 'Date': ['2019']}
title: Spotify 's premium subscribers 2015 to 2019
X_Axis['Quarter']: ['Q4_2019', 'Q3_2019', 'Q2_2019', 'Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018', 'Q4_2017', 'Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015', 'Q3_2015', 'Q2_2015', 'Q1_2015']
Y_Axis['Paying', 'subscribers', 'in', 'millions']: ['124', '113', '108', '100', '96', '87', '83', '75', '71', '62', '59', '52', '48', '40', '36', '30', '28', '24', '22', '18']

gold: How many paid subscribers does Spotify have ? As of the fourth quarter of 2019 , Spotify had 124 million premium subscribers worldwide , up from 96 million in the corresponding quarter of 2018 . Spotify 's subscriber base has increased dramatically in the last few years , more than doubling in just three years . Spotify and competitors Spotify is a music streaming service originally founded in 2006 in Sweden .
gold_template: How many paid templateYLabel[1] does templateTitleSubject[0] have ? As of the fourth templateXLabel[0] of templateXValue[0] , templateTitleSubject[0] had templateYValue[max] templateScale templateTitle[2] templateYLabel[1] worldwide , up from templateYValue[4] templateScale in the corresponding templateXLabel[0] of templateXValue[4] . templateTitleSubject[0] templateTitle[1] subscriber base has templatePositiveTrend dramatically in the last few years , more than doubling in just three years . templateTitleSubject[0] and competitors templateTitleSubject[0] is a music streaming service originally founded in 2006 in Sweden .

generated_template: This statistic shows the total templateYLabel[0] of templateTitleSubject[0] in the first templateXLabel[0] of templateXValue[0] from the first templateXLabel[0] of templateXValue[last] to the first templateXLabel[0] of templateXValue[0] . In the fourth templateXLabel[0] of templateXValue[0] , the templateTitle[0] templateYLabel[3] of templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the total Paying of Spotify in the first Quarter of Q4_2019 from the first Quarter of Q1_2015 to the first Quarter of Q4_2019 .  In the fourth Quarter of Q4_2019 , the Spotify millions of 124 millions .

Example 884:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2009', '2018']}
title: South Korea : e-commerce sales volume 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['E-commerce', 'sales', 'volume', 'in', 'trillion', 'Korean', 'won']: ['113.73', '91.99', '64.91', '53.89', '45.3', '38.5', '34.07', '29.07', '25.2', '20.64']

gold: This timeline shows online shopping transaction volume in South Korea from 2009 to 2018 . In 2018 , the online shopping transactions totaled more than 113 trillion South Korean won , up from around 92 trillion won in the previous year .
gold_template: This timeline shows online shopping transaction templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the online shopping transactions totaled more than 113 templateScale templateTitleSubject[0] templateYLabel[4] templateYLabel[5] , up from around templateYValue[1] templateScale templateYLabel[5] in the previous templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] employed approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the E-commerce of the sales of South Korea from 2009 to 2018 .  In 2018 , South Korea employed approximately 113.73 trillion Korean .

Example 885:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2008', '2018']}
title: Infant mortality rate in the Netherlands 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Infant', 'mortality', 'per', '1,000', 'live', 'born', 'children']: ['3.5', '3.6', '3.5', '3.3', '3.6', '3.8', '3.7', '3.6', '3.8', '3.8', '3.8']

gold: In 2018 , the infant mortality rate in the Netherlands was 3.5 . This means that out of every 1,000 newborns , nearly four died before their first birthday . Infant mortality was lowest in 2015 , when the rate was 3.3 deaths per 1,000 live born children .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateTitle[2] in the templateTitleSubject[0] was templateYValue[idxmax(X)] . This means that out of every 1,000 newborns , nearly templateYValue[0] died before their first birthday . templateYLabel[0] templateYLabel[1] was lowest in templateXValue[3] , when the templateTitle[2] was templateYValue[min] deaths templateYLabel[2] 1,000 templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] . The highest templateYLabel[0] templateYLabel[1] group in templateXValue[idxmax(Y)] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Infant mortality of the 1,000 2008 of Netherlands from 2008 to 2018 .  In 2018 , the mortality of Netherlands was 3.5 per 1,000 .  The highest Infant mortality group in 2013 was 3.5 per 1,000 .

Example 886:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Number of assaults by weapon U.S. 2018
X_Axis['Weapon', 'used']: ['Other_weapon', 'Firearm', 'Personal_weapons', 'Knife_or_cutting_instrument']
Y_Axis['Number', 'of', 'assaults']: ['223997', '185437', '179008', '123253']

gold: In 2018 , there were 185,437 aggravated assaults where firearms were used . In comparison , there were 123,253 aggravated assaults where knives or other cutting instruments were used . What are firearms ? Firearms are portable weapons used to shoot bullets .
gold_template: In templateTitleDate[0] , there were templateYValue[1] aggravated templateYLabel[1] where firearms were templateXLabel[1] . In comparison , there were templateYValue[min] aggravated templateYLabel[1] where knives or templateXValue[0] templateXValue[last] instruments were templateXLabel[1] . What are firearms ? Firearms are portable templateXValue[2] templateXLabel[1] to shoot bullets .

generated_template: As of 2019 , it was found that over templateYValue[max] templateScale of templateYLabel[3] of templateTitle[4] templateTitle[5] users in templateTitleSubject[0] . templateXValue[1] was the second largest templateYLabel[0] that year , with a templateYLabel[0] of templateYValue[1] templateScale templateYLabel[3] .
generated: As of 2019 , it was found that over 223997 % of assaults U.S. 2018 users in U.S. Firearm was the second largest Number that year , with a Number of 185437 % assaults .

Example 887:
titleEntities: {'Subject': ['English'], 'Date': ['2018/19', '2018/19']}
title: Average per game attendance English Premier League 2018/19
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10']
Y_Axis['Average', 'attendance']: ['38484', '38495', '35838', '36490', '36163', '36691', '35903', '34646', '35356', '34215']

gold: The statistic shows the average per game attendance at games of the English Premier League from the 2009/10 season to the 2018/19 season . In 2018/19 , the average attendance at the games was at 38,484 .
gold_template: The statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] templateYLabel[1] at games of the templateTitleSubject[0] templateTitle[5] templateTitle[6] from the templateXValue[last] season to the templateXValue[0] season . In templateXValue[0] , the templateYLabel[0] templateYLabel[1] at the games was at templateYValue[0] .

generated_template: This graph depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitleSubject[0] was templateYValue[0] templateYValue[idxmax(X)]
generated: This graph depicts the Average per game attendance of the English from 2009/10 to 2018/19 .  In 2018/19 , the Average attendance at games of the English was 38484 . 

Example 888:
titleEntities: {'Subject': ['Major League Baseball'], 'Date': ['2019']}
title: Average age of players in Major League Baseball by club 2019
X_Axis['Major', 'League', 'Baseball', 'Player']: ['Washington_Nationals', 'Oakland_Athletics', 'Houston_Astros', 'Chicago_Cubs', 'Atlanta_Braves', 'New_York_Yankees', 'Milwaukee_Brewers', 'San_Francisco_Giants', 'New_York_Mets', 'St._Louis_Cardinals', 'Minnesota_Twins', 'Tampa_Bay_Rays', 'Boston_Red_Sox', 'Chicago_White_Sox', 'Los_Angeles_Dodgers', 'Seattle_Mariners', 'Miami_Marlins', 'Cleveland_Indians', 'Texas_Rangers', 'Los_Angeles_Angels', 'Colorado_Rockies', 'Pittsburgh_Pirates', 'Cincinnati_Reds', 'Philadelphia_Phillies', 'Kansas_City_Royals', 'Arizona_Diamondbacks', 'Toronto_Blue_Jays', 'Detroit_Tigers', 'San_Diego_Padres', 'Baltimore_Orioles']
Y_Axis['Average', 'player', 'age', 'in', 'years']: ['30.1', '29.9', '29.8', '29.5', '29.5', '29.5', '29.3', '29.0', '29.0', '28.6', '28.4', '28.3', '28.3', '28.3', '28.2', '27.8', '27.8', '27.7', '27.7', '27.7', '27.5', '27.4', '27.3', '27.3', '27.1', '27.0', '26.8', '26.5', '26.0', '25.7']

gold: This graph depicts Major League Baseball clubs by average player age in 2019 . Players on the New York Yankees ' roster had an average age of 29.5 years . Major League Baseball players - additional information Amongst all teams in Major League Baseball , the Washington Nationals have the roster with the highest average player age in 2019 .
gold_template: This graph depicts templateXLabel[0] templateXLabel[1] templateXLabel[2] clubs templateTitle[6] templateYLabel[0] templateXLabel[3] templateYLabel[2] in templateTitleDate[0] . templateTitle[2] on the templateXValue[5] Yankees ' roster had an templateYLabel[0] templateYLabel[2] of templateYValue[3] templateYLabel[3] . templateXLabel[0] templateXLabel[1] templateXLabel[2] templateTitle[2] - additional information Amongst all teams in templateXLabel[0] templateXLabel[1] templateXLabel[2] , the templateXValue[0] have the roster with the highest templateYLabel[0] templateXLabel[3] templateYLabel[2] in templateTitleDate[0] .

generated_template: The graph shows a ranking of the templateTitle[3] templateTitle[4] templateTitle[5] at the templateTitle[7] templateTitle[8] in templateTitleDate[0] . The templateXValue[0] Giants had an templateYLabel[0] templateYLabel[2] of templateYValue[max] templateYLabel[3] .
generated: The graph shows a ranking of the Major League Baseball at the club 2019 in .  The Washington_Nationals Giants had an Average age of 30.1 years .

Example 889:
titleEntities: {'Subject': ['Canada'], 'Date': ['2017', '2017']}
title: Leading convention centers in Canada as of June 2017 , by size
X_Axis['Buildings']: ['Enercare_Centre', 'The_Toronto_Congress_Centre', 'The_International_Centre_Toronto', 'Metro_Toronto_Convention_Centre', 'Evraz_Place', 'Olympic_Park', 'Northlands', 'Calgary_Stampede_Park', 'Place_Bonaventure', 'Vancouver_Convention_Centre']
Y_Axis['Exhibition', 'space', 'in', 'thousand', 'square', 'feet']: ['1000.0', '1000.0', '500.0', '460.0', '452.0', '410.0', '401.0', '400.0', '315.0', '311.5']

gold: This statistic shows the leading convention centers in Canada as of June 2017 , by size . The Enercare Centre and The Toronto Congress Centre were the largest convention centers in Canada at one million square feet each .
gold_template: This statistic shows the templateTitle[0] templateXValue[3] templateTitle[2] in templateTitleSubject[0] as of templateTitle[4] templateTitleDate[0] , templateTitle[6] templateTitle[7] . The templateXValue[0] and The templateXValue[1] Centre were the largest templateXValue[3] templateTitle[2] in templateTitleSubject[0] at templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] each .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of the templateTitle[0] templateXLabel[1] in templateTitleSubject[0] in templateTitleDate[0] . The ranking with the highest templateYLabel[0] templateYLabel[1] rate of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Exhibition space thousand square of the Leading Buildings in Canada 2017 .  The ranking with the highest Exhibition space rate of 1000.0 thousand square .

Example 890:
titleEntities: {'Subject': ['Ivory Coast'], 'Date': ['2024']}
title: Inflation rate in Ivory Coast 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2', '2', '2', '2', '2', '1', '0.42', '0.69', '0.72', '1.24', '0.45', '2.58', '1.3', '4.9', '1.37', '1.01', '6.32', '1.9', '2.47', '3.88', '1.46', '3.3', '3.08', '4.36', '2.53', '0.74', '4.5', '6.3', '2.7', '14.1', '25.96', '2.13', '4.22', '1.58', '-0.66', '0.99', '6.94', '6.98', '6.84', '1.75', '4.28']

gold: This statistic shows the average inflation rate in Ivory Coast from 1984 to 2017 , with projections up until 2024 . In 2017 , the inflation rate in Ivory Coast amounted to about 0.69 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[7] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Ivory Coast from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Ivory Coast amounted to about 0.42 % compared to the previous Year .

Example 891:
titleEntities: {'Subject': ['Tampa Bay Buccaneers'], 'Date': ['2019']}
title: Average ticket price of the Tampa Bay Buccaneers 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['82.59', '85.07', '74.11', '63.59', '63.59', '63.59', '69.72', '71.47', '72.1', '74.25', '90.13', '72.44', '67.97']

gold: This graph depicts the average ticket price for Tampa Bay Buccaneers games in the National Football League from 2006 to 2019 . In 2019 , the average ticket price was at 82.59 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] Buccaneers games in the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: The graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: The graph depicts the Average ticket price for Tampa Bay Buccaneers games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 82.59 U.S. dollars .

Example 892:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2000']}
title: Crude suicide rate in Vietnam 2000 to 2015
X_Axis['Year']: ['2015', '2010', '2005', '2000']
Y_Axis['Crude', 'suicide', 'rate', 'per', '100,000', 'inhabitants']: ['7.4', '7.1', '6.5', '6.8']

gold: This statistic shows the crude suicide rate in Vietnam from 2000 to 2015 . In 2015 , the crude suicide rate in Vietnam was approximately 7.4 deaths per 100,000 inhabitants .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was approximately templateYValue[idxmax(X)] deaths templateYLabel[3] 100,000 templateYLabel[5] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] was approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Crude suicide of the rate Vietnam 2000 2015 in Vietnam from 2000 to 2015 .  In 2015 , the Crude suicide of rate Vietnam 2000 was approximately 7.4 rate per .

Example 893:
titleEntities: {'Subject': ['Price Index'], 'Date': ['1990', '2019']}
title: Producer Price Index for commodities 1990 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90"]
Y_Axis['Producer', 'Price', 'Index', '(100=1982)']: ['199.8', '202.0', '193.5', '185.4', '190.4', '205.3', '203.4', '202.2', '201.0', '184.7', '172.9', '189.6', '172.6', '164.7', '157.4', '146.7', '138.1', '131.1', '134.2', '132.7', '125.5', '124.4', '127.6', '127.7', '124.7', '120.4', '118.9', '117.2', '116.5', '116.3']

gold: This statistic shows the Producer Price Index for commodities in the United States of America form 1990 to 2019 . In 2019 , the PPI stood at 199.8 . The Producer Price Index ( PPI ) program measures the average change over time in the selling prices received by domestic producers for their output .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] in the country of America form templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the PPI stood at templateYValue[0] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] ( PPI ) program measures the average change over time in the selling prices received by domestic producers templateTitle[3] their output .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitleSubject[0] templateTitle[3] templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] was templateYValue[0] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Producer Price of the Producer Price Index for commodities from 1990 to 2019 .  In 2019 , the Producer Price of the for commodities was 199.8 Index (100=1982) .

Example 894:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. : reported motor vehicle theft rate 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Rate', 'per', '100,000', 'population']: ['228.9', '237.7', '237.3', '222.2', '215.4', '221.3', '230.4', '230.0', '239.1', '259.2', '315.4', '364.9', '400.2', '416.8', '421.5', '433.7', '432.9', '430.6', '412.2', '422.5', '459.8', '505.8', '525.5', '560.4', '591.3', '606.1', '631.5', '658.9', '657.8']

gold: This graph shows the reported motor vehicle theft rate in the United States from 1990 to 2018 . In 2018 , the nationwide rate was 228.9 cases per 100,000 population .
gold_template: This graph shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the nationwide templateYLabel[0] was templateYValue[idxmax(X)] cases templateYLabel[1] 100,000 templateYLabel[3] .

generated_template: This graph shows the templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] in the country from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the nationwide templateYLabel[0] was templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[1] 100,000 of the templateYLabel[3] .
generated: This graph shows the reported motor vehicle Rate in the country from 1990 to 2018 .  In 2018 , the nationwide Rate was 228.9 population per 100,000 of the population .

Example 895:
titleEntities: {'Subject': ['Brunei'], 'Date': ['2024']}
title: Inflation rate in Brunei Darussalam 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['0.2', '0.2', '0.2', '0.2', '0.15', '0.07', '0.15', '-0.17', '-0.74', '-0.42', '-0.21', '0.39', '0.11', '0.14', '0.22', '1.04', '2.09', '0.97', '0.15', '1.09', '0.9', '0.3', '-2.29', '0.6', '1.17', '-0.01', '-0.42', '1.71', '1.97', '5.97', '2.45', '4.29', '1.26', '1.58', '2.13', '1.31', '1.19', '1.28', '1.78', '2.3', '3.1']

gold: This statistic shows the average inflation rate in Brunei Darussalam from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Brunei Darussalam amounted to about 0.15 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] templateTitle[3] amounted to about templateYValue[4] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Brunei from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Brunei amounted to about 0.15 % compared to the previous Year .

Example 896:
titleEntities: {'Subject': ['Egypt'], 'Date': ['2019']}
title: Youth unemployment rate in Egypt in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['32.43', '32.6', '32.95', '34.28', '34.68', '32.65', '34.37', '34.54', '29.44', '24.43', '25.44', '25.77', '25.04', '30.23', '31.18', '28.32', '29.15', '26.23', '27.49', '24.38', '20.13']

gold: The statistic shows the youth unemployment rate in Egypt from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Egypt was at 32.43 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Youth unemployment rate in Egypt from 1999 and 2019 .  According to the source , the data are ILO estimates .  In 2019 , the estimated Youth unemployment rate in Egypt was at 32.43 % .

Example 897:
titleEntities: {'Subject': ['Mexico', 'FDI'], 'Date': ['2019', '2019']}
title: Mexico : foreign direct investment ( FDI ) 3rd quarter 2019 , by country
Data:
                     0       1        2      3        4            5          6      7      8
Country  United_States  Canada  Germany  Spain  Belgium  Netherlands  Australia  Japan  Italy
Q2 2019          36.07   34.97     9.81   6.32        0            0          0      0   3.18
Q3 2019          24.93    1.46    19.38  26.79     0.01         0.71       1.42   7.64   6.94 

gold: In the third quarter of 2019 , less than one third of foreign direct investments ( FDI ) in Mexico came from the United States . During that same period , nearly 27 percent of Mexican FDI originated from Spain . Moreover , FDI in Mexico throughout the second quarter of each year has decreased lately .
gold_template: In the third templateTitle[6] of templateTitleDate[0] , less than templateValue[2][1] third of templateTitle[1] templateTitle[2] investments ( templateTitleSubject[1] ) in templateTitleSubject[0] came from the templateValue[0][0] . During that same period , nearly templateValue[2][3] templateScale of Mexican templateTitleSubject[1] originated from templateValue[0][3] . Moreover , templateTitleSubject[1] in templateTitleSubject[0] throughout the second templateTitle[6] of each year has templateNegativeTrend lately .

generated_template: This statistic illustrates the number of templateTitleSubject[0] and templateLabel[2][0] templateLabel[2][1] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . In that year , the templateValue[0][0] of templateTitleSubject[0] employees were templateLabel[1][0] in the world .
generated: This statistic illustrates the number of Mexico and Q3 2019 investment FDI 3rd in 2019 .  In that year , the United_States of Mexico employees were Q2 in the world .

Example 898:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Average annual consumer spending , by type U.S. 2018
X_Axis['Spending', 'Type']: ['Housing', 'Transportation', 'Food', 'Personal_insurance_and_pensions', 'Healthcare', 'Entertainment', 'All_other_expenditures', 'Cash_contributions', 'Apparel_and_services']
Y_Axis['Spending', 'in', 'U.S.', 'dollars']: ['20091', '9761', '7923', '7296', '4968', '3226', '2030', '1888', '1866']

gold: This statistic shows the average annual expenditures of consumer units in the United States in 2018 , by type . In 2018 , the average U.S. consumer unit spent about 7,923 U.S. dollars on food . The total average U.S. consumer spending amounted to 61,224 U.S. dollars .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateXValue[6] of templateTitle[2] units in the templateTitle[6] in templateTitleDate[0] , templateTitle[4] templateXLabel[1] . In templateTitleDate[0] , the templateTitle[0] templateYLabel[1] templateTitle[2] unit spent about templateYValue[2] templateYLabel[1] templateYLabel[2] on templateXValue[2] . The total templateTitle[0] templateYLabel[1] templateTitle[2] templateXLabel[0] amounted to 61,224 templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the distribution of templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . As of that year , templateXValue[0] was the templateTitle[0] templateXValue[0] , with a total of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the distribution of U.S. the Average annual consumer spending in 2018 , .  As of that year , Housing was the Average Housing , with a total of 20091 dollars .

Example 899:
titleEntities: {'Subject': ['Global'], 'Date': ['2017']}
title: Global papaya production 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Million', 'metric', 'tons']: ['13.02', '13.05', '12.08', '12.67', '12.36', '12.01', '11.3', '11.19', '10.73', '10.02', '9.53', '8.91', '8.03', '8.67', '8.16', '8.42', '8.55', '7.25']

gold: This statistic depicts the production of papaya worldwide from 2000 to 2017 . According to the report , global papaya production amounted to approximately 12.67 million metric tons in 2014 .
gold_template: This statistic depicts the templateTitle[2] of templateTitle[1] worldwide from templateXValue[min] to templateXValue[max] . According to the report , templateTitleSubject[0] templateTitle[1] templateTitle[2] amounted to approximately templateYValue[3] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[3] .
generated: This statistic shows the Million metric of the 2000 2017 of Global from 2000 to 2017 .  In 2017 , the average Million metric of Global was 13.02 tons .

Example 900:
titleEntities: {'Subject': ['Nepal'], 'Date': ['2024']}
title: Inflation rate in Nepal 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['5.3', '5.6', '5.8', '6', '6.1', '4.5', '4.15', '4.45', '9.93', '7.21', '9.04', '9.87', '8.31', '9.57', '9.57', '12.62', '6.68', '6.2', '7.96', '4.54', '3.96', '4.74', '2.9', '2.44', '3.39', '11.38', '8.33', '8.1', '7.18', '7.68', '8.95', '8.87', '21.06', '7.94', '8.93', '8.08', '11.02', '13.29', '15.85', '4.13', '6.21']

gold: In 2018 , the average inflation rate in Nepal was at 4.15 percent , a slight drop compared to the previous year . The inflation rate is calculated using the price increase of a defined product basket . This product basket contains products and services on which the average consumer spends money throughout the year .
gold_template: In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[6] templateScale , a slight drop templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . The templateYLabel[0] templateYLabel[1] is calculated using the price templatePositiveTrend of a defined product basket . This product basket contains products and services on which the average consumer spends money throughout the templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Nepal from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Nepal amounted to about 4.15 % compared to the previous Year .

Example 901:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2011']}
title: U.S. college students average study hours , by major 2011
X_Axis['Month']: ['Architecture', 'Chemical_Engineering', 'Physics', 'Chemistry', 'Art', 'Nursing', 'Music', 'Biology', 'Mathematics', 'Philosophy', 'Theater', 'Anthropology', 'English', 'Elementary_Education', 'Accounting', 'History', 'Computer_Science', 'Political_Science', 'Economics', 'Psychology', 'Sociology', 'Finance', 'Business_Administration', 'Journalism', 'Communications', 'Marketing', 'Physical_Education', 'Leisure_Studies', 'Speech']
Y_Axis['Number', 'of', 'hours', 'spent', 'studying', 'per', 'week']: ['23.7', '21.6', '19.7', '18.4', '18.1', '18.0', '17.5', '16.7', '16.4', '16.2', '16.0', '16.0', '15.9', '15.2', '15.1', '15.0', '14.7', '14.6', '14.4', '13.9', '13.8', '13.3', '13.2', '12.8', '12.5', '12.1', '11.8', '11.1', '10.8']

gold: This statistic shows the average number of hours that students spent studying per week in the United States in 2011 . The results are divided by major . On average an architecture major spent 23.7 hours per week studying .
gold_template: This statistic shows the templateTitle[3] templateYLabel[0] of templateYLabel[1] that templateTitle[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in the templateTitle[0] in templateTitleDate[0] . The results are divided templateTitle[6] templateTitle[7] . On templateTitle[3] an templateXValue[0] templateTitle[7] templateYLabel[2] templateYValue[max] templateYLabel[1] templateYLabel[4] templateYLabel[5] templateYLabel[3] .

generated_template: This statistic shows the distribution of templateYLabel[2] templateYLabel[3] of the templateTitle[0] templateTitle[1] in templateTitleDate[0] . templateXValue[0] had the highest templateTitle[0] templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] , followed by templateXValue[1] with templateYValue[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the distribution of spent studying of the U.S. college in 2011 .  Architecture had the highest U.S. Number of 23.7 spent studying , followed by Chemical_Engineering with 21.6 spent studying .

Example 902:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2018', '2019']}
title: Average price of cut flowers sold in auctions in the Netherlands 2018 to 2019
X_Axis['Month']: ["Dec_'19", "Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18"]
Y_Axis['Average', 'price', 'per', 'flower', 'in', 'eurocent']: ['35', '36', '33', '28', '25', '22', '25', '35', '24', '24', '28', '26', '35', '34', '31', '28', '27', '21', '28', '30']

gold: This statistic displays the average price of cut flowers sold in auctions in the Netherlands from May 2018 to December 2019 ( in eurocent per flower ) . In December 2019 , the average price of a flower sold on an auction in the Netherlands was 35 eurocent .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] in the templateTitleSubject[0] from templateXValue[7] templateTitleDate[min] to 2019 ( in templateYLabel[4] templateYLabel[2] templateYLabel[3] ) . In 2019 , the templateYLabel[0] templateYLabel[1] of a templateYLabel[3] templateTitle[4] on an auction in the templateTitleSubject[0] was templateYValue[0] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitle[3] on templateTitleSubject[0] from 2016 to 2019 . In 2019 , the templateTitle[0] of templateYLabel[1] was valued at templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitle[0] templateTitle[1] templateYLabel[0] is based on the prices .
generated: This statistic shows the Average of price Average flowers on Netherlands from 2016 to 2019 .  In 2019 , the Average of price was valued at 35 per flower eurocent .  The Average price is based on the prices .

Example 903:
titleEntities: {'Subject': ['National Health Service', 'United Kingdom'], 'Date': ['2015']}
title: Cost of a liver transplant on the National Health Service in the United Kingdom 2015
X_Axis['Cost', 'in', 'British', 'pounds']: ['Less_than_760', '760-2300', '2300-5000', '5000-10000', '10000-20000', 'More_than_20000']
Y_Axis['Share', 'of', 'respondents']: ['17', '17', '9', '21', '19', '18']

gold: This statistic shows the results of a survey asking individuals in the United Kingdom how much they think the unit cost is to have a liver transplant on the NHS in 2015 . The average cost of a liver transplant is estimated to be 12,279 British pounds . The majority of respondents , 64 percent , predicted the cost to be less than ten thousand British pounds .
gold_template: This statistic shows the results of a survey asking individuals in the templateTitleSubject[1] how much they think the unit templateXLabel[0] is to have a templateTitle[1] templateTitle[2] on the NHS in templateTitleDate[0] . The average templateXLabel[0] of a templateTitle[1] templateTitle[2] is estimated to be 12,279 templateXLabel[1] templateXLabel[2] . The majority of templateYLabel[1] , 64 templateScale , predicted the templateXLabel[0] to be templateXValue[0] ten thousand templateXLabel[1] templateXLabel[2] .

generated_template: According to a templateTitleDate[0] survey , it was found that templateYValue[max] templateScale of templateYLabel[1] felt that templateXValue[0] a quarter templateTitleSubject[1] templateXValue[0] or templateTitle[1] templateTitle[2] in the templateTitleSubject[0] . A further templateYValue[1] templateScale of templateYLabel[1] claimed that they used templateXValue[1] on a templateXValue[1] basis .
generated: According to a 2015 survey , it was found that 21 % of respondents felt that Less_than_760 a quarter United Kingdom Less_than_760 or liver transplant in the National Health Service .  A further 17 % of respondents claimed that they used 760-2300 on a 760-2300 basis .

Example 904:
titleEntities: {'Subject': ['Bundesliga'], 'Date': []}
title: Prices of season tickets of the Bundesliga clubs in 2019/2020
Data:
                                 0            1                    2           3           4               5                  6                   7           8           9               10             11                  12                        13           14                15               16             17
Year                  FSV_Mainz_05  SC_Freiburg  Eintracht_Frankfurt  1._FC_Köln  Schalke_04  Bayern_München  Borussia_Dortmund  Fortuna_Düsseldorf  Hertha_BSC  RB_Leipzig  SC_Paderborn_07  Werder_Bremen  1._FC_Union_Berlin  Borussia_Mönchengladbach  FC_Augsburg  Bayer_Leverkusen  1899_Hoffenheim  VFL_Wolfsburg
Standing places               179€         195€                 184€        165€       1905€            145€               219€                195€        199€        180€             225€           195€                187€                      190€         199€              190€             150€           145€
Most expensive seats          950€         865€                 827€        785€        776€            765€               759€                759€        749€        740€             735€           730€                680€                      675€         629€              595€             584€           440€ 

gold: The statistic presents the prices of season tickets first Bundesliga club matches in Germany in 2019/2020 . Season tickets for standing places for SC Paderborn and Borussia Dortmund matches were the most expensive at 225 and 219 euros respectively .
gold_template: The statistic presents the templateTitle[0] of templateTitle[1] templateTitle[2] first templateTitleSubject[0] club matches in Germany in templateTitle[5] . templateTitle[1] templateTitle[2] for templateLabel[1][0] templateLabel[1][1] for templateValue[0][1] templateValue[0][10] and templateValue[0][6] matches were the templateLabel[2][0] templateLabel[2][1] at 225 and 219 euros respectively .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateValue[0][last] to templateValue[0][0] . According to the survey , it was found that templateValue[1][0] templateScale of the respondents of people .
generated: This statistic shows the Prices of the season tickets Bundesliga clubs in the 2019/2020 from VFL_Wolfsburg to FSV_Mainz_05 .  According to the survey , it was found that 179€ % of the respondents of people .

Example 905:
titleEntities: {'Subject': ['Malawi'], 'Date': ['2018']}
title: Infant mortality rate in Malawi 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['35.3', '36.8', '38.3', '40.3', '42.3', '45.2', '48.4', '51.8', '55.4', '57.4', '59.2']

gold: The statistic shows the infant mortality rate in Malawi from 2008 to 2018 . In 2018 , the infant mortality rate in Malawi was at about 35.3 deaths per 1,000 live births .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality rate in Malawi from 2008 to 2018 .  In 2018 , the Infant mortality rate in Malawi was at about 35.3 Deaths per 1,000 live births .

Example 906:
titleEntities: {'Subject': ['Red'], 'Date': ['2019']}
title: Red list : threatened species 2019
X_Axis['Threatened', 'Species']: ['Flowering_Plants', 'Fishes', 'Molluscs', 'Amphibians', 'Insects', 'Birds', 'Reptiles', 'Mammals', 'Crustaceans', 'Gymnosperms', 'Ferns_and_Allies', 'Corals', 'Arachnids', 'Mosses', 'Other_invertebrates', 'Mushrooms', 'Lichens', 'Velvet_Worms', 'Red_Algae', 'Brown_Algae', 'Horseshoe_Crabs', 'Green_Algae']
Y_Axis['Number', 'of', 'species']: ['14938', '2674', '2250', '2200', '1647', '1486', '1409', '1244', '733', '402', '261', '237', '197', '164', '146', '140', '24', '9', '9', '6', '2', '0']

gold: In 2019 , 14,938 flowering plants were considered to be threatened species . Flowering plants , more colloquially known as flowers , are the most diverse group of land plants . They are also the largest group within the plant kingdom as regards the number of described species .
gold_template: In templateTitleDate[0] , templateYValue[max] templateXValue[0] were considered to be templateXLabel[0] templateXLabel[1] . templateXValue[0] , more colloquially known as flowers , are the most diverse group of land templateXValue[0] . They are also the largest group within the plant kingdom as regards the templateYLabel[0] of described templateXLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] of the templateTitle[0] templateTitle[1] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[min] to templateTitleDate[max] . The templateYLabel[0] of the templateTitleSubject[0] stood at templateYValue[7] templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: This statistic shows the Number of species the Red list in the United Kingdom ( Red ) from 2019 to .  The Number of the Red stood at 1244 species in 2019 .

Example 907:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2006', '2016']}
title: Annual rainfall in Belgium 2006 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Rainfall', 'in', 'millimeters']: ['942', '737', '784', '816', '977', '815', '914', '764', '862', '880', '835']

gold: Does it always rain in Belgium ? In some weeks that may seem to be the case , but in the last decade Belgium actually counted between 180 and 212 rain days a year . And in none of these years , the annual rainfall surpassed the thousand millimeters . Rainfall peaked in 2016 , at roughly 940 millimeters .
gold_template: Does it always rain in templateTitleSubject[0] ? In some weeks that may seem to be the case , but in the last decade templateTitleSubject[0] actually counted between 180 and 212 rain days a templateXLabel[0] . And in none of these years , the templateTitle[0] templateYLabel[0] surpassed the thousand templateYLabel[1] . templateYLabel[0] peaked in templateXValue[max] , at roughly 940 templateYLabel[1] .

generated_template: templateTitleSubject[0] has seen its templateTitle[1] templateYLabel[0] vary since templateXValue[min] , but it usually received more than 1,500 templateYLabel[1] ( mm ) of rain per templateXLabel[0] . However , in templateXValue[1] the recorded templateYLabel[0] amounted to 1,395mm , which was the fourth lowest during this period . templateYLabel[0] peaked in templateXValue[8] at 1,886mm .
generated: Belgium has seen its rainfall vary since 2006 , but it usually received more than 1,500 millimeters ( mm ) of rain per Year .  However , in 2015 the recorded Rainfall amounted to 1,395mm , which was the fourth lowest during this period .  Rainfall peaked in 2008 at 1,886mm .

Example 908:
titleEntities: {'Subject': ['Share'], 'Date': ['2007']}
title: Share of economic sectors in the global gross domestic product from 2007 to 2017
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2007   2008   2009   2010   2011   2012   2013   2014   2015   2016   2017
Agriculture   3.76   3.74   3.68   3.66    3.7   3.64   3.69   3.59   3.52   3.52   3.43
Industry     28.59  28.43  26.72   27.2  27.39  26.98  26.61  26.39  25.56  25.12  25.44
Services     61.64  61.95  63.89  63.21  62.92  63.42  63.71  63.97  64.86  65.26  65.03 

gold: This statistic shows the share of economic sectors in the global gross domestic product ( GDP ) from 2007 to 2017 . In 2017 , agriculture contributed 3.43 percent , industry contributed approximately 25.44 percent and services contributed about 65.03 percent to the global gross domestic product . See global GDP for comparison .
gold_template: This statistic shows the templateTitleSubject[0] of templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] ( GDP ) templateTitle[7] templateValue[0][0] to templateValue[0][last] . In templateValue[0][last] , templateLabel[1][0] contributed templateValue[1][last] templateScale , templateLabel[2][0] contributed approximately templateValue[2][last] templateScale and templateLabel[3][0] contributed about templateValue[3][last] templateScale to the templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . See templateTitle[3] GDP for comparison .

generated_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .
generated: The statistic shows the distribution of Share in economic sectors global from 2017 to 2007 .  In 2007 , 3.76 % of the employees in Share were active in the agricultural global , 28.59 % in Industry and 61.64 % in the service global .

Example 909:
titleEntities: {'Subject': ['Syria'], 'Date': ['2010']}
title: Gross domestic product ( GDP ) in Syria 2010
X_Axis['Year']: ['2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['60.04', '53.94', '52.63', '40.49', '33.82', '28.88', '25.2', '21.7', '22.76', '20.98', '19.86', '16.79', '16.14', '16.57', '17.76', '16.56', '15.11', '13.8', '13.26', '12.74', '12.3', '9.85', '16.54', '32.5', '25.43', '21.18', '19.17']

gold: The statistic shows gross domestic product ( GDP ) in Syria from 1984 to 2010 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Syria from 1984 to 2004 , with projections up until 2010 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 910:
titleEntities: {'Subject': ['Leading'], 'Date': ['2019']}
title: Leading financial centers worldwide 2019
X_Axis['State']: ['New_York', 'London', 'Hong_Kong', 'Singapore', 'Shanghai', 'Tokyo', 'Beijing', 'Dubai', 'Shenzhen', 'Sydney', 'Toronto', 'San_Francisco', 'Los_Angeles', 'Zurich', 'Frankfurt', 'Chicago', 'Paris', 'Boston', 'Melbourne', 'Montreal']
Y_Axis['Points', 'on', 'the', 'Global', 'Financial', 'Centres', 'Index']: ['790', '773', '771', '762', '761', '757', '748', '740', '739', '738', '737', '736', '735', '734', '733', '732', '728', '728', '720', '716']

gold: This statistic presents the most attractive financial centers worldwide as of September 2019 . The private institute Z/Yen has constructed an index for financial center rating , in which a multitude of factors are integrated . New York proved to be the most attractive financial center in 2019 , with 790 points assigned in the Z/Yen report .
gold_template: This statistic presents the most attractive templateYLabel[2] templateTitle[2] templateTitle[3] as of 2019 . The private institute Z/Yen has constructed an templateYLabel[4] for templateYLabel[2] center rating , in which a multitude of factors are integrated . templateXValue[0] proved to be the most attractive templateYLabel[2] center in templateTitleDate[0] , with templateYValue[max] templateYLabel[0] assigned in the Z/Yen report .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] the templateTitle[7] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In that year , the templateXValue[1] of templateXValue[0] had the second highest templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] with templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Leading of financial centers worldwide the 2019 in , 2019 State .  In that year , the London of New_York had the second highest Leading financial centers worldwide with 790 Financial Centres .

Example 911:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2017']}
title: Life expectancy of women in Mexico 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Life', 'expectancy', 'at', 'birth', 'in', 'years']: ['77.83', '77.83', '77.84', '77.86', '77.89', '77.92', '77.95', '77.98', '78.0', '78.03', '78.04']

gold: The statistic shows the life expectancy of women at birth in Mexico from 2007 to 2017 . In 2017 , the average life expectancy of women at birth in Mexico was about 77.83 years .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitle[2] at templateYLabel[2] in templateTitleSubject[0] was about templateYValue[idxmax(X)] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of women in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitle[2] at templateYLabel[2] in templateTitleSubject[0] was about templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] .
generated: The statistic shows the Life expectancy of women in Mexico from 2007 to 2017 .  In 2017 , the average Life expectancy of women at birth in Mexico was about 77.83 years .

Example 912:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1991', '2015']}
title: Median age of first alcohol use among U.S. youth 1991 to 2015
X_Axis['Year']: ['2013–2015', '2012–2014', '2011–2013', '2010–2012', '2009–2011', '2008–2010', '2007–2009', '2006–2008', '2005–2007', '2004–2006', '2003–2005', '2002–2004', '2001–2003', '2000–2002', '1999–2001', '1998–2000', '1997–1999', '1996–1998', '1995–1997', '1994–1996', '1993–1995', '1992–1994', '1991–1993']
Y_Axis['Average', 'age', 'in', 'years']: ['14.64', '14.56', '14.47', '14.4', '14.35', '14.29', '14.22', '14.14', '14.06', '13.97', '13.9', '13.85', '13.82', '13.81', '13.81', '13.88', '13.91', '13.94', '13.85', '13.8', '13.76', '13.76', '13.65']

gold: This statistic shows the average age youths aged 12 to 20 years first drank alcohol in the U.S. from 1991 to 2015 . In the period 2013 to 2015 , on average those aged 12 to 20 years first drank alcohol when they were 14.64 years old .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] youths aged 12 to 20 templateYLabel[2] templateTitle[2] drank templateTitle[3] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In the period 2013 to templateTitleDate[max] , on templateYLabel[0] those aged 12 to 20 templateYLabel[2] templateTitle[2] drank templateTitle[3] when they were templateYValue[max] templateYLabel[2] old .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] in the templateTitle[2] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the templateYLabel[1] of templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[2] .
generated: This statistic shows the Average age of U.S. in the first from 1991–1993 to 2013–2015 .  In 2013–2015 , the age of U.S. amounted to 14.64 years .

Example 913:
titleEntities: {'Subject': ['FMCG', 'United Kingdom', 'UK'], 'Date': ['2018']}
title: Leading FMCG brands in the United Kingdom ( UK ) 2018 , by consumer reach
X_Axis['Food', 'Groups']: ['Warburtons', 'Heinz', "Mc_Vitie's", 'Hovis', 'Walkers', 'Coca-Cola', 'Kingsmill', 'Muller', "Cadbury's_Dairy_Milk", 'Birds_Eye']
Y_Axis['Consumer', 'Reach', 'Points', 'in', 'millions']: ['514', '367', '284', '274', '270', '232', '222', '217', '181', '172']

gold: This statistic shows the leading ten fast moving consumer goods ( FMCG ) brands in the United Kingdom ( UK ) as of 2018 . That year , Warburtons ranked highest with 514 million consumer reach points , followed by Heinz with 365 million points . Fast moving consumer goods refer to products that are sold quickly and at a relatively low cost .
gold_template: This statistic shows the templateTitle[0] ten fast moving templateYLabel[0] goods ( templateTitleSubject[0] ) templateTitle[2] in the templateTitleSubject[1] ( templateTitleSubject[2] ) as of templateTitleDate[0] . That year , templateXValue[0] ranked highest with templateYValue[max] templateScale templateYLabel[0] templateYLabel[1] templateYLabel[2] , followed templateTitle[7] templateXValue[1] with 365 templateScale templateYLabel[2] . Fast moving templateYLabel[0] goods refer to products that are sold quickly and at a relatively low cost .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] average end of the templateYLabel[2] templateYLabel[3] templateTitle[4] in templateTitleDate[0] . The templateXValue[0] templateXValue[1] , with the largest templateYLabel[0] of . In that year , there were templateYValue[1] templateYLabel[2] templateYLabel[3] that year .
generated: The statistic shows the Leading FMCG brands average end of the Points millions Kingdom in 2018 .  The Warburtons Heinz , with the largest Consumer of .  In that year , there were 367 Points millions that year .

Example 914:
titleEntities: {'Subject': ['Ireland'], 'Date': ['2019']}
title: Employment by economic sector in Ireland 2019
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2019   2018   2017   2016   2015   2014   2013   2012   2011   2010   2009
Agriculture   4.98   5.02   5.06   5.33   5.33   5.43   5.76   5.77   5.73   5.75   5.59
Industry     18.27  18.47  18.83  18.56   18.1   17.3  17.23  16.98  17.45  18.09  19.85
Services     76.75  76.51  76.12  76.11  76.57  77.27  77.01  77.25  76.82  76.16  74.56 

gold: The statistic shows the distribution of employment in Ireland by economic sector from 2009 to 2019 . In 2019 , 4.98 percent of the employees in Ireland were active in the agricultural sector , 18.27 percent in industry and 76.75 percent in the service sector .
gold_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .

generated_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .
generated: The statistic shows the distribution of Employment in Ireland by economic sector from 2009 to 2019 .  In 2019 , 4.98 % of the employees in Ireland were active in the agricultural sector , 18.27 % in Industry and 76.75 % in the service sector .

Example 915:
titleEntities: {'Subject': ['Greece'], 'Date': ['2024']}
title: Budget balance in Greece 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'in', 'billion', 'euros']: ['-3.59', '-2.98', '-2.42', '-2.18', '-1.97', '-0.59', '1.77', '1.91', '0.98', '-4.91', '-7.27']

gold: The statistic shows the budget balance in Greece from 2014 to 2018 , with projections until 2024 . A positive value indicates a state surplus ; a negative value , a budget deficit . In 2018 , the budget surplus of Greece was around 1.77 billion euros .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections until templateXValue[max] . A positive value indicates a state surplus ; a negative value , a templateYLabel[0] deficit . In templateXValue[6] , the templateYLabel[0] surplus of templateTitleSubject[0] was around templateYValue[6] templateScale templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a state surplus ; a negative value , a state deficit . In templateXValue[6] , the state deficit of templateTitleSubject[0] was around 37.51 templateScale templateYLabel[3] .
generated: The statistic shows the Budget balance in Greece from 2014 to 2018 , with projections up until 2024 .  A positive value indicates a state surplus ; a negative value , a state deficit .  In 2018 , the state deficit of Greece was around 37.51 billion euros .

Example 916:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018', '2019']}
title: Number of earthquakes in Italy 2018 - 2019 , by magnitude
X_Axis['Magnitude']: ['Magnitude_0+', 'Magnitude_2+', 'Magnitude_3+', 'Magnitude_4+', 'Magnitude_5.5+']
Y_Axis['Number', 'of', 'earthquakes']: ['21869', '2384', '226', '20', '0']

gold: The statistic shows the number of earthquakes in Italy from March 2018 to March 2019 , broken down by magnitude . According to data , no earthquakes with 5.5+ magnitude occurred in Italy over the period considered .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from 2018 to 2019 , broken down templateTitle[5] templateXValue[0] . According to data , no templateYLabel[1] with templateXValue[last] templateXValue[0] occurred in templateTitleSubject[0] over the period considered .

generated_template: This statistic gives information on the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] as of 2019 . The survey period , templateValue[1][0] templateScale of all templateTitle[0] templateTitle[1] .
generated: This statistic gives information on the Number of earthquakes in the Italy as of 2019 .  The survey period , percentage of all Number earthquakes .

Example 917:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2005', '2018']}
title: Expenditure on beer in the United Kingdom 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Expenditure', 'in', 'million', 'GBP']: ['5631', '5091', '4640', '4571', '4573', '4435', '4188', '4119', '3994', '4142', '3677', '3802', '3976', '3714']

gold: In 2018 , consumers spent 5.6 billion British pounds on beer in the United Kingdom ( UK ) . This is the highest consumer spending recorded in the past thirteen years and the second time spending surpassed five billion pounds . Spending has generally grown since 2010 .
gold_template: In templateXValue[max] , consumers spent templateYValue[max] templateScale British pounds on templateTitle[1] in the templateTitleSubject[0] ( UK ) . This is the highest consumer spending recorded in the past thirteen years and the second time spending surpassed templateYValue[1] templateScale pounds . Spending has generally grown since templateXValue[8] .

generated_template: This statistic shows total consumer spending on templateTitle[1] , templateTitle[3] , and templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateYLabel[0] on templateYLabel[1] amounted to approximately templateYValue[1] templateScale British pounds .
generated: This statistic shows total consumer spending on beer , Kingdom and United from 2005 to 2018 .  In 2017 , the Expenditure on million amounted to approximately 5091 million British pounds .

Example 918:
titleEntities: {'Subject': ['Alabama'], 'Date': ['2019']}
title: Number of active physicians in Alabama 2019 , by specialty area
X_Axis['Specialty', 'area']: ['Psychiatry', 'Surgery', 'Anesthesiologists', 'Emergency_medicine', 'Radiology', 'Cardiology', 'Oncology_(cancer)', 'Endocrinology_diabetes_&_metabolism', 'All_other_specialities', 'Total_specialty']
Y_Axis['Number', 'of', 'physicians']: ['506', '754', '602', '465', '674', '410', '241', '71', '2619', '6342']

gold: This statistic depicts the number of active physicians in Alabama as of March 2019 , sorted by specialty area . In that year , there were 602 anesthesiologists active in Alabama . Thus , this was one of the three largest specialty groups in Alabama .
gold_template: This statistic depicts the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] as of 2019 , sorted templateTitle[5] templateXValue[last] templateXLabel[1] . In that year , there were templateYValue[2] templateXValue[2] templateTitle[1] in templateTitleSubject[0] . Thus , this was one of the three largest templateXValue[last] groups in templateTitleSubject[0] .

generated_template: This statistic depicts the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] as of 2019 , ordered templateTitle[5] templateXValue[last] templateXLabel[1] . At that time , there were templateYValue[2] templateXValue[2] templateTitle[1] in templateTitleSubject[0] . In templateXValue[last] , there were over 13,700 registered templateXValue[last] templateYLabel[1] in templateTitleSubject[0] during that time .
generated: This statistic depicts the Number of active physicians in Alabama as of 2019 , ordered by Total_specialty area .  At that time , there were 602 Anesthesiologists active in Alabama .  In Total_specialty , there were over 13,700 registered Total_specialty physicians in Alabama during that time .

Example 919:
titleEntities: {'Subject': ['U.S. January TV'], 'Date': ['2020', '2020']}
title: Leading trailers in the U.S. January 2020 , by weekly TV ad spend
X_Axis['Year']: ['1917', 'Dolittle', 'Bad_Boys_for_Life', 'Like_a_Boss', 'Just_Mercy']
Y_Axis['Spending', 'in', 'million', 'U.S.', 'dollars']: ['10.41', '5.77', '4.18', '3.9', '3.72']

gold: The leading movie commercial in the United States based on weekly television advertising spending for the week ending January 5 , 2020 was for war drama film ' 1917 ' _ , with a 10.41 million U.S. dollar spend by studio Universal Pictures . Universal also spent 5.77 million U.S. dollars on TV promotion of 'Dolittle ' _ .
gold_template: The templateTitle[0] movie commercial in the templateTitle[2] based on templateTitle[6] television advertising templateYLabel[0] templateXValue[2] the week ending templateTitleSubject[0] 5 , templateTitleDate[0] was templateXValue[2] war drama film ' templateXValue[0] ' _ , with a templateYValue[max] templateScale templateYLabel[2] dollar templateTitle[9] templateTitle[5] studio Universal Pictures . Universal also spent templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] on templateTitleSubject[0] promotion of 'Dolittle ' _ .

generated_template: This statistic illustrates the templateTitle[3] templateYLabel[0] of the templateTitle[1] in the country from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the templateTitle[1] templateYLabel[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic illustrates the January Spending of the trailers in the country from Just_Mercy to 1917 .  In 1917 , the trailers Spending amounted to 10.41 million U.S. dollars .

Example 920:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010', '2016']}
title: Average price for a soak-off gel application in nail salons in the U.S. 2010 to 2016
X_Axis['Year']: ['2010', '2011', '2012', '2013', '2014', '2015', '2016']
Y_Axis['Average', 'price', 'in', 'U.S.', 'dollars']: ['28.68', '31.74', '28.93', '35.54', '36.64', '30.03', '29.96']

gold: This statistic shows the average price for a soak-off gels application in nail salons in the United States from 2010 to 2016 . Nail salons charged an average price of 36.64 for an initial soak-off gel application in 2014 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] a templateTitle[3] gels templateTitle[5] in templateTitle[6] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . templateTitle[6] templateTitle[7] charged an templateYLabel[0] templateYLabel[1] of templateYValue[max] templateTitle[2] an initial templateTitle[3] templateTitle[4] templateTitle[5] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] a templateTitle[3] templateTitle[4] of templateTitle[5] templateTitle[6] in templateTitle[7] templateTitle[8] in the templateTitle[9] from templateXValue[min] to templateXValue[max] . templateTitle[7] templateTitle[8] charged an templateYLabel[0] templateYLabel[1] of templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateTitle[2] a templateTitle[3] templateTitle[4] of templateTitle[5] templateTitle[6] in templateXValue[max] .
generated: This statistic shows the Average price for a soak-off gel of application nail in salons U.S. in the 2010 from to 2016 .  salons U.S. charged an Average price of 29.96 U.S. dollars for a soak-off gel of application nail in 2016 .

Example 921:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2012', '2019']}
title: Favorite social networks of U.S. teens 2012 to 2019
Data:
                   0            1          2            3            4          5            6          7            8          9           10         11           12
Response   Fall_2012  Spring_2013  Fall_2013  Spring_2014  Spring_2015  Fall_2015  Spring_2016  Fall_2016  Spring_2017  Fall_2017  Spring_2018  Fall_2018  Spring_2019
Snapchat           0            0          0            0           11         17           24         35           39         47           45         46           41
Instagram         12           17         27           30           29         29           23         24           23         24           26         32           35
Twitter           27           30         31           27           21         18           16         13           11          7            9          6            6
Facebook          42           33         27           23           12         13           15         13           11          9            8          6            6
Tumblr             3            4          5            5            4          3            2          0            0          0            0          0            0
Pinterest          2            2          2            2            2          1            1          1            1          0            1          1            1
Google+            6            5          4            4            1          1            1          1            0          0            0          0            0
Other              2            4          2            4            8          8            5          0            0          0            0          0            0 

gold: This statistic shows the preferred social networks of U.S. teenagers . During the spring 2019 survey , it was found that Snapchat was the most important social network for 41 percent of U.S. teens . Facebook was ranked fourth with six percent of teenagers in the United States stating it to be their favourite .
gold_template: This statistic shows the preferred templateTitle[1] templateTitle[2] of templateTitleSubject[0] teenagers . During the templateValue[0][1] templateValue[0][12] survey , it was found that templateLabel[1][0] was the most important templateTitle[1] network for templateValue[1][last] templateScale of templateTitleSubject[0] templateTitle[4] . templateLabel[4][0] was ranked fourth with templateValue[3][last] templateScale of teenagers in the templateTitle[3] stating it to be their favourite .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[4] as of 2019 , sorted templateTitle[6] templateTitle[7] . During the survey , templateValue[1][0] templateScale of respondents stated that they had used templateLabel[1][0] templateLabel[1][1] .
generated: This statistic gives information on the Favorite social networks in the teens as of 2019 , sorted 2019 .  During the survey , 0 % of respondents stated that they had used Snapchat .

Example 922:
titleEntities: {'Subject': ['Afghanistan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Afghanistan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['GDP', 'in', 'billion', 'U.S.', 'dollars']: ['25.19', '23.24', '21.54', '20', '18.86', '18.73', '19.63', '20.24', '19.43', '20.06', '20.62', '20.17', '20.29', '17.89', '15.33', '12.07', '10.3', '8.56', '6.93', '6.17', '5.15']

gold: This timeline shows the gross domestic product ( GDP ) in Afghanistan from 2004 to 2018 , with projections up until 2024 . In 2018 , Afghanistan 's gross domestic product amounted to 19.63 billion U.S. dollars . For comparison , global GDP was estimated at almost more than 75 trillion U.S. dollars that same year .
gold_template: This timeline shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] templateTitle[2] amounted to templateYValue[6] templateScale templateYLabel[2] templateYLabel[3] . For comparison , global templateYLabel[0] was estimated at almost more than 75 templateScale templateYLabel[2] templateYLabel[3] that same templateXLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the Gross domestic product ( GDP ) in the Afghanistan from 2004 to 2018 , with projections up until 2024 .  GDP refers to the total market value of all goods and services that are produced within a country per Year .  It is an important indicator of the economic strength of a country .

Example 923:
titleEntities: {'Subject': ['Indiana Pacers', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Indiana Pacers ( NBA ) games 2015/16
X_Axis['Year']: ['2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['38.97', '32.59', '31.62', '31.62', '30.59', '29.13', '30.02', '41.09', '42.39', '42.39']

gold: This graph depicts the average ticket price for Indiana Pacers games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 42.39 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Indiana Pacers games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Average ticket price was 42.39 U.S. dollars .

Example 924:
titleEntities: {'Subject': ['EU'], 'Date': ['2018']}
title: Payroll taxes in EU countries 2018
X_Axis['Country']: ['Sweden', 'France', 'Lithuania', 'Italy', 'Austria', 'Czech_Republic', 'Belgium', 'Slowakia', 'Estonia', 'Spain', 'Euro_area', 'Greece', 'Netherlands', 'EU', 'Latvia', 'Germany', 'Portugal', 'Finland', 'Hungary', 'Poland', 'United_Kingdom', 'Slovenia', 'Croatia', 'Bulgaria', 'Ireland', 'Cyprus', 'Denmark', 'Luxembourg', 'Malta', 'Romania']
Y_Axis['Payroll', 'taxes', 'of', '100', 'euros', 'gross', 'earnings']: ['48', '45', '43', '40', '38', '37', '37', '37', '35', '35', '34', '33', '30', '30', '29', '27', '26', '25', '25', '22', '20', '19', '19', '19', '18', '16', '16', '13', '8', '0']

gold: The statistic shows payroll taxes of 100 euros gross earnings in member states of the European Union in 2018 . Payroll taxes include social contributions , the cost of professional education and training , the taxes on payroll or number of employees , and other special expenses . In 2018 , payroll taxes of 100 euros gross earning amounted to 30 euros in the European Union .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in member states of the European Union in templateTitleDate[0] . templateYLabel[0] templateYLabel[1] include social contributions , the cost of professional education and training , the templateYLabel[1] on templateYLabel[0] or number of employees , and other special expenses . In templateTitleDate[0] , templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] templateYLabel[4] earning amounted to templateYValue[12] templateYLabel[3] in the European Union .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in selected countries templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleDate[0] . The templateTitle[0] of the templateTitle[2] templateTitle[3] with a total population of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic displays the Payroll taxes of EU countries in selected countries 2018 in .  The Payroll of the EU countries with a total population of 48 100 euros .

Example 925:
titleEntities: {'Subject': ['Countries'], 'Date': ['2001', '2010']}
title: Countries with the fastest growing economies from 2001 to 2010
X_Axis['Country']: ['Angola', 'China', 'Myanmar', 'Nigeria', 'Ethiopia', 'Kazakhstan', 'Chad', 'Mozambique', 'Cambodia', 'Rwanda']
Y_Axis['Annual', 'average', 'GDP', 'growth', 'in', 'percent']: ['11.1', '10.5', '10.3', '8.9', '8.4', '8.2', '7.9', '7.9', '7.7', '7.6']

gold: This statistic shows the ten countries with the fastest growing economies in the world from 2001 to 2010 . Over the past decade , Angola has demonstrated the fastest economic growth rate with average annual GDP growth sitting as high as 11.1 percent . The overall quarterly GDP growth in the United States can be found here .
gold_template: This statistic shows the templateYValue[2] templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] in the world templateTitle[5] templateTitleDate[min] to templateTitleDate[max] . Over the past decade , templateXValue[0] has demonstrated the templateTitle[2] economic templateYLabel[3] rate templateTitle[1] templateYLabel[1] templateYLabel[0] templateYLabel[2] templateYLabel[3] sitting as high as templateYValue[max] templateScale . The overall quarterly templateYLabel[2] templateYLabel[3] in the country can be found here .

generated_template: This statistic shows the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] in templateTitleDate[0] , based on templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[0] had the templateTitle[0] templateTitleSubject[0] templateTitle[3] with a templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 20 Countries fastest the growing economies in 2001 , based on 2010 Country .  In 2001 , the Angola had the Countries economies with a Annual of 11.1 GDP growth .

Example 926:
titleEntities: {'Subject': ['Saba', 'Caribbean Netherlands'], 'Date': ['2011', '2019']}
title: Population of Saba ( Caribbean Netherlands ) 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'people']: ['1915', '2155', '2010', '1947', '1811', '1846', '1991', '1971', '1797']

gold: Between 2011 and 2019 , the population of the Caribbean island Saba increased slightly . Saba , one of the three special municipalities of the Netherlands , counted roughly 1,900 inhabitants in 2019 . Population peaked in 2018 , at approximately 2,150 .
gold_template: Between templateXValue[min] and templateXValue[max] , the templateTitle[0] of the templateTitleSubject[1] island templateTitleSubject[0] templatePositiveTrend slightly . templateTitleSubject[0] , one of the three special municipalities of the templateTitleSubject[1] , counted roughly 1,900 inhabitants in templateXValue[max] . templateTitle[0] peaked in templateXValue[1] , at approximately 2,150 .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitleSubject[0] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] amounted to templateYValue[min] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Population Saba Number of the Saba Netherlands 2011 from to 2019 .  In 2019 , the Population Saba of Netherlands 2011 amounted to 1797 thousand people .

Example 927:
titleEntities: {'Subject': ['PC'], 'Date': ['2015']}
title: Most played PC games 2015
X_Axis['Video', 'Game']: ['League_of_Legends', 'Counter-Strike:_Global_Offensive', 'Fallout_4', 'DOTA_2', 'World_of_Warcraft', 'World_of_Tanks', 'Hearthstone:_Heroes_of_Warcraft', 'Minecraft', 'Smite', 'Grand_Theft_Auto_V', 'Heroes_of_the_Storm', 'ARK:_Survival_Evolved', 'Guild_Wars_2', 'Star_Wars:The_Old_Republic', 'Diablo_III', 'StarCraft_II', 'Spider_Solitaire', 'Battlefield_4', 'Final_Fantasy_XIV_Online', 'Call_of_Duty:_Black_Ops_III']
Y_Axis['Share', 'of', 'playing', 'time']: ['22.92', '6.88', '5.78', '5.09', '4.82', '3.48', '2.14', '1.97', '1.3', '1.29', '1.16', '1.15', '1.12', '1.1', '1.08', '1.04', '0.89', '0.8', '0.73', '0.72']

gold: The statistic presents the most-played PC games on the online gaming platform Raptr in November 2015 , by share of total time played . According to Raptr 's blog , 4.82 percent of all time spent gaming on their platform in November 2015 was dedicated to playing World of Warcraft . In the fourth quarter of 2014 , World of Warcraft gained more than 10 million subscribers , mostly owing to 'Warlords of Draenor ' expansion release in November 2014 .
gold_template: The statistic presents the most-played templateTitleSubject[0] templateTitle[3] on the templateXValue[18] gaming platform Raptr in 2015 , by templateYLabel[0] of total templateYLabel[2] templateTitle[1] . According to Raptr 's blog , templateYValue[4] templateScale of all templateYLabel[2] spent gaming on their platform in 2015 was dedicated to templateYLabel[1] templateXValue[4] of templateXValue[4] . In the fourth quarter of 2014 , templateXValue[4] of templateXValue[4] templatePositiveTrend more than 10 templateScale subscribers , mostly owing to 'Warlords of Draenor ' expansion release in 2014 .

generated_template: This statistic presents the 20 templateTitle[0] templateTitle[1] templateXLabel[1] in templateTitleSubject[0] in templateTitleDate[0] , ranked by their templateYLabel[0] templateYLabel[1] . According to the source , it was found that templateXValue[0] had the largest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic presents the 20 Most played Game in PC 2015 , ranked by their Share playing .  According to the source , it was found that League_of_Legends had the largest Share playing of 22.92 time .

Example 928:
titleEntities: {'Subject': ['PV U.S.'], 'Date': ['2016']}
title: Residential solar PV : U.S. market value 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Market', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['4.7', '3.7', '2.9', '2.3', '1.7', '1.2']

gold: This statistic shows the projected residential solar PV market value in the United States from 2011 to 2016 . In 2012 , it is estimated that the residential solar PV industry will have a market value of around 2.3 billion U.S. dollars .
gold_template: This statistic shows the projected templateTitle[0] templateTitle[1] templateTitleSubject[0] templateYLabel[0] templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , it is estimated that the templateTitle[0] templateTitle[1] templateTitleSubject[0] industry will have a templateYLabel[0] templateYLabel[1] of around templateYValue[3] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic depicts the templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[1] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[4] templateTitle[1] templateTitle[2] templateYLabel[0] was at templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic depicts the solar Market value of the market solar 2016 from 2011 to 2016 .  In 2016 , the market solar PV Market was at 4.7 billion U.S. dollars .

Example 929:
titleEntities: {'Subject': ['Norway'], 'Date': ['2019']}
title: Most popular social media sites in Norway 2019 , by platform
X_Axis['Platform']: ['Facebook', 'Snapchat', 'YouTube', 'Instagram', 'LinkedIn', 'Twitter', 'Pinterest', 'Whatsapp', 'Reddit', 'Tumblr']
Y_Axis['Share', 'of', 'respondents']: ['83', '61', '60', '57', '21', '20', '16', '16', '6', '2']

gold: Facebook was the social media platform that was most common among Norwegians as of 2019 . A survey , conducted in the third quarter half of 2019 , showed that 83 percent of the respondents used Facebook . Snapchat came second and third in the ranking , with 60 percent of the interviewed Norwegians using it .
gold_template: templateXValue[0] was the templateTitle[2] templateTitle[3] templateXLabel[0] that was templateTitle[0] common among Norwegians as of templateTitleDate[0] . A survey , conducted in the third quarter half of templateTitleDate[0] , showed that templateYValue[max] templateScale of the templateYLabel[1] used templateXValue[0] . templateXValue[1] came second and third in the ranking , with templateYValue[2] templateScale of the interviewed Norwegians using it .

generated_template: This statistic presents the templateYLabel[0] of users of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] platforms templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] in templateTitleDate[0] . During the survey , it was found that templateYValue[max] templateScale of the templateYLabel[1] stated that they used templateXValue[1] .
generated: This statistic presents the Share of users the Most popular social media platforms Norway 2019 by in Norway 2019 .  During the survey , it was found that 83 % of the respondents stated that they used Snapchat .

Example 930:
titleEntities: {'Subject': ['Zimbabwe'], 'Date': ['2018']}
title: Urbanization in Zimbabwe 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['32.21', '32.24', '32.3', '32.39', '32.5', '32.65', '32.83', '33.02', '33.2', '33.38', '33.56']

gold: This statistic shows the degree of urbanization in Zimbabwe from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 32.21 percent of Zimbabwe 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Zimbabwe from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 32.21 % of Zimbabwe 's total population lived in urban areas and cities .

Example 931:
titleEntities: {'Subject': ['YouTube'], 'Date': ['2017']}
title: YouTube : all-time beauty content category views 2017
X_Axis['Selected', 'Cosmetics']: ['Personal_care_-_overall', 'Deodorant', 'Body_care', 'Beauty_-_overall', 'Cosmetics', 'Hair', 'Skincare', 'Fragrance', 'Nails']
Y_Axis['Amount', 'of', 'category', 'views', 'in', 'billions']: ['28', '17', '16', '219', '156', '87', '73', '24', '4']

gold: This statistic presents the content views of selected beauty-related categories on YouTube as of July 2017 . As of that month , cosmetics content had generated 156 billion views on the video platform . Hair content had generated 87 billion video views .
gold_template: This statistic presents the templateTitle[3] templateYLabel[2] of templateXLabel[0] beauty-related categories on templateTitleSubject[0] as of 2017 . As of that month , templateXValue[4] templateTitle[3] had generated templateYValue[4] templateScale templateYLabel[2] on the video platform . templateXValue[5] templateTitle[3] had generated templateYValue[5] templateScale video templateYLabel[2] .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among people aged 16 to 29 years in templateTitleSubject[0] in templateTitleDate[0] . The source defined as the total templateYLabel[0] of people who were templateXValue[1] , with a group that year .
generated: This statistic shows the results of a 2017 survey among people aged 16 to 29 years in YouTube 2017 .  The source defined as the total Amount of people who were Deodorant , with a group that year .

Example 932:
titleEntities: {'Subject': ['Puma'], 'Date': ['2019']}
title: Global revenue of Puma 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['5.5', '4.65', '4.14', '3.63', '3.39', '2.97', '2.99', '3.27', '3.01', '2.71', '2.45', '2.52', '2.37', '2.37', '1.78', '1.53', '1.27', '0.91', '0.6', '0.46']

gold: This statistic shows data on the global revenue of sporting goods manufacturer Puma SE from 2000 to 2019 . In 2019 , Puma generated a global revenue of roughly 5.5 billion euros . Together with Nike and Adidas , Puma is one of the largest sporting goods manufacturers in the world .
gold_template: This statistic shows data on the templateTitle[0] templateYLabel[0] of sporting goods manufacturer templateTitleSubject[0] SE from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated a templateTitle[0] templateYLabel[0] of roughly templateYValue[idxmax(X)] templateScale templateYLabel[2] . Together with Nike and Adidas , templateTitleSubject[0] is templateYValue[16] of the largest sporting goods manufacturers in the world .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateYLabel[0] from the templateTitle[1] templateTitle[2] templateTitle[3] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] .
generated: The statistic shows the Revenue of the revenue Puma 2000 in Puma between 2000 and 2019 .  In 2019 , the Revenue from the revenue Puma 2000 amounted to 5.5 billion euros .

Example 933:
titleEntities: {'Subject': ['Burger King', 'Asia Pacific'], 'Date': ['2012', '2013']}
title: Number of Burger King restaurants in the Asia Pacific region from 2012 to 2013
Data:
                 0      1            2            3      4         5          6          7            8       9        10     11
Country  Australia  China  South_Korea  New_Zealand  Japan  Malaysia  Indonesia  Singapore  Philippines  Taiwan  Thailand  Other
2012           357     86          139           82     64        43         40         41           29      41        29     59
2013           371    190          164           86     82        56         48         43           36      36        34     85 

gold: The statistic shows the number of Burger King restaurants in the Asia Pacific region from 2012 to 2013 , by country . In 2013 , there were 371 Burger King restaurants in Australia , 190 restaurants in China and 164 restaurants in South Korea .
gold_template: The statistic shows the templateTitle[0] of templateTitleSubject[0] restaurants in the templateTitleSubject[1] region templateTitle[7] templateTitleDate[min] to templateTitleDate[max] , by templateLabel[0][0] . In templateTitleDate[max] , there were templateValue[2][max] templateTitleSubject[0] restaurants in templateValue[0][0] , templateValue[2][1] templateTitle[3] in templateValue[0][1] and templateValue[2][2] templateTitle[3] in templateValue[0][2] .

generated_template: This statistic depicts the templateTitle[2] of templateTitleSubject[0] restaurants in the templateTitleSubject[1] templateTitle[5] in templateTitleDate[min] and templateTitleDate[max] . In templateTitleDate[max] , there were templateValue[2][max] templateTitleSubject[0] restaurants in templateValue[0][0] , templateValue[2][1] templateTitle[3] in templateValue[0][1] and templateValue[2][2] templateTitle[3] in templateValue[0][2] .
generated: This statistic depicts the King of Burger King restaurants in the Asia Pacific Pacific in 2012 and 2013 .  In 2013 , there were 371 Burger King restaurants in Australia , 190 restaurants in China and 164 restaurants in South_Korea .

Example 934:
titleEntities: {'Subject': ['Tanzania'], 'Date': ['2024']}
title: Inflation rate in Tanzania 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['5', '4.85', '4.66', '4.48', '4.19', '3.57', '3.51', '5.32', '5.17', '5.59', '6.13', '7.87', '16', '12.69', '7.19', '12.14', '10.28', '7.03', '7.25', '4.36', '4.14', '4.43', '4.56', '5.15', '5.96', '7.89', '12.78', '16.14', '21.05', '26.77', '37.9', '26.1', '20.7', '25.2', '36.4', '25.8', '31.2', '29.9', '32.4', '33.3', '36.1']

gold: This statistic shows the average inflation rate in Tanzania from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Tanzania amounted to about 3.51 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[min] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Tanzania from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Tanzania amounted to about 3.51 % compared to the previous Year .

Example 935:
titleEntities: {'Subject': ['UK'], 'Date': ['2009', '2014']}
title: Market share of UK gambling industry sectors by GGY 2009 to 2014
Data:
               0        1        2        3        4
Year     2013/14  2012/13  2011/12  2010/11  2009/10
Betting       46       48       50       52       52
Casino        16       16       15       15       14
Remote        19       16       13       12       12
Bingo          9       10       12       11       11
Arcades        5        5        5        7        8
Lottery        5        4        5        3        3 

gold: This statistic shows the market share of gambling industry sectors by gross gambling yield ( GGY ) in the United Kingdom from 2009 to 2014 . From October 2012 to September 2013 , Bingo accounted for 10 percent of the gambling market .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] gross templateTitle[3] yield ( templateTitle[7] ) in the United Kingdom from templateTitleDate[min] to templateTitleDate[max] . From October 2012 to 2013 , templateLabel[4][0] accounted for templateValue[4][1] templateScale of the templateTitle[3] templateTitle[0] .

generated_template: The statistic shows the results of a survey conducted templateTitle[4] the templateTitle[0] of templateTitleSubject[0] . The templateTitle[0] of the templateLabel[5][0] workforce in the United Kingdom ( templateTitleSubject[1] ) from the templateValue[0][0] to templateValue[0][last] , broken down templateTitle[7] templateTitle[8] group stood at templateValue[1][last] British pounds in templateValue[0][last] .
generated: The statistic shows the results of a survey conducted industry the Market of UK .  The Market of the Arcades workforce in the United Kingdom ( UK ) from the 2013/14 to 2009/10 , broken down GGY 2009 group stood at 52 British pounds in 2009/10 .

Example 936:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2018']}
title: Population in Sweden 2018 , by level of education
X_Axis['Level', 'Of', 'Education']: ['Primary_and_lower_education_(less_than_9_years)', 'Lower_secondary_education_(9_years)', 'Upper_secondary_education_(less_than_3_years)', 'Upper_secondary_education_(3_years_or_more)', 'Post-secondary_education_(less_than_3_years)', 'Post-secondary_education_(3_years_or_more)', 'Post-graduate_education', 'No_information_about_level_of_education']
Y_Axis['Share', 'of', 'population']: ['4.1', '13.1', '19.5', '23.4', '14.5', '21.7', '1.12', '2.7']

gold: The number of Swedes with higher education , a post-secondary education of three years or more , increased since 2000 , while the number without decreased . In 2017 , around 37 percent of the population had at least some level of post-secondary education . Among them , 21.7 percent had an upper secondary education of three years or more .
gold_template: The number of Swedes with higher templateXValue[0] , a templateXValue[4] templateXValue[0] of templateXValue[2] templateXValue[3] or more , templatePositiveTrend since 2000 , while the number without templateNegativeTrend . In 2017 , around 37 templateScale of the templateYLabel[1] had at least some templateXValue[last] of templateXValue[4] templateXValue[0] . Among them , templateYValue[5] templateScale had an templateXValue[2] templateXValue[1] templateXValue[0] of templateXValue[2] templateXValue[3] or more .

generated_template: The statistic provides information on the templateTitle[1] templateTitle[2] of templateTitle[0] in templateTitleSubject[0] as of templateTitleDate[0] , sorted by templateXLabel[0] . During the survey period , it was found that templateYValue[1] templateScale of internet users in the templateTitleSubject[1] accessed the templateXValue[last] of templateTitle[1] .
generated: The statistic provides information on the Sweden 2018 of Population in Sweden as of 2018 , sorted by Level .  During the survey period , it was found that 13.1 % of internet users in the Sweden accessed the No_information_about_level_of_education of Sweden .

Example 937:
titleEntities: {'Subject': ['Air Products Chemicals'], 'Date': ['2009', '2019']}
title: Air Products and Chemicals ' sales 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['8919', '8930', '8188', '7504', '7824', '8384', '8313', '9612', '9674', '8616', '7847']

gold: This statistic shows the revenues of Air Products and Chemicals from 2009 to 2019 . Air Products and Chemicals , Inc. is a United States-based international corporation whose major business is selling gases and chemicals for industrial uses . In 2019 , the company generated approximately 8.92 billion U.S. dollars of revenue .
gold_template: This statistic shows the revenues of templateTitleSubject[0] and templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] and templateTitleSubject[0] , Inc. is a United States-based international corporation whose major business is selling gases and templateTitleSubject[0] for industrial uses . In templateXValue[max] , the company generated approximately templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] of templateYLabel[0] .

generated_template: The statistic presents the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] are owned by Mark Attanasio , who bought the franchise for 223 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[2] .
generated: The statistic presents the Revenue of the Air Products Chemicals from 2009 to 2019 .  In 2019 , the Revenue of the Major League Baseball franchise amounted to 8919 million U.S. dollars.The Air Products Chemicals are owned by Mark Attanasio , who bought the franchise for 223 million U.S. dollars in 2017 .

Example 938:
titleEntities: {'Subject': ['South Sudan'], 'Date': ['2024']}
title: Inflation rate in South Sudan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['7.95', '8.21', '8.61', '9.74', '16.89', '24.47', '83.49', '187.87', '379.85', '52.81', '1.66']

gold: This statistic shows the average inflation rate in South Sudan from 2014 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in South Sudan amounted to about 83.49 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price templatePositiveTrend of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: The statistic shows the Inflation rate in the South Sudan from 2014 to 2018 , with projections up until 2024 .  The Inflation rate is calculated using the price increase of a defined product basket .  This product basket contains products and services , on which the average consumer spends money throughout the Year .

Example 939:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2015']}
title: Purchasing power change in the Netherlands 2015 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Year', 'on', 'year', 'percentage', 'change']: ['1.3', '1.6', '0.3', '0.3', '2.6', '1']

gold: In 2018 , the purchasing power in the Netherlands increased for the fourth year in a row . Purchasing power growth slowed down considerably though in 2017 and 2018 in comparison to 2015 and especially 2016 . In the next two years , the purchasing power was forecast to increase further .
gold_template: In templateXValue[2] , the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] templatePositiveTrend for the fourth templateXLabel[0] in a row . templateTitle[0] templateTitle[1] growth slowed down considerably though in templateXValue[3] and templateXValue[2] in comparison to templateXValue[min] and especially templateXValue[4] . In the next templateYValue[1] years , the templateTitle[0] templateTitle[1] was forecast to templatePositiveTrend further .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the average templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to templateYValue[idxmin(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Year of percentage change of the change Netherlands 2015 2020 in Netherlands from 2015 to 2020 .  In 2015 , the average Year change Netherlands 2015 amounted to 1 percentage change .

Example 940:
titleEntities: {'Subject': ['Denso'], 'Date': ['2008', '2019']}
title: Denso - global revenue 2008 to 2019
X_Axis['Fiscal', 'year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Net', 'sales', 'in', 'million', 'Japanese', 'yen']: ['5362772', '5108291', '4527148', '4524522', '4309787', '4094960', '3580923', '3154630', '3131460', '2976709', '3142665', '4025076']

gold: This statistic illustrates Denso 's global revenue from the fiscal year of 2008 to the fiscal year of 2019 . In the fiscal year of 2019 , Denso had global net sales of around 5.36 trillion yen ( or about 48 billion U.S. dollars ) .
gold_template: This statistic illustrates templateTitleSubject[0] 's templateTitle[1] templateTitle[2] from the templateXLabel[0] templateXLabel[1] of templateXValue[last] to the templateXLabel[0] templateXLabel[1] of templateXValue[0] . In the templateXLabel[0] templateXLabel[1] of templateXValue[0] , templateTitleSubject[0] had templateTitle[1] templateYLabel[0] templateYLabel[1] of around templateYValue[max] templateScale templateYLabel[4] ( or about 48 templateScale U.S. dollars ) .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] of the templateTitleSubject[0] employees amounted to over templateYValue[3] templateScale templateYLabel[3] .
generated: The statistic shows the Denso of global revenue 2008 2019 the Denso from 2008 to 2019 .  In 2008 , the Denso of the Denso employees amounted to over 4524522 million Japanese .

Example 941:
titleEntities: {'Subject': ['Europe'], 'Date': ['2014']}
title: Ultra-rich women in Europe in 2014 , by personal fortune value
X_Axis['Month']: ['Liliane_Bettencourt_(L’Oréal_France)', 'Susanne_Klatten_(BMW_Germany)', 'Johanna_Quandt_(BMW_Germany)', 'Charlene_Carvalho_Heineken_(Heineken_Netherlands)', 'Margarita_Louis_Dreyfus_(Louis_Dreyfus_Switzerland)', 'Antonia_Axson_Johnson_(Axel_Johnson_Group_Sweden)', 'Sandra_Ortega_Mera_(Inditex_Spain)', 'Daniela_Herz_(Mayfair_Germany)', 'Kirsten_Rausing_(Tetra_Sweden)', 'Elisabeth_Mohn_(Bertelsmann_Germany)']
Y_Axis['Value', 'of', 'wealth', 'in', 'billion', 'euros']: ['25.1', '13.1', '10.7', '8.1', '5.6', '5.6', '5.2', '4.6', '4.5', '4.3']

gold: The statistic displays the ranking of ten wealthiest women in Europe as of July 2014 . At that time , the richest woman in Europe was Liliane Bettencourt , the heiress of the French cosmetics and beauty company L'Oreal , with personal fortune amounting to approximately 25 billion euros .
gold_template: The statistic displays the ranking of ten wealthiest templateTitle[1] in templateTitleSubject[0] as of 2014 . At that time , the richest woman in templateTitleSubject[0] was templateXValue[0] , the heiress of the French cosmetics and beauty company L'Oreal , with templateTitle[5] templateTitle[6] amounting to approximately templateYValue[max] templateScale templateYLabel[3] .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among people were using templateTitle[0] . Some templateYValue[0] templateScale of templateYLabel[1] who were templateXValue[0] templateYLabel[3] . The templateXValue[2] with a templateYValue[2] templateScale of people , followed by templateXValue[1] with templateYValue[1] templateScale of templateYLabel[1] .
generated: This statistic shows the results of a 2014 survey among people were using Ultra-rich .  Some 25.1 billion of wealth who were Liliane_Bettencourt_(L’Oréal_France) euros .  The Johanna_Quandt_(BMW_Germany) with a 10.7 billion of people , followed by Susanne_Klatten_(BMW_Germany) with 13.1 billion of wealth .

Example 942:
titleEntities: {'Subject': ['Global'], 'Date': ['2018']}
title: Global rum market : leading brands based on sales volume 2018
X_Axis['Alcohol', '']: ['Tanduay', 'Bacardi', 'Captain_Morgan', "McDowell's_No._1_Celebration", 'Havana_Club', 'Barceló', 'Bozkov', 'Old_Port', 'Appleton_Estate', 'Montilla', 'Negrita']
Y_Axis['Million', '9', 'liter', 'cases']: ['20.1', '17.1', '11.7', '11.2', '4.6', '2.2', '1.7', '1.3', '1.1', '1.0', '1.0']

gold: This statistic presents the leading rum brands worldwide in 2018 , based on sales volume . Bacardi was the second largest global rum brand that year , with a sales volume of about 17.1 million-9 liter cases . Global Spirits Industry The term `` spirits '' refers to an alcoholic beverage that has been distilled , contains no added sugar , and has at least 20 percent alcohol by volume .
gold_template: This statistic presents the templateTitle[3] templateTitle[1] templateTitle[4] worldwide in templateTitleDate[0] , templateTitle[5] on templateTitle[6] templateTitle[7] . templateXValue[1] was the second largest templateTitleSubject[0] templateTitle[1] brand that year , with a templateTitle[6] templateTitle[7] of about templateYValue[1] million-9 templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] Spirits Industry The term `` spirits '' refers to an alcoholic beverage that has been distilled , contains no added sugar , and has at least templateYValue[max] templateScale templateXLabel[0] by templateTitle[7] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the United Kingdom ( templateTitleSubject[0] ) as of templateTitleSubject[0] templateTitleDate[0] . According to the source , the templateTitle[0] templateYLabel[0] of the templateXValue[0] stood at templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Global rum market leading the United Kingdom ( Global ) as of Global 2018 .  According to the source , the Global Million of the Tanduay stood at 20.1 million cases .

Example 943:
titleEntities: {'Subject': ['British'], 'Date': ['2019', '2019']}
title: British soccer teams with most League Cup titles 2019
X_Axis['Club', 'Name']: ['Liverpool_FC', 'Manchester_City', 'Aston_Villa', 'Chelsea_FC', 'Manchester_United', 'Nottingham_Forest', 'Tottenham_Hotspur', 'Leicester_City', 'Arsenal_FC', 'Birmingham_City', 'Norwich_City', 'Wolverhampton_Wanderers', 'Blackburn_Rovers', 'Leeds_United', 'Luton_Town', 'Middlesbrough_FC', 'Oxford_United', 'Queens_Park_Rangers', 'Sheffield_Wednesday', 'Stoke_City', 'Swansea_City', 'Swindon_Town', 'West_Bromwich_Albion']
Y_Axis['Number', 'of', 'titles']: ['8', '6', '5', '5', '5', '4', '4', '3', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']

gold: This statistic shows the winners of the Football League Cup from 1961 to 2019 . Liverpool FC have won the most League Cup titles , having lifted the trophy a total of eight times .
gold_template: This statistic shows the winners of the Football templateTitle[5] templateTitle[6] from 1961 to templateTitleDate[0] . templateXValue[0] have won the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[1] , having lifted the trophy a total of templateYValue[max] times .

generated_template: templateXValue[0] are currently the record for templateTitle[0] templateTitle[1] templateXValue[1] templateTitle[2] templateTitle[5] in the history . templateXValue[0] 's templateTitle[0] templateTitle[1] is currently the most recently with templateYValue[max] times times , followed templateTitle[6] templateXValue[1] with templateYValue[1] in the templateXValue[2] with templateYValue[2] times .
generated: Liverpool_FC are currently the record for British soccer Manchester_City teams Cup in the history .  Liverpool_FC 's British soccer is currently the most recently with 8 times , followed titles Manchester_City with 6 in the Aston_Villa with 5 times .

Example 944:
titleEntities: {'Subject': ['Kickstarter'], 'Date': ['2019']}
title: Most funded Kickstarter projects 2019
X_Axis['Kickstarter', 'Project']: ['Pebble_Time_(2015)', 'Coolest_Cooler_(2014)', 'Pebble_2_Time_2_+_All-New_Pebble_Core_(2016)', 'Kingdom_Death:_Monster_1.5_(2017)', 'Travel_Tripod_by_Peak_Design_(2019)', 'Critical_Role:_The_Legend_of_Vox_Machina_Animated_Special_(2019)', 'Pebble:_e-paper_Watch_for_iPhone_and_Android_(2012)', "The_World's_Best_Travel_Jacket_(2015)", 'Exploding_Kittens_(2015)', 'OUYA:_A_New_Kind_of_Video_Game_Console_(2012)', 'Snapmaker_2.0:_Modular_3-in-1_3D_Printers_(2019)', 'THE_7th_CONTINENT_–_What_Goes_Up_Must_Come_Down_(2017)', 'The_Everyday_Backpack_Tote_and_Sling_(2016)', 'Fidget_Cube:_A_Vinyl_Desk_Toy_(2016)', 'Shenmue_3_(2015)']
Y_Axis['Funds', 'raised', 'in', 'million', 'U.S.', 'dollars']: ['20.34', '13.29', '12.78', '12.39', '12.14', '11.39', '10.27', '9.19', '8.78', '8.6', '7.85', '7.07', '6.57', '6.47', '6.33']

gold: This statistic shows a ranking of the most successfully completed Kickstarter projects as of July 2019 . At the end of August 2014 , the Coolest Cooler finished its Kickstarter run with more than 13.28 million U.S. dollars in project funding . Up until then , Pebble e-paper watch had held the title for the highest funded project on the crowdfunding website with 10.27 million US dollars .
gold_template: This statistic shows a ranking of the templateTitle[0] successfully completed templateXLabel[0] templateTitle[3] as of 2019 . At the end of 2014 , the templateXValue[1] finished its templateXLabel[0] run with more than 13.28 templateScale templateYLabel[3] templateYLabel[4] in templateXLabel[1] funding . templateXValue[11] until then , templateXValue[0] templateXValue[6] had held the title templateXValue[6] the highest templateTitle[1] templateXLabel[1] on the crowdfunding website with templateYValue[6] templateScale US templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[7] templateXLabel[0] templateXLabel[1] . With over templateYValue[max] templateScale templateYLabel[3] of templateXValue[0] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] that year .
generated: This statistic shows the Most funded Kickstarter projects in the United Kingdom ( Kickstarter ) in 2019 , Kickstarter Project .  With over 20.34 million U.S. of Pebble_Time_(2015) , the Most funded Kickstarter projects 2019 Funds that year .

Example 945:
titleEntities: {'Subject': ['Qdoba'], 'Date': ['2011', '2017']}
title: Number of Qdoba restaurants from 2011 to 2017
Data:
                  0     1     2     3     4     5     6
Year           2017  2016  2015  2014  2013  2012  2011
Franchise       341   332   339   328   319   311   338
Company-owned   385   367   322   310   296   316   245 

gold: Fast casual restaurant chain Qdoba Mexican Eats had a total of 726 restaurants in 2017 . Of these , 385 were company-owned restaurants and 341 were franchised . This figure steadily increased over the course of the last four years , reaching its peak in 2017 .
gold_template: Fast casual restaurant chain templateTitleSubject[0] Mexican Eats had a total of 726 templateTitle[2] in templateValue[0][0] . Of these , templateValue[2][0] were templateLabel[2][0] templateTitle[2] and templateValue[1][0] were franchised . This figure steadily templatePositiveTrend over the course of the last four years , reaching its peak in templateValue[0][0] .

generated_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] in the templateTitleSubject[0] templateTitle[3] templateTitle[4] templateValue[0][last] to templateValue[0][0] . templateTitleSubject[0] in the templateTitleSubject[1] , owned by templateTitleSubject[0] in the templateTitleSubject[0] Inc. , had templateValue[2][0] templateLabel[2][0] templateTitle[3] and templateValue[1][0] templateLabel[1][0] templateTitle[3] in templateValue[0][0] .
generated: This statistic shows the Number of Qdoba in the Qdoba from 2011 to 2017 .  Qdoba in the Qdoba , owned by Qdoba in the Qdoba Inc. , had 385 Company-owned from and 341 Franchise from in 2017 .

Example 946:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000']}
title: Number of cats in the U.S. 2000 to 2017
X_Axis['Year']: ['2017', '2015', '2013', '2011', '2008', '2006', '2004', '2002', '2000']
Y_Axis['Number', 'of', 'cats', 'in', 'millions']: ['94.2', '85.8', '95.6', '86.4', '93.6', '88.3', '90.5', '77.7', '73.0']

gold: According to a national pet owners survey , there was a total of approximately 95.6 million cats living in households in the United States in 2017 . In the same year , some 68 percent of all U.S. households owned at least one pet . Increasing pet expenditure Whilst the number of households owning cats , and pets in general , has remained relatively consistent over the last few years , pet industry expenditure has steadily grown .
gold_template: According to a national pet owners survey , there was a total of approximately templateYValue[max] templateScale templateYLabel[1] living in households in the templateTitle[2] in templateXValue[max] . In the same templateXLabel[0] , some 68 templateScale of all templateTitleSubject[0] households owned at least one pet . templatePositiveTrend pet expenditure Whilst the templateYLabel[0] of households owning templateYLabel[1] , and pets in general , has remained relatively consistent over the last few years , pet industry expenditure has steadily grown .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[3] templateTitle[4] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number of cats millions of 2000 2017 U.S. from 2000 to 2017 .  In 2017 , the Number cats of U.S. amounted to 94.2 millions .

Example 947:
titleEntities: {'Subject': ['Republican'], 'Date': ['2016']}
title: 2016 U.S. election : delegates won by Republican candidates
Data:
                     0        1       2        3         4           5         6            7         8                     9       10       11      12     13        14       15    16      17        18         19     20        21             22        23         24           25        26       27        28      29             30          31          32        33              34                        35    36        37      38            39           40            41              42            43         44     45    46       47              48        49          50             51         52       53
State            Total  Alabama  Alaska  Arizona  Arkansas  California  Colorado  Connecticut  Delaware  District_of_Columbia  Florida  Georgia  Hawaii  Idaho  Illinois  Indiana  Iowa  Kansas  Kentucky  Louisiana  Maine  Maryland  Massachusetts  Michigan  Minnesota  Mississippi  Missouri  Montana  Nebraska  Nevada  New_Hampshire  New_Jersey  New_Mexico  New_York  North_Carolina  Northern_Mariana_Islands  Ohio  Oklahoma  Oregon  Pennsylvania  Puerto_Rico  Rhode_Island  South_Carolina  South_Dakota  Tennessee  Texas  Utah  Vermont  Virgin_Islands  Virginia  Washington  West_Virginia  Wisconsin  Wyoming
Donald J. Trump   1447       36      11       58        16         172         0           28        16                     0       99       42      11     12        54       57     7       9        17         28      9        38             22        25          8           25        37       27        36      14             11          51          24        89              29                         9     0        13      18            17            0            12              50            29         33     48     0        8               1        17          40             30          6        1
Ted Cruz           551       13      12        0        15           0        30            0         0                     0        0       18       7     20         9        0     8      24        15         18     12         0              4        17         13           15        15        0         0       6              3           0           0         0              27                         0     0        15       5             0            0             2               0             0         16    104    40        0               1         8           0              0         36       23
Marco Rubio        167        1       5        0         9           0         0            0         0                    10        0       16       1      0         0        0     7       6         7          0      0         0              8         0         17            0         0        0         0       7              1           0           0         0               6                         0     0        12       0             0           23             0               0             0          9      3     0        0               2        16           0              0          0        1
John Kasich        161        0       0        0         0           0         0            0         0                     9        0        0       0      0         6        0     1       1         7          0      2         0              8        17          0            0         0        0         0       1              4           0           0         6               9                         0    66         0       5             0            0             5               0             0          0      0     0        8               0         5           0              1          0        0 

gold: This statistic shows the number of delegates Republican Party candidates won during primaries in the race for the nomination to the U.S. presidential election in November 2016 . After the primaries , Donald Trump had earned 1,447 delegates . U.S. 2016 Elections : Iowa Republican caucus - additional information - additional information The 2016 election year has kicked off on February 1 with the Iowa caucuses , effectively the first step towards the nomination of both the Republican and Democrat candidates for the presidential elections .
gold_template: This statistic shows the number of templateTitle[3] templateTitleSubject[0] Party templateTitle[7] templateTitle[4] during primaries in the race for the nomination to the templateTitle[1] presidential templateTitle[2] in 2016 . After the primaries , templateLabel[1][0] templateLabel[1][2] had earned templateValue[1][0] templateTitle[3] . templateTitle[1] templateTitleDate[0] Elections : templateValue[0][16] templateTitleSubject[0] caucus - additional information - additional information The templateTitleDate[0] templateTitle[2] year has kicked off on 1 with the templateValue[0][16] caucuses , effectively the first step towards the nomination of both the templateTitleSubject[0] and Democrat templateTitle[7] for the presidential elections .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[min] to templateTitleDate[max] . templateValue[0][0] , the average templateTitle[0] of templateLabel[1][0] templateLabel[1][1] in templateLabel[4][0] .
generated: This statistic shows the 2016 of the U.S. election delegates won in the United Kingdom ( Republican ) from 2016 to .  Total , the average 2016 of Donald J. in John .

Example 948:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2019']}
title: Average price for a full set of acrylics at nail salons in the U.S. 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Average', 'price', 'in', 'U.S.', 'dollars']: ['46.01', '46.31', '45.56', '38.41', '43.84', '45.29', '44.07', '43.58', '43.9', '42.92', '42.95', '50.1', '43.75', '43.71']

gold: How much are acrylic nails in the U.S. ? Nail salons in the U.S. charged an average price of 46.01 U.S. dollars for a full set of acrylics in 2019 . Nail salons in the U.S . The nail salon industry in the United States is a large and thriving industry , which is active across all 50 states .
gold_template: How much are acrylic nails in the templateYLabel[2] ? templateTitle[6] templateTitle[7] in the templateYLabel[2] charged an templateYLabel[0] templateYLabel[1] of templateYValue[0] templateYLabel[2] templateYLabel[3] templateTitle[2] a templateTitle[3] templateTitle[4] of templateTitle[5] in templateXValue[max] . templateTitle[6] templateTitle[7] in the templateTitle[8] . The templateTitle[6] salon industry in the templateTitle[8] is a large and thriving industry , which is active across all templateYValue[max] states .

generated_template: The statistic above shows the templateYLabel[0] templateTitle[1] templateTitle[2] templateYLabel[1] of templateYLabel[2] templateTitle[5] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateTitle[1] templateTitle[2] templateYLabel[1] of a magazine was templateYValue[6] templateYLabel[2] templateYLabel[3] . Here you can find data on templateYLabel[0] magazine circulation per issue .
generated: The statistic above shows the Average price for of U.S. acrylics nail 2006 to 2019 .  In 2013 , the Average price for of a magazine was 44.07 U.S. dollars .  Here you can find data on Average magazine circulation per issue .

Example 949:
titleEntities: {'Subject': ['Piracy'], 'Date': ['2019']}
title: Piracy - actual and attempted attacks worldwide by country 2019
X_Axis['Country']: ['Nigeria', 'Indonesia', 'Singapore_Staits', 'Malaysia', 'Peru', 'Venezuela', 'Cameroon']
Y_Axis['Number', 'of', 'incidents']: ['35', '25', '12', '11', '10', '6', '6']

gold: The statistic represents the total number of actual and attempted piracy attacks in the world 's most perilous territorial waters in 2019 . That year , there were six actual and attempted piracy attacks off the Venezuelan coast .
gold_template: The statistic represents the total templateYLabel[0] of templateTitle[1] and templateTitle[2] templateTitleSubject[0] templateTitle[3] in the world 's most perilous territorial waters in templateTitleDate[0] . That year , there were templateYValue[min] templateTitle[1] and templateTitle[2] templateTitleSubject[0] templateTitle[3] off the Venezuelan coast .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] , by templateXLabel[0] . In templateXValue[0] , there were templateYValue[max] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateXValue[0] .
generated: The statistic shows the Number of incidents attempted attacks worldwide by in 2019 , by Country .  In Nigeria , there were 35 attempted attacks worldwide by in Nigeria .

Example 950:
titleEntities: {'Subject': ['Kenya'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Kenya 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['5.85', '5.84', '5.84', '5.82', '6.05', '5.6', '6.32', '4.86', '5.88', '5.72', '5.36']

gold: The statistic shows the growth in real GDP in Kenya from 2014 to 2018 , with projections up until 2024 . In 2018 , Kenya 's real gross domestic product grew by around 6.32 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[max] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Kenya from 2014 to 2018 , with projections up until 2024 .  In 2018 , Kenya 's real Gross domestic product increased by around 6.32 % compared to the previous Year .

Example 951:
titleEntities: {'Subject': ['World Cup Number'], 'Date': ['1930', '2018']}
title: FIFA World Cup : Number of goals scored at each tournament 1930 to 2018
X_Axis['Countries']: ['Russia_2018', 'Brazil_2014', 'South_Africa_2010', 'Germany_2006', 'South_Korea_/_Japan_2002', 'France_1998', 'USA_1994', 'Italy_1990', 'Mexico_1986', 'Spain_1982', 'Argentina_1978', 'Germany_1974', 'Mexico_1970', 'England_1966', 'Chile_1962', 'Sweden_1958', 'Switzerland_1954', 'Brazil_1950', 'France_1938', 'Italy_1934', 'Uruguay_1930']
Y_Axis['Number', 'of', 'goals', 'scored']: ['169', '171', '143', '147', '161', '171', '141', '115', '132', '146', '102', '97', '95', '89', '89', '126', '140', '88', '84', '70', '70']

gold: How many goals were scored in the 2014 FIFA World Cup ? A total of 171 goals were scored across the 2014 FIFA World Cup held in Brazil . This marked a joint record for the tournament along with France 1998 . The goal-fest in 2014 was thanks to some unusually high-scoring games , including Netherlands beating Spain 5 - 1 in the group stages and , most famously , Germany embarrassing Brazil 7 - 1 at the semi-final stage .
gold_template: How many templateYLabel[1] were templateYLabel[2] in the templateXValue[1] templateTitle[0] templateTitleSubject[0] ? A total of templateYValue[max] templateYLabel[1] were templateYLabel[2] across the templateXValue[1] templateTitle[0] templateTitleSubject[0] held in templateXValue[1] . This marked a joint record for the templateTitle[7] along with templateXValue[5] . The goal-fest in templateXValue[1] was thanks to some unusually high-scoring games , including Netherlands beating templateXValue[9] 5 - 1 in the group stages and , most famously , templateXValue[3] embarrassing templateXValue[1] 7 - 1 at the semi-final stage .

generated_template: This statistic shows the templateTitle[0] templateYLabel[3] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[min] to templateTitleDate[max] . It can be seen that templateXValue[0] , with a templateTitle[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the FIFA scored of World Cup Number goals in the United Kingdom ( World Cup Number ) from 1930 to 2018 .  It can be seen that Russia_2018 , with a FIFA of 171 scored .

Example 952:
titleEntities: {'Subject': ['Faroe Islands'], 'Date': ['1993', '2019']}
title: World ranking of Faroe Islands ' national football team 1993 to 2019
X_Axis['Year']: ['1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['FIFA', 'World', 'Ranking', 'position']: ['115', '133', '120', '135', '117', '125', '112', '117', '117', '114', '126', '131', '132', '181', '194', '184', '117', '136', '116', '153', '170', '104', '97', '83', '95', '98', '102']

gold: In 2016 , the Faroe Island 's national football team , controlled by the Football Association of the Faroe Islands , reached its highest position in the FIFA World Ranking . The team took part in the qualifying for the UEFA European Championship 2016 . Out of the ten qualifying matches , the Faroe Island 's national football team won both matches against Greece .
gold_template: In templateXValue[23] , the templateTitleSubject[0] Island 's templateTitle[5] templateTitle[6] templateTitle[7] , controlled by the templateTitle[6] Association of the templateTitleSubject[0] , reached its highest templateYLabel[3] in the templateYLabel[0] templateYLabel[1] templateYLabel[2] . The templateTitle[7] took part in the qualifying for the UEFA European Championship templateXValue[23] . Out of the ten qualifying matches , the templateTitleSubject[0] Island 's templateTitle[5] templateTitle[6] templateTitle[7] won both matches against Greece .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the Norwegian templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . The highest templateYLabel[3] ever reached was templateYValue[min] in templateXValue[idxmin(Y)] . Rank templateYValue[max] was the lowest result of the templateTitle[6] , which was reached in templateXValue[idxmax(Y)] .
generated: The statistic shows the FIFA World Ranking of the Norwegian ' national football from 1993 to 2019 .  The highest position ever reached was 83 in 2016 .  Rank 194 was the lowest result of the football , which was reached in 2007 .

Example 953:
titleEntities: {'Subject': ['Russia'], 'Date': ['2018', '2018']}
title: Average player age of participating national teams at the 2018 World Cup in Russia
X_Axis['Country']: ['Costa_Rica', 'Mexico', 'Argentina', 'Egypt', 'Panama', 'Russia', 'Saudi_Arabia', 'Brazil', 'Iceland', 'Japan', 'Spain', 'Portugal', 'Poland', 'Sweden', 'Uruguay', 'Colombia', 'Australia', 'Croatia', 'South_Korea', 'Belgium', 'Peru', 'Senegal', 'Switzerland', 'Morocco', 'Iran', 'Germany', 'Denmark', 'Serbia', 'Tunisia', 'France', 'England', 'Nigeria']
Y_Axis['Average', 'player', 'age', 'in', 'years']: ['29.6', '29.4', '29.3', '29.0', '28.9', '28.8', '28.7', '28.6', '28.6', '28.5', '28.4', '28.3', '28.2', '28.1', '28.1', '28.1', '28.1', '27.9', '27.8', '27.6', '27.4', '27.2', '27.2', '27.2', '27.2', '27.1', '27.1', '26.8', '26.5', '26.0', '26.0', '25.9']

gold: While Costa Rica might have crashed out of the 2018 World Cup in the first round having finished bottom of their group , they were able to claim the title of the oldest average team . Their squad had an average age of 29.6 years , almost four years older than the squad of Nigeria , who had the youngest squad at the tournament . France 's stars bring home the trophyFrance 's squad was full of superstars such as Kylian Mbappé , Paul Pogba , and Antoine Griezmann , making it the most expensive squad assembled at the 2018 World Cup .
gold_template: While templateXValue[0] might have crashed out of the templateTitleDate[0] templateTitle[7] templateTitle[8] in the first round having finished bottom of their group , they were able to claim the title of the oldest templateYLabel[0] team . Their squad had an templateYLabel[0] templateYLabel[2] of templateYValue[max] templateYLabel[3] , almost four templateYLabel[3] older than the squad of templateXValue[last] , who had the youngest squad at the tournament . templateXValue[29] 's stars bring home the trophyFrance 's squad was full of superstars such as Kylian Mbappé , Paul Pogba , and Antoine Griezmann , making it the most expensive squad assembled at the templateTitleDate[0] templateTitle[7] templateTitle[8] .

generated_template: The statistic shows a ranking of the templateTitle[3] templateTitle[4] templateTitle[5] at the templateTitleDate[0] FIFA templateTitle[7] templateTitle[8] in templateXValue[5] by templateYLabel[0] templateYLabel[2] of players . The templateYLabel[0] templateYLabel[2] of the templateXValue[26] squad for the templateTitle[7] templateTitle[8] in templateXValue[5] is templateYValue[26] templateYLabel[3] . templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleDate[0] templateTitle[7] templateTitle[8] squads - additional information There were a total of 736 players across 32 templateTitle[5] in the templateTitle[7] templateTitle[8] , picked from domestic leagues in 52 countries .
generated: The statistic shows a ranking of the participating national teams at the 2018 FIFA World Cup in Russia by Average age of players .  The Average age of the Denmark squad for the World Cup in Russia is 27.1 years .  Average player age of the 2018 World Cup squads - additional information There were a total of 736 players across 32 teams in the World Cup , picked from domestic leagues in 52 countries .

Example 954:
titleEntities: {'Subject': ['China', 'Europe'], 'Date': ['2018']}
title: Number of outbound trips from China to Europe in 2018 , by region of destination
X_Axis['Europe', 'European', 'Union']: ['Central/Eastern_Europe', 'Western_Europe', 'Northern_Europe', 'Southern/Mediterranean_Europe']
Y_Axis['Arrivals', 'in', 'millions']: ['5.93', '5.9', '1.15', '1.12']

gold: This statistic shows the number of outbound trips from China to Europe in 2018 , by region of destination . There were approximately 1.15 million trips from China to Northern Europe in 2018 .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] to templateXValue[0] in templateTitleDate[0] , templateTitle[7] templateTitle[8] of templateTitle[9] . There were approximately templateYValue[2] templateScale templateTitle[2] templateTitle[3] templateTitleSubject[0] to templateXValue[2] templateXValue[0] in templateTitleDate[0] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] the templateTitle[4] to templateXValue[0] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . According to the source , templateXValue[0] generated approximately approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: This statistic shows the Number of outbound trips from the China to Central/Eastern_Europe in 2018 , by region .  According to the source , Central/Eastern_Europe generated approximately 5.93 millions in 2018 .

Example 955:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Net profit margin of the leading 10 consumer products companies in the U.S. 2013
X_Axis['Company']: ['Apple_Inc.', 'The_Procter_&_Gamble_Company', 'PepsiCo_Inc.', 'The_Coca-Cola_Company', 'Mondeléz_International', 'Tyson_Foods_Inc.', 'Mars_Inc.', 'Philip_Morris_International_Inc.', 'NIKE_Inc.', 'Kimberly-Clark_Corporation']
Y_Axis['Net', 'profit', 'margin']: ['21.7', '14.2', '10.2', '18.4', '11.1', '2.3', '0', '28.3', '9.7', '10.5']

gold: This statistic presents the net profit margin of the leading 10 consumer products companies in the United States in 2013 . In that year , the Coca-Cola Company had a net profit margin of 18.4 percent . The Coca-Cola Company , founded in 1892 , is headquartered in Atlanta , Georgia .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[3] templateYValue[2] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[8] in templateTitleDate[0] . In that year , the templateXValue[3] templateXValue[1] had a templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[3] templateScale . The templateXValue[3] templateXValue[1] , founded in 1892 , is headquartered in Atlanta , Georgia .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[2] templateXValue[4] , based on templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] . According to the source , templateXValue[0] was the ranking with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net profit of the margin Mondeléz_International , based on 10 Net profit margin .  According to the source , Apple_Inc. was the ranking with a Net profit of 28.3 % margin .

Example 956:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015', '2018']}
title: Smartphone ownership in the U.S. 2015 - 2018 , by age group
Data:
          0     1     2     3
Year   2018  2017  2016  2015
18-29    96    94    92    86
30-49    92    89    88    83
50-64    79    73    74    58
65+      53    46    42    30 

gold: Here you can see a breakdown of adults who owned a smartphone in the United States from 2015 to 2018 , by age group . The survey found that 96 percent of those aged between 18 and 29 years old owned a smartphone in 2018 .
gold_template: Here you can see a breakdown of adults who owned a templateTitle[0] in the templateTitle[2] from templateValue[0][last] to templateValue[0][0] , templateTitle[5] templateTitle[6] templateTitle[7] . The survey found that templateValue[1][max] templateScale of those aged between 18 and 29 years old owned a templateTitle[0] in templateValue[0][idxmax(1)] .

generated_template: This statistic shows the templateScale of online consumers in the templateTitle[0] who were using templateTitleSubject[0] between templateValue[0][last] and templateValue[0][0] , sorted templateTitle[7] group . In templateValue[0][0] , templateValue[1][min] templateScale of templateTitleSubject[0] dollars aged 18 to 29 years old were employed in the templateTitle[7] .
generated: This statistic shows the percentage of online consumers in the Smartphone who were using U.S. between 2015 and 2018 , sorted group .  In 2018 , 86 % of U.S. dollars aged 18 to 29 years old were employed in the group .

Example 957:
titleEntities: {'Subject': ['ID'], 'Date': ['2016']}
title: Preferred social login ID worldwide 2016
X_Axis['Platform']: ['Facebook', 'GooglePlus', 'Twitter', 'LinkedIn', 'Yahoo', 'Vkontakte', 'Live']
Y_Axis['Share', 'of', 'logins']: ['53.1', '44.8', '1.01', '0.56', '0.29', '0.15', '0.11']

gold: This statistic gives information on the preferred social login ID used by internet users worldwide . During the second quarter of 2016 , data analysis revealed that 53.1 percent of social logins were through a Facebook account . Approximately one percent of internet users used their Twitter account to login to a website .
gold_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitleSubject[0] used by internet users templateTitle[4] . During the second quarter of templateTitleDate[0] , data analysis revealed that templateYValue[max] templateScale of templateTitle[1] templateYLabel[1] were through a templateXValue[0] account . Approximately templateYValue[2] templateScale of internet users used their templateXValue[2] account to templateTitle[2] to a website .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] , based on reach . During the survey period , it was found that templateXValue[0] was ranked first with a total of templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the Share of logins login in ID 2016 , based on reach .  During the survey period , it was found that Facebook was ranked first with a total of 53.1 million logins .

Example 958:
titleEntities: {'Subject': ['Volcanic'], 'Date': ['2016']}
title: Volcanic eruptions - people affected worldwide up to 2016
X_Axis['Location', 'and', 'Date']: ['Volcanic_eruption_in_the_Philippines_(June_9_1991)', 'Volcano_eruption_in_Ecuador_(August_14_2015)', 'Volcanic_eruption_in_Nicaragua_(April_9_1992)', 'Volcano_eruption_in_Ecuador_(August_14_2006)', 'Volcano_eruption_in_Indonesia_(April_5_1982)', 'Volcano_eruption_in_Indonesia_(1969)', 'Volcanic_eruption_in_Comoros_(November_24_2005)', 'Volcanic_eruption_in_the_Philippines_(Feb._6_1993)', 'Volcanic_eruption_in_Papua_New_Guinea_(September_19_1994)', 'Volcanic_eruption_in_Indonesia_(October_24_2002)']
Y_Axis['Number', 'of', 'victims']: ['1036065', '800000', '300075', '300013', '300000', '250000', '245000', '165009', '152002', '137140']

gold: The statistic shows the number of people , who were affected by the world 's most significant volcanic eruptions from 1900 to 2016  . In 1991 , total 1,036,035 were affected due to volcanic eruption in Philippines .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[2] , who were templateTitle[3] by the world 's most significant templateXValue[0] templateTitle[1] from 1900 to templateTitleDate[0] . In 1991 , total 1,036,035 were templateTitle[3] due to templateXValue[0] in templateXValue[0] .

generated_template: This statistic provides a ranking of the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] , based on the templateYLabel[0] of across across the last templateYValue[3] times . The second place with templateYValue[1] templateYLabel[1] , of this figure that year .
generated: This statistic provides a ranking of the Volcanic eruptions people in Volcanic 2016 , based on the Number of across the last 300013 times .  The second place with 800000 victims , of this figure that year .

Example 959:
titleEntities: {'Subject': ['Super Bowl'], 'Date': ['2011', '2020']}
title: Super Bowl - monetary compensation for players playing in the game 2011 to 2020
Data:
                 0     1     2     3     4     5     6     7     8     9
Year          2020  2019  2018  2017  2016  2015  2014  2013  2012  2011
Winning team   130   124   118   112   107   102    97    92    88    88
Loosing team    65    62    59    56    53    51    49    46    44    44 

gold: This graph depicts the amount a NFL player will be paid for playing in the Super Bowl from 2011 to 2020 . In 2015 , a player on the winnning team in the Super Bowl would receive 102,000 U.S. dollars for winning the Super Bowl . The Pittsburgh Steelers have the most Super Bowl wins of all NFL teams .
gold_template: This graph depicts the amount a NFL player will be paid templateTitle[4] templateTitle[6] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][5] , a player on the winnning templateLabel[1][1] in the templateTitleSubject[0] would receive 102,000 U.S. dollars templateTitle[4] templateLabel[1][0] the templateTitleSubject[0] . The Pittsburgh Steelers have the most templateTitleSubject[0] wins of all NFL teams .

generated_template: This graph shows the number of people that finding the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][5] , there were templateValue[1][0] templateLabel[1][0] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] .
generated: This graph shows the number of people that finding the Super Bowl monetary compensation for in Super Bowl from 2011 to 2020 .  In 2015 , there were 130 Winning monetary compensation for in the players .

Example 960:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019', '2019']}
title: Canada : Gross Domestic Product ( GDP ) by industry December 2019
X_Axis['Industry']: ['Real_estate_and_rental_and_leasing', 'Manufacturing', 'Mining_quarrying_and_oil_and_gas_extraction', 'Construction', 'Health_care_and_social_assistance', 'Public_administration', 'Finance_and_insurance', 'Professional_scientific_and_technical_services', 'Educational_services', 'Wholesale_trade', 'Retail_trade', 'Transportation_and_warehousing', 'Information_and_cultural_industries', 'Administrative_and_support_waste_management_and_remediation_services', 'Accommodation_and_food_services', 'Utilities', 'Agriculture_forestry_fishing_and_hunting', 'Other_services_(except_public_administration)', 'Arts_entertainment_and_recreation', 'Management_of_companies_and_enterprises']
Y_Axis['GDP', 'in', 'million', 'chained', '2012', 'Canadian', 'dollars']: ['254294', '199234', '145909', '142530', '142028', '134370', '133599', '120820', '104771', '103082', '102619', '89599', '64323', '52649', '45160', '43668', '40058', '38127', '15699', '9303']

gold: This statistic shows the Gross Domestic Product ( GDP ) of Canada in December 2019 , distinguished by major industry . In December 2019 , the construction industry of Canada contributed about 142.5 billion Canadian dollars to the total Canadian GDP .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) of templateTitleSubject[0] in templateTitle[7] templateTitleDate[0] , distinguished templateTitle[5] major templateXLabel[0] . In templateTitle[7] templateTitleDate[0] , the templateXValue[3] templateXLabel[0] of templateTitleSubject[0] contributed about templateYValue[3] templateScale templateYLabel[4] templateYLabel[5] to the total templateYLabel[4] templateYLabel[0] .

generated_template: This statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateYLabel[0] of the templateXValue[4] templateXLabel[0] in templateTitleSubject[0] was 50.72 templateScale templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the gross domestic product ( GDP ) of Canada in 2019 , GDP Industry .  In 2019 , the GDP of the Health_care_and_social_assistance Industry in Canada was 50.72 million chained 2012 Canadian dollars .

Example 961:
titleEntities: {'Subject': ['Zion National Park U.S.'], 'Date': ['2008', '2019']}
title: Number of visitors to Zion National Park in the U.S. 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'visitors', 'in', 'millions']: ['4.49', '4.32', '4.5', '4.3', '3.65', '3.19', '2.81', '2.97', '2.83', '2.67', '2.74', '2.69']

gold: The number of visitors to Zion National Park has been increasing in recent years . In 2019 , the park saw an increase in the number of visitors with 4.49 million visitors , compared to previous year 's 4.32 million . Zion National Park is located in Utah in the United States .
gold_template: The templateYLabel[0] of templateYLabel[1] to templateTitleSubject[0] Park has been templatePositiveTrend in recent years . In templateXValue[max] , the templateTitleSubject[0] saw an templatePositiveTrend in the templateYLabel[0] of templateYLabel[1] with templateYValue[idxmax(X)] templateScale templateYLabel[1] , compared to previous templateXLabel[0] 's templateYValue[1] templateScale . templateTitleSubject[0] Park is located in Utah in the templateTitle[5] .

generated_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[1] to the templateTitleSubject[0] Memorial in the templateTitle[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] to the templateTitleSubject[0] Memorial amounted to approximately templateYValue[0] templateYValue[idxmax(X)] in templateXValue[max] .
generated: This statistic shows the Number of recreational visitors to the Zion National Park U.S. Memorial in the U.S. from 2008 to 2019 .  The Number of visitors to the Zion National Park U.S. Memorial amounted to approximately 4.49 in 2019 .

Example 962:
titleEntities: {'Subject': ['Price Index'], 'Date': ['2019']}
title: Consumer Price Index of all urban consumers , by region 2019
X_Axis['Country']: ['U.S._city_average', 'Northeast', 'Midwest', 'South', 'West']
Y_Axis['Consumer', 'Price', 'Index', '(100=1982-1984)']: ['255.66', '269.39', '237.78', '246.27', '270.35']

gold: This statistic shows the annual average Consumer Price Index for urban consumers in the United States of America in 2019 , by region . In 2019 , the CPI in the Western United States amounted to 270.35 . The monthly inflation rate for the country can be accessed here and a projection of the annual inflation rate can be found here and the annual consumer price index for urban consumers in the U.S. can be accessed here .
gold_template: This statistic shows the annual templateXValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitle[4] templateTitle[5] in the country of America in templateTitleDate[0] , templateTitle[6] templateTitle[7] . In templateTitleDate[0] , the CPI in the Western country amounted to templateYValue[max] . The monthly inflation rate for the templateXLabel[0] can be accessed here and a projection of the annual inflation rate can be found here and the annual templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitle[4] templateTitle[5] in the templateXValue[0] can be accessed here .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] worldwide in templateTitleDate[0] , templateTitle[5] templateTitle[6] . According to the source , the templateXValue[0] held the largest templateYLabel[2] of the world templateTitle[1] , with templateYValue[max] templateYLabel[2] .
generated: This statistic presents the Consumer Price Index worldwide in 2019 , consumers by .  According to the source , the U.S._city_average held the largest Index of the world Price , with 270.35 Index .

Example 963:
titleEntities: {'Subject': ['India'], 'Date': ['2010', '2019']}
title: India 's petroleum imports by value 2010 to 2019
X_Axis['Years']: ['FY_2019', 'FY_2018', 'FY_2017', 'FY_2016', 'FY_2015', 'FY_2014', 'FY_2013', 'FY_2012', 'FY_2011']
Y_Axis['Import', 'value', 'in', 'billion', 'Indian', 'rupees']: ['9860.2', '7003.2', '5832.2', '5405.0', '8428.74', '9978.85', '8918.71', '7430.75', '4822.82']

gold: India imported petroleum products worth over 9.8 trillion Indian rupees in fiscal year 2019 . Petroleum made up a share of over 27 percent of all imports into the country that year . The increasing trend in recent years was reflected in the price of fuel and its impact on various industries across the south Asian country .
gold_template: templateTitleSubject[0] imported templateTitle[2] products worth over 9.8 templateScale templateYLabel[3] templateYLabel[4] in fiscal year templateXValue[0] . templateTitle[2] made up a share of over 27 templateScale of all templateTitle[3] into the country that year . The templatePositiveTrend trend in recent templateXLabel[0] was reflected in the price of fuel and its impact on various industries across the south Asian country .

generated_template: In financial templateXLabel[1] templateXValue[0] , the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in the templateTitleSubject[0] amounted to over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] templateYLabel[4] . This was the second largest templateTitle[4] accounted for over ten templateScale of the world 's population . It is estimated that the country that the largest alone .
generated: In financial Years FY_2019 , the India 's petroleum ( Import ) in the India amounted to over 9978.85 billion Indian rupees .  This was the second largest by accounted for over ten billion of the world 's population .  It is estimated that the country that the largest alone .

Example 964:
titleEntities: {'Subject': ['Trust'], 'Date': ['2014', '2018']}
title: Trust in online customer reviews 2014 to 2018
Data:
                   0                                           1                                               2                                                   3                                             4                                5
Response  Yes_always  Yes_if_I_believe_the_reviews_are_authentic  Yes_for_some_types_of_businesses_no_for_others  Yes_if_there_are_multiple_customer_reviews_to_read  No_I_am_often_skeptical_about_online_reviews  No_I_don't_trust_reviews_at_all
2014               0                                          22                                              34                                                  26                                             0                               17
2015               8                                          31                                              22                                                  19                                             0                               20
2016              18                                          27                                              19                                                  20                                            12                                4
2017              19                                          27                                              20                                                  20                                            13                                3
2018              19                                          19                                              15                                                  25                                            16                                6 

gold: This statistic compares consumers trust in online reviews compared to personal recommendations . During the October 2018 survey it was found that 19 percent of respondents trusted online reviews if they believed them to be authentic .
gold_template: This statistic compares consumers templateValue[0][5] in templateValue[0][4] templateValue[0][1] compared to personal recommendations . During the October templateTitleDate[max] survey it was found that templateValue[2][3] templateScale of respondents trusted templateValue[0][4] templateValue[0][1] they believed them to be templateValue[0][1] .

generated_template: This statistic shows the estimated templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[min] , the templateTitle[0] templateValue[1][0] templateScale of respondents said that they had a templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] templateLabel[1][3] .
generated: This statistic shows the estimated Trust online customer of reviews 2014 2018 in the United Kingdom ( Trust ) from 2014 to 2018 .  In 2014 , the Trust 0 % of respondents said that they had a 2014 .

Example 965:
titleEntities: {'Subject': ['Goldman Sachs'], 'Date': ['2019']}
title: Stock price of Goldman Sachs 2004 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Stock', 'price', 'in', 'U.S.', 'dollars']: ['229.93', '167.05', '254.76', '239.45', '180.23', '193.83', '177.26', '127.56', '90.43', '168.16', '168.84', '84.39', '215.05', '199.35', '127.71', '104.04']

gold: The stock price of Goldman Sachs was 229.93 points at the close of 2019 . This is down from its height in 2018 , but still well above the slumps in 2008 and 2011 due to the Financial Crisis and its aftermath . What does the stock price depend on ? The stock price is how much it costs to buy one share of Goldman Sachs .
gold_template: The templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[0] points at the close of templateXValue[max] . This is down from its height in templateXValue[1] , but still well above the slumps in templateXValue[11] and templateXValue[8] due to the Financial Crisis and its aftermath . What does the templateYLabel[0] templateYLabel[1] depend on ? The templateYLabel[0] templateYLabel[1] is how much it costs to buy one share of templateTitleSubject[0] .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[1] of the templateYLabel[2] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the data from the templateTitle[0] of the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Stock of the price of the U.S. 2004 to 2019 .  In 2019 , the data from the Stock of the Goldman Sachs amounted to 229.93 U.S. dollars .

Example 966:
titleEntities: {'Subject': ['Nissan', 'Europe'], 'Date': ['2003', '2018']}
title: Nissan car sales in Europe 2003 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Number', 'of', 'units', 'sold']: ['487017', '560415', '547343', '554046', '477703', '421134', '436169', '470004', '411084', '372096', '338169', '313437', '332150', '387325', '409717', '409511']

gold: This statistic shows the number of cars sold by Nissan in Europe between 2003 and 2018 . European sales of the Nissan cars rose from 400 thousand units sold in 2003 to over 560 thousand units sold by 2017 . In 2018 , there were 487 thousand units of Nissan cars sold in Europe .
gold_template: This statistic shows the templateYLabel[0] of cars templateYLabel[2] by templateTitleSubject[0] in templateTitleSubject[1] between templateXValue[min] and templateXValue[max] . European templateTitle[2] of the templateTitleSubject[0] cars templatePositiveTrend from 400 thousand templateYLabel[1] templateYLabel[2] in templateXValue[min] to over templateYValue[max] thousand templateYLabel[1] templateYLabel[2] by templateXValue[idxmax(Y)] . In templateXValue[max] , there were templateYValue[0] thousand templateYLabel[1] of templateTitleSubject[0] cars templateYLabel[2] in templateTitleSubject[1] .

generated_template: The statistic shows the templateYLabel[0] of new templateTitle[0] cars templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , there were a total of templateYValue[0] templateScale templateYLabel[1] in templateYLabel[2] in templateXValue[max] .
generated: The statistic shows the Number of new Nissan cars sold in the Nissan from 2003 to 2018 .  According to the report , there were a total of 487017 million units in sold 2018 .

Example 967:
titleEntities: {'Subject': ['Malawi'], 'Date': ['2019']}
title: Unemployment rate in Malawi 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['5.38', '5.43', '5.47', '5.71', '5.79', '5.88', '5.95', '6.18', '6.37', '6.32', '6.43', '6.18', '6.46', '7.09', '7.8', '8.09', '8.23', '8.31', '8.32', '8.4', '8.43']

gold: This statistic shows the unemployment rate in Malawi from 1999 to 2019 . In 2019 , the unemployment rate in Malawi was 5.38 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Malawi from 1999 to 2019 .  In 2019 , the Unemployment rate in Malawi was at approximately 5.38 % .

Example 968:
titleEntities: {'Subject': ['Saudi Arabia'], 'Date': ['2019']}
title: Youth unemployment rate in Saudi Arabia in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['25.55', '25.77', '25.25', '24.5', '28.98', '30.12', '28.97', '27.84', '28.77', '29.09', '29.46', '28.82', '29.6', '33.82', '32.59', '30.67', '28.8', '27.46', '24.93', '24.34', '23.18']

gold: The statistic shows the youth unemployment rate in Saudi Arabia from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Saudi Arabia was at 25.55 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] templateYValue[idxmax(X)] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Youth unemployment rate in Saudi Arabia from 1999 and 2019 .  According to the source , the data are ILO estimates .  In 2019 , the estimated Youth unemployment rate in Saudi Arabia was at 25.55 % .

Example 969:
titleEntities: {'Subject': ['India'], 'Date': ['2024']}
title: National debt of India 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'billion', 'U.S.', 'dollars']: ['3299.94', '2984.05', '2701.91', '2449.0', '2219.37', '2009.81', '1801.98', '1615.18', '1448.03', '1319.32', '1160.56']

gold: In 2018 , the national debt of India amounted to around 1.8 trillion U.S. dollars . Projections show an upward trend , with a significant increase each year . Honor thy national debt National debt , also called government debt or public debt , is money owed by the federal government .
gold_template: In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] . Projections show an upward trend , with a significant templatePositiveTrend each templateXLabel[0] . Honor thy templateYLabel[0] templateYLabel[1] templateYLabel[0] templateYLabel[1] , also called government templateYLabel[1] or public templateYLabel[1] , is money owed by the federal government .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] was at around templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the National debt of the India from 2014 to 2018 , with projections up until 2024 .  In 2018 , the National debt in the India was at around 1801.98 billion U.S. dollars .

Example 970:
titleEntities: {'Subject': ['Volkswagen', 'EU'], 'Date': ['2019']}
title: Volkswagen 's EU share : new-car registrations in 2019
X_Axis['Month']: ['Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18']
Y_Axis['Market', 'share']: ['10.7', '12.3', '12.1', '9.2', '11.9', '11.8', '11.3', '11', '11.3', '10.5', '11.4', '11.5', '11.5', '11.8', '10.2', '6.6', '12.7']

gold: In December 2019 , the VW brand 's share of new-car registrations in the European Union stood at around 10.7 percent , 0.8 points less than the share in December 2018 . Many brands see upward trend Despite an increase in December sales for many brands , Volkswagen and other marques lost their market shares . The VW group saw monthly sales increase 21 percent but its market share dropped by 0.1 percent .
gold_template: In 2019 , the VW brand templateTitle[1] templateYLabel[1] of templateTitle[4] templateTitle[5] in the European Union stood at around templateYValue[0] templateScale , 0.8 points less than the templateYLabel[1] in 2018 . Many brands see upward trend Despite an templatePositiveTrend in sales for many brands , templateTitleSubject[0] and other marques lost their templateYLabel[0] shares . The VW group saw monthly sales templatePositiveTrend 21 templateScale but its templateYLabel[0] templateYLabel[1] templateNegativeTrend by 0.1 templateScale .

generated_template: This statistic illustrates the monthly templateYLabel[0] templateYLabel[1] of the templateTitle[1] manufacturer templateTitleSubject[0] in the templateTitleSubject[1] ( UK ) between 2016 and 2019 . The company 's templateYLabel[0] templateYLabel[1] has gradually templateNegativeTrend from in this time period from templateYValue[29] templateScale in 2016 to templateYValue[0] templateScale in 2019 . templateTitleSubject[0] is one of the leading templateTitle[1] manufacturers in the UK .
generated: This statistic illustrates the monthly Market share of the 's manufacturer Volkswagen in the EU ( UK ) between 2016 and 2019 .  The company 's Market share has gradually decreased from in this time period from 12.7 % in 2016 to 10.7 % in 2019 .  Volkswagen is one of the leading 's manufacturers in the UK .

Example 971:
titleEntities: {'Subject': ['National Hockey League'], 'Date': ['2005', '2019']}
title: National Hockey League - New Jersey Devils home attendance 2005 to 2019
Data:
                          0        1        2        3        4        5        6        7        8        9       10       11       12       13
Year                2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09  2007/08  2006/07  2005/06
Total attendance     608210   623240   597261   618029   622783   625570   410739   631258   605803   636975   647397   638144   581225   583448
Average attendance    14834    15200    14567    15073    15189    15257    17114    15396    14775    15535    15790    15564    14176    14230 

gold: This graph depicts the total/average regular season home attendance of the New Jersey Devils franchise of the National Hockey League from the 2005/06 season to the 2018/19 season . In 2018/19 , the total regular season home attendance of the franchise was 608,210 .
gold_template: This graph depicts the total/average regular season templateTitle[6] templateTitle[7] of the templateTitle[3] templateTitle[4] templateTitle[5] franchise of the templateTitleSubject[0] League from the templateValue[0][last] season to the templateValue[0][0] season . In templateValue[0][0] , the templateLabel[1][0] regular season templateTitle[6] templateTitle[7] of the franchise was templateValue[1][0] .

generated_template: This graph depicts the total/average regular season templateTitle[5] templateTitle[6] of the templateTitleSubject[0] templateTitle[4] franchise of the templateTitleSubject[0] League from the templateValue[0][last] season to the templateValue[0][0] season . In templateValue[0][0] , the templateLabel[1][0] regular season templateTitle[5] templateTitle[6] of the franchise was templateValue[1][0] .
generated: This graph depicts the total/average regular season Devils home of the National Hockey League Jersey franchise of the National Hockey League League from the 2005/06 season to the 2018/19 season .  In 2018/19 , the Total regular season Devils home of the franchise was 608210 .

Example 972:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2017']}
title: Brazil : poverty headcount ratio at 3.20 U.S. dollars a day 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Percentage', 'of', 'population']: ['9.6', '9.3', '8', '6.9', '8.2', '8.7', '10.3', '12.5', '13.5', '15.7', '17.1', '19.7']

gold: In 2017 , the poverty headcount ratio at 3.20 U.S. dollars a day in Brazil amounted to 9.6 percent , which means that almost 10 percent of the Brazilian population was living on less than 3.20 U.S. dollars per day . The poverty rate has increased since 2014 , when the headcount ratio was below seven percent .
gold_template: In templateXValue[max] , the templateTitle[1] templateTitle[2] templateTitle[3] at templateTitle[4] templateTitle[5] templateTitle[6] a templateTitle[7] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale , which means that almost templateYValue[0] templateScale of the Brazilian templateYLabel[1] was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] . The templateTitle[1] rate has templatePositiveTrend since templateXValue[3] , when the templateTitle[2] templateTitle[3] was below templateYValue[min] templateScale .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the poverty headcount in Brazil from 2005 to 2017 .  In 2017 , about 9.6 percentage of Brazil 's population lived below the poverty line .

Example 973:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000', '2018']}
title: Canada : violent crime rate 2000 to 2018
X_Axis['Year']: ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Violent', 'crime', 'rate']: ['1494.21', '1473.43', '1440.77', '1434.79', '1404.21', '1388.91', '1386.69', '1353.85', '1334.33', '1321.89', '1291.62', '1235.72', '1198.78', '1095.65', '1044.23', '1070.26', '1076.07', '1112.82', '1143.5']

gold: This statistic shows the reported rate of violent crime in Canada from 2000 to 2018 . There were roughly 1,143.5 violent crimes reported per 100,000 residents in Canada in 2018 .
gold_template: This statistic shows the reported templateYLabel[2] of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . There were roughly templateYValue[idxmax(X)] templateYLabel[0] crimes reported per 100,000 residents in templateTitleSubject[0] in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[2] templateYLabel[3] templateTitle[4] templateYLabel[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the data from the templateTitle[0] of the templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Violent of the rate 2000 between and 2018 .  In 2018 , the data from the Canada of the Canada was 1143.5 rate .

Example 974:
titleEntities: {'Subject': ['Brunei'], 'Date': ['2019']}
title: Unemployment rate in Brunei Darussalam 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['9.25', '9.22', '9.32', '8.56', '7.76', '6.97', '7.03', '6.9', '6.72', '6.66', '6.42', '5.7', '5.62', '5.74', '5.87', '5.92', '5.88', '5.77', '5.63', '5.6', '5.57']

gold: This statistic shows the unemployment rate in Brunei Darussalam from 1999 to 2019 . In 2019 , the estimated unemployment rate in Brunei Darussalam was at approximately 9.25 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] templateTitle[3] was at approximately templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Brunei from 1999 to 2019 .  In 2019 , the Unemployment rate in Brunei was at approximately 9.25 % .

Example 975:
titleEntities: {'Subject': ['Revenue'], 'Date': ['2006', '2015']}
title: Revenue earned from sports merchandise worldwide 2006 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Merchandising', 'revenue', 'in', 'billion', 'U.S.', 'dollars']: ['20.07', '19.57', '18.55', '18.0', '17.57', '17.62', '17.59', '21.26', '20.41', '19.43']

gold: This graph depicts the total revenue generated worldwide from sports merchandising rights from 2006 to 2010 and corresponding revenue forecasts for the years 2011 to 2015 . In 2006 , total revenues from merchandising were 19.43 billion U.S. dollars .
gold_template: This graph depicts the total templateYLabel[1] generated templateTitle[5] templateTitle[2] templateTitle[3] templateYLabel[0] rights templateTitle[2] templateXValue[min] to templateXValue[5] and corresponding templateYLabel[1] forecasts for the years templateXValue[4] to templateXValue[max] . In templateXValue[min] , total revenues templateTitle[2] templateYLabel[0] were templateYValue[idxmin(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the most recently reported period , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitle[2] amounted to templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Merchandising revenue of the from sports merchandise in Revenue from 2006 to 2015 .  In the most recently reported period , the Revenue Merchandising of from amounted to 20.07 U.S. dollars .

Example 976:
titleEntities: {'Subject': ['Airbus'], 'Date': ['2002', '2019']}
title: Airbus - worldwide aircraft deliveries 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Number', 'of', 'aircraft', 'deliveries']: ['863', '800', '718', '688', '635', '629', '626', '588', '534', '510', '498', '483', '453', '434', '378', '320', '305', '303']

gold: Airbus delivered 863 aircraft to 99 customers worldwide in 2019 , representing their highest delivery total in the company 's existence . This figure puts Airbus on track to surpass its closest rival , Boeing .
gold_template: templateTitleSubject[0] delivered templateYValue[max] templateYLabel[1] to 99 customers templateTitle[1] in templateXValue[idxmax(Y)] , representing their highest delivery total in the company 's existence . This figure puts templateTitleSubject[0] on track to surpass its closest rival , Boeing .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[7] distribution of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number aircraft of the 2019 distribution of Airbus from 2002 to 2019 .  In 2019 , the Airbus Number aircraft of Airbus amounted to approximately 863 deliveries .

Example 977:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2018']}
title: Population growth in Mexico 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Population', 'growth', 'compared', 'to', 'the', 'previous', 'year']: ['1.13', '1.16', '1.2', '1.24', '1.28', '1.32', '1.36', '1.39', '1.44', '1.48', '1.5']

gold: The statistic shows the population growth in Mexico from 2008 to 2018 . In 2018 , the total population of Mexico grew by about 1.13 percent compared to the previous year . Among the largest cities in Mexico , Mexico City is leading with more than 8.5 million inhabitants .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the total templateYLabel[0] of templateTitleSubject[0] templatePositiveTrend by about templateYValue[idxmax(X)] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . Among the largest cities in templateTitleSubject[0] , templateTitleSubject[0] City is leading with more than 8.5 templateScale inhabitants .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . In templateXValue[max] , the templateYLabel[0] in the templateTitleSubject[0] templatePositiveTrend by approximately templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Population growth in the Mexico from 2008 to 2018 , compared to the previous Year .  In 2018 , the Population in the Mexico increased by approximately 1.13 % .

Example 978:
titleEntities: {'Subject': ['LINE'], 'Date': ['2014', '2016']}
title: LINE : number of monthly active users 2014 to 2016
X_Axis['Quarter']: ["Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14"]
Y_Axis['Number', 'of', 'monthly', 'users', 'in', 'millions']: ['217.0', '220.0', '220.0', '218.4', '215.0', '212.0', '211.0', '205.0', '190.0', '179.0', '170.0']

gold: This statistic shows a timeline with the amount of monthly active LINE users worldwide as of the fourth quarter of 2016 . As of that period , the mobile messaging app announced more than 217 million monthly active users . In October 2014 , LINE had also reported 560 million registered users worldwide .
gold_template: This statistic shows a timeline with the amount of templateYLabel[1] templateTitle[3] templateTitleSubject[0] templateYLabel[2] worldwide as of the fourth templateXLabel[0] of templateTitleDate[max] . As of that period , the mobile messaging app announced more than templateYValue[0] templateScale templateYLabel[1] templateTitle[3] templateYLabel[2] . In October templateTitleDate[min] , templateTitleSubject[0] had also reported 560 templateScale registered templateYLabel[2] worldwide .

generated_template: Messaging app templateTitle[0] is templateTitleSubject[0] 's most popular online communication tool , peaking at approximately templateYValue[max] templateScale templateYLabel[1] actives templateYLabel[2] in templateTitleSubject[0] during the fourth templateXLabel[0] of templateTitleDate[max] . templateTitle[0] 's main target group are young adults aged 15 to 34 years , representing more than 50 templateScale of the company 's Japanese user base in 2018 . The rise of templateTitle[0] in templateTitleSubject[0] The success story of messaging service templateTitle[0] , a Japanese subsidiary of South Korean internet powerhouse Naver , started back in 2011 during the Great East templateTitleSubject[0] Earthquake .
generated: Messaging app LINE is 's most popular online communication tool , peaking at approximately 220.0 millions monthly actives users in LINE during the fourth Quarter of 2016 .  LINE 's main target group are young adults aged 15 to 34 years , representing more than 50 millions of the company 's Japanese user base in 2018 .  The rise of LINE in The success story of messaging service LINE , a Japanese subsidiary of South Korean internet powerhouse Naver , started back in 2011 during the Great East LINE Earthquake .

Example 979:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1995', '2019']}
title: Share of non-performing loans held by banks in the U.S. 1995 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95"]
Y_Axis['Share', 'of', 'non-performing', 'loans', 'in', 'total', 'loans']: ['0.89', '1.02', '1.22', '1.47', '1.67', '2.21', '3.04', '3.95', '4.5', '5.3', '4.7', '2.21', '1.01', '0.73', '0.77', '0.96', '1.3', '1.47', '1.3', '1.02', '0.96', '0.95', '0.99', '1.11', '1.25']

gold: In 2019 , 0.89 percent of the loans that banks in the United States held were non-performing . This means that 99 percent of loan recipients were repaying their bank back at that time , which is a significant improvement from the 5.3 percent of non-performing loans in the aftermath of the Financial Crisis . What are non-performing loans ? Banks do not expect all the loans they issue to be fully repaid .
gold_template: In templateTitleDate[max] , templateYValue[0] templateScale of the templateYLabel[2] that templateTitle[5] in the templateTitle[6] templateTitle[3] were templateYLabel[1] . This means that 99 templateScale of loan recipients were repaying their bank back at that time , which is a significant improvement from the templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] in the aftermath of the Financial Crisis . What are templateYLabel[1] templateYLabel[2] ? templateTitle[5] do not expect all the templateYLabel[2] they issue to be fully repaid .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] was templateYValue[0] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Share non-performing of the Share non-performing U.S. held from 1995 to 2019 .  In 2019 , the Share non-performing was 0.89 loans total .

Example 980:
titleEntities: {'Subject': ['UK'], 'Date': ['2000', '2019']}
title: Full-time weekly hours of work , by gender 2000 - 2019 | UK Statistic
Data:
          0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19
Year   2019  2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  2000
Men    39.0  39.0  39.3  39.3  39.3  39.4  39.5  39.3  39.0  39.0  38.5  38.9  39.0  38.9  39.1  39.2  39.2  39.3  39.7  39.8
Women  34.5  34.1  34.2  34.3  34.2  34.5  34.2  34.0  33.7  33.8  33.7  33.8  33.9  34.0  34.0  33.9  34.0  34.1  34.3  34.2 

gold: In 2019 , male full-time employees in the United Kingdom worked an average of 39 hours a week , with women working an average of 34.5 hours a week . During the period between 2000 and 2019 , the year with the highest average weekly hours for men was 2000 when it was 39.8 hours , and for women it was 2014 and 2019 at 34.5 hours .
gold_template: In templateValue[0][0] , male templateTitle[0] employees in the United Kingdom worked an average of templateValue[1][0] templateTitle[2] a week , with templateLabel[2][0] working an average of templateValue[2][0] templateTitle[2] a week . During the period between templateValue[0][last] and templateValue[0][0] , the templateLabel[0][0] with the highest average templateTitle[1] templateTitle[2] for templateLabel[1][0] was templateValue[0][last] when it was templateValue[1][last] templateTitle[2] , and for templateLabel[2][0] it was templateValue[0][5] and templateValue[0][0] at templateValue[2][0] templateTitle[2] .

generated_template: The templateTitle[0] templateTitle[1] at templateTitle[2] templateTitle[3] in templateTitleSubject[0] templatePositiveTrend among both templateLabel[1][0] and templateLabel[2][0] during the period from templateValue[0][last] to templateValue[0][0] . The templateTitle[0] templateTitle[1] at templateTitle[2] templateTitle[3] among templateLabel[2][0] templatePositiveTrend templateTitle[7] 1.5 years during the ten-year period and was templateValue[2][0] years in templateValue[0][0] . Among templateLabel[1][0] , the templateTitle[1] templatePositiveTrend was not as large as for templateLabel[2][0] .
generated: The Full-time weekly at hours work in UK increased among both Men and Women during the period from 2000 to 2019 .  The Full-time weekly at hours work among Women increased 2019 1.5 years during the ten-year period and was 34.5 years in 2019 .  Among Men , the weekly increase was not as large for Women .

Example 981:
titleEntities: {'Subject': ['Goldman Sachs'], 'Date': ['2018']}
title: Assets under supervision at Goldman Sachs 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Assets', 'under', 'supervision', 'in', 'billion', 'U.S.', 'dollars']: ['1542', '1494', '1379', '1252', '1178', '1042', '965', '895', '917', '871']

gold: Assets under supervision include assets under management and other client assets on which the bank earns fees for advisory and other services . Goldman Sachs supervised 1.54 trillion U.S. dollars in assets in 2018 , up from 871 billion U.S. dollars in 2009 . Goldman Sachs within the banking industry Headquartered in New York City , Goldman Sachs is one of the largest banks worldwide , by market capitalization .
gold_template: templateYLabel[0] templateYLabel[1] templateYLabel[2] include templateYLabel[0] templateYLabel[1] management and other client templateYLabel[0] on which the bank earns fees for advisory and other services . templateTitleSubject[0] supervised templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] in templateYLabel[0] in templateXValue[idxmax(Y)] , up from templateYValue[min] templateScale templateYLabel[4] templateYLabel[5] in templateXValue[idxmin(Y)] . templateTitleSubject[0] within the banking industry Headquartered in New York City , templateTitleSubject[0] is one of the largest banks worldwide , by market capitalization .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] employees amounted to approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .
generated: This statistic shows the Assets of under Goldman Sachs worldwide from 2009 to 2018 .  The Assets under of Goldman Sachs employees amounted to approximately 1542 supervision billion in 2018 .

Example 982:
titleEntities: {'Subject': ['England'], 'Date': ['2008', '2012']}
title: Daily added sugar intake in England in 2008 - 2012 , by age group
X_Axis['Age', 'Group']: ['Children_1.5-3_years', 'Children_4-10_years', 'Teenagers_11-18_years', 'Adults_19-64_years', 'Adults_65_years_and_over']
Y_Axis['Sugar', 'intake', 'in', 'grams']: ['36.1', '60.8', '74.2', '58.8', '51.6']

gold: This statistic displays the daily added sugar intake in England from 2008 to 2012 , by age group . In the four year period of investigation teenagers aged between 11 and 18 years old had the highest daily added intake of sugar with 74.2 grams of added sugar in their diet . The dietary intake of sugar for children in this age bracket was 15.4 percent of total energy intake .
gold_template: This statistic displays the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[7] templateXLabel[0] templateXLabel[1] . In the four year period of investigation templateXValue[2] aged between 11 and 18 templateXValue[0] old had the highest templateTitle[0] templateTitle[1] templateYLabel[1] of templateYLabel[0] with templateYValue[max] templateYLabel[2] of templateTitle[1] templateYLabel[0] in their diet . The dietary templateYLabel[1] of templateYLabel[0] for templateXValue[0] in this templateXLabel[0] bracket was 15.4 templateScale of total energy templateYLabel[1] .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] as of 2019 , based on templateYLabel[0] templateYLabel[1] . According to the source , templateXValue[0] was ranked first with a total of templateYValue[max] templateYLabel[2] templateYLabel[3] of templateXValue[idxmax(Y)] .
generated: This statistic presents the Daily added sugar intake in England as of 2019 , based on Sugar intake .  According to the source , Children_1.5-3_years was ranked first with a total of 74.2 grams of Teenagers_11-18_years .

Example 983:
titleEntities: {'Subject': ['New Zealand'], 'Date': ['2015']}
title: Median age of the population in New Zealand 2015
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Median', 'age', 'in', 'years']: ['43.7', '43.1', '42.3', '41.3', '40.0', '38.9', '38.0', '37.3', '36.6', '35.5', '34.3', '32.6', '31.1', '29.5', '27.9', '26.3', '25.6', '25.8', '27.4', '28.7', '29.4']

gold: This statistic shows the median age of the population in New Zealand from 1950 to 2050 . The median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .
generated: This statistic shows the Median age of the population in New Zealand from 1950 to 2050 .  The Median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older .  It is a single index that summarizes the age distribution of a population .

Example 984:
titleEntities: {'Subject': ['United States'], 'Date': ['2014']}
title: Market share of streaming devices in the United States 2014
X_Axis['Streaming', 'Device']: ['Apple_TV', 'Roku', 'Chromecast', 'Tivo', 'Other']
Y_Axis['Market', 'share']: ['43', '24', '14', '6', '13']

gold: The statistic shows a breakdown of the U.S. American streaming devices market according to market share in 2014 . Google 's Chromecast held a market share of 14 percent in 2014 .
gold_template: The statistic shows a breakdown of the templateTitle[5] American templateXLabel[0] templateTitle[3] templateYLabel[0] according to templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . Google 's templateXValue[2] held a templateYLabel[0] templateYLabel[1] of templateYValue[2] templateScale in templateTitleDate[0] .

generated_template: This statistic displays the breakdown of templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitleSubject[0] in templateTitleDate[0] , based on sales . In this year , the templateXValue[0] accounted for over templateYValue[max] templateScale of all templateYLabel[2] in the templateTitleSubject[1] .
generated: This statistic displays the breakdown of Market share in the United States in 2014 , based on sales .  In this year , the Apple_TV accounted for over 43 % of all share in the United States .

Example 985:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000']}
title: Steel concrete reinforcing bars - production in the U.S. 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Production', 'in', 'million', 'metric', 'tons']: ['6.39', '6.02', '5.84', '7.0', '6.56', '5.65', '5.06', '5.74', '4.62', '7.01', '7.28', '6.83', '6.35', '7.35', '7.38', '6.06', '6.29', '6.27']

gold: The statistic illustrates the volume of concrete reinforcing bars that were produced in the United States between 2000 and 2017 . In 2017 , some 6.4 million metric tons of such products were produced here .
gold_template: The statistic illustrates the volume of templateTitle[1] templateTitle[2] templateTitle[3] that were produced in the templateTitle[5] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , some templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] of such products were produced here .

generated_template: This statistic displays the templateTitle[0] templateYLabel[0] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] were produced .
generated: This statistic displays the Steel Production in the concrete from 2000 to 2017 .  In 2017 , about 6.39 million metric tons of Steel concrete were produced .

Example 986:
titleEntities: {'Subject': ['Outback Steakhouse'], 'Date': ['2011', '2018']}
title: Number of Outback Steakhouse restaurants worldwide from 2011 to 2018 , by location
Data:
                  0     1     2     3     4     5     6     7
Year           2018  2017  2016  2015  2014  2013  2012  2011
United States   733   740   755   755   753   768   771   776
International   256   249   239   224   222   220   204   191 

gold: In 2018 , Florida-based casual dining chain Outback Steakhouse operated a total of 989 restaurants worldwide . The largest share of the company 's restaurants was in its native country of the United States . Outback 's parent company is U.S.-based Bloomin ' Brands – other subsidiaries include Carrabba 's Italian Grill , Bonefish Grill and Fleming 's Prime Steakhouse & Wine Bar .
gold_template: In templateValue[0][0] , Florida-based casual dining chain templateTitleSubject[0] operated a total of 989 templateTitle[3] templateTitle[4] . The largest share of the company 's templateTitle[3] was in its native country of the country . templateTitleSubject[0] 's parent company is U.S.-based Bloomin ' Brands – other subsidiaries include Carrabba 's Italian Grill , Bonefish Grill and Fleming 's Prime templateTitleSubject[0] & Wine Bar .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] in the templateTitle[4] from templateValue[0][last] to templateValue[0][0] , templateTitle[5] templateTitle[6] . In templateValue[0][0] , templateTitleSubject[0] generated approximately templateValue[2][0] templateScale U.S. dollars in templateTitle[2] .
generated: This statistic shows the Number Outback of Outback Steakhouse in the worldwide from 2011 to 2018 , from 2011 .  In 2018 , Outback Steakhouse generated approximately 256 million U.S. dollars in Steakhouse .

Example 987:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2001', '2019']}
title: U.S. air conditioners shipments 2001 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Number', 'of', 'shipments', 'in', 'millions']: ['5.36', '5.4', '5.19', '4.9', '4.55', '4.5', '4.2', '3.92', '3.75', '3.42', '3.52', '3.97', '4.51', '4.95', '6.47', '5.52', '5.18', '5.26', '4.84']

gold: Around 5.4 million air conditioners ( A/Cs ) were shipped in the United States during 2019 . Total shipment figures fluctuate year to year , but generally fall between 3.5 and 6 million , with a few notable exceptions such as the 6.47 million sold in 2005 . Air conditioner market Having been invented over a century ago , air conditioner products comprise a mature and relatively stable market of the overall household appliance industry .
gold_template: Around templateYValue[0] templateScale templateTitle[1] templateTitle[2] ( A/Cs ) were shipped in the templateTitle[0] during templateXValue[max] . Total shipment figures fluctuate templateXLabel[0] to templateXLabel[0] , but generally fall between templateYValue[10] and templateYValue[max] templateScale , with a few notable exceptions such as the templateYValue[max] templateScale sold in templateXValue[idxmax(Y)] . templateTitle[1] conditioner market Having been invented over a century ago , templateTitle[1] conditioner products comprise a mature and relatively stable market of the overall household appliance industry .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] templateYLabel[5] . The highest in this statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[3] was valued at templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number shipments of the millions of U.S. worldwide from 2001 to 2019 .  In 2019 , the average Number shipments of U.S. was 5.36 millions .  The highest in this statistic shows the Number shipments of millions was valued at 6.47 millions .

Example 988:
titleEntities: {'Subject': ['Banco Santander'], 'Date': ['2012', '2019']}
title: Branch numbers of Banco Santander globally 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'branches']: ['11952', '13217', '13697', '12235', '13030', '12951', '13781', '14392']

gold: The number of bank branches for the Banco Santander Group decreased with some fluctuation globally between 2012 and 2019 . The Spanish bank employs approximately 196 thousand staff and has a customer base that grew by one million between 2018 and 2019 . As of the end of 2019 , the number of Banco Santander branches in Spain fell by more than one thousand .
gold_template: The templateYLabel[0] of bank templateYLabel[1] for the templateTitleSubject[0] Group templateNegativeTrend with some fluctuation templateTitle[4] between templateXValue[min] and templateXValue[max] . The Spanish bank employs approximately 196 thousand staff and has a customer base that templatePositiveTrend by one templateScale between templateXValue[1] and templateXValue[max] . As of the end of templateXValue[max] , the templateYLabel[0] of templateTitleSubject[0] branches in Spain templateNegativeTrend by more than one thousand .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . As of the most recently reported period , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic presents the Number of branches the Banco Santander worldwide from 2012 to 2019 .  As of the most recently reported period , the Number of the Banco Santander was 14392 11952 branches .

Example 989:
titleEntities: {'Subject': ['Usage'], 'Date': ['2019']}
title: Usage of top level domains for websites 2019
X_Axis['DNS', 'names']: ['.com', '.ru', '.org', '.net', '.de', '.br', '.uk', '.in', '.au', '.jp']
Y_Axis['Share', 'of', 'global', 'TLD']: ['48', '5.2', '4.9', '3.5', '2.7', '1.9', '1.8', '1.5', '1.3', '1.3']

gold: This statistic gives information on the distribution of worldwide top level domains as of November 2019 . As of that month , 48 percent of all global websites used a .com top-level domain . A further 3.5 percent used a .net TLD .
gold_template: This statistic gives information on the distribution of worldwide templateTitle[1] templateTitle[2] templateTitle[3] as of 2019 . As of that month , templateYValue[max] templateScale of all templateYLabel[1] templateTitle[5] used a templateXValue[idxmax(Y)] top-level domain . A further templateYValue[3] templateScale used a templateXValue[3] templateYLabel[2] .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] average templateXValue[0] on templateXValue[0] in the United Kingdom ( templateTitleSubject[0] ) as of 2019 . The templateXValue[0] , templateXValue[0] was ranked first with a score of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic presents the Usage top average .com on in the United Kingdom ( Usage ) as of 2019 .  The .com , was ranked first with a score of 48 TLD .

Example 990:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2008', '2018']}
title: Number of divorces in Sweden 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'divorces']: ['24958', '24210', '24258', '24876', '26143', '26933', '25005', '24884', '25151', '23534', '22580']

gold: The number of divorces in Sweden peaked in 2013 , when almost 27 thousand couples got divorced . Since then the divorce number decreased until 2018 , when it again increased and amounted to almost 25 thousand divorces . A similar trend can be seen in the neighbor country Norway , where the number of divorces decreased for several years but increased again in 2017 .
gold_template: The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] peaked in templateXValue[5] , when almost templateYValue[max] thousand couples got divorced . Since then the divorce templateYLabel[0] templateNegativeTrend until templateXValue[max] , when it again templatePositiveTrend and amounted to almost templateYValue[0] thousand templateYLabel[1] . A similar trend can be seen in the neighbor country Norway , where the templateYLabel[0] of templateYLabel[1] templateNegativeTrend for several years but templatePositiveTrend again in templateXValue[1] .

generated_template: The statistic shows the templateTitle[1] templateYLabel[0] of the templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[3] , the templateTitle[0] of the templateTitleSubject[0] amounted to templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the divorces Number of the Number between 2008 and 2018 .  In 2015 , the Number of the Sweden amounted to 24876 % divorces .

Example 991:
titleEntities: {'Subject': ['Senate'], 'Date': ['1983', '2019']}
title: U.S. Senate - composition by political party affiliation 1983 to 2019
Data:
                                 0                    1                    2                    3                    4                    5                    6                    7                    8                    9                   10                   11                   12                  13                  14                   15                   16                  17                  18
Year           116th_Congress_2019  115th_Congress_2017  114th_Congress_2015  113th_Congress_2013  112th_Congress_2011  111th_Congress_2009  110th_Congress_2007  109th_Congress_2005  108th_Congress_2003  107th_Congress_2001  106th_Congress_1999  105th_Congress_1997  104th_Congress_1995  103d_Congress_1993  102d_Congress_1991  101st_Congress_1989  100th_Congress_1987  99th_Congress_1985  98th_Congress_1983
Democrat                        45                   46                   44                   53                   51                   55                   49                   44                   48                   50                   45                   45                   45                  57                  57                   55                   55                  47                  46
Republican                      53                   52                   54                   45                   47                   41                   49                   55                   51                   50                   55                   55                   55                  43                  43                   45                   45                  53                  54
Other parties                    2                    2                    2                    2                    2                    2                    2                    1                    1                    0                    0                    0                    0                   0                   0                    0                    0                   0                   0 

gold: This graph shows the composition of the U.S. Senate by political party affiliation since 1983 . The 116th Congress consists of 45 Democratic and 53 Republican senators .
gold_template: This graph shows the templateTitle[2] of the templateTitle[0] templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] since templateValue[0][18] . The templateValue[0][0] consists of templateValue[1][0] Democratic and templateValue[1][3] templateLabel[2][0] senators .

generated_template: The statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . The templateTitle[0] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] was templateValue[1][0] U.S. dollars .
generated: The statistic shows the U.S. of the Senate composition by political in the party from 1983 to 2019 .  The U.S. of the Senate composition by political in 1983 was 45 U.S. dollars .

Example 992:
titleEntities: {'Subject': ['December'], 'Date': ['2019', '2019']}
title: Gasoline price per liter in selected countries - December 2019
X_Axis['Country']: ['Italy', 'France', 'United_Kingdom', 'Germany', 'Spain', 'Japan', 'Canada', 'United_States']
Y_Axis['Price', 'per', 'liter', 'in', 'U.S.', 'dollars']: ['1.76', '1.69', '1.64', '1.54', '1.45', '1.36', '0.89', '0.68']

gold: This statistic shows the price of gasoline per liter in selected countries in December 2019 . The prices concern unleaded premium ( 95 RON ) for France , Germany , Italy , Spain , and the United Kingdom , and regular unleaded for Canada , Japan , and the United States . In December 2019 , the gasoline price per liter in the United States stood at 0.675 U.S. dollars per liter .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] templateTitle[5] in templateTitleSubject[0] templateTitleDate[0] . The prices concern unleaded premium ( 95 RON ) for templateXValue[1] , templateXValue[3] , templateXValue[0] , templateXValue[4] , and the templateXValue[2] , and regular unleaded for templateXValue[6] , templateXValue[5] , and the templateXValue[2] templateXValue[last] . In templateTitleSubject[0] templateTitleDate[0] , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateXValue[2] templateXValue[last] stood at 0.675 templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] .

generated_template: In templateTitleSubject[0] templateTitleDate[0] , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateXValue[0] templateXValue[last] stood at 0.807 templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] . In the templateXValue[0] in the same month , the templateYLabel[0] stood at 1.704 templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] . This data concerns the templateYLabel[0] of automotive templateTitle[0] ( for non-commercial use ) templateYLabel[1] templateYLabel[2] in templateTitle[4] templateTitle[5] .
generated: In December 2019 , the Gasoline Price per liter in the Italy United_States stood at 0.807 U.S. dollars per liter .  In the Italy in the same month , the Price stood at 1.704 U.S. dollars per liter .  This data concerns the Price of automotive Gasoline ( for non-commercial use ) per liter in selected countries .

Example 993:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2030']}
title: U.S. corporate income tax revenues and forecast 2000 to 2030
X_Axis['Year']: ["'30", "'29", "'28", "'27", "'26", "'25", "'24", "'23", "'22", "'21", "'20", "'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00"]
Y_Axis['Revenues', 'in', 'billion', 'U.S.', 'dollars']: ['406.0', '398.0', '390.0', '382.0', '385.0', '386.0', '362.0', '334.0', '292.0', '257.0', '234.0', '230.0', '204.7', '297.0', '299.6', '343.8', '320.7', '273.5', '242.3', '181.1', '191.4', '138.2', '304.3', '370.2', '353.9', '278.3', '189.4', '131.8', '148.0', '151.1', '207.3']

gold: The statistic shows the U.S. corporate income tax revenues from 2000 to 2019 with an additional forecast from 2020 to 2030 . Revenues from corporate income tax amounted to 230 billion U.S. dollars in 2019 . The forecast predicts an increase in corporate income tax revenues up to 406 billion U.S. dollars in 2030 .
gold_template: The statistic shows the templateYLabel[2] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] from templateTitleDate[min] to 2019 with an additional templateTitle[5] from 2020 to templateTitleDate[max] . templateYLabel[0] from templateTitle[1] templateTitle[2] templateTitle[3] amounted to templateYValue[11] templateScale templateYLabel[2] templateYLabel[3] in 2019 . The templateTitle[5] predicts an templatePositiveTrend in templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] up to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateTitleDate[max] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[2] population in the templateTitle[0] from templateTitleDate[min] to templateTitleDate[max] . The templateYLabel[0] of the templateTitle[2] amounted to templateYValue[max] templateScale in templateXValue[idxmax(Y)] .
generated: The statistic shows the Revenues of the income population in the U.S. from 2000 to 2030 .  The Revenues of the income amounted to 406.0 billion in '30 .

Example 994:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Travel spending of U.S. travelers by age group 2015
Data:
                                            0                1              2
Year                          21-34_years_old  35-54_years_old  55+_years_old
Less than 1,000 U.S. dollars               15               12             23
1,000-3,999 U.S. dollars                   43               33             36
4,000-3,000 U.S. dollars                   19               26             18
8,000-11,999 U.S. dollars                   6               14             10
12,000-15,999 U.S. dollars                  3                6              5
16,000-19,999 U.S. dollars                  7                2              3
20,000-24,999 U.S. dollars                  3                3              2
25,000 U.S. dollars or more                 4                4              4 

gold: This statistic shows amount spent on travel by U.S. travelers in 2015 , by age group . The survey revealed that 15 percent of 21 - 34 year olds spent less than 1,000 U.S. dollars on travel in the past 12 months .
gold_template: This statistic shows amount spent on templateTitle[0] templateTitle[4] templateTitleSubject[0] templateTitle[3] in templateTitleDate[0] , templateTitle[4] templateTitle[5] templateTitle[6] . The survey revealed that templateValue[1][0] templateScale of 21 - 34 templateLabel[0][0] olds spent templateLabel[1][0] templateLabel[1][1] 1,000 templateTitleSubject[0] templateLabel[1][4] on templateTitle[0] in the past templateValue[1][1] months .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among American templateValue[0][0] in the United Kingdom ( templateTitleSubject[0] ) from templateValue[0][0] to templateValue[0][last] . The share of the workforce in this age group group between the ages of templateLabel[1][1] to templateValue[0][0] .
generated: This statistic shows the results of a 2015 survey among American 21-34_years_old in the United Kingdom ( U.S. ) from 21-34_years_old to 55+_years_old .  The share of the workforce in this age group between the ages of than to 21-34_years_old .

Example 995:
titleEntities: {'Subject': ['BSH Group'], 'Date': ['2018']}
title: Revenue of the BSH Group 2003 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['13.4', '13.8', '13.1', '12.6', '11.4', '10.5', '9.8', '9.65', '9.07', '8.41', '8.76', '8.82', '8.31', '7.34', '6.84', '6.3']

gold: This statistic represents the annual revenue of the BSH Group ( BSH Haushaltsgeraete GmbH ) between the 2003 to 2018 fiscal years . In its 2018 fiscal year , the BSH Group generated approximately 13.4 billion euros in revenue .
gold_template: This statistic represents the annual templateYLabel[0] of the templateTitleSubject[0] ( templateTitleSubject[0] Haushaltsgeraete GmbH ) between the templateXValue[min] to templateXValue[max] fiscal years . In its templateXValue[max] fiscal templateXLabel[0] , the templateTitleSubject[0] generated approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] in templateYLabel[0] .

generated_template: The statistic shows the total templateYLabel[0] of the templateTitle[1] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateTitle[2] templateYLabel[0] amounted to roughly templateYValue[idxmax(X)] templateScale templateYLabel[2] .
generated: The statistic shows the total Revenue of the BSH BSH Group from 2003 to 2018 .  In 2018 , the BSH Group Revenue amounted to roughly 13.4 billion euros .

Example 996:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017', '2019']}
title: Twitter : number of monetizable daily active U.S. users 2017 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17"]
Y_Axis['Number', 'of', 'monetizable', 'daily', 'active', 'users', 'in', 'millions']: ['31', '30', '29', '28', '27', '26', '26', '26', '25', '26', '25', '26']

gold: This statistic shows a timeline with the amount of monetizable daily active Twitter users in the United States as of the fourth quarter of 2019 . In the last reported quarter , the number of mDAU in the U.S. amounted to 31 million users . Total monthly active Twitter users in the United States amounted to 68 million .
gold_template: This statistic shows a timeline with the amount of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[0] templateYLabel[4] in the templateTitle[5] as of the fourth templateXLabel[0] of templateTitleDate[max] . In the last reported templateXLabel[0] , the templateYLabel[0] of mDAU in the templateTitleSubject[0] amounted to templateYValue[max] templateScale templateYLabel[4] . Total monthly templateYLabel[3] templateTitle[0] templateYLabel[4] in the templateTitle[5] amounted to 68 templateScale .

generated_template: As of the fourth templateXLabel[0] of templateTitleDate[0] , the templateTitle[0] templateYLabel[3] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitleSubject[0] reported templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . The second templateXLabel[0] of templateXValue[4] was the templateTitle[7] with templateYValue[4] templateScale templateYLabel[2] templateYLabel[3] in the United Kingdom , up from templateYValue[4] templateScale in the second templateXLabel[0] of templateTitleDate[max] .
generated: As of the fourth Quarter of 2017 , the Twitter active of U.S. monetizable daily U.S. reported 31 millions daily active .  The second Quarter of Q4_'18 was the 2017 with 27 millions daily active in the United Kingdom , up from 27 millions in the second Quarter of 2019 .

Example 997:
titleEntities: {'Subject': ['Germany'], 'Date': ['2010', '2024']}
title: National debt of Germany in relation to gross domestic product 2010 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['National', 'debt', 'as', 'percent', 'of', 'GDP']: ['43.7', '46.02', '48.48', '51.1', '53.82', '56.93', '59.75', '63.86', '67.85', '70.84', '74.67', '77.42', '79.86', '78.63', '80.96']

gold: This statistic shows the national debt of Germany from 2010 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In 2018 , the national debt of Germany amounted to approximately 60 percent of the GDP .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale of the templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to an estimate of approximately templateYValue[6] templateScale of the templateYLabel[3] .
generated: The statistic shows the National debt of Germany from 2010 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 .  The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances .  In 2018 , the National debt of Germany amounted to an estimate of approximately 59.75 percent of the GDP .

Example 998:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2024']}
title: Unemployment rate in South Korea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Unemployment', 'rate']: ['3.8', '3.9', '4', '4.1', '4.15', '4', '3.84', '3.68', '3.68', '3.59', '3.49']

gold: The statistic shows the unemployment rate in South Korea from 2014 to 2018 , with projections up until 2024 . In 2018 , the unemployment rate in South Korea was at around 3.84 percent . See the figures for the population of South Korea for comparison .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at around templateYValue[6] templateScale . See the figures for the population of templateTitleSubject[0] for comparison .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[min] templateScale .
generated: The statistic shows the Unemployment rate in South Korea from 2014 to 2024 .  In 2018 , the Unemployment rate in South Korea was at 3.49 % .

Example 999:
titleEntities: {'Subject': ['Rocket League'], 'Date': ['2016']}
title: Rocket League sales revenue worldwide 2016
X_Axis['Month']: ['June_2016', 'December_2015']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['110', '70']

gold: The statistic presents information on the sales revenue generated by Rocket League worldwide as of June 2016 . According to estimates , the game developed by Psyonix generated 110 million U.S. dollars in sales revenue in the presented period .
gold_template: The statistic presents information on the templateTitle[2] templateYLabel[0] generated by templateTitleSubject[0] worldwide as of templateXValue[0] . According to estimates , the game developed by Psyonix generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateTitle[2] templateYLabel[0] in the presented period .

generated_template: This statistic gives information on the templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[0] from 2011 to 2019 . During the last measured period , the mobile app had a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . The templateTitleSubject[0] – additional information The templateTitleSubject[0] is one of the most popular social network that is part of the internet users worldwide .
generated: This statistic gives information on the Rocket Rocket League sales Revenue from 2011 to 2019 .  During the last measured period , the mobile app had a Revenue of 110 million U.S. dollars .  The Rocket League – additional information The Rocket League is one of the most popular social network that is part of the internet users worldwide .

Example 1000:
titleEntities: {'Subject': ['France'], 'Date': ['2005', '2018']}
title: France : number of public primary schools 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2005']
Y_Axis['Number', 'of', 'public', 'schools']: ['44902', '45401', '45877', '46435', '46962', '47306', '47672', '48139', '48522', '48975', '49498', '49928', '50668']

gold: This graphic shows the total number of public primary schools in France from 2005 to 2018 . Over nine years , the number of public primary schools gradually diminished and reached 44,902 in 2018 .
gold_template: This graphic shows the total templateYLabel[0] of templateYLabel[1] templateTitle[3] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Over nine years , the templateYLabel[0] of templateYLabel[1] templateTitle[3] templateYLabel[2] gradually diminished and reached templateYValue[idxmax(X)] in templateXValue[idxmin(Y)] .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[2] templateYLabel[3] templateTitle[4] of goods and services from templateXValue[min] to templateXValue[max] . According to the source , the templateTitle[0] of templateYLabel[2] in templateTitleSubject[0] reached approximately templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Number of the schools of goods and services from 2005 to 2018 .  According to the source , the France of schools in France reached approximately 45877 thousand schools .

Example 1001:
titleEntities: {'Subject': ['Oman'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Oman 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['1.63', '-0.61', '0.9', '4.32', '3.68', '0.04', '1.76', '0.35', '4.9', '4.66', '1.42']

gold: The statistic shows the growth in real GDP in Oman from 2014 to 2024  . In 2018 , Oman 's real gross domestic product increased by around 1.76 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Oman from 2014 to 2018 , with projections up until 2024 .  In 2018 , Oman 's real Gross domestic product increased by around 1.76 % compared to the previous Year .

Example 1002:
titleEntities: {'Subject': ['FC Bayern Munich'], 'Date': ['2018']}
title: Number of members of FC Bayern Munich 2005 to 2018
X_Axis['Month']: ['Nov_2018', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Number', 'of', 'members', 'in', 'thousands']: ['291.0', '290.0', '284.04', '270.33', '251.32', '233.43', '195.69', '187.87', '171.35', '162.18', '151.23', '147.07', '135.75', '121.12']

gold: Over the past 10 years the number of members of the FC Bayern Munich , one of the most prominent football club of the German Bundesliga , the top tier football league of Germany , almost doubled . In 2008 there were roughly 151 thousand members of the club and in 2018 there were 291 thousand . In a survey based on public interest among Germans from 2018 , Bayern Munich lead in as the most popular club and the number of fan club members also came close to doubling in the last 10 years .
gold_template: Over the past 10 years the templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] Munich , one of the most prominent football club of the German Bundesliga , the top tier football league of Germany , almost doubled . In 2008 there were roughly templateYValue[10] thousand templateYLabel[1] of the club and in templateXValue[0] there were templateYValue[max] thousand . In a survey based on public interest among Germans from templateXValue[0] , templateTitleSubject[0] lead in as the most popular club and the templateYLabel[0] of fan club templateYLabel[1] also came close to doubling in the last 10 years .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitleSubject[0] from the templateXValue[last] to templateXValue[0] . As of the last reported period , the templateTitle[0] of templateXValue[0] amounted to templateYValue[max] thousand templateYLabel[3] .
generated: This statistic shows the Number members of the Number FC Bayern Munich from the 2005/06 to Nov_2018 .  As of the last reported period , the Number of Nov_2018 amounted to 291.0 thousand thousands .

Example 1003:
titleEntities: {'Subject': ['OECD'], 'Date': ['2016']}
title: Time per day spent on leisure activities in OECD countries by gender , as of 2016
Data:
                        0                      1              2                  3               4               5               6              7                    8                   9                   10                     11                12                     13                    14             15             16               17            18                19                 20                   21            22             23            24               25            26            27             28
Year    Finland_(2009-10)  United_Kingdom_(2005)  Norway_(2010)  Germany_(2001-02)  Denmark_(2001)  Belgium_(2005)  Ireland_(2005)  Canada_(2010)  Estonia_(2009-2010)  Slovenia_(2000-01)  South_Africa_(2010)  New_Zealand_(2009-10)  Poland_(2003-04)  Netherlands_(2005-06)  United_States_(2014)  France_(2009)  Sweden_(2010)  Spain_(2009-10)  Korea_(2009)  Australia_(2006)  Austria_(2008-09)  Hungary_(1999-2000)  Italy_(2008)  Turkey_(2006)  India_(1999)  Portugal_(1999)  Japan_(2011)  China_(2008)  Mexico_(2009)
Male                  386                    382            364                351             340             350             341            336                  345                 337                  334                    306               323                    301                   320            318            314              310           314               297                299                  303           309            279           283              289           240           248            236
Female                336                    339            355                326             320             307             290            294                  278                 283                  282                    295               273                    290                   271            270            272              273           268               269                262                  255           248            247           221              200           244           211            206 

gold: This statistic provides a comparison of the average amount of time spent leisure activities by gender in OECD member countries as well as China , India and South Africa . As of 2016 , women in Portugal spent an average of 200 minutes per day on leisure activities rather low when compared with women from Norway who spent an average of 355 minutes .
gold_template: This statistic provides a comparison of the average amount of templateTitle[0] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[8] templateTitle[9] in templateTitleSubject[0] member templateTitle[7] as well as templateValue[0][27] , templateValue[0][24] and templateValue[0][10] . As of templateTitleDate[0] , women in templateValue[0][25] templateTitle[3] an average of templateValue[2][25] minutes templateTitle[1] templateTitle[2] on templateTitle[4] templateTitle[5] rather low when compared with women from templateValue[0][2] who templateTitle[3] an average of templateValue[2][2] minutes .

generated_template: This statistic provides a comparison of the average amount of time templateTitle[4] on templateTitle[5] housework by gender in templateTitleSubject[0] member templateTitle[8] as well as templateValue[0][21] , templateValue[0][2] and templateValue[0][12] . As of templateTitleDate[0] , Portuguese men templateTitle[4] templateValue[1][3] templateTitle[1] templateTitle[2] templateTitle[3] on unpaid templateTitle[6] on templateTitle[0] while for women the templateTitle[0] was templateValue[2][3] templateTitle[1] .
generated: This statistic provides a comparison of the average amount of time leisure on activities housework by gender in OECD member by as well Hungary_(1999-2000) , Norway_(2010) and Poland_(2003-04) .  As of 2016 , Portuguese men leisure 351 per day spent on unpaid OECD on Time while for women the Time was 326 per .

Example 1004:
titleEntities: {'Subject': ['Arizona Republic'], 'Date': ['2018']}
title: Circulation of the Arizona Republic 2018
X_Axis['Day']: ['Sunday', 'Daily']
Y_Axis['Circulation']: ['378602', '130283']

gold: This statistic shows the daily circulation of the Arizona Republic newspaper in the United States in 2018 . The newspaper had a Sunday circulation of just under 380 thousand in that year .
gold_template: This statistic shows the templateXValue[last] templateYLabel[0] of the templateTitleSubject[0] newspaper in the country in templateTitleDate[0] . The newspaper had a templateXValue[0] templateYLabel[0] of just under 380 thousand in that year .

generated_template: This statistic shows the average templateYLabel[0] of the templateTitle[1] templateTitle[2] in the templateTitle[0] in templateTitleDate[0] , templateTitle[7] on templateXLabel[0] . In that year , the templateXValue[0] accounted for templateYValue[max] templateScale of the total templateYLabel[0] of the templateTitleSubject[0] .
generated: This statistic shows the average Circulation of the Arizona Republic in the Circulation in 2018 , on Day .  In that year , the Sunday accounted for 378602 % of the total Circulation of the Arizona Republic .

Example 1005:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2003']}
title: The largest lakes in the U.S. in 2003 , by area
X_Axis['Lakes', 'In', 'The', 'Us']: ['Lake_Superior', 'Lake_Huron', 'Lake_Michigan', 'Lake_Erie', 'Lake_Ontario', 'Great_Salt_Lake', 'Lake_of_the_Woods', 'Iliamna_Lake', 'Lake_Oahe_', 'Lake_Okeechobee', 'Lake_Pontchartrain', 'Lake_Sakakawea_', 'Lake_Champlain', 'Becharof_Lake', 'Lake_St._Clair', 'Red_Lake', 'Selawik_Lake', 'Fort_Peck_Lake_', 'Salton_Sea', 'Rainy_Lake']
Y_Axis['Area', 'in', 'square', 'miles']: ['31700', '23000', '22300', '9910', '7340', '2117', '1485', '1014', '685', '662', '631', '520', '490', '453', '430', '427', '404', '393', '347', '345']

gold: This statistic shows the largest lakes in the U.S. as of 2003 , by area . Lake Superior covers an area of 31,700 square miles and is therefore the largest lake in the U.S .
gold_template: This statistic shows the templateTitle[0] templateXLabel[0] in the templateTitleSubject[0] as of templateTitleDate[0] , templateTitle[4] templateYLabel[0] . templateXValue[0] covers an templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] and is therefore the templateTitle[0] templateXValue[0] in the templateTitle[2] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of the templateTitle[2] in the templateTitleSubject[0] in templateTitleDate[0] . The templateTitle[0] templateYLabel[0] of templateYLabel[1] that year was templateXValue[0] , with over templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the largest Area of the U.S. in the U.S. in 2003 .  The largest Area of square that year was Lake_Superior , with over 31700 million miles .

Example 1006:
titleEntities: {'Subject': ['Italy'], 'Date': ['2016']}
title: Public opinions on gay marriage in Italy 2016
X_Axis['Response']: ['I_don´t_know', 'In_favor', 'Against']
Y_Axis['Share', 'of', 'respondents']: ['8', '40', '52']

gold: The statistic illustrates the public opinion on gay marriage among Italians in 2016 . According to the data , more than half of Italian adults were against same-sex marriage ( 52 percent ) . Italy recognized same-sex civil unions ( not marriage ) in June 2016 , granting to same-sex couples most of the rights of marriage except parenting ( stepchild or joint adoption ) .
gold_template: The statistic illustrates the templateTitle[0] opinion on templateTitle[2] templateTitle[3] among Italians in templateTitleDate[0] . According to the data , more than half of Italian adults were templateXValue[last] same-sex templateTitle[3] ( templateYValue[max] templateScale ) . templateTitleSubject[0] recognized same-sex civil unions ( not templateTitle[3] ) in 2016 , granting to same-sex couples most of the rights of templateTitle[3] except parenting ( stepchild or joint adoption ) .

generated_template: As of 2019 , it was found that templateYValue[max] templateScale of people living in templateTitle[2] on the templateTitle[4] templateTitle[5] in the templateTitleSubject[0] . A further templateYValue[1] templateScale of templateYLabel[1] claimed that they templateXValue[0] on the social network .
generated: As of 2019 , it was found that 52 % of people living in gay on the Italy 2016 in the Italy .  A further 40 % of respondents claimed that they I_don´t_know on the social network .

Example 1007:
titleEntities: {'Subject': ['Games industry M A'], 'Date': ['2010', '2016']}
title: Games industry M & A transactions cumulative value worldwide 2010 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Value', 'in', 'million', 'U.S.', 'dollars']: ['1146.8', '4362.5', '7757.4', '4479.6', '692.0', '1423.5', '81.0']

gold: This statistic shows the Cumulative value of games market mergers and acquisitions ( M & A ) worldwide from 2010 to 2016 . In 2015 , based on the values of disclosed transactions , the industry 's M & A deals amounted to 4.36 billion U.S. dollars .
gold_template: This statistic shows the templateTitle[5] templateYLabel[0] of templateTitleSubject[0] market mergers and acquisitions ( templateTitleSubject[0] templateTitle[3] A ) templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , based on the values of disclosed templateTitle[4] , the templateTitleSubject[0] 's templateTitleSubject[0] templateTitle[3] A deals amounted to templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic presents the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[4] from templateXValue[min] to templateXValue[max] . According to the source , the templateTitle[0] templateTitle[1] templateTitle[2] templateNegativeTrend by templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] .
generated: The statistic presents the Value of the industry M transactions from 2010 to 2016 .  According to the source , the Games industry M decreased by 81.0 million U.S. dollars in 2010 .

Example 1008:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2017', '2023']}
title: Smartphone user penetration in Argentina 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['61', '60', '59', '57', '55', '52', '48']

gold: The statistic shows the smartphone user penetration in Argentina as share of the population from 2017 to 2023 . In 2018 , 52 percent of the population in Argentina used a smartphone .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] as templateYLabel[0] of the templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateYValue[5] templateScale of the templateYLabel[1] in templateTitleSubject[0] used a templateTitle[0] .

generated_template: This statistic presents the templateTitle[0] networking reach in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the templateYLabel[1] in the country accessed templateTitle[0] templateTitle[1] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] templateScale .
generated: This statistic presents the Smartphone networking reach in Argentina from 2017 to 2023 .  In 2017 , 48 % of the population in the country accessed Smartphone user .  In 2019 , this Share is projected to reach 55 % .

Example 1009:
titleEntities: {'Subject': ['Australia'], 'Date': ['2013', '2013']}
title: Internet service provider market share in Australia fourth quarter 2013
X_Axis['Telecommunications', 'Company']: ['Telstra', 'Optus', 'iiNet', 'TPG', 'Australia_Other']
Y_Axis['Market', 'share']: ['46.3', '15.9', '15.1', '11.6', '11']

gold: The statistic depicts a breakdown of the internet service provider ( ISP ) market in Australia in the fourth quarter of 2013 . The internet service provider Telstra held a market share of 46.3 percent . The total number of internet service subscribers in Australia was at around 6.15 million in the fourth quarter of 2013 .
gold_template: The statistic depicts a breakdown of the templateTitle[0] templateTitle[1] templateTitle[2] ( ISP ) templateYLabel[0] in templateXValue[last] in the templateTitle[6] templateTitle[7] of templateTitleDate[0] . The templateTitle[0] templateTitle[1] templateTitle[2] templateXValue[0] held a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale . The total number of templateTitle[0] templateTitle[1] subscribers in templateXValue[last] was at around 6.15 templateScale in the templateTitle[6] templateTitle[7] of templateTitleDate[0] .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[4] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . According to the source , templateXValue[0] accounted for over templateYValue[max] templateScale of all templateYLabel[2] in the templateTitle[4] in templateTitleDate[0] .
generated: The statistic presents the Market share of Internet service provider in the share in 2013 , Australia Telecommunications .  According to the source , Telstra accounted for over 46.3 % of all share in the share in 2013 .

Example 1010:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2017']}
title: Leading brands on Facebook from Indonesia 2017 , by number of fans
X_Axis['Month']: ['Mario_Teguh', 'DRAWING_PENCIL', 'Kompas.com', 'PERSIB_Bandung', 'Prabowo_Subianto', 'Kang_Dedi_Mulyadi', 'Facebook_Indonesia', 'Iwan_Fals', 'Tribunnews.com', 'Liputan6']
Y_Axis['Number', 'of', 'fans', 'on', 'Facebook', 'in', 'millions']: ['19.99', '12.91', '11.05', '9.63', '9.62', '9.14', '9.03', '8.51', '8.42', '8.19']

gold: This statistic provides information on the most popular Facebook brands from Indonesia as of November 2017 . As of that month , motivational speaker Mario Teguh had 19.99 million global fans on the social network .
gold_template: This statistic provides information on the most popular templateXValue[6] templateTitle[1] templateTitle[3] templateXValue[6] as of 2017 . As of that templateXLabel[0] , motivational speaker templateXValue[0] had templateYValue[max] templateScale global templateYLabel[1] on the social network .

generated_template: This statistic gives information on the most popular templateXValue[5] templateTitle[1] worldwide as of 2019 . During the measured period , templateXValue[0] had the most popular templateXValue[5] templateXValue[0] with over templateYValue[max] templateScale templateYLabel[1] worldwide .
generated: This statistic gives information on the most popular Kang_Dedi_Mulyadi brands worldwide as of 2019 .  During the measured period , Mario_Teguh had the most popular Kang_Dedi_Mulyadi Mario_Teguh with over 19.99 millions fans worldwide .

Example 1011:
titleEntities: {'Subject': ['European'], 'Date': ['1980', '2030']}
title: U.S. and European natural gas price 1980 to 2030
Data:
           0     1     2     3     4     5     6     7     8     9     10    11    12    13    14    15    16    17
Year    2030  2025  2022  2021  2020  2019  2018  2017  2016  2015   2014  2013  2012  2011  2010  2000  1990  1980
Europe   7.0   5.6   4.9   4.7   4.5   4.5   7.7   5.7   4.6   7.3  10.05  11.1   9.6   8.6   7.3   4.3   2.9   5.5
U.S.     4.0   3.3   2.9   2.8   2.7   2.5   3.2   3.0   2.5   2.6   4.37   3.5   2.3   3.3   3.9   4.8   1.8   2.1 

gold: This statistic represents the price of European and US natural gas from 1980 through 2018 , and provides projections up to 2030 . In 2030 , the price of natural gas in Europe is expected to reach about seven constant U.S. dollars per million British thermal units . Natural gas prices For millions of years , a combination of decayed plants and animals has been trapped beneath sand , silt and rock .
gold_template: This statistic represents the templateTitle[4] of templateTitleSubject[0] and US templateTitle[2] templateTitle[3] from templateValue[0][last] through templateValue[0][6] , and provides projections up to templateValue[0][0] . In templateValue[0][0] , the templateTitle[4] of templateTitle[2] templateTitle[3] in templateLabel[1][0] is expected to reach about templateValue[1][0] constant templateTitle[0] dollars per templateScale British thermal units . templateTitle[2] templateTitle[3] prices For templateScale of years , a combination of decayed plants and animals has been trapped beneath sand , silt and rock .

generated_template: The graph shows the templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , around templateValue[1][0] templateScale of the templateTitle[2] templateTitle[3] templateTitle[4] were employed in the templateTitle[6] .
generated: The graph shows the of European natural gas price in the 1980 from to 2030 .  In 2030 , around 7.0 % of the natural gas price were employed in the 2030 .

Example 1012:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1998', '2019']}
title: Total lobbying spending U.S. 1998 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998']
Y_Axis['Spending', 'in', 'billion', 'U.S.', 'dollars']: ['3.47', '3.46', '3.38', '3.16', '3.22', '3.25', '3.24', '3.3', '3.32', '3.51', '3.5', '3.31', '2.87', '2.63', '2.44', '2.19', '2.06', '1.83', '1.63', '1.56', '1.44', '1.45']

gold: This statistic shows the total lobbying spending in the United States from 1998 to 2019 . In 2019 , the total lobbying spending amounted to 3.47 billion U.S. dollars . Additional information on lobbying in the United States Since the turn of the millennium , the amount spent on lobbying in the United States has more than doubled .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] . Additional information on templateTitle[1] in the templateTitle[3] Since the turn of the millennium , the amount spent on templateTitle[1] in the templateTitle[3] has more than doubled .

generated_template: This statistic shows the worldwide templateYLabel[0] for templateTitle[2] sponsorship from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitleSubject[0] spendings on templateTitle[2] templateTitle[3] amounted to templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the worldwide Spending for sponsorship from 1998 to 2019 .  In 2016 , U.S. spendings on spending U.S. amounted to 3.16 billion U.S. dollars .

Example 1013:
titleEntities: {'Subject': ['Norway'], 'Date': ['2019']}
title: Ranking of the most popular blogs in Norway 2019
X_Axis['Month']: ['Sophieelise', 'Annarasmussen', 'Isabelleeriksen', 'Straad', 'Sofienils1', 'Pilotfrue', 'Stina', 'Sofiemk', 'Andreaasveinsdottir', 'Martinelunde']
Y_Axis['Number', 'of', 'visitors']: ['34173', '33363', '27246', '23413', '22692', '21834', '18455', '16394', '16346', '11963']

gold: Sophie Elise ranked first among popular blogs in Norway , as of August 2019 . Her channel covered a variety of topics , but the 21-year-old described herself as blogger , influencer , DJ and author on her page . She reached over 34 thousand visitors for the evaluated period .
gold_template: Sophie Elise ranked first among templateTitle[2] templateTitle[3] in templateTitleSubject[0] , as of 2019 . Her channel covered a variety of topics , but the 21-year-old described herself as blogger , influencer , DJ and author on her page . She reached over templateYValue[max] thousand templateYLabel[1] for the evaluated period .

generated_template: As of 2019 , it was found that templateXValue[0] had the highest templateYLabel[0] of any templateTitle[0] with templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateXValue[last] of people from followed by templateXValue[1] with templateYValue[1] templateScale of employees . The templateXValue[last] statistic presents presents the photo sharing sharing app had .
generated: As of 2019 , it was found that Sophieelise had the highest Number of any Ranking with 34173 million visitors .  The Martinelunde of people from followed by Annarasmussen with 33363 % of employees .  The Martinelunde statistic presents the photo sharing app had .

Example 1014:
titleEntities: {'Subject': ['West Virginia'], 'Date': ['2000', '2018']}
title: West Virginia - poverty rate from 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['17.8', '19.1', '17.9', '17.9', '18.3', '18.5', '17.8', '18.6', '18.1', '17.7', '17', '16.9', '17.3', '18', '17.9', '18.5', '17.2', '17.2', '18.6']

gold: This graph shows the poverty rate in West Virginia from 2000 to 2018 . In 2018 , 17.8 percent of West Virginia 's population lived below the poverty line .
gold_template: This graph shows the templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[2] line .

generated_template: This graph shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This graph shows the Virginia poverty in West Virginia from 2000 to 2018 .  In 2018 , about 17.8 percentage of West Virginia 's population lived below the Virginia line .

Example 1015:
titleEntities: {'Subject': ['Germany'], 'Date': ['2017']}
title: Main export partners of Germany 2017
X_Axis['Country']: ['United_States', 'France', 'China', 'Netherlands', 'United_Kingdom', 'Italy', 'Austria', 'Poland', 'Switzerland']
Y_Axis['Share', 'in', 'total', 'exports']: ['8.8', '8.2', '6.8', '6.7', '6.6', '5.1', '4.9', '4.7', '4.2']

gold: This statistic shows the main export partners of Germany as of 2017 . That year , Germany 's main export partner were the United States with a 8.8 percent share in Germany 's total exports .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] as of templateTitleDate[0] . That year , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] partner were the templateXValue[0] with a templateYValue[max] templateScale templateYLabel[0] in templateTitleSubject[0] 's templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the estimated templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] were the UK 's most important templateTitle[1] partner with a templateYLabel[0] of templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the estimated Main export partners Germany in 2017 .  In 2017 , United_States were the UK 's most important export partner with a Share of 8.8 % of total exports .

Example 1016:
titleEntities: {'Subject': ['Cineplex'], 'Date': ['2010', '2018']}
title: Attendance at Cineplex cinemas 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Attendance', 'in', 'millions']: ['69.27', '70.4', '74.6', '77.0', '73.6', '72.7', '71.2', '66.1', '67.0']

gold: The timeline presents the attendance figures at Cineplex from 2010 to 2018 . In 2018 , 69.27 million people attended movies at the Canadian movie theater chain , down from 70.4 million visitors a year earlier .
gold_template: The timeline presents the templateYLabel[0] figures at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale people attended movies at the Canadian movie theater chain , down from templateYValue[1] templateScale visitors a templateXLabel[0] earlier .

generated_template: The statistic shows the total templateYLabel[0] of the templateTitle[1] templateTitle[2] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateTitle[2] had an estimated templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the total Attendance of the Cineplex cinemas in the 2018 from 2010 to 2018 .  In 2018 , the Cineplex cinemas had an estimated Attendance of 69.27 millions .

Example 1017:
titleEntities: {'Subject': ['Philippines'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in the Philippines 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['4673.78', '4328.9', '4008.76', '3714.03', '3484.9', '3294.47', '3104.22', '2989.13', '2953.29', '2882.77', '2849.27', '2768.47', '2591.63', '2379.94', '2155.41', '1851.07', '1941.0', '1683.69', '1405.21', '1208.93', '1093.48', '1024.77', '1013.42', '970.38', '1051.97', '1078.0', '958.16', '1240.77', '1276.66', '1200.43', '1062.28', '922.19', '919.7', '806.94', '805.63', '794.1', '723.48', '648.26', '597.84', '627.54', '659.36']

gold: The statistic shows gross domestic product ( GDP ) per capita in the Philippines from 1984 to 2017 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in the Philippines from 1984 to 2017 , with projections up until 2024 .  GDP is the total value of all goods and services produced in a Year .  It is considered to be a very important indicator of the economic strength of a country .

Example 1018:
titleEntities: {'Subject': ['Case Shiller National Home Price Index'], 'Date': ['2017', '2019']}
title: U.S. housing : Case Shiller National Home Price Index 2017 to 2019
X_Axis['Month']: ['Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17']
Y_Axis['Index', 'value']: ['212.06', '211.63', '210.87', '209.65', '207.96', '206.06', '204.72', '204.46', '204.94', '205.34', '205.59', '205.6', '205.55', '205.18', '204.27', '202.66', '200.8', '198.76', '197.08', '196.29', '196.02', '195.61', '195.24', '194.97', '194.5', '193.66', '192.4', '190.65', '188.65', '186.64', '185.14']

gold: This statistic presents the values of S & P Case Shiller U.S. National Home Price Index from February 2017 to August 2019 . The index value was equal to 100 as of January 2000 , so if the index value is equal to 130 in a given month , for example , it means that the house prices increased by 30 percent since 2000 . The value of the S & P Case Shiller National Home Price Index amounted to 212.06 in August 2019 .
gold_template: This statistic presents the values of S & P templateTitleSubject[0] U.S. templateTitleSubject[0] Price templateYLabel[0] from 2017 to 2019 . The templateYLabel[0] templateYLabel[1] was equal to 100 as of 2000 , so if the templateYLabel[0] templateYLabel[1] is equal to 130 in a given templateXLabel[0] , for example , it means that the house prices templatePositiveTrend by 30 templateScale since 2000 . The templateYLabel[1] of the S & P templateTitleSubject[0] National templateTitleSubject[0] Index amounted to templateYValue[max] in 2019 .

generated_template: This statistic shows the total templateYLabel[0] of the templateTitle[1] templateTitleSubject[0] between 2018 and 2019 , based on a templateYLabel[1] of templateYValue[0] templateScale compared to the previous templateXLabel[0] . The templateTitleSubject[0] is a four largest manufacturer of goods and services produced in 2019 .
generated: This statistic shows the total Index of the housing Case Shiller National Home Price Index between 2018 and 2019 , based on a value of 212.06 % compared to the previous Month .  The Case Shiller National Home Price Index is a four largest manufacturer of goods and services produced in 2019 .

Example 1019:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: Refugee admissions in the U.S. 1990 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90"]
Y_Axis['Number', 'of', 'persons']: ['30000', '22491', '53716', '84994', '69933', '69987', '69926', '58238', '56424', '73311', '74654', '60191', '48282', '41223', '53813', '52873', '28403', '27131', '69886', '73147', '85525', '77080', '70488', '76403', '99974', '112981', '119448', '132531', '113389', '122066']

gold: This statistic shows the number of refugees admitted to the U.S. from the fiscal year of 1990 to the fiscal year of 2019 . During the fiscal year of 2019 , 30,000 refugees were admitted to the United States .
gold_template: This statistic shows the templateYLabel[0] of refugees admitted to the templateTitleSubject[0] from the fiscal templateXLabel[0] of templateTitleDate[min] to the fiscal templateXLabel[0] of templateTitleDate[max] . During the fiscal templateXLabel[0] of templateTitleDate[max] , templateYValue[0] refugees were admitted to the templateTitle[2] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[0] templateTitle[1] of templateTitle[2] in the templateTitle[4] was templateYValue[last] templateTitle[4] . The templateTitle[0] templateTitle[1] of the templateTitleSubject[0] templateTitle[3] – additional information As of templateTitleDate[max] , the templateTitle[4] was templateYValue[last] templateTitle[4] .
generated: This statistic gives information on the Refugee admissions U.S. in the 2019 from 1990 to 2019 .  In 2019 , the Refugee admissions of U.S. in the 2019 was 122066 2019 .  The Refugee admissions of the U.S. 1990 – additional information As of 2019 , the 2019 was 122066 2019 .

Example 1020:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Public opinion on adoption rights for same-sex couples in the U.S. 2014
X_Axis['Response']: ['Yes_should', 'No_should_not', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['63', '35', '2']

gold: This statistic shows the results of a survey , conducted by Gallup in the United States in May 2014 , on adoption rights for gay and lesbian couples . During this survey , 63 percent of the respondents were in favor of adoption rights for gay and lesbian couples in the United States .
gold_template: This statistic shows the results of a survey , conducted by Gallup in the templateTitle[7] in 2014 , on templateTitle[2] templateTitle[3] templateTitle[4] gay and lesbian templateTitle[6] . During this survey , templateYValue[max] templateScale of the templateYLabel[1] were in favor of templateTitle[2] templateTitle[3] templateTitle[4] gay and lesbian templateTitle[6] in the templateTitle[7] .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] regarding their templateTitle[2] . During the survey period , it was found that templateYValue[max] templateScale of the templateYLabel[1] stated that they are templateXValue[0] of the templateTitle[0] .
generated: This statistic shows the results of a survey among U.S. regarding their adoption .  During the survey period , it was found that 63 % of the respondents stated that they are Yes_should of the Public .

Example 1021:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2017']}
title: Life expectancy in Mexico 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Life', 'expectancy', 'at', 'birth', 'in', 'years']: ['74.95', '74.92', '74.9', '74.91', '74.93', '74.97', '75.01', '75.07', '75.13', '75.19', '75.26']

gold: The statistic shows the life expectancy at birth in Mexico from 2007 to 2017 . In 2017 , the average life expectancy at birth in Mexico was about 74.95 years .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] was about templateYValue[idxmax(X)] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[3] .
generated: The statistic shows the Life expectancy at birth in Mexico from 2007 to 2017 .  In 2017 , the average Life expectancy at birth in Mexico was 74.95 years .

Example 1022:
titleEntities: {'Subject': ['Boston Red Sox'], 'Date': ['2006', '2019']}
title: Boston Red Sox average ticket price 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['59.32', '56.97', '54.79', '52.34', '52.32', '53.38', '53.38', '53.38', '52.32', '50.24', '48.8', '47.71', '46.46']

gold: This graph depicts the average ticket price for Boston Red Sox games in Major League Baseball from 2006 to 2019 . In 2019 , the average ticket price was at 59.32 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] Sox games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Boston Red Sox games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 59.32 U.S. dollars .

Example 1023:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2015', '2018']}
title: Market share of leading insurance companies in Belgium 2015 to 2018
Data:
                              0     1       2     3        4    5        6        7    8                 9        10       11        12       13     14     15         16           17
Insurance Company  AG_Insurance   AXA  Ethias   KBC  Belfius  P&V  Allianz  Baloise   NN  ERGO_-_DKV_-_DAS  Generali  Argenta  Federale  Credimo  Fidea  Foyer  Integrale  Delta_Lloyd
2015                       20.8  14.2       9   8.7      6.1  5.9      5.4      4.8  1.9                 4       2.3      3.1       1.2        0    0.9      0          0          3.3
2016                       22.4  12.5       9  10.1      5.6  5.9      4.5      5.1  4.7                 4       2.8      2.2       1.4        0    0.9      0        0.8            0
2017                         21  12.5     9.1   9.4      6.4    6      5.5        5  4.4               3.8       2.6      2.7       1.4        0    1.1    0.7          0            0
2018                       21.7  12.1     9.3   8.8      6.8  5.7      5.3      4.9  4.9               3.6       2.7      2.5       1.5      1.1    1.1      0          0            0 

gold: AG Insurance was the largest active company on the Belgian insurance market , reaching a market share of more than 20 percent in 2018 . AXA and KBC reached market share of 12 percent and 9.3 percent respectively . When it comes to health insurance , however , the market shares change .
gold_template: templateValue[0][0] was the largest active templateLabel[0][1] on the Belgian templateValue[0][0] templateTitle[0] , reaching a templateTitle[0] templateTitle[1] of more than 20 templateScale in templateTitleDate[max] . templateValue[0][1] and templateValue[0][3] reached templateTitle[0] templateTitle[1] of templateValue[4][1] templateScale and templateValue[4][2] templateScale respectively . When it comes to health templateValue[0][0] , however , the templateTitle[0] shares change .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the average templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[3] of the templateTitleSubject[0] was held templateTitle[9] with an average of templateValue[1][0] Canadian dollars .
generated: The statistic shows the Market share leading insurance companies Belgium in the United Kingdom ( Belgium ) from 2015 to 2018 .  In 2018 , the average Market share leading insurance of the Belgium was held 2018 with an average of 20.8 Canadian dollars .

Example 1024:
titleEntities: {'Subject': ['France'], 'Date': ['2005', '2018']}
title: Average daily television viewing time in France 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Time', 'in', 'hours', 'and', 'minutes', '(hh.mm)']: ['3.36', '3.42', '3.43', '3.44', '3.41', '3.46', '3.5', '3.47', '3.32', '3.25', '3.24', '3.27', '3.24', '3.26']

gold: This statistic shows the average time individuals spent watching television per day in France from 2005 to 2018 . The data refers to television owners in metropolitan France aged four years and older . In 2018 , the average daily television viewing time in France came to three hours and 36 minutes , six minute less than one year earlier and a continuous slight decrease since 2015 .
gold_template: This statistic shows the templateTitle[0] templateYLabel[0] individuals spent watching templateTitle[2] per day in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The data refers to templateTitle[2] owners in metropolitan templateTitleSubject[0] aged templateYValue[max] years and older . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] in templateTitleSubject[0] came to templateYValue[0] templateYLabel[1] and 36 templateYLabel[2] , six minute less than one templateXLabel[0] earlier and a continuous slight templateNegativeTrend since templateXValue[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Time hours of the viewing time of France from 2005 to 2018 .  In 2018 , the Time hours of France was approximately 3.36 minutes (hh.mm) .

Example 1025:
titleEntities: {'Subject': ['Northern Ireland', 'UK'], 'Date': ['2000', '2019']}
title: Unemployment rate in Northern Ireland ( UK ) 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Unemployment', 'rate']: ['2.7', '3.6', '4.6', '5.7', '6.1', '6.4', '7.5', '7.4', '7.2', '7.1', '6.4', '4.4', '3.9', '4.4', '4.6', '5', '5.6', '5.9', '6', '6.2']

gold: This statistic shows the unemployment rate in Northern Ireland from 2000 to 2019 . Unemployment in Northern Ireland peaked in 2013 when there were 7.5 percent of the population unemployed , compared with just 2.7 percent in the most recent reporting year of 2019 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] in templateTitleSubject[0] peaked in templateXValue[6] when there were templateYValue[max] templateScale of the population unemployed , compared with just templateYValue[min] templateYValue[idxmax(X)] in the most recent reporting templateXLabel[0] of templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Northern Ireland from 2000 to 2019 .  In 2019 , the Unemployment rate in Northern Ireland was at approximately 2.7 % .

Example 1026:
titleEntities: {'Subject': ['Academy Awards'], 'Date': ['2020']}
title: Academy Awards nominations in 2020 , by film
X_Axis['Year']: ['Joker', 'The_Irishman', '1917', 'Once_Upon_a_Time_in_Hollywood', 'Little_Women', 'Jojo_Rabbit', 'Parasite', 'Marriage_Story', 'Ford_v_Ferrari']
Y_Axis['Number', 'of', 'nominations']: ['11', '10', '10', '10', '6', '6', '6', '6', '4']

gold: In 2020 , the movie with the most Oscar nominations was 'Joker ' _ , with 11 in total . 'Joker ' was directed by Todd Philips , whose work includes movies within 'The Hangover ' franchise , 'Due Date ' _ , and 'War Dogs ' _ . Philips also produced major hit ' A Star is Born ' _ .
gold_template: In templateTitleDate[0] , the movie with the most Oscar templateYLabel[1] was 'Joker ' _ , with templateYValue[max] in total . 'Joker ' was directed templateTitle[4] Todd Philips , whose work includes movies within 'The Hangover ' franchise , 'Due Date ' _ , and 'War Dogs ' _ . Philips also produced major hit ' A Star is Born ' _ .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[min] to templateTitleDate[max] . The templateTitle[0] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] was templateXValue[0] , with templateYValue[0] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Academy of Awards nominations 2020 in the Academy Awards ( ) from 2020 to .  The Academy of the Academy Awards Awards nominations 2020 was Joker , with 11 nominations .

Example 1027:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Italy : natural gas imports 2018 , by country of origin
X_Axis['Country']: ['Russia', 'Algeria', 'Qatar', 'Libya', 'Norway', 'Holland', 'Others']
Y_Axis['Natural', 'gas', 'imports', 'in', 'million', 'cubic', 'meters']: ['32321', '17970', '6535', '4466', '3159', '1003', '2419']

gold: In 2018 , Russia – with 32,321 million cubic meters of natural gas exported to Italy - was the main supplier of this fuel . In the same year , Italy imported 17,970 million cubic meters of natural gas from Algeria and 6,535 million cubic meters from Qatar . Overall , the volume of natural gas imported from Russia oscillated throughout the past years , even though it was quite steady from 2014 to 2017 .
gold_template: In templateTitleDate[0] , templateXValue[0] – with templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] of templateYLabel[0] templateYLabel[1] exported to templateTitleSubject[0] - was the main supplier of this fuel . In the same year , templateTitleSubject[0] imported templateYValue[1] templateScale templateYLabel[4] templateYLabel[5] of templateYLabel[0] templateYLabel[1] from templateXValue[1] and templateYValue[2] templateScale templateYLabel[4] templateYLabel[5] from templateXValue[2] . Overall , the volume of templateYLabel[0] templateYLabel[1] imported from templateXValue[0] oscillated throughout the past years , even though it was quite steady from 2014 to 2017 .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in selected countries templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleDate[0] . The templateTitle[0] of the templateTitle[2] templateTitle[3] templateTitle[4] was templateXValue[0] , with a templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Italy natural gas imports 2018 in selected countries by country origin in 2018 .  The Italy of the gas imports 2018 was Russia , with a Natural of 32321 imports million .

Example 1028:
titleEntities: {'Subject': ['Milwaukee Brewers'], 'Date': ['2019']}
title: Regular season average attendance of the Milwaukee Brewers 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Average', 'attendance']: ['36090', '35195', '31589', '28575', '31389', '34535', '31248', '34955', '37918', '34278', '37499']

gold: This graph depicts the average regular season home attendance of the Milwaukee Brewers from 2009 to 2019 . In 2019 , the average regular season home attendance of the Milwaukee Brewers was 36,090 . • Milwaukee Brewers total home attendance • Major League Baseball average per game attendance • Major League Baseball total attendance
gold_template: This graph depicts the templateYLabel[0] templateTitle[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateTitle[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] was templateYValue[0] templateYValue[idxmax(X)] • templateTitleSubject[0] total home templateYLabel[1] • Major League Baseball templateYLabel[0] per game templateYLabel[1] • Major League Baseball total templateYLabel[1]

generated_template: This graph depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitleSubject[0] was templateYValue[0] templateYValue[idxmax(X)]
generated: This graph depicts the Average season attendance of the Milwaukee Brewers from 2009 to 2019 .  In 2019 , the Average attendance at games of the Milwaukee Brewers was 36090 . 

Example 1029:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Leading causes of death among black U.S. residents in 2017
X_Axis['Month']: ['Diseases_of_the_heart', 'Malignant_neoplasms_(cancers)', 'Accidents_(unintentional_injuries)', 'Cerebrovascular_diseases_(stroke)', 'Diabetes_mellitus', 'Chronic_lower_respiratorydiseases_(CLRD)', 'Assault_(homicide)', 'Nephritis_nephrotic_syndrome_and_nephrosis_(kidney_diseases)', "Alzheimer's_disease", 'Septicemia']
Y_Axis['Percentage', 'of', 'all', 'causes', 'of', 'death']: ['23.3', '20.8', '5.9', '5.7', '4.4', '3.3', '3', '2.8', '2.7', '2']

gold: The leading causes of death among black residents in the United States in 2017 included diseases of the heart , cancer , stroke , and unintentional injuries . The leading causes of death for African Americans generally reflects the leading causes of death for the entire United States population . However , a major exception is that death from assault or homicide is the seventh leading cause of death among African Americans but is not among the ten leading causes for the general population .
gold_template: The templateTitle[0] templateYLabel[2] of templateYLabel[3] templateTitle[3] templateTitle[4] templateTitle[6] in the templateTitle[5] in templateTitleDate[0] included templateXValue[0] of the templateXValue[0] , cancer , stroke , and unintentional injuries . The templateTitle[0] templateYLabel[2] of templateYLabel[3] for African Americans generally reflects the templateTitle[0] templateYLabel[2] of templateYLabel[3] for the entire templateTitle[5] population . However , a major exception is that templateYLabel[3] from templateXValue[6] or homicide is the seventh templateTitle[0] cause of templateYLabel[3] templateTitle[3] African Americans but is not templateTitle[3] the ten templateTitle[0] templateYLabel[2] for the general population .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateXValue[0] on templateTitle[4] templateYLabel[0] templateYLabel[1] . In that year , the templateTitle[0] of templateXValue[7] was templateXValue[0] with a templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] , followed by templateXValue[1] with just over templateYValue[1] templateScale templateYLabel[3] .
generated: This statistic shows the Leading causes death in the among in 2017 , Diseases_of_the_heart on black Percentage all .  In that year , the Leading of Nephritis_nephrotic_syndrome_and_nephrosis_(kidney_diseases) was Diseases_of_the_heart with a Percentage of 23.3 causes death , followed by Malignant_neoplasms_(cancers) with just over 20.8 percentage death .

Example 1030:
titleEntities: {'Subject': ['November'], 'Date': ['2018', '2018']}
title: U.S. states with state cyber bullying laws as of November 2018
X_Axis['Category', 'Of', 'Cyber', 'Bullying', 'Law']: ['Includes_cyber_bullying_or_online_harassment', 'Criminal_Sanction_for_cyber_bullying_or_electronic_harassment', 'School_sanction_for_cyber_bullying', 'School_policy', 'Includes_off-campus']
Y_Axis['State', 'totals']: ['48', '44', '45', '49', '17']

gold: This statistic presents the number of US states with state cyber bullying laws , by policy implementation . As of November 2018 , 48 states had electronic harassment laws which explicitly included cyber bullying . A total of 44 states included criminal sanctions in their cyber bullying laws .
gold_template: This statistic presents the number of US templateTitle[1] templateTitle[2] templateYLabel[0] templateXValue[0] laws , by templateXValue[3] implementation . As of templateTitleSubject[0] templateTitleDate[0] , templateYValue[0] templateTitle[1] had templateXValue[1] templateXValue[0] templateTitle[6] which explicitly included templateXValue[0] . A total of templateYValue[1] templateTitle[1] included templateXValue[1] sanctions in their templateXValue[0] laws .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[0] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . According to the survey , it was found that templateYValue[1] templateScale of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[4] group stood at templateXValue[1] .
generated: This statistic presents the State of totals in the U.S. in 2018 , .  According to the survey , it was found that 44 % of totals in the bullying group stood at Criminal_Sanction_for_cyber_bullying_or_electronic_harassment .

Example 1031:
titleEntities: {'Subject': ['Australia'], 'Date': ['2018']}
title: Degree of urbanization in Australia 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['86.01', '85.9', '85.8', '85.7', '85.6', '85.5', '85.4', '85.3', '85.18', '85.06', '84.94']

gold: This statistic shows the degree of urbanization in Australia from 2008 to 2018 . In 2018 , 86.01 percent of Australia 's population lived in cities .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[2] lived in cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of in Australia from 2008 to 2018 .  Degree means the Share of urban population in the total population of a country .  In 2018 , 86.01 % of Australia 's total population lived in urban areas and cities .

Example 1032:
titleEntities: {'Subject': ['Clans', 'Clash Royale'], 'Date': ['2018']}
title: Clash of Clans and Clash Royale mobile revenue 2018
X_Axis['Game']: ['Clash_of_Clans', 'Clash_Royale']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['567', '597']

gold: The statistic presents information on the mobile revenue generated by Clash of Clans and Clash Royale in 2018 . In the measured period , both of the Supercell 's games generated over 500 million U.S. dollars in revenue , with Clash Royale 's revenue reaching 597 million . The game , released on the market in March 2016 , has been considerably more successful in terms of ARPUs .
gold_template: The statistic presents information on the templateTitle[4] templateYLabel[0] generated by templateXValue[0] of templateXValue[0] and templateXValue[0] templateXValue[last] in templateTitleDate[0] . In the measured period , both of the Supercell 's games generated over 500 templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] , with templateXValue[0] templateXValue[last] 's templateYLabel[0] reaching templateYValue[max] templateScale . The templateXLabel[0] , released on the market in 2016 , has been considerably more successful in terms of ARPUs .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] amounted to templateYValue[min] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Clans Clash in the Clans in 2018 , Game .  In 2018 , the Revenue of the Clans amounted to 567 million U.S. dollars .

Example 1033:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: Per capita consumption of tree nuts in the U.S. 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['5.1', '5.0', '4.8', '4.1', '4.1', '4.0', '4.2', '3.8', '3.8', '3.7', '3.6', '3.6', '3.3', '2.69', '3.53', '3.52', '3.35', '2.88', '2.61']

gold: The timeline shows the per capita consumption of tree nuts ( shelled ) in the United States from 2000 to 2018 . The U.S. per capita consumption of tree nuts amounted to five pounds in 2017 .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] ( shelled ) in the templateTitle[5] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] amounted to templateYValue[max] templateYLabel[3] in templateXValue[1] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of nuts U.S. in the Per from 2000 to 2018 .  According to the report , the U.S. Per capita consumption of nuts U.S. amounted to 5.1 pounds in 2018 .

Example 1034:
titleEntities: {'Subject': ['Smartphone', 'Canada'], 'Date': ['2013', '2023']}
title: Smartphone users in Canada 2013 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Smartphone', 'users', 'in', 'millions']: ['30.4', '29.6', '28.7', '27.7', '26.6', '25.4', '24.1', '22.63', '21.09', '18.0', '15.2']

gold: This forecast shows the number of smartphone users in Canada from 2013 to 2023 . For 2019 , the number of smartphone users in Canada is estimated to reach 26.6 million . Smartphone users in Canada – additional information Smartphones are mobile phones that have more advanced computing capabilities and connectivity than regular mobile phones .
gold_template: This forecast shows the number of templateYLabel[0] templateYLabel[1] in templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . For templateXValue[4] , the number of templateYLabel[0] templateYLabel[1] in templateTitleSubject[1] is estimated to reach templateYValue[4] templateScale . templateYLabel[0] templateYLabel[1] in templateTitleSubject[1] – additional information Smartphones are mobile phones that have more advanced computing capabilities and connectivity than regular mobile phones .

generated_template: The number of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] has templatePositiveTrend steadily in recent years , and is expected to keep templatePositiveTrend until at least templateXValue[max] . By templateXValue[max] , there will be an expected templateYValue[idxmax(X)] templateScale templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] . Smartphones : reduced barriers to ownership Just as they reach a higher share of the global population , smart phones also ( there is an `` are '' missing in between those two ) becoming more accessible .
generated: The number of Smartphone users in Smartphone has increased steadily in recent years , and is expected to keep increasing until at least 2023 .  By 2023 , there will be an expected 30.4 millions Smartphone users in Smartphone .  Smartphones : reduced barriers to ownership Just as they reach a higher share of the global population , smart phones also ( there is an `` are '' missing in between those two ) becoming more accessible .

Example 1035:
titleEntities: {'Subject': ['Share'], 'Date': ['2015', '2030']}
title: Share of projected base oil demand worldwide by type 2015 to 2030
Data:
                         0     1     2
Year                  2015  2020  2030
Paraffinic Group I      54    44    39
Paraffinic Group II     22    25    31
Paraffinic Group III     8    12    16
Naphthenic              10    10     7
Synthetic                5     5     8
Other                    1     2     0 

gold: This statistic outlines the distribution of the projected base oil demand worldwide in 2015 , 2020 , and 2030 , by type . Naphthenic base oils have a seven percent projected share of global base oil demand in 2030 , a slight decrease from the ten percent projected share of demand of naphthenic base oils in 2020 .
gold_template: This statistic outlines the distribution of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateValue[0][0] , templateValue[0][1] , and templateValue[0][last] , templateTitle[6] templateTitle[7] . templateLabel[4][0] templateTitle[2] oils have a templateValue[4][last] templateScale templateTitle[1] templateTitleSubject[0] of global templateTitle[2] templateTitle[3] templateTitle[4] in templateValue[0][last] , a slight templateNegativeTrend from the templateValue[4][0] templateScale templateTitle[1] templateTitleSubject[0] of templateTitle[4] of templateLabel[4][0] templateTitle[2] oils in templateValue[0][1] .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . The average templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] is expected to grow to templateValue[1][0] British pounds in templateValue[0][0] .
generated: This statistic shows the Share of the projected base oil in the United Kingdom ( Share ) from 2030 to 2015 , type 2015 .  The average Share projected base oil is expected to grow 54 British pounds in 2015 .

Example 1036:
titleEntities: {'Subject': ['LINE Corporation'], 'Date': ['2013', '2019']}
title: LINE Corporation : consolidated revenue 2013 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13"]
Y_Axis['Revenue', 'in', 'billion', 'Japanese', 'yen']: ['60.8', '56.0', '55.4', '55.3', '56.0', '51.9', '50.6', '48.7', '47.9', '44.3', '41.6', '40.7', '37.5', '35.9', '33.9', '33.5', '32.3', '31.9', '28.1', '28.1', '25.8', '23.8', '20.2', '16.6', '15.9', '12.6', '8.2']

gold: In the fourth quarter of 2019 , the Japanese chat app company LINE Corporation generated almost 61 billion Japanese yen in consolidated sales revenues . The number of monthly active users in Japan during this quarter amounted to approximately 83 million people .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[max] , the templateYLabel[2] chat app company templateTitleSubject[0] generated almost templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateTitle[2] sales revenues . The number of monthly active users in Japan during this templateXLabel[0] amounted to approximately 83 templateScale people .

generated_template: In the third templateXLabel[0] of templateTitleDate[max] , the social network templateTitleSubject[0] generated more than templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateTitle[2] , up from templateYValue[4] templateScale templateYLabel[3] in the corresponding templateXLabel[0] in the previous templateXLabel[0] . The number of monthly templateTitle[2] templateYLabel[3] in Japan during this quarter amounted to approximately 83 templateScale people .
generated: In the third Quarter of 2019 , the social network LINE Corporation generated more than 60.8 billion Japanese yen in consolidated , up from 56.0 billion yen in the corresponding Quarter in the previous Quarter .  The number of monthly consolidated yen in Japan during this quarter amounted to approximately 83 billion people .

Example 1037:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1995', '2018']}
title: Economic damage caused by floods in the U.S. 1995 to 2018
X_Axis['Year']: ['1995', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Damage', 'in', 'million', 'U.S.', 'dollars']: ['1251', '1255', '1220', '655', '2541', '1696', '1538', '3768', '1278', '3406', '1050', '3927', '7978', '494', '2172', '2626', '2749', '10907', '60700', '1644']

gold: This statistic shows the economic damage caused by floods and flash floods in the United States from 1995 to 2018 . In 2018 , floods caused approximately 1.6 billion U.S. dollars worth of property and crop damage across the United States .
gold_template: This statistic shows the templateTitle[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] and flash templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[4] templateTitle[2] approximately templateYValue[last] templateScale templateYLabel[2] templateYLabel[3] worth of property and crop templateYLabel[0] across the templateTitle[5] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[4] resulted in approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] worth of templateYLabel[0] across the templateTitle[5] .
generated: This statistic shows the Economic Damage caused by floods in the U.S. from 1995 to 2018 .  In 2018 , floods resulted in approximately 1644 million U.S. dollars worth of Damage across the U.S. .

Example 1038:
titleEntities: {'Subject': ['Shipping TEUs'], 'Date': ['2016', '2016']}
title: Hanjin Shipping - number of TEUs 2016
Data:
                          0               1             2             3            4              5              6                7
Month      September_7_2016  August_15_2016  July_12_2016  June_13_2016  May_18_2016  April_20_2016  March_21_2016  February_9_2016
Owned                292311          292311        274078        274078       274078         274078         274078           274078
Chartered            318250          318843        342284        351338       339733         339315         347165           346692
Orderbook             30400           30400             0             0         9030           9030           9030            18060 

gold: The statistic shows Hanjin Shipping 's number of twenty-foot equivalent units between February 9 and September 7 , 2016 . Hanjin Shipping owned ships with a capacity of around 274,000 TEUs in May 2016 . The abbreviation TEU stands for twenty-foot equivalent units .
gold_template: The statistic shows templateTitle[0] templateTitleSubject[0] 's templateTitle[2] of twenty-foot equivalent units between templateValue[0][7] and templateValue[0][0] , templateValue[0][0] . templateTitle[0] templateTitleSubject[0] templateLabel[1][0] ships with a capacity of around 274,000 templateTitleSubject[0] in templateValue[0][4] templateValue[0][0] . The abbreviation TEU stands for twenty-foot equivalent units .

generated_template: The statistic gives information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[4] templateTitle[2] templateTitle[4] as of 2019 , sorted templateTitle[6] templateTitle[7] . According to the survey , it was found that templateValue[1][0] templateScale of respondents stated that templateValue[2][0] templateScale of people living .
generated: The statistic gives information on the Shipping TEUs Shipping number 2016 as of 2019 , sorted 2016 .  According to the survey , it was found that 292311 % of respondents stated that 318250 % of people living .

Example 1039:
titleEntities: {'Subject': ['UK'], 'Date': ['2015']}
title: Comic book reading among young people in the UK 2015 , by demographic
X_Axis['Demographic']: ['Boys', 'Girls', 'KS2', 'KS3', 'KS4', 'KS5', 'White', 'Mixed', 'Asian', 'Black']
Y_Axis['Share', 'of', 'respondents']: ['34', '19.3', '29.3', '27.5', '17.6', '14.4', '26.2', '30.8', '28.6', '29.1']

gold: This statistic illustrates the proportion of young people reading comics at least once a month in the United Kingdom in 2015 , by demographic group . In 2015 , 30.8 percent of children from a Mixed ethnic background reported reading comics at least once a month .
gold_template: This statistic illustrates the proportion of templateTitle[4] templateTitle[5] templateTitle[2] comics at least once a month in the United Kingdom in templateTitleDate[0] , templateTitle[8] templateXLabel[0] group . In templateTitleDate[0] , templateYValue[7] templateScale of children from a templateXValue[7] ethnic background reported templateTitle[2] comics at least once a month .

generated_template: This statistic presents the distribution of templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] , broken down templateTitle[7] templateTitle[8] . According to the survey , templateYValue[max] templateScale of responding responding templateTitleSubject[0] was templateXValue[0] with templateYValue[1] templateScale of the templateYLabel[1] that year .
generated: This statistic presents the distribution of book reading in UK 2015 , broken down 2015 by .  According to the survey , 34 % of responding UK was Boys with 19.3 % of the respondents that year .

Example 1040:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Percentage of U.S. population engaged in leisure and sports per day 2010 to 2018
Data:
          0     1     2     3     4     5     6     7     8
Year   2018  2017  2016  2015  2014  2013  2012  2011  2010
All    95.6  95.6  95.8  96.1  95.7  95.3  96.2  95.2  95.8
Men    96.2  95.6  96.4  96.6  95.8  95.1  96.9  95.7  96.1
Women  94.9  95.6  95.3  95.6  95.7  95.4  95.6  94.8  95.4 

gold: This graph depicts the daily percentage of U.S. Americans engaged in leisure and sports activities from 2010 to 2018 . According to the source , 95.6 percent of all men were engaged in leisure and sports activities in 2018 .
gold_template: This graph depicts the daily templateScale of templateTitleSubject[0] Americans templateTitle[3] in templateTitle[4] and templateTitle[5] activities from templateValue[0][last] to templateValue[0][0] . According to the source , templateValue[1][0] templateScale of templateLabel[1][0] templateLabel[2][0] were templateTitle[3] in templateTitle[4] and templateTitle[5] activities in templateValue[0][0] .

generated_template: This graph depicts the average templateScale of the templateTitleSubject[0] templateTitle[2] participating in templateTitle[4] , templateTitle[5] and recreational activities templateTitle[6] templateTitle[7] by gender from templateValue[0][last] to templateValue[0][0] . According to the source , templateValue[2][0] templateScale of templateLabel[2][0] participated in templateTitle[4] , templateTitle[5] and recreational activities every templateTitle[7] in templateValue[0][0] .
generated: This graph depicts the average percentage of the U.S. population participating in leisure , sports and recreational activities per day by gender from 2010 to 2018 .  According to the source , 96.2 % of Men participated in leisure , sports and recreational activities every day in 2018 .

Example 1041:
titleEntities: {'Subject': ['Ecuadorian-American U.S.'], 'Date': ['2010']}
title: Largest Ecuadorian-American population groups in the U.S. , by county 2010
X_Axis['Population', 'Group']: ['Queens_County_NY', 'Kings_County_NJ', 'Hudson_County_NJ', 'Essex_County_NJ', 'Bronx_County_NY', 'Westchester_County_NY', 'Miami-Dade_County_FL', 'Los_Angeles_County_CA', 'Cook_County_IL', 'Suffolk_County_NY']
Y_Axis['Population', 'number']: ['98512', '28684', '26650', '25169', '23206', '22460', '19832', '19588', '19450', '17638']

gold: This statistic depicts the largest Ecuadorian-American population groups living in different counties across the United States as of 2010 . At this time there were 98,512 people of Ecuadorian origin living in Queens County in New York .
gold_template: This statistic depicts the templateTitle[0] templateTitleSubject[0] templateXLabel[0] templateTitle[3] living in different counties across the templateTitle[4] as of templateTitleDate[0] . At this time there were templateYValue[max] people of Ecuadorian origin living in templateXValue[0] in New York .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateYLabel[0] in different counties across the templateTitle[4] as of templateTitleDate[0] . At this time there were around templateYValue[max] templateScale of all templateYLabel[2] in templateXValue[0] .
generated: This statistic gives information on the Largest Ecuadorian-American Population in different counties across the U.S. as of 2010 .  At this time there were around 98512 % of all number in Queens_County_NY .

Example 1042:
titleEntities: {'Subject': ['EU'], 'Date': ['2018']}
title: Number of live births in the EU in 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'live', 'births', 'in', 'millions']: ['4.96', '5.07', '5.15', '5.11', '5.14', '5.08', '5.23', '5.27', '5.41', '5.41', '5.47']

gold: In 2017 , about 5.07 million babies were born in the European Union , a slight decrease from the year before . Of those 5.06 million , some 785,000 were born in Germany , the most of any EU member state . Live births are the births of children excluding stillbirths ; a key figure that can provide insight to demographic analyses , such as population growth .
gold_template: In templateXValue[1] , about templateYValue[1] templateScale babies were born in the European Union , a slight templateNegativeTrend from the templateXLabel[0] before . Of those 5.06 templateScale , some 785,000 were born in Germany , the most of any templateTitleSubject[0] member state . templateYLabel[1] templateYLabel[2] are the templateYLabel[2] of children excluding stillbirths ; a key figure that can provide insight to demographic analyses , such as population growth .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the National Football League ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] .
generated: This statistic shows the Number live of the National Football League ( EU ) from 2008 to 2018 .  In 2018 , the Number live of EU amounted to approximately 4.96 millions .

Example 1043:
titleEntities: {'Subject': ['New York'], 'Date': ['2000', '2018']}
title: New York - poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['13.6', '14.1', '14.7', '15.4', '15.9', '16', '15.9', '16', '14.9', '14.2', '13.6', '13.7', '14.2', '13.8', '14.2', '13.5', '13.1', '13.4', '13.1']

gold: This statistic shows the poverty rate in New York from 2000 to 2018 . 13.6 percent of New York 's population lived below the poverty line in 2018 .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[2] line in templateXValue[max] .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the York poverty in New York from 2000 to 2018 .  In 2018 , about 13.6 percentage of New York 's population lived below the York line .

Example 1044:
titleEntities: {'Subject': ['Singapore'], 'Date': ['2018', '2018']}
title: Singapore resident population as of June 2018 by ethnic group
X_Axis['Ethnicity']: ['Chinese', 'Malays', 'Indians', 'Others']
Y_Axis['Population', 'in', 'thousands']: ['2969.28', '535.82', '360.53', '128.65']

gold: As of June 2017 , the population of Singaporeans who were ethnic Chinese was around 2.97 million . Singapore is a multi-ethnic and multi-religious society , with residents categorized into four main ethnic groups : Chinese , Malay , Indian , and Other ethnicities .
gold_template: As of templateTitle[3] 2017 , the templateYLabel[0] of Singaporeans who were templateTitle[6] templateXValue[0] was around templateYValue[max] templateScale . templateTitleSubject[0] is a multi-ethnic and multi-religious society , with residents categorized into four main templateTitle[6] groups : templateXValue[0] , Malay , Indian , and Other ethnicities .

generated_template: This statistic shows the distribution of templateYLabel[2] templateYLabel[3] in templateXLabel[0] templateTitle[4] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . According to the survey findings , it was found that templateXValue[3] had a templateTitle[1] of templateYValue[3] templateScale in the templateXValue[5] .
generated: This statistic shows the distribution of thousands in Ethnicity 2018 in , by ethnic .  According to the survey findings , it was found that Others had a resident of 128.65 thousands in the Others .

Example 1045:
titleEntities: {'Subject': ['GM'], 'Date': ['2015', '2019']}
title: GM 's research and development expenditures 2015 to 2019
X_Axis['Year']: ['2015', '2016', '2017', '2018', '2019']
Y_Axis['Expenditures', 'in', 'billion', 'U.S.', 'dollars']: ['6.0', '6.6', '7.3', '7.8', '6.8']

gold: General Motors incurred around 6.8 billion U.S. dollars in R & D costs in 2019 . This figure puts the car manufacturer 's R & D intensity at five percent . That said , the company declared interest to enter into cost sharing arrangements with third parties .
gold_template: General Motors incurred around templateYValue[last] templateScale templateYLabel[2] templateYLabel[3] in R & D costs in templateXValue[max] . This figure puts the car manufacturer templateTitle[1] R & D intensity at five templateScale . That said , the company declared interest to enter into cost sharing arrangements with third parties .

generated_template: The statistic shows the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateTitle[2] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the GM Expenditures of GM worldwide from 2015 to 2019 .  In 2019 , the GM Expenditures research in GM amounted to about 6.8 billion U.S. dollars .

Example 1046:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2008', '2012']}
title: Quantity of ice cream consumed in the United Kingdom ( UK ) 2008 - 2012 , by age
X_Axis['Year']: ['1.5-3_years', '4-10_years', '11-18_years', '19-64_years', '65_years_and_over']
Y_Axis['Consumption', 'in', 'grams']: ['7', '13', '8', '5', '7']

gold: This statistic displays the quantity of ice cream consumed per day in the United Kingdom between 2008 and 2012 , by age . In this four year period , individuals aged between 4 and 10 years old consumed 13 grams of ice cream per day .
gold_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] between templateTitleDate[min] and templateTitleDate[max] , templateTitle[9] templateTitle[10] . In this four templateXLabel[0] period , individuals aged between 4 and 10 templateXValue[0] old templateTitle[3] templateYValue[max] templateYLabel[1] of templateTitle[1] templateTitle[2] per day .

generated_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] between templateTitleDate[min] and templateTitleDate[max] , templateTitle[8] templateTitle[9] . In this templateYValue[min] templateXLabel[0] period , individuals aged between 11 and 18 templateXValue[0] old templateTitle[3] templateYValue[max] templateYLabel[1] of templateTitle[1] templateTitle[2] per day .
generated: This statistic displays the Quantity of ice cream consumed per day in the United Kingdom between 2008 and 2012 , by .  In this 5 Year period , individuals aged between 11 and 18 1.5-3_years old consumed 13 grams of ice cream per day .

Example 1047:
titleEntities: {'Subject': ['UEFA Europa League'], 'Date': ['1971', '2019']}
title: UEFA Europa League all-time top goalscorers 1971 to 2019
X_Axis['Month']: ['Henrik_Larsson', 'Klaas_Jan_Huntelaar', 'Aritz_Aduriz', 'Radamel_Falcao', 'Dieter_Müller', 'Shota_Arveladze', 'Kevin_Gameiro', 'Mladen_Petrić', 'Alessandro_Altobelli', 'Vágner_Love', 'Claudio_Pizarro', 'Jermain_Defoe', 'Alan', 'Óscar_Cardozo', 'Dimitris_Salpingidis', 'Jupp_Heynckes', 'Ivan_Tričkovski', 'Jürgen_Klinsmann', 'Dennis_Bergkamp', 'Aleksandr_Kerzhakov', 'Martin_Chivers', 'Karl-Heinz_Rummenigge', 'Manuel_Fernandes']
Y_Axis['Goals', 'scored']: ['40', '34', '31', '31', '29', '27', '26', '25', '25', '24', '24', '24', '23', '23', '23', '23', '23', '22', '22', '22', '22', '22', '22']

gold: As of December 2019 , Henrik Larsson is the European football player with the most goals scored in the UEFA Europa League with a total of 40 goals , followed by Klaas Jan Huntelaar with 34 . The record for most matches played in the Europa League is held by Giuseppe Bergomi with 96 games played before Frank Rost with 90 games under his belt . Further records When it comes to number of appearances of clubs in the Europa League , Sporting Lisbon ranked first , having taken part 32 times in the League and Club Brugge ranked second with 30 appearances .
gold_template: As of 2019 , templateXValue[0] is the European football player with the most templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] League with a total of templateYValue[max] templateYLabel[0] , followed by templateXValue[1] Huntelaar with templateYValue[1] . The record for most matches played in the templateTitleSubject[0] is held by Giuseppe Bergomi with 96 games played before Frank Rost with 90 games under his belt . Further records When it comes to number of appearances of clubs in the templateTitleSubject[0] , Sporting Lisbon ranked first , having taken part 32 times in the templateTitleSubject[0] and Club Brugge ranked second with 30 appearances .

generated_template: As of 23rd 2020 , templateXValue[0] is the templateTitle[8] templateTitle[9] leader in templateYLabel[0] templateYLabel[1] for the templateTitle[1] templateTitle[2] team of templateTitleSubject[0] templateTitle[4] a total of templateYValue[max] templateYLabel[0] templateYLabel[1] , followed rather closely by templateXValue[1] with templateYValue[1] templateYLabel[0] . templateXValue[1] has passed away back in 1979 and templateXValue[0] retired from the templateTitle[1] already back in 1974 so these records are exceptionally old . templateTitleSubject[0] not at World Cup 2018 As templateTitleSubject[0] is generally a very prominent country for templateTitle[2] and even part of the Big Five , which are the biggest templateTitle[2] league market countries ( England , Germany , Spain , templateTitleSubject[0] .
generated: As of 23rd 2020 , Henrik_Larsson is the 2019 leader in Goals scored for the Europa League team of UEFA Europa League top a total of 40 Goals scored , followed rather closely by Klaas_Jan_Huntelaar with 34 Goals .  Klaas_Jan_Huntelaar has passed away back in 1979 and Henrik_Larsson retired from the Europa already back in 1974 so these records are exceptionally old .  UEFA Europa League not at World Cup 2018 As UEFA Europa League is generally a very prominent country for League and even part of the Big Five , which are the biggest League market countries ( England , Germany Spain , UEFA Europa League .

Example 1048:
titleEntities: {'Subject': ['Italy'], 'Date': ['2019']}
title: Distribution of the workforce across economic sectors in Italy 2019
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2019   2018   2017   2016   2015   2014   2013   2012   2011   2010   2009
Agriculture   3.72   3.75   3.78   3.88   3.75   3.64    3.6   3.69   3.68   3.77   3.69
Industry     25.62  25.81     26  26.12   26.6   26.9  27.05  27.58  28.29  28.61  29.24
Services     70.66  70.44  70.21  69.99  69.65  69.46  69.35  68.72  68.03  67.62  67.07 

gold: The statistic shows the distribution of the workforce across economic sectors in Italy from 2009 to 2019 . In 2019 , 3.72 percent of the workforce in Italy were employed in agriculture , 25.62 percent in industry and 70.66 percent in services .
gold_template: The statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the templateTitle[1] in templateTitleSubject[0] were employed in templateLabel[1][0] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in templateLabel[3][0] .

generated_template: The statistic shows the distribution of templateTitle[0] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the employees in templateTitleSubject[0] were active in the agricultural templateTitle[3] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in the service templateTitle[3] .
generated: The statistic shows the distribution of in Italy workforce across economic from 2009 to 2019 .  In 2019 , 3.72 % of the employees in Italy were active in the agricultural economic , 25.62 % in Industry and 70.66 % in the service economic .

Example 1049:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Brazil 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['2296.03', '2188.89', '2084.3', '1987.82', '1893.01', '1847.02', '1867.82', '2052.81', '1795.37', '1799.88', '2456.11', '2471.56', '2464.4', '2613.99', '2207.62', '1667.68', '1694.87', '1396.11', '1106.37', '890.67', '668.43', '557.68', '509.36', '559.96', '655.44', '599.87', '865.12', '884.31', '850.42', '786.54', '546.57', '429.03', '382.33', '399.11', '455.17', '439.28', '319.99', '286.44', '263.16', '226.86', '142.91']

gold: The statistic shows gross domestic product ( GDP ) in Brazil from 1984 to 2018 , with projections up until 2024 . Gross domestic product denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Brazil from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 1050:
titleEntities: {'Subject': ['Nigeria'], 'Date': ['2018']}
title: Urbanization in Nigeria 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['50.34', '49.52', '48.68', '47.84', '46.98', '46.12', '45.25', '44.37', '43.48', '42.59', '41.7']

gold: This statistic shows the degree of urbanization in Nigeria from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 50.34 percent of Nigeria 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Nigeria from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 50.34 % of Nigeria 's total population lived in urban areas and cities .

Example 1051:
titleEntities: {'Subject': ['Mozambique'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Mozambique 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['11.47', '9.24', '4', '4', '6.04', '1.8', '3.27', '3.74', '3.76', '6.59', '7.44']

gold: The statistic shows the growth in real GDP in Mozambique from 2014 to 2018 , with projections up until 2024 . In 2018 , Mozambique 's real gross domestic product increased by around 3.27 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Mozambique from 2014 to 2018 , with projections up until 2024 .  In 2018 , Mozambique 's real Gross domestic product increased by around 3.27 % compared to the previous Year .

Example 1052:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['1980', '2018']}
title: Defense spending as share of GDP in the United Kingdom ( UK ) 1980 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984', '1983', '1982', '1981', '1980']
Y_Axis['Defense', 'spending', 'as', 'share', 'of', 'GDP']: ['2.1', '2.1', '2.1', '2.1', '2.2', '2.3', '2.2', '2.4', '2.5', '2.5', '2.4', '2.3', '2.3', '2.4', '2.2', '2.3', '2.4', '2.5', '2.5', '2.5', '2.6', '2.7', '3', '3.1', '3.4', '3.6', '3.8', '4.3', '4.1', '4.1', '4.2', '4.6', '4.9', '5.2', '5.5', '5.3', '5.4', '4.8', '5']

gold: In 2018 , the United Kingdom 's defense spending as a share of Gross Domestic Product was 2.1 percent . Since 1980 , the UK 's defense spending was at it 's highest in 1984 when 5.5 percent of the UK 's GDP was spent on the military . After 1984 , defense spending declined gradually , and then at a much faster pace after the end of the Cold War in 1991 , with the United Kingdom only just reaching the two percent benchmark set by NATO by 2018 .
gold_template: In templateXValue[max] , the templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] as a templateYLabel[2] of Gross Domestic Product was templateYValue[min] templateYValue[idxmax(X)] . Since templateXValue[min] , the templateTitleSubject[1] 's templateYLabel[0] templateYLabel[1] was at it 's highest in templateXValue[34] when templateYValue[max] templateScale of the templateTitleSubject[1] 's templateYLabel[3] was spent on the military . After templateXValue[idxmax(Y)] , templateYLabel[0] templateYLabel[1] declined gradually , and then at a much faster pace after the end of the Cold War in templateXValue[27] , with the templateTitleSubject[0] only just reaching the templateYValue[min] templateScale benchmark set by NATO by templateXValue[idxmin(Y)] templateXValue[idxmin(Y)]

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[4] templateTitle[7] of the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] was templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Defense spending of the GDP United 1980 of the Defense from 1980 to 2018 .  In 2018 , the average Defense spending of the United Kingdom was 5.5 share GDP .

Example 1053:
titleEntities: {'Subject': ['Spain'], 'Date': ['2015']}
title: Spain : number of hotels and similar accommodation by number of rooms 2015
X_Axis['Year']: ['Less_than_25_rooms', 'From_25_to_99_rooms', '100_rooms_or_more', 'From_100_to_249_rooms', '250_rooms_or_more']
Y_Axis['Number', 'of', 'hotels']: ['12170', '5111', '2437', '1758', '679']

gold: This statistic shows the number of hotels and similar accommodation in Spain in 2015 , by number of rooms . In 2015 there were over 12 thousand hotels with less than 25 rooms in Spain .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] and templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateYLabel[0] of templateXValue[0] . In templateTitleDate[0] there were over templateYValue[max] thousand templateYLabel[1] with templateXValue[0] 25 templateXValue[0] in templateTitleSubject[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] templateTitle[3] from templateXValue[last] to templateXValue[0] . As of the third quarter of templateTitleDate[0] , there were templateYValue[max] templateYLabel[1] worldwide .
generated: This statistic shows the Number of Spain hotels similar from 250_rooms_or_more to Less_than_25_rooms .  As of the third quarter of 2015 , there were 12170 hotels worldwide .

Example 1054:
titleEntities: {'Subject': ['Prevalence'], 'Date': ['2000', '2018']}
title: Prevalence of undernourishment worldwide 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2005', '2000']
Y_Axis['Proportion', 'of', 'undernourished', 'people']: ['10.8', '10.8', '10.7', '10.6', '10.7', '11', '11.3', '11.5', '11.8', '13.8', '14.5']

gold: The statistic shows the prevalence of undernourishment in the world population from 2000 to 2018 . In 2018 , about 10.8 percent of the world population were undernourished .
gold_template: The statistic shows the templateTitleSubject[0] of templateTitle[1] in the world population from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of the world population were templateYLabel[1] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Prevalence Proportion undernourished of the United Kingdom ( Prevalence ) from 2000 to 2018 .  In 2018 , the Prevalence Proportion undernourished of Prevalence was 10.8 people .

Example 1055:
titleEntities: {'Subject': ['Global'], 'Date': ['1980', '2016']}
title: Global electricity consumption 1980 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2005', '2000', '1995', '1990', '1985', '1980']
Y_Axis['Net', 'consumption', 'in', 'billion', 'kilowatt', 'hours']: ['21801', '21192', '20761', '20340', '19713', '19324', '18643', '15717', '13253', '11486', '10395', '8657', '7323']

gold: This statistic represents worldwide electricity consumption between 1980 and 2016 . In 2016 , the world 's electricity consumption amounted to approximately 21.8 trillion kilowatt hours . One quadrillion watts is approximately equal to one petawatt .
gold_template: This statistic represents worldwide templateTitle[1] templateYLabel[1] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the world 's templateTitle[1] templateYLabel[1] amounted to approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . One quadrillion watts is approximately equal to one petawatt .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] of the templateYLabel[2] templateTitle[4] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of the templateYLabel[1] that templateXLabel[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Net of the consumption of the billion 2016 Global from 1980 to 2016 .  The Net of the consumption that Year was 21801 billion kilowatt .

Example 1056:
titleEntities: {'Subject': ['Volvo Group'], 'Date': ['2003', '2018']}
title: Volvo Group - global number of employees 2003 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Number', 'of', 'employees']: ['105175', '99488', '94914', '99501', '92828', '95533', '98717', '98162', '90409', '90208', '101380', '101700', '83190', '81860', '81080', '75740']

gold: This statistic represents the global number of Volvo Group employees from 2002 to 2018 . In 2018 , the Volvo Group had around 105,175 employees worldwide . This figure is considerably lower than that of other vehicle manufacturers : General Motors employed around 180,000 people in 2017 .
gold_template: This statistic represents the templateTitle[2] templateYLabel[0] of templateTitleSubject[0] employees from 2002 to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] had around templateYValue[max] templateYValue[idxmax(X)] worldwide . This figure is considerably lower than that of other vehicle manufacturers : General Motors employed around 180,000 people in templateXValue[1] .

generated_template: The statistic illustrates the templateYLabel[0] of templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . According to the source , the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateNegativeTrend by over 4 templateScale .
generated: The statistic illustrates the Number of employees the Group global number employees 2003 to 2018 .  According to the source , the Number of the Group global decreased by over 4 % .

Example 1057:
titleEntities: {'Subject': ['Reptile-ownership'], 'Date': ['2007', '2013']}
title: Reptile-ownership : household penetration rates 2007 to 2013
X_Axis['Year']: ['2007', '2008', '2009', '2010', '2011', '2012', '2013']
Y_Axis['Penetration', 'rates']: ['2', '2.1', '1.5', '1.8', '1.7', '1.8', '2.7']

gold: The statistic shows the household penetration rates for reptile-ownership in the United States from 2007 to 2013 . The household penetration rate for reptile-ownership amounted to about 2 percent in 2007 .
gold_template: The statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] in the country from templateXValue[min] to templateXValue[max] . The templateTitle[1] templateYLabel[0] rate for templateTitleSubject[0] amounted to about templateYValue[idxmin(X)] templateScale in templateXValue[min] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Reptile-ownership Penetration rates of the rates household in Reptile-ownership from 2007 to 2013 .  In 2013 , the Reptile-ownership Penetration rates in Reptile-ownership was at 2.7 rates .

Example 1058:
titleEntities: {'Subject': ['Asian'], 'Date': ['2013']}
title: Most popular social network sites in Asian countries in 2013
X_Axis['Country', 'and', 'Platform']: ['China_(QZone)', 'India_(Facebook)', 'Indonesia_(Facebook)', 'Japan_(LINE)', 'Philippines_(Facebook)', 'South_Korea_(Kakaotalk)', 'Thailand_(Facebook)', 'Taiwan_(Facebook)', 'Malaysia_(Facebook)', 'Vietnam_(Facebook)', 'Pakistan_(Facebook)', 'Hong_Kong_(Facebook)', 'Bangladesh_(Facebook)', 'Singapore_(Facebook)', 'Sri_Lanka_(Facebook)']
Y_Axis['Number', 'of', 'registered', 'users', '(in', 'millions)']: ['598.0', '63.0', '48.0', '36.0', '30.0', '19.0', '18.0', '13.0', '13.0', '12.0', '8.0', '3.7', '3.4', '2.7', '1.6']

gold: This statistic shows the most popular social networking sites in selected Asian countries , based on user figures . As of May 2013 , Facebook was the most popular social network in Singapore with 2.7 million registered users .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] networking templateTitle[4] in selected templateTitleSubject[0] templateTitle[6] , based on user figures . As of 2013 , Facebook was the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateXValue[13] with templateYValue[13] templateScale templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitle[3] templateTitle[4] templateXValue[0] in templateTitleDate[0] . The templateXValue[0] , with the largest templateYLabel[0] of the templateTitleSubject[0] with over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Most popular social of the network sites China_(QZone) in 2013 .  The China_(QZone) , with the largest Number of the Asian with over 598.0 million (in millions) .

Example 1059:
titleEntities: {'Subject': ['India'], 'Date': ['2018', '2018']}
title: India : mobile activities as of January 2018
X_Axis['Response']: ['Visit_a_social_network', 'Watch_videos', 'Use_a_search_engine', 'Look_for_product_information', 'Play_games']
Y_Axis['Share', 'of', 'respondents']: ['21', '16', '15', '4', '4']

gold: This statistic presents the most popular smartphone activities in India as of January 2018 . During a survey , it was found that 21 percent of the respondents visited a social network , while 16 percent watched videos .
gold_template: This statistic presents the most popular smartphone templateTitle[2] in templateTitleSubject[0] as of templateTitle[3] templateTitleDate[0] . During a survey , it was found that templateYValue[max] templateScale of the templateYLabel[1] visited a templateXValue[0] , while templateYValue[1] templateScale watched templateXValue[1] .

generated_template: This statistic presents the distribution of users in the templateTitle[0] who have used templateTitle[2] templateXValue[last] as of 2017 . During the survey , it was found that templateYValue[max] templateScale of templateYLabel[1] stated that they watched templateTitle[0] videos templateXValue[0] a templateXValue[last] .
generated: This statistic presents the distribution of users in the India who have used activities Play_games as of 2017 .  During the survey , it was found that 21 % of respondents stated that they watched India videos Visit_a_social_network a Play_games .

Example 1060:
titleEntities: {'Subject': ['Consumer Price Index'], 'Date': ['1990', '2019']}
title: U.S. Consumer Price Index excluding food and energy 1990 to 2019
X_Axis['Year']: ['1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['Consumer', 'Price', 'Index', '(100=1982-1984)']: ['135.5', '142.1', '147.3', '152.2', '156.5', '161.2', '165.6', '169.5', '173.4', '177.0', '181.3', '186.1', '190.5', '193.2', '196.6', '200.9', '205.9', '210.73', '215.57', '219.24', '221.34', '225.01', '229.76', '233.81', '237.9', '242.25', '247.6', '252.17', '257.57', '263.21']

gold: This statistic shows the Consumer Price Index for all items , excluding food and energy , for urban consumers in the United States of America from 1990 to 2019 . This selection , also called Core CPI , excludes goods with high price volatility like food and energy , which are non-systemic . In 2019 , the CPI stood at 263.21 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] for all items , templateTitle[4] templateTitle[5] and templateTitle[6] , for urban consumers in the templateTitle[0] of America from templateXValue[min] to templateXValue[max] . This selection , also called Core CPI , excludes goods with high templateYLabel[1] volatility like templateTitle[5] and templateTitle[6] , which are non-systemic . In templateXValue[max] , the CPI stood at templateYValue[idxmax(X)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitle[4] templateTitle[5] in the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the CPI was templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Consumer Price Index for excluding food in the National Football League from 1990 to 2019 .  In 2019 , the CPI was 263.21 Price Index .

Example 1061:
titleEntities: {'Subject': ['GDP'], 'Date': ['2019']}
title: Countries with the largest gross domestic product ( GDP ) 2019
X_Axis['Country']: ['United_States', 'China', 'Japan', 'Germany', 'India', 'United_Kingdom', 'France', 'Italy', 'Brazil', 'Canada', 'Russia', 'Korea', 'Spain', 'Australia', 'Mexico', 'Indonesia', 'Netherlands', 'Saudi_Arabia', 'Turkey', 'Switzerland']
Y_Axis['GDP', 'in', 'billion', 'U.S.', 'dollars']: ['21439.45', '14140.16', '5154.48', '3863.34', '2935.57', '2743.59', '2707.07', '1988.64', '1847.02', '1730.91', '1637.89', '1629.53', '1397.87', '1376.26', '1274.18', '1111.71', '902.36', '779.29', '743.71', '715.36']

gold: The statistic shows the 20 countries with the largest gross domestic product ( GDP ) in 2019 . In 2019 , Turkey ranked 19th with an estimated GDP of about 743.71 billion U.S. dollars . Global gross domestic product Global gross domestic product ( GDP ) amounts to almost 75 trillion U.S. dollars , with the United States making up more than 18 percent of it alone .
gold_template: The statistic shows the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[0] ) in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[18] ranked 19th templateTitle[1] an estimated templateYLabel[0] of about templateYValue[18] templateScale templateYLabel[2] templateYLabel[3] . Global templateTitle[3] templateTitle[4] templateTitle[5] Global templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[0] ) amounts to almost 75 templateScale templateYLabel[2] templateYLabel[3] , templateTitle[1] the templateXValue[0] making up more than 18 templateScale of it alone .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitleDate[0] , sorted templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[0] generated approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of templateYLabel[4] in templateYLabel[0] .
generated: This statistic shows the Countries largest gross ( GDP ) in 2019 , sorted product Country .  In 2019 , the United_States generated approximately 21439.45 billion U.S. dollars of in GDP .

Example 1062:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2018']}
title: Average monthly salary in Sweden 2018 , by industry
X_Axis['Industry']: ['Financial_institutions_and_insurance_companies', 'Information_and_communication_companies', 'Professional_scientific_and_technical_companies', 'Electricity_gas_steam_and_hot_water_plants', 'Mines_and_quarries', 'Real_estate_companies', 'Public_authorities_and_national_defence', 'Manufacturing_industry', 'Construction_industry', 'Trade;_repair_establishments_for_motor_vehicles_and_motorcycles', 'Water_works;_sewage_plants_waste-disposal_plants', 'Other_service_companies', 'Human_health_and_social_work_establishments', 'Educational_establishments', 'Transport_and_storage_companies', 'Administrative_and_support_service_companies', 'Establishments_for_culture_entertainment_and_recreation', 'Companies_in_agriculture_forestry_and_fishing', 'Hotels_and_restaurants']
Y_Axis['Average', 'salary', 'in', 'SEK']: ['51700', '45100', '42800', '42600', '39900', '37200', '37000', '36600', '35500', '34100', '33800', '32200', '31800', '31800', '31300', '30100', '29800', '28200', '26100']

gold: The highest average monthly salary in Sweden in 2018 was found within the sector of financial institutions and insurance companies . The average monthly salary for employees at companies within this sector amounted to almost 52 thousand Swedish kronor in 2018 , which is significantly higher than the average monthly salary for individuals with a tertiary education in Sweden . Increasing insurance companies and employees The number of insurance companies at the market in Sweden was decreasing for several years , but increased again in 2017 , and almost 21 thousand individuals in Sweden were employed within insurance companies during 2017 .
gold_template: The highest templateYLabel[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] in templateTitleDate[0] was found within the sector of templateXValue[0] and templateXValue[0] . The templateYLabel[0] templateTitle[1] templateYLabel[1] templateXValue[9] employees at templateXValue[0] within this sector amounted to almost templateYValue[max] thousand Swedish kronor in templateTitleDate[0] , which is significantly higher than the templateYLabel[0] templateTitle[1] templateYLabel[1] templateXValue[9] individuals with a tertiary education in templateTitleSubject[0] . templatePositiveTrend templateXValue[0] and employees The number of templateXValue[0] at the market in templateTitleSubject[0] was templateNegativeTrend templateXValue[9] several years , but templatePositiveTrend again in 2017 , and almost 21 thousand individuals in templateTitleSubject[0] were employed within templateXValue[0] during 2017 .

generated_template: As of 2019 , it was templateXValue[0] , with over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of employees , making it the templateTitle[0] of the templateXValue[2] with , and templateXValue[2] were templateXValue[7] or templateXValue[2] . According to the survey , the templateTitle[0] of all time . What is the people of people .
generated: As of 2019 , it was Financial_institutions_and_insurance_companies , with over 51700 million SEK of employees , making it the Average of the Professional_scientific_and_technical_companies with , and Professional_scientific_and_technical_companies were Manufacturing_industry or Professional_scientific_and_technical_companies .  According to the survey , the Average of all time .  What is the people of .

Example 1063:
titleEntities: {'Subject': ['General Electric'], 'Date': ['2005', '2018']}
title: General Electric : number of employees 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Number', 'of', 'employees', 'in', 'thousands']: ['283', '313', '295', '333', '305', '307', '305', '301', '287', '304', '323', '327', '319', '316']

gold: This statistic shows the approximate number of employees at General Electric each year from 2005 to 2018 . In 2018 , General Electric employed around 283,000 people worldwide . Total number of GE employees General Electric conducts business in virtually every part of the world , with over 180 countries served .
gold_template: This statistic shows the approximate templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] each templateXLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] employed around templateYValue[min] people worldwide . Total templateYLabel[0] of GE templateYLabel[1] templateTitleSubject[0] conducts business in virtually every part of the world , with over 180 countries served .

generated_template: This statistic depicts the templateTitle[0] templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[idxmax(X)] thousand templateYLabel[1] in templateTitleSubject[0] .
generated: This statistic depicts the General Number of employees in the General Electric ( ) from 2005 to 2018 .  In 2018 , there were approximately 283 thousand employees in General Electric .

Example 1064:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014', '2019']}
title: Number of people freelancing in U.S. 2014 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Number', 'of', 'people', 'doing', 'freelance', 'work', 'in', 'million']: ['57.0', '56.7', '57.3', '55.0', '53.7', '53.0']

gold: This statistic shows the number of freelance workers in the United States from 2014 to 2019 . In 2019 , there were 57 million people doing freelance work in the U.S .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[3] workers in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the templateTitle[3] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number of people U.S. worldwide from 2014 to 2019 .  In 2019 , the company 's Number people amounted to 57.0 doing freelance .

Example 1065:
titleEntities: {'Subject': ['Olive Garden'], 'Date': ['2016', '2019']}
title: Total sales of Olive Garden in the U.S. 2016 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016']
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['4.29', '4.08', '3.94', '3.84']

gold: This statistic shows the total sales of Olive Garden in the United States from 2016 to 2019 . In 2019 , Olive Garden 's U.S. sales reached approximately 4.29 billion U.S. dollars , up from 4.08 billion in 2018 .
gold_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[2] templateYLabel[0] reached approximately templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateScale in templateXValue[1] .

generated_template: The statistic depicts the annual templateTitle[1] templateYLabel[0] of smartphones in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] of templateYLabel[1] of templateTitle[1] amounted to templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the annual sales of smartphones in the Total from 2016 to 2019 .  In 2016 , the Sales of billion sales amounted to 3.84 billion U.S. dollars .

Example 1066:
titleEntities: {'Subject': ['Number M A', 'Asia'], 'Date': ['2015', '2017']}
title: Number of M & A transactions in Asia 2015 - 2017 , by country
Data:
                       0        1        2        3        4        5        6        7        8
Quarter          Q3_2017  Q2_2017  Q1_2017  Q4_2016  Q3_2016  Q2_2016  Q1_2016  Q4_2015  Q3_2015
Australia            328      328      321      404      364      358      362      402      394
China/Hong Kong      983      930      771     1059      916      732      746      932      896
India                156      173      228      221      258      254      286      241      271
Japan                492      456      446      445      437      394      425      384      381 

gold: The statistic presents the number of merger and acquisition transactions in Asia from the third quarter of 2015 to the third quarter of 2017 , by country . In the third quarter of 2017 , there were 983 M & A deals in China and Hong Kong .
gold_template: The statistic presents the templateTitleSubject[0] of merger and acquisition templateTitle[3] in templateTitleSubject[1] from the third templateLabel[0][0] of templateValue[0][7] to the third templateLabel[0][0] of templateValue[0][0] , templateTitle[7] templateTitle[8] . In the third templateLabel[0][0] of templateValue[0][0] , there were templateValue[2][0] templateTitleSubject[0] templateTitle[2] A deals in China and Hong templateLabel[2][1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] templateTitle[3] from templateValue[0][0] to templateValue[0][last] , sorted templateTitle[5] templateTitle[6] . In the second templateLabel[0][0] of templateValue[0][6] , the 16 templateScale of templateTitle[1] were templateLabel[2][0] in the templateTitleSubject[0] .
generated: This statistic shows the Number M of the Number M A transactions from Q3_2017 to Q3_2015 , sorted 2015 2017 .  In the second Quarter of Q1_2016 , the 16 % of M were China/Hong in the Number M A .

Example 1067:
titleEntities: {'Subject': ['European'], 'Date': ['2018']}
title: Leading European city tourism destinations in 2018 , by number of bednights
X_Axis['European', 'City', 'Destination']: ['London', 'Paris', 'Berlin', 'Rome', 'Istanbul', 'Madrid', 'Barcelona', 'Prague', 'Vienna', 'Munich', 'Amsterdam', 'Stockholm', 'Hamburg', 'Milan', 'Lisbon']
Y_Axis['Number', 'of', 'bednights', 'in', 'millions']: ['71.16', '52.55', '32.87', '28.55', '20.98', '19.83', '19.29', '18.25', '17.41', '17.12', '16.94', '14.59', '14.53', '12.29', '10.76']

gold: With approximately 71 million bednights , London is the most popular European city destination in the ECM benchmarking report . International visitors to London continually increased over the decade and measured over 19 million in 2018 . In second place was London 's competitor , Paris - the French capital accounted for around 53 million bednights .
gold_template: With approximately templateYValue[max] templateScale templateYLabel[1] , templateXValue[0] is the most popular templateXLabel[0] templateXLabel[1] templateXLabel[2] in the ECM benchmarking report . International visitors to templateXValue[0] continually templatePositiveTrend over the decade and measured over templateYValue[6] templateScale in templateTitleDate[0] . In second place was templateXValue[0] 's competitor , templateXValue[1] - the French capital accounted for around templateYValue[1] templateScale templateYLabel[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[3] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . The templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] stood at templateYValue[2] templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: This statistic shows the Leading European Number of the tourism in European 2018 , number bednights .  The Leading European Number bednights of European stood at 32.87 millions in 2018 .

Example 1068:
titleEntities: {'Subject': ['United States'], 'Date': []}
title: United States : light vehicle sales by manufacturer 2017/18
Data:
                               0                   1                          2        3                     4        5                       6                  7                            8                     9       10                      11            12                       13                        14
Car Manufacturer  General_Motors  Ford_Motor_Company  Toyota_Motor_Sales_U.S.A.      FCA  Nissan_North_America    Honda  Hyundai_Kia_Auto_Group  Subaru_of_America  Volkswagen_Group_of_America  BMW_of_North_America  Daimler  Mazda_Motor_of_America  Tesla_Motors  Jaguar_Land_Rover_N._A.  Volvo_Cars_North_America
2017                     2986737             2575200                    2433749  2073147               1697150  1639352                 1275222             647956                       620772                352787   375311                  289469         50067                   114212                     81434
2018                     2934742             2490175                    2426646  2238307               1611951  1604561                 1278740             680135                       634509                354698   350360                  300325        197517                   122397                     98786 

gold: This graph shows year-to-date passenger car sales in the United States between 2017 and 2018 , sorted by manufacturer . By the end of December 2018 , Toyota Motor had sold more than 2.4 million passenger cars here . Japan 's Toyota is one of the largest automobile manufacturers worldwide .
gold_template: This graph shows year-to-date passenger templateLabel[0][0] templateValue[0][2] in the templateTitleSubject[0] between templateLabel[1][0] and templateLabel[2][0] , sorted templateTitle[5] templateLabel[0][1] . templateTitle[5] the end of 2018 , templateValue[0][2] templateValue[0][1] had sold more than templateValue[1][2] templateScale passenger templateValue[0][14] here . Japan 's templateValue[0][2] is one of the largest automobile manufacturers worldwide .

generated_template: The statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] of employees in the templateTitle[4] from templateTitleDate[min] and templateTitleDate[max] . During the survey period , it was found that templateValue[2][0] templateScale of employees employees on templateValue[0][0] .
generated: The statistic gives information on the United States light of employees in the sales from and .  During the survey period , it was found that 2934742 % of employees on General_Motors .

Example 1069:
titleEntities: {'Subject': ['Revenue'], 'Date': ['2012', '2022']}
title: Revenue of the global eSports market 2012 to 2022
X_Axis['Year']: ['2022', '2019', '2018', '2017', '2016', '2015', '2014', '2012']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['1790', '1096', '865', '655', '493', '325', '194', '130']

gold: This statistic shows the global eSports market revenue from 2012 to 2022 . In 2018 , the global eSports market was valued at nearly 865 million U.S. dollars . According to the source 's estimates , global eSports market revenue will reach 1.79 billion U.S. dollars in 2022 .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateTitle[1] templateTitle[2] templateTitle[3] was valued at nearly templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] . According to the source 's estimates , templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] will reach templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] are owned by Ken Kendrick , who bought the franchise for 238 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[14] .
generated: The statistic depicts the Revenue of the Revenue from 2012 to 2022 .  In 2022 , the Revenue of the Major League Baseball franchise amounted to 1790 million U.S. dollars.The Revenue are owned by Ken Kendrick , who bought the franchise for 238 million U.S. dollars in 2012 .

Example 1070:
titleEntities: {'Subject': ['Red Bull'], 'Date': ['2011', '2016']}
title: Red Bull 's number of cans sold worldwide 2011 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'cans', 'in', 'billions']: ['6.06', '5.96', '5.61', '5.39', '5.23', '4.08']

gold: The popular energy drink Red Bull sold 6.06 billion cans in 2016 worldwide , up from just over 4 billion cans in 2011 . Red Bull in the U.S. Red Bull is one of the most popular energy drinks in the United States , controlling about 26.4 percent of the market . Red Bull Sugar Free and Red Bull Blue Edition are also among the top ten energy drink brands in the United States , accounting for 6.8 and 1.9 percent of the total market , respectively .
gold_template: The popular energy drink templateTitleSubject[0] sold templateYValue[max] templateScale templateYLabel[1] in templateXValue[idxmax(Y)] templateTitle[6] , up from just over templateYValue[min] templateScale templateYLabel[1] in templateXValue[idxmin(Y)] . templateTitleSubject[0] in the U.S. templateTitleSubject[0] is one of the most popular energy drinks in the country , controlling about 26.4 templateScale of the market . templateTitleSubject[0] Sugar Free and templateTitleSubject[0] Blue Edition are also among the top ten energy drink brands in the country , accounting for 6.8 and 1.9 templateScale of the total market , respectively .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to approximately templateYValue[idxmax(X)] thousand templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number of the Bull 's from 2011 to 2016 .  In 2016 , the Red Bull 's Number amounted to approximately 6.06 thousand billions .

Example 1071:
titleEntities: {'Subject': ['Italy'], 'Date': ['2014', '2018']}
title: Italy : market share of coffee pods and capsules 2014 - 2018 , by segment
Data:
           0     1     2     3     4
Year    2018  2017  2016  2015  2014
Family  71.3  70.6  69.9  69.1  68.2
Office  24.3    25  25.7  26.5  27.4
HoReCa   4.4   4.4   4.4   4.4   4.4 

gold: This timeline illustrates the market share of coffee pods and capsules in Italy between 2014 and 2018 , broken down by segment . Over the survey period , the market share of coffee pods and capsules designated to families has increased from 68.2 percent of the whole market to 71.3 percent . On the contrary , the market share of coffee pods and capsules for the HoReCa industry had been stable around four percent .
gold_template: This timeline illustrates the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] and templateTitle[5] in templateTitleSubject[0] between templateValue[0][last] and templateValue[0][0] , broken down templateTitle[8] templateTitle[9] . Over the survey period , the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] and templateTitle[5] designated to families has templatePositiveTrend from templateValue[1][last] templateScale of the whole templateTitle[1] to templateValue[1][0] templateScale . On the contrary , the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] and templateTitle[5] for the templateLabel[3][0] industry had been stable around templateValue[3][last] templateScale .

generated_template: In templateValue[0][0] , the templateTitle[2] of templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to nearly templateValue[2][0] templateScale U.S. dollars . This was an templatePositiveTrend of GDP per templateScale U.S. dollars in templateValue[0][last] . The forecast is projected to grow to about templateValue[2][0] templateScale U.S. dollars in templateValue[0][0] .
generated: In 2018 , the share of Italy market in Italy amounted to nearly 24.3 billion U.S. dollars .  This was an increase of GDP per million U.S. dollars in 2014 .  The forecast is projected to grow about 24.3 billion U.S. dollars in 2018 .

Example 1072:
titleEntities: {'Subject': ['Royal Bank Canada'], 'Date': ['2014', '2019']}
title: Total assets of Royal Bank of Canada 2014 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Total', 'assets', 'in', 'billion', 'Canadian', 'dollars']: ['1428.94', '1334.73', '1212.85', '1180.26', '1074.21', '940.55']

gold: Royal Bank of Canada ( RBC ) has been increasing its assets over the past few years . They stood at 1.43 trillion Canadian dollars in 2019 , up from 941 billion Canadian dollars in 2014 . How big is RBC ? The assets given in this statistic make RBC the largest bank in Canada , followed closely by Toronto-Dominion Bank ( TD Bank ) .
gold_template: templateTitleSubject[0] of templateTitleSubject[0] ( RBC ) has been templatePositiveTrend its templateYLabel[1] over the past few years . They stood at templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] , up from templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmin(Y)] . How big is RBC ? The templateYLabel[1] given in this statistic make RBC the largest templateTitleSubject[0] in templateTitleSubject[0] , followed closely by Toronto-Dominion templateTitleSubject[0] ( TD templateTitleSubject[0] ) .

generated_template: The statistic shows the templateYLabel[0] of the employees of the templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Total of the employees of the Total in Royal Bank Canada from 2014 to 2019 .  In 2019 , the Total of the Royal Bank Canada amounted to 1428.94 billion Canadian .

Example 1073:
titleEntities: {'Subject': ['U.S. Q4'], 'Date': ['2010', '2019']}
title: Percentage of e-commerce in total U.S. Q4 retail sales 2010 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q4_'18", "Q4_'17", "Q4_'16", "Q4_'15", "Q4_'14", "Q4_'13", "Q4_'12", "Q4_'11", "Q4_'10"]
Y_Axis['Estimated', 'share', 'of', 'e-commerce', 'in', 'total', 'sales']: ['11.5', '10.1', '9.1', '8.2', '7.5', '6.6', '6.1', '5.5', '5.1', '4.6']

gold: This statistic shows the estimated U.S. total and e-commerce figures of U.S. retail in the fourth quarter of 2010 to 2019 . In the fourth quarter of 2010 , the share of e-commerce in total U.S. retail sales was 4.6 percent and more than doubled in 2019 , where e-commerce sales amounted to 11.5 percent of total retail sales in the holiday quarter .
gold_template: This statistic shows the templateYLabel[0] templateTitleSubject[0] templateYLabel[3] and templateYLabel[2] figures of templateTitleSubject[0] templateTitle[5] in the fourth templateXLabel[0] of templateTitleDate[min] to templateTitleDate[max] . In the fourth templateXLabel[0] of templateTitleDate[min] , the templateYLabel[1] of templateYLabel[2] in templateYLabel[3] templateTitleSubject[0] templateTitle[5] templateYLabel[4] was templateYValue[min] templateScale and more than doubled in templateTitleDate[max] , where templateYLabel[2] templateYLabel[4] amounted to templateYValue[max] templateScale of templateYLabel[3] templateTitle[5] templateYLabel[4] in the holiday templateXLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] templateYLabel[1] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[min] to templateTitleDate[max] , in templateScale templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] . In the first templateXLabel[0] of templateTitleDate[max] , the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] templateYLabel[1] templatePositiveTrend to templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the Percentage e-commerce total U.S. Q4 Estimated share in the United Kingdom ( U.S. Q4 ) from 2010 to 2019 , in percentage total sales .  In the first Quarter of 2019 , the total U.S. Q4 Estimated share increased to 11.5 % total .

Example 1074:
titleEntities: {'Subject': ['Digital'], 'Date': ['2012', '2019']}
title: Digital advertising revenue of leading online companies 2012 to 2019
Data:
               0         1        2     3       4
Platform  Google  Facebook  Twitter   AOL  Yahoo!
2012       43.69      4.28     0.27  1.42    4.03
2013       51.07      6.99     0.59  1.61    3.69
2014       59.62     11.49     1.25  1.85    3.71
2015       67.39     17.08     1.99     0     4.2
2016       79.38     26.89     2.25     0    4.65
2017       95.58     39.94     2.11     0       0
2018      116.46     55.01     2.62     0       0
2019      134.81     69.66     2.99     0       0 

gold: This statistic presents a ranking of selected online companies based on total digital advertising revenue based on their annual reporting , excluding virtual currencies and goods . In 2019 , Facebook earned 69.66 billion U.S. dollars in digital revenue through online advertising . During the same period of time , search market leader Google generated 134.8 billion U.S. dollars through digital advertising channels .
gold_template: This statistic presents a ranking of selected templateTitle[4] templateTitle[5] based on total templateTitleSubject[0] templateTitle[1] templateTitle[2] based on their annual reporting , excluding virtual currencies and goods . In templateTitleDate[max] , templateValue[0][1] earned templateValue[8][1] templateScale U.S. dollars in templateTitleSubject[0] templateTitle[2] through templateTitle[4] templateTitle[1] . During the same period of time , search market leader templateValue[0][0] generated templateValue[8][max] templateScale U.S. dollars through templateTitleSubject[0] templateTitle[1] channels .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] in templateTitleSubject[0] was templateValue[5][0] templateScale .
generated: This statistic shows the Digital advertising revenue leading of online in Digital from 2012 to 2019 .  In 2019 , the Digital advertising revenue leading of online in Digital was 79.38 % .

Example 1075:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016', '2016']}
title: Most googled hotel-related keywords in the United Kingdom ( UK ) in January 2016
X_Axis['Hotel', 'Related', 'Keyword']: ['Hotels', 'Hotels_in_London', 'Cheap_hotels', 'London_hotels', 'Last_minute_hotels', 'Hotel', 'Amsterdam_hotels', 'Hotels_in_Machester', 'Hotels_in_York', 'Hoterls_in_Edinburgh', 'Other_related_keywords']
Y_Axis['Searches', 'in', 'thousands']: ['201.0', '90.5', '74.0', '74.0', '74.0', '49.5', '40.5', '40.5', '40.5', '40.5', '3059.58']

gold: This statistic displays the most googled hotel-related keywords on Google UK in January 2016 . Most searched was the term `` hotels '' which was searched 201 thousand times that month . Second most popular was `` hotels in london '' .
gold_template: This statistic displays the templateTitle[0] templateTitle[1] templateTitle[2] templateXValue[last] on Google templateTitleSubject[1] in templateTitle[7] templateTitleDate[0] . templateTitle[0] searched was the term `` templateXValue[0] '' which was searched templateYValue[0] thousand times that month . Second templateTitle[0] popular was `` templateXValue[0] in templateXValue[1] '' .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateXValue[last] as of 2019 , based on sales . As of that month , templateXValue[0] accounted for templateYValue[max] templateScale of all templateYLabel[2] templateYLabel[3] in the templateTitle[4] .
generated: This statistic gives information on the Most googled hotel-related Other_related_keywords as of 2019 , based on sales .  As of that month , Hotels accounted for 3059.58 thousands of all thousands in the United .

Example 1076:
titleEntities: {'Subject': ['Italian', 'Dolce Gabbana'], 'Date': ['2014', '2018']}
title: Net profits/loss of Italian company Dolce & Gabbana in 2014 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014']
Y_Axis['Net', 'profits/loss', 'in', 'million', 'euros']: ['-24.11', '27.55', '-3.73', '-20.89', '13.71']

gold: The Italian fashion Group Dolce & Gabbana S.r.l. , well known worldwide for designing , producing and distributing high-end clothing , leather goods , footwear , jewelry and accessories , reported a net loss of 24.11 million euros in 2018 . During the period considered , this number represented the highest net loss of the Group , which registered a net profit of 27.55 million euros in 2017 . Italian roots , global reach Due to a very distinctive Mediterranean look , Dolce & Gabbana started attracting the attention of artists and famous people around the world already in the 90s .
gold_template: The templateTitleSubject[0] fashion Group templateTitleSubject[1] templateTitle[5] templateTitleSubject[1] S.r.l. , well known worldwide for designing , producing and distributing high-end clothing , leather goods , footwear , jewelry and accessories , reported a templateYLabel[0] loss of 24.11 templateScale templateYLabel[3] in templateXValue[max] . During the period considered , this number represented the highest templateYLabel[0] loss of the Group , which registered a templateYLabel[0] profit of templateYValue[max] templateScale templateYLabel[3] in templateXValue[idxmax(Y)] . templateTitleSubject[0] roots , global reach Due to a very distinctive Mediterranean look , templateTitleSubject[1] templateTitle[5] templateTitleSubject[1] started attracting the attention of artists and famous people around the world already in the 90s .

generated_template: This statistic shows the templateYLabel[0] of the templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] stood at approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Net of the profits/loss of Italian from 2014 to 2018 .  In 2018 , the Net of Italian stood at approximately -24.11 million euros .

Example 1077:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2008', '2018']}
title: Luxury retail segment growth in the United Kingdom ( UK ) 2008 to 2018
Data:
                        0          1
Year            2013-2018  2008-2013
Luxury segment       24.8         24
All retail           11.9        7.1 

gold: This statistic presents the growth rate of the luxury retail segment compared to all retail segments in the United Kingdom ( UK ) from 2008 to 2013 and 2013 to 2018 . The luxury retail sector grew by 24 percent between 2008 to 2013 and is expected to grow at a similar rate over the following five years .
gold_template: This statistic presents the templateTitle[3] rate of the templateTitle[0] templateTitle[1] templateTitle[2] compared to templateLabel[2][0] templateTitle[1] segments in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[min] to 2013 and 2013 to templateTitleDate[max] . The templateTitle[0] templateTitle[1] sector templatePositiveTrend by templateValue[1][last] templateScale between templateTitleDate[min] to 2013 and is expected to grow at a similar rate over the following five years .

generated_template: This statistic gives information on the templateScale of templateTitle[1] of the templateTitle[0] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . During the survey period , it was found that templateTitle[7] templateValue[0][0] accounted for templateValue[1][0] templateScale of the templateTitle[0] in the templateTitleSubject[0] .
generated: This statistic gives information on the percentage of retail the Luxury in 2008 , 2018 .  During the survey period , it was found that 2008 2013-2018 accounted for 24.8 % of the Luxury in the United Kingdom .

Example 1078:
titleEntities: {'Subject': ['Amazon'], 'Date': ['2007', '2019']}
title: Amazon : quarterly net revenue 2007 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10", "Q2_'10", "Q1_'10", "Q4_'09", "Q3_'09", "Q2_'09", "Q1_'09", "Q4_'08", "Q3_'08", "Q2_'08", "Q1_'08", "Q4_'07", "Q3_'07", "Q2_'07", "Q1_'07"]
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['87.44', '69.98', '63.4', '59.7', '72.38', '56.58', '52.89', '51.04', '60.5', '43.74', '37.96', '35.71', '43.74', '32.71', '30.4', '29.13', '35.75', '25.36', '23.19', '22.72', '29.33', '20.58', '19.34', '19.74', '25.59', '17.09', '15.7', '16.07', '21.27', '13.81', '12.83', '13.19', '17.43', '10.88', '9.91', '9.86', '12.95', '7.56', '6.57', '7.13', '9.52', '5.45', '4.65', '4.89', '6.7', '4.26', '4.06', '4.14', '5.67', '3.26', '2.89', '3.02']

gold: During the fourth quarter of 2019 , Amazon generated total net sales of almost 87.44 billion U.S. dollars , up from 69.98 billion U.S. dollars in the preceding quarter . The majority of Amazon revenues were via net product sales . Amazon – additional information Launched in 1995 in the United States as an online bookshop , Amazon has grown into an international e-commerce company with separate retail websites in North America for the United States and Canada .
gold_template: During the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] generated total templateTitle[2] sales of almost templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the preceding templateXLabel[0] . The majority of templateTitleSubject[0] revenues were via templateTitle[2] product sales . templateTitleSubject[0] – additional information Launched in 1995 in the country as an online bookshop , templateTitleSubject[0] has grown into an international e-commerce company with separate retail websites in North America for the country and Canada .

generated_template: In the last measured templateXLabel[0] , The templateTitleSubject[0] Company generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] . templateTitleSubject[0] 's first fiscal templateXLabel[0] of templateTitleDate[max] was a strong one , which the templateTitleSubject[0] partially attributed to the success of its new streaming service , templateTitleSubject[0] Plus ( stylized as Disney+ ) . The templateTitleSubject[0] Company : financial dataDisney 's templateTitle[3] net income often varies wildly throughout each fiscal year , sometimes surpassing four or five templateScale templateYLabel[2] templateYLabel[3] and other times dipping below one templateScale .
generated: In the last measured Quarter , The Amazon Company generated 87.44 billion U.S. dollars in Revenue .  Amazon 's first fiscal Quarter of 2019 was a strong one , which the Amazon partially attributed to the success of its new streaming service , Amazon Plus ( stylized as Disney+ ) .  The Amazon Company : financial dataDisney 's revenue net income often varies wildly throughout each fiscal year , sometimes surpassing four or five billion U.S. dollars and other times dipping below one billion .

Example 1079:
titleEntities: {'Subject': ['Apple App Store'], 'Date': ['08', '17']}
title: Number of available apps in the Apple App Store 2008 to 2017
X_Axis['Month']: ["Jan_'17", "Jun_'16", "Jun_'15", "Jan_'15", "Sep_'14", "Jun_'14", "Oct_'13", "Jun_'13", "May_'13", "Jan_'13", "Sep_'12", "Jun_'12", "Mar_'12", "Oct_'11", "Jun_'11", "Jan_'11", "Oct_'10", "Jun_'10", "Mar_'10", "Nov_'09", "Jul_'09", "Apr_'09", "Sep_'08", "Jul_'08"]
Y_Axis['Number', 'of', 'available', 'apps']: ['2200000', '2000000', '1500000', '1400000', '1300000', '1200000', '1000000', '900000', '850000', '800000', '700000', '650000', '585000', '500000', '425000', '350000', '300000', '225000', '150000', '100000', '65000', '35000', '3000', '800']

gold: This statistic gives information on the number of available apps in the Apple App Store since July 2008 . As of January 2017 , 2.2 million mobile apps were available to download for various iOS devices . Apple App Store – additional information The Apple App Store allows users to browse and download applications to their iPhone , iPod Touch , of iPad .
gold_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] Store since 2008 . As of 2017 , templateYValue[max] templateScale mobile templateYLabel[2] were templateYLabel[1] to download for various iOS devices . templateTitleSubject[0] Store – additional information The templateTitleSubject[0] Store allows users to browse and download applications to their iPhone , iPod Touch , of iPad .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] applications in the templateTitle[0] templateTitleSubject[0] Store from 2009 to 2019 . The templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[0] templateTitleSubject[0] Store was most recently placed at templateYValue[0] templateScale templateYLabel[2] , after surpassing templateYValue[17] templateScale templateYLabel[2] in 2013 . templateTitle[0] templateTitleSubject[0] was originally launched in October 2008 under the name Android Market .
generated: This statistic shows the Number of available applications in the Number Apple App Store Store from 2009 to 2019 .  The Number of available apps in the Number Apple App Store Store was most recently placed at 2200000 million apps , after surpassing 225000 million apps in 2013 .  Number Apple App Store was originally launched in October 2008 under the name Android Market .

Example 1080:
titleEntities: {'Subject': ['Construction'], 'Date': ['2012']}
title: Construction costs for selected airports up to 2012
X_Axis['Airport']: ['Hong_Kong_(China)', 'Kansai/Osaka_(Japan)', 'Doha_(Katar)', 'Kobe_(Japan)', 'London-Heathrow_(Terminal_5_GB)', 'Berlin-Brandenburg_(Germany)']
Y_Axis['Costs', 'in', 'billion', 'euros']: ['15.9', '15.9', '8.8', '6.9', '5.6', '3.0']

gold: This statistic shows the construction costs for selected airports ( as of 2012  ) . The construction costs for the Berlin-Brandebug airport amounted to at least 3 billion euros . The costs for the airport Kobe ( Japan ) amounted to approximately 7 billion euros .
gold_template: This statistic shows the templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] ( as of templateTitleDate[0] ) . The templateTitleSubject[0] templateYLabel[0] templateTitle[2] the Berlin-Brandebug templateXLabel[0] amounted to at least templateYValue[min] templateScale templateYLabel[2] . The templateYLabel[0] templateTitle[2] the templateXLabel[0] templateXValue[3] ( Japan ) amounted to approximately templateYValue[3] templateScale templateYLabel[2] .

generated_template: The statistic presents the templateTitle[2] of people in the templateTitle[2] in templateTitleDate[0] , based on templateTitle[4] templateYLabel[0] templateYLabel[1] . According to the source , templateXValue[0] was the templateTitle[2] network , with templateYValue[max] templateScale of templateYLabel[1] templateYLabel[3] , followed by templateXValue[1] with templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the for of people in the for in 2012 , based on airports Costs billion .  According to the source , Hong_Kong_(China) was the for network , with 15.9 billion of euros , followed by Kansai/Osaka_(Japan) with 15.9 billion euros .

Example 1081:
titleEntities: {'Subject': ['California'], 'Date': ['2000', '2018']}
title: Personal income in California - income per capita 2000 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00"]
Y_Axis['Income', 'in', 'U.S.', 'dollars']: ['63557', '60156', '57739', '55758', '52324', '49277', '48798', '46170', '43634', '42044', '43890', '43629', '42088', '39279', '37369', '35415', '34214', '34043', '33364']

gold: This statistic shows the per capita personal income in California from 2000 to 2018 . In 2018 , the per capita personal income in California was 63,557 U.S. dollars . Personal income is the income that is received by persons from all sources .
gold_template: This statistic shows the templateTitle[4] templateTitle[5] templateTitle[0] templateYLabel[0] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[4] templateTitle[5] templateTitle[0] templateYLabel[0] in templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] . templateTitle[0] templateYLabel[0] is the templateYLabel[0] that is received by persons from all sources .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateYLabel[0] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[0] of templateTitle[1] templateYLabel[0] in templateTitleSubject[0] was templateYValue[min] templateScale . templateTitle[0] templateYLabel[0] is one of the world .
generated: This statistic shows the Personal of income in California from 2000 to 2018 .  In 2018 , the Personal of income in California was 33364 % .  Personal Income is one of the world .

Example 1082:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2017']}
title: Indonesia social media user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018_', '2017']
Y_Axis['Share', 'of', 'group']: ['37', '36', '35', '34', '32', '30', '28']

gold: This statistic presents the social networking reach in Indonesia from 2017 to 2023 . In 2017 , 28 percent of the population in the country accessed social media . In 2019 , this share is projected to reach 32 percent .
gold_template: This statistic presents the templateTitle[1] networking reach in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[last] , templateYValue[min] templateScale of the population in the country accessed templateTitle[1] templateTitle[2] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] templateScale .

generated_template: This statistic shows the change in templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , it is expected that templateYValue[5] templateScale of the templateTitleSubject[0] will be accessing the social network .
generated: This statistic shows the change in Indonesia from 2017 to 2023 .  In 2017 , it is expected that 30 % of the Indonesia will be accessing the social network .

Example 1083:
titleEntities: {'Subject': ['PC'], 'Date': ['2019']}
title: Global market share held by PC vendors 2019
X_Axis['Brand']: ['Lenovo', 'HP_Inc', 'Dell', 'Apple', 'Acer', 'Asus', 'Others']
Y_Axis['Share', 'of', 'shipments']: ['24.1', '22.2', '16.8', '7', '5.7', '5.5', '18.7']

gold: The overall market leader in the global personal computer market in 2019 was Lenovo with a market share of 24.1 percent with Hewlett Packard a close second with a 22.2 percent share . In terms of unit sales , Lenovo had almost 63 million shipments , while HP had around 58 million shipments . With the overall technology sector moving into a direction centered more on software and services , the PC hardware market has declined over the past years .
gold_template: The overall templateTitle[1] leader in the templateTitle[0] personal computer templateTitle[1] in templateTitleDate[0] was templateXValue[0] with a templateTitle[1] templateYLabel[0] of templateYValue[max] templateScale with Hewlett Packard a close second with a templateYValue[1] templateScale templateYLabel[0] . In terms of unit sales , templateXValue[0] had almost 63 templateScale templateYLabel[1] , while templateXValue[1] had around 58 templateScale templateYLabel[1] . With the overall technology sector moving into a direction centered more on software and services , the templateTitleSubject[0] hardware templateTitle[1] has declined over the past years .

generated_template: This statistic shows the 20 templateTitle[0] templateTitle[1] in the templateTitleSubject[0] as of 2014 , templateTitle[4] templateXLabel[0] . As of templateTitleDate[0] , templateXValue[0] was the templateTitle[0] templateTitle[1] templateXLabel[0] templateXLabel[1] templateXValue[0] with a templateYLabel[1] of templateYValue[max] templateScale .
generated: This statistic shows the 20 Global market in the PC as of 2014 , by Brand .  As of 2019 , Lenovo was the Global market Brand Lenovo with a shipments of 24.1 billion .

Example 1084:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Vietnam 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['394.89', '363.03', '334.5', '308.63', '284.85', '261.64', '241.27', '220.38', '201.33', '191.29', '185.76', '170.44', '155.48', '136.6', '112.77', '101.63', '98.27', '77.52', '66.39', '57.65', '49.52', '39.56', '35.1', '32.52', '31.18', '28.7', '27.23', '26.89', '24.69', '20.8', '16.31', '13.18', '9.87', '7.64', '6.47', '6.29', '23.23', '42.05', '33.87', '15.0', '48.18']

gold: In 2018 , Vietnam 's gross domestic product ( GDP ) amounted to around 241.27 billion U.S. dollars , and is expected to increase to almost 395 billion U.S. dollars by 2024 . Gross domestic product denotes the aggregate value of all services and goods produced within a country in any given year , and it is an important indicator of a country 's economic power . Vietnam 's economy Vietnam 's economy has a strong agrarian base , with key agricultural exports of wet rice , coffee , and black pepper .
gold_template: In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) amounted to around templateYValue[6] templateScale templateYLabel[4] templateYLabel[5] , and is expected to templatePositiveTrend to almost templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] by templateXValue[idxmax(Y)] . templateYLabel[0] templateYLabel[1] templateYLabel[2] denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] , and it is an important indicator of a country 's economic power . templateTitleSubject[0] 's economy templateTitleSubject[0] 's economy has a strong agrarian base , with key agricultural exports of wet rice , coffee , and black pepper .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Vietnam from 1984 to 2018 , with projections up until 2024 .  Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  GDP is an important indicator of a country 's economic power .

Example 1085:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Financial cyber crime losses in the U.S. 2018 , by victim state
X_Axis['State']: ['California', 'New_York', 'Texas', 'Florida', 'North_Carolina', 'Ohio', 'Illinois', 'Michigan', 'New_Jersey', 'Massachusetts']
Y_Axis['Annual', 'loss', 'in', 'million', 'U.S.', 'dollars']: ['450.48', '201.09', '195.61', '178.14', '137.23', '97.73', '82.85', '80.93', '79.71', '68.24']

gold: This statistic gives information on the U.S. states with the largest losses through reported cyber crime in 2018 . That year , California reported a loss of more than 450 million U.S. dollars through cyber crime , more than double the amount of second-placed New York , which reported 201 million U.S. dollars of losses to the IC3 .
gold_template: This statistic gives information on the templateYLabel[3] states with the largest templateTitle[3] through reported templateTitle[1] templateTitle[2] in templateTitleDate[0] . That year , templateXValue[0] reported a templateYLabel[1] of more than templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] through templateTitle[1] templateTitle[2] , more than double the amount of second-placed templateXValue[1] , which reported templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] of templateTitle[3] to the IC3 .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] in the templateTitle[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . According to the source , the templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of a templateTitle[4] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Annual of the cyber crime in the Financial in 2018 , U.S. State .  According to the source , the California had the highest Annual loss of a U.S. of 450.48 million U.S. .

Example 1086:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. motorcycle registrations by state 2017
X_Axis['State']: ['California', 'Florida', 'Ohio', 'New_York', 'Pennsylvania', 'Texas', 'Illinois', 'Wisconsin', 'Montana', 'Michigan', 'Indiana', 'Minnesota', 'Washington', 'Georgia', 'Iowa', 'Virginia', 'Colorado', 'North_Carolina', 'Massachusetts', 'Tennessee', 'Arizona', 'New_Jersey', 'Oregon', 'Missouri', 'Oklahoma', 'Maryland', 'South_Carolina', 'South_Dakota', 'Louisiana', 'Alabama', 'Kentucky', 'Kansas', 'Connecticut', 'Arkansas', 'Utah', 'New_Hampshire', 'Nevada', 'Idaho', 'West_Virginia', 'New_Mexico', 'Nebraska', 'North_Dakota', 'Maine', 'Hawaii', 'Alaska', 'Vermont', 'Rhode_Island', 'Wyoming', 'Mississippi', 'Delaware', 'Dist._of_Col.']
Y_Axis['Registrations', 'in', 'units']: ['842543', '586267', '410187', '392178', '377158', '364690', '333943', '324670', '306655', '258487', '250579', '241556', '231401', '203922', '194603', '193951', '190002', '188843', '168931', '165968', '164055', '152979', '142738', '138294', '136190', '118277', '118132', '117461', '113664', '112185', '101163', '95892', '90131', '89457', '83993', '78798', '76032', '63297', '60582', '57718', '55736', '51941', '51467', '35576', '31859', '30955', '30914', '28960', '28124', '27810', '4290']

gold: The statistic shows the number of publicly owned , private and commercial motorcycles registered in U.S. states and the District of Columbia in 2017 . In that year , there were about 27,800 registered motorcycles in Delaware . U.S. motorcycle registrations by state In 2016 , California reported the highest amount of registered private and commercial motorcycles in the country , followed by Florida , Ohio , Pennsylvania and New York ( all of them with over 390,000 registrations that year ) .
gold_template: The statistic shows the number of publicly owned , private and commercial motorcycles registered in templateTitleSubject[0] states and the District of Columbia in templateTitleDate[0] . In that year , there were about 27,800 registered motorcycles in templateXValue[49] . templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateXLabel[0] In 2016 , templateXValue[0] reported the highest amount of registered private and commercial motorcycles in the country , followed templateTitle[3] templateXValue[1] , templateXValue[2] , templateXValue[4] and templateXValue[3] ( all of them with over 390,000 templateYLabel[0] that year ) .

generated_template: This statistic represents the templateYLabel[0] templateYLabel[1] templateTitleSubject[0] templateXLabel[0] , in templateTitleDate[0] . In templateTitleDate[0] , the German templateXLabel[0] with the highest templateYLabel[0] templateYLabel[1] on templateTitle[1] and templateTitle[2] . In templateXValue[1] , the highest templateYLabel[0] templateYLabel[1] of templateTitle[0] was registered templateYValue[1] thousand .
generated: This statistic represents the Registrations units U.S. State , in 2017 .  In 2017 , the German State with the highest Registrations units on motorcycle and registrations .  In Florida , the highest Registrations units of U.S. was registered 586267 thousand .

Example 1087:
titleEntities: {'Subject': ['Profit'], 'Date': ['2018']}
title: Profit of leading mutual property/casualty insurance companies 2018
X_Axis['Insurance', 'Companies']: ['State_Farm_Insurance_Cos.', 'Auto-Owners_Insurance', 'Erie_Insurance_Group', 'Nationwide', 'Sentry_Insurance_Group', 'Country_Financial', 'Amica_Mutual_Insurance', 'Farmers_Insurance_Exchange']
Y_Axis['Profit', 'in', 'million', 'U.S.', 'dollars']: ['8788.4', '754.5', '686.3', '512.6', '250.6', '215.2', '147.1', '-70.5']

gold: This statistic displays the profits of leading mutual property and casualty insurance companies in the United States in 2018 . In that year , the profits of Nationwide amounted to approximately 512.6 million U.S. dollars .
gold_template: This statistic displays the profits of templateTitle[1] templateXValue[6] property and casualty templateXValue[0] templateXLabel[1] in the country in templateTitleDate[0] . In that year , the profits of templateXValue[3] amounted to approximately templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] of the templateXValue[3] with the largest templateYLabel[0] in the world in templateTitleDate[0] . According to the survey , it was that the templateXValue[0] accounted for templateYValue[max] templateScale of all people .
generated: This statistic shows the Profit of the Nationwide with the largest Profit in the world in 2018 .  According to the survey , it was that the State_Farm_Insurance_Cos. accounted for 8788.4 million of all people .

Example 1088:
titleEntities: {'Subject': ['Carnival Corporation plc'], 'Date': ['2007', '2019']}
title: Number of Carnival Corporation & plc passengers worldwide 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'passengers', 'in', 'millions']: ['12.9', '12.4', '12.1', '11.52', '10.84', '10.57', '10.06', '9.83', '9.56', '9.15', '8.52', '8.18', '7.67']

gold: This statistic shows the number of Carnival Corporation & plc passengers worldwide from 2007 to 2019 . In 2019 , the cruise company Carnival Corporation carried approximately 12.9 million passengers on its ships ..
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] & templateTitleSubject[0] templateYLabel[1] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the cruise company templateTitleSubject[0] carried approximately templateYValue[max] templateYValue[idxmax(X)] templateYLabel[1] on its ships ..

generated_template: In templateXValue[max] , the total templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] amounted to approximately templateYValue[max] templateScale . This figure has templatePositiveTrend over the last few years , when the templateTitle[3] templateYLabel[0] of templateYLabel[1] occurred in the previous templateXLabel[0] . The average templateYLabel[1] of templateTitleSubject[0] is one of the most populous region in the world .
generated: In 2019 , the total Number of passengers in Carnival Corporation plc amounted to approximately 12.9 millions .  This figure has increased over the last few years , when the & Number of passengers occurred in the previous Year .  The average passengers of Carnival Corporation plc is one of the most populous region in the world .

Example 1089:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2009', '2018']}
title: Devices used to play video games in the United Kingdom ( UK ) 2009 to 2018
Data:
                           0     1     2     3     4     5     6     7     8     9
Year                    2018  2017  2016  2015  2014  2013  2012  2011  2010  2009
Games console and TV      17    15    15    16    17    24    25    27    24    27
Computer                  12     9    11    14    16    17    15    13    11    14
Mobile phone              22    21    19    26    26    19    13    12     9     6
Handheld games console     6     5     5     6     7    12    12    12    13    16
Tablet                    11    12    12    13    15    10     5     2     1     0
Portable media player      0     0     0     0     0     3     3     2     2     1
Smart TV                   2     2     1     1     1     1     0     0     0     0 

gold: In an Ofcom survey looking into the use and attitudes towards media usage of UK adults , a total of 62 percent of respondents indicated that they played some form of game . Since 2014 , the popularity of mobile gaming overtook games consoles and TV for the first time . As of 2018 , 22 percent of respondents said that they played games in the home or elsewhere on their mobile phone .
gold_template: In an Ofcom survey looking into the use and attitudes towards templateLabel[6][1] usage of templateTitleSubject[1] adults , a total of 62 templateScale of respondents indicated that they played some form of game . Since templateValue[0][4] , the popularity of templateLabel[3][0] gaming overtook templateTitle[4] consoles and templateLabel[1][2] for the first time . As of templateValue[0][0] , templateValue[3][0] templateScale of respondents said that they played templateTitle[4] in the home or elsewhere on their templateLabel[3][0] templateLabel[3][1] .

generated_template: This statistic shows the results of a survey conducted irregularly between templateValue[0][0] and templateValue[0][last] templateValue[0][last] , templateTitle[8] templateTitle[9] . In templateValue[0][0] , the average of templateValue[1][last] templateScale of templateLabel[1][0] templateLabel[1][1] were templateLabel[1][0] templateLabel[1][1] templateTitle[7] templateTitle[8] in the templateTitle[8] group .
generated: This statistic shows the results of a survey conducted irregularly between 2018 and 2009 , 2018 .  In 2018 , the average of 27 % of Games console were Games console UK 2009 in the 2009 group .

Example 1090:
titleEntities: {'Subject': ['Groupon'], 'Date': ['2017', '2019']}
title: Groupon : TTM gross profit per average active customer 2017 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17"]
Y_Axis['TTM', 'gross', 'billings', 'per', 'average', 'active', 'customer', 'in', 'U.S.', 'dollars']: ['27.19', '27.45', '27.51', '27.59', '27.42', '27.51', '27.27', '27.16', '26.93', '26.43']

gold: In the fourth quarter of 2019 , the Groupon 's average TTM gross profit per active customer were 27.19 US dollars , down from 27.42 US dollars in the corresponding quarter of the previous year . As of the fourth quarter of 2019 , Groupon had 43.6 million active customers worldwide .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[max] , the templateTitleSubject[0] 's templateYLabel[4] templateYLabel[0] templateYLabel[1] templateTitle[3] templateYLabel[3] templateYLabel[5] templateYLabel[6] were templateYValue[0] US templateYLabel[8] , down from templateYValue[4] US templateYLabel[8] in the corresponding templateXLabel[0] of the previous year . As of the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] had 43.6 templateScale templateYLabel[5] customers worldwide .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] employees from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the fourth templateXLabel[0] of templateTitleDate[max] , the social network templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the TTM gross of Groupon employees from the first Quarter of 2017 to the fourth Quarter of 2019 .  In the fourth Quarter of 2019 , the social network Groupon 's TTM gross amounted to 27.59 billings per .

Example 1091:
titleEntities: {'Subject': ['Europe'], 'Date': ['12', '19']}
title: Europe : semiconductor sales 2012 - 2019 , by month
X_Axis['Month']: ["Jan._'12", "Febr._'12", "Mar._'12", "Apr_'12", "May_'12", "Jun._'12", "Jul._'12", "Aug_'12", "Sep_'12", "Oct_'12", "Nov_'12", "Dec_'12", "Jan_'13", "Feb_'13", "Mar_'13", "Apr_'13", "May_'13", "Jun_'13", "Jul_'13", "Aug_'13", "Sep_'13", "Oct_'13", "Nov_'13", "Dec_'13", "Jan_'14", "Feb_'14", "Mar_'14", "Apr_'14", "May_'14", "Jun_'14", "Jul_'!4", "Aug_'14", "Sep_'14", "Oct_'14", "Nov_'14", "Dec_'14", "Jan_'15", "Feb_'15", "Mar_'15", "Apr_'15", "May_'15", "Jun_'15", "Jul_'15", "Aug_'15", "Sep_'15", "Oct_'15", "Nov_'15", "Dec_'15", "Jan_'16", "Feb_'16", "Mar_'16", "Apr_'16", "May_'16", "Jun_'16", "Jul_'16", "Aug_'16", "Sep_'16", "Oct_'16", "Nov_'16", "Dec_'16", "Jan_'17", "Feb_'17", "Mar_'17", "Apr_'17", "May_'17", "Jun_'17", "Jul_'17", "Aug_'17", "Sep_'17", "Oct_'17", "Nov_'17", "Dec_'17", "Jan_'18", "Feb_'18", "Mar_'18", "Apr_'18", "May_'18", "Jun_'18", "Jul_'18", "Aug_'18", "Sep_'18", "Oct_'18", "Nov_'18", "Dec_'18", "Jan_'19", "Feb_'19", "Mar_'19", "Apr_'19", "May_'19", "Jun_'19", "Jul_'19", "Aug_'19", "Sep_'19", "Oct_'19", "Nov_'19", "Dec_'19"]
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['2.77', '2.71', '2.83', '2.82', '2.84', '2.82', '2.82', '2.77', '2.79', '2.79', '2.8', '2.63', '2.65', '2.69', '2.84', '2.83', '2.84', '2.84', '2.85', '2.91', '2.98', '3.05', '3.08', '2.96', '2.95', '2.96', '3.08', '3.06', '3.13', '3.18', '3.27', '3.22', '3.21', '3.21', '3.19', '3.01', '2.95', '2.88', '2.96', '2.89', '2.87', '2.83', '2.63', '2.81', '2.87', '2.91', '2.93', '2.77', '2.72', '2.66', '2.67', '2.64', '2.63', '2.68', '2.7', '2.71', '2.76', '2.82', '2.88', '2.8', '2.84', '2.82', '2.96', '2.99', '3.11', '3.16', '3.2', '3.22', '3.28', '3.36', '3.42', '3.37', '3.41', '3.44', '3.6', '3.65', '3.7', '3.67', '3.58', '3.53', '3.57', '3.64', '3.64', '3.47', '3.42', '3.34', '3.36', '3.37', '3.35', '3.27', '3.28', '3.27', '3.36', '3.37', '3.35', '3.2']

gold: As per recent data , in December 2019 , semiconductor sales in Europe amounted to 3.2 billion U.S. dollars , a fall from the 3.35 billion U.S. dollars seen in the previous month . Semiconductor sales in Europe in December 2019 were also down on the figures seen in the same month of the previous year where 3.47 billion U.S. dollars worth were sold .
gold_template: As per recent data , in 2019 , templateTitle[1] templateYLabel[0] in templateTitleSubject[0] amounted to templateYValue[29] templateScale templateYLabel[2] templateYLabel[3] , a fall from the templateYValue[88] templateScale templateYLabel[2] templateYLabel[3] seen in the previous templateXLabel[0] . templateTitle[1] templateYLabel[0] in templateTitleSubject[0] in 2019 were also down on the figures seen in the same templateXLabel[0] of the previous year where templateYValue[83] templateScale templateYLabel[2] templateYLabel[3] worth were sold .

generated_template: This graph depicts the templateYLabel[0] of users of the templateTitle[0] templateTitleSubject[0] and templateTitleSubject[1] from templateTitleDate[min] to 2019 . The templateTitle[0] templateTitleSubject[0] templateYLabel[0] of templateYLabel[1] amounted to templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in 2019 .
generated: This graph depicts the Sales of users the Europe and from 12 to 2019 .  The Europe Europe Sales of billion amounted to 2.77 billion U.S. dollars in 2019 .

Example 1092:
titleEntities: {'Subject': ['Pandora'], 'Date': ['2006', '2017']}
title: Pandora 's advertising revenue 2006 to 2017
X_Axis['Month']: ['Jan_to_Dec_2017', 'Jan_to_Dec_2016', 'Jan_to_Dec_2015', 'Jan_to_Dec_2014', 'Feb_to_Dec_2013', 'Feb_2012_to_Jan_2013', 'Feb_2011_to_Jan_2012', 'Feb_2010_to_Jan_2011', 'Feb_2009_to_Jan_2010', 'Feb_2008_to_Jan_2009', 'Feb_2007_to_Jan_2008', 'Feb_2006_to_Jan_2007']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['1074.93', '1072.49', '933.31', '732.34', '489.34', '375.22', '239.96', '119.33', '50.15', '18.25', '13.31', '3.25']

gold: The statistic shows data on the advertising revenue generated by Pandora Media , Inc. in the fiscal periods between February 2006 and December 2017 . In the fiscal year which ended December 31 , 2016 , Pandora generated 1.07 billion U.S. dollars in advertising revenue , the first time that the revenue has exceeded one billion U.S. dollars .
gold_template: The statistic shows data on the templateTitle[2] templateYLabel[0] generated by templateTitleSubject[0] Media , Inc. in the fiscal periods between 2006 and 2017 . In the fiscal year which ended 31 , templateXValue[1] , templateTitleSubject[0] generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateTitle[2] templateYLabel[0] , the first time that the templateYLabel[0] has exceeded templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic displays the templateYLabel[0] of the templateTitle[1] templateTitle[2] and templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[7] from templateTitleDate[min] to templateTitleDate[max] . The templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] of the templateTitleSubject[0] templateTitle[4] amounted to templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: The statistic displays the Revenue of the 's advertising and 2006 2017 in the 2017 from 2006 to 2017 .  The Pandora 's advertising Revenue of the Pandora 2006 amounted to 1074.93 million U.S. dollars in Feb_2012_to_Jan_2013 .

Example 1093:
titleEntities: {'Subject': ['Election'], 'Date': ['2012']}
title: Election 2012 exit polls : percentage of votes by income
Data:
                        0               1              2               3
Year          Under_30000  30000_to_49999  50000_or_more  100000_or_more
Barack Obama           63              57             45              44
Mitt Romney            35              42             53              54 

gold: This graph shows the percentage of votes of the 2012 presidential elections in the United States on November 6 , 2012 , by income . According to the exit polls , about 63 percent of voters with an annual income of less than 30,000 U.S. dollars nationwide have voted for Barack Obama .
gold_template: This graph shows the templateScale of templateTitle[5] of the templateTitleDate[0] presidential elections in the country on 6 , templateTitleDate[0] , templateTitle[6] templateTitle[7] . According to the templateTitle[2] templateTitle[3] , about templateValue[1][0] templateScale of voters with an annual templateTitle[7] of less than templateValue[0][0] U.S. dollars nationwide have voted for templateLabel[1][0] templateLabel[1][1] .

generated_template: This graph shows the templateScale of templateTitle[5] of the templateTitleDate[0] presidential elections in the country on 6 , templateTitleDate[0] , templateTitle[6] templateTitle[7] . According to the templateTitle[2] templateTitle[3] , about templateValue[1][0] templateScale of 18 to 29 year-old voters nationwide have voted for templateLabel[1][0] templateLabel[1][1] .
generated: This graph shows the percentage of votes the 2012 presidential elections in the country on 6 , 2012 by income .  According to the exit polls , about 63 percentage of 18 to 29 year-old voters nationwide have voted for Barack Obama .

Example 1094:
titleEntities: {'Subject': ['Mavericks'], 'Date': ['2010', '2016']}
title: Dallas Mavericks concession stand prices ( beer , soft drink , hot dog ) 2010 to 2016
Data:
                  0        1        2        3        4        5
Year        2015/16  2014/15  2013/14  2012/13  2011/12  2010/11
Beer              5        6        6        6        6        6
Soft drink        3        4        4        4        4        4
Hot dog         3.5      4.0      4.0      4.0      4.0      4.0 

gold: The statistic depicts the concession stand prices at games of the Dallas Mavericks ( NBA ) from 2010/11 to 2015/16 . In the 2012/13 season , a 18 ounce beer cost six U.S. dollars .
gold_template: The statistic depicts the templateTitle[2] templateTitle[3] templateTitle[4] at games of the templateTitle[0] templateTitleSubject[0] ( NBA ) from templateValue[0][last] to templateValue[0][0] . In the templateValue[0][3] season , a 18 ounce templateTitle[5] cost templateValue[1][last] U.S. dollars .

generated_template: The statistic depicts the templateTitle[2] templateTitle[3] templateTitle[4] at games of the templateTitleSubject[0] Thunder ( NBA ) from templateValue[0][last] to templateValue[0][0] . In the templateValue[0][3] season , a 16 ounce templateTitle[5] cost templateValue[1][last] U.S. dollars .
generated: The statistic depicts the concession stand prices at games of the Mavericks Thunder ( NBA ) from 2010/11 to 2015/16 .  In the 2012/13 season , a 16 ounce beer cost 6 U.S. dollars .

Example 1095:
titleEntities: {'Subject': ['Luxottica'], 'Date': ['2018']}
title: Share of global net sales of Luxottica by geographical area 2018
X_Axis['Geographical', 'area']: ['North_America', 'Europe', 'Asia-Pacific', 'Latin_America', 'Rest_of_the_world']
Y_Axis['Share', 'of', 'net', 'sales']: ['58', '21', '13', '6', '2']

gold: This statistic depicts the share of net sales of Luxottica worldwide in 2018 , by geographical area . In that year , 58 percent of Luxottica 's global net sales came from North America . Founded in 1961 in Agordo , Italy , the Luxottica Group S.p.A. is the world 's largest eyewear company .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] worldwide in templateTitleDate[0] , templateTitle[5] templateXLabel[0] templateXLabel[1] . In that year , templateYValue[max] templateScale of templateTitleSubject[0] 's templateTitle[1] templateYLabel[1] templateYLabel[2] came from templateXValue[0] . Founded in 1961 in Agordo , Italy , the templateTitleSubject[0] Group S.p.A. is the templateXValue[last] 's largest eyewear company .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] . In this year , the templateYLabel[0] templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] with a total of templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic shows the Share of the global net sales Luxottica in the United Kingdom ( Luxottica ) in 2018 .  In this year , the Share net of the global net sales with a total of 58 million sales .

Example 1096:
titleEntities: {'Subject': ['Angola'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Angola 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['3.78', '4.13', '3.33', '2.87', '1.15', '-0.27', '-1.2', '-0.15', '-2.58', '0.94', '4.82']

gold: The statistic shows the growth in real GDP in Angola from 2014 to 2018 , with projections up until 2024 . In 2018 , Angola 's real gross domestic product fell by around 1.2 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templateNegativeTrend by around templateYValue[4] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Angola from 2014 to 2018 , with projections up until 2024 .  In 2018 , Angola 's real Gross domestic product increased by around -1.2 % compared to the previous Year .

Example 1097:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017', '2018']}
title: Countries with highest ransomware infection rates 2017 to 2018
X_Axis['Country']: ['Thailand', 'United_Arab_Emirates', 'Iran', 'Bangladesh', 'Vietnam', 'Saudi_Arabia', 'China', 'India', 'Algeria', 'Turkey']
Y_Axis['Share', 'of', 'users', 'attacked', 'with', 'ransomware', 'out', 'of', 'all', 'users', 'encountering', 'malware']: ['9.57', '8.67', '8.47', '7.62', '6.17', '5.45', '5.36', '4.28', '3.59', '3.22']

gold: This statistic shows the countries with highest ransomware infection rates from 2017 to 2018 . In the measured period , Thailand had the biggest share of users attacked with ransomware out of all users encountering malware - 9.57 percent .
gold_template: This statistic shows the templateTitleSubject[0] templateYLabel[3] templateTitle[2] templateYLabel[4] templateTitle[4] templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . In the measured period , templateXValue[0] had the biggest templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of templateYLabel[6] templateYLabel[1] templateYLabel[8] templateYLabel[9] - templateYValue[max] templateScale .

generated_template: This statistic shows the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] in templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] templateYLabel[0] templateYLabel[1] . In that year , the templateXValue[0] was ranked first with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] of templateYLabel[4] .
generated: This statistic shows the 20 Countries highest the ransomware infection in 2017 , sorted 2017 2018 Share users .  In that year , the Thailand was ranked first with a Share users of 9.57 attacked ransomware of out .

Example 1098:
titleEntities: {'Subject': ['NHL', 'Canada'], 'Date': ['2014']}
title: Fans of NHL hockey in Canada 2014 , by age group
Data:
          0            1            2            3            4          5
Year  Total  18-34_years  35-44_years  45-54_years  55-64_years  65+_years
Yes      53           60           49           57           48         47
No       47           40           51           43           52         53 

gold: The statistic shows the results of a survey which asked people in Canada if they consider themselves NHL hockey fans . During the survey , 60 percent of the respondents aged 18 - 34 years said that they are NHL hockey fans .
gold_template: The statistic shows the results of a survey which asked people in templateTitleSubject[1] if they consider themselves templateTitleSubject[0] templateTitle[2] templateTitle[0] . During the survey , templateValue[1][1] templateScale of the respondents aged 18 - 34 templateValue[0][1] said that they are templateTitleSubject[0] templateTitle[2] templateTitle[0] .

generated_template: This statistic shows the templateScale of templateTitle[1] in the templateTitleSubject[0] templateTitle[2] templateTitle[3] ever templateTitle[4] templateTitle[5] or templateTitle[6] templateTitle[7] templateTitle[8] as of templateTitleDate[0] , templateTitle[10] templateTitle[11] . It was found that templateValue[1][0] templateScale of those aged 18 - 29 templateValue[0][0] templateTitle[3] templateTitle[4] templateTitle[5] or templateTitle[7] templateTitle[8] .
generated: This statistic shows the percentage of NHL in the NHL hockey Canada ever 2014 by or age group as of 2014 , group .  It was found that 53 % of those aged 18 - 29 Total Canada 2014 by or group .

Example 1099:
titleEntities: {'Subject': ['National Hockey League'], 'Date': ['2005', '2019']}
title: National Hockey League - Florida Panthers home attendance 2005 to 2019
Data:
                          0        1        2        3        4        5        6        7        8        9       10       11       12       13
Year                2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09  2007/08  2006/07  2005/06
Total attendance     543734   567897   599447   630746   461877   581286   407806   681763   643116   620986   640496   632881   630183   656587
Average attendance    13261    13851    14620    15384    11265    14177    16991    16628    15685    15146    15621    15436    15370    16014 

gold: This graph depicts the total/average regular season home attendance of the Florida Panthers franchise of the National Hockey League from the 2005/06 season to the 2018/19 season . In 2018/19 , the total regular season home attendance of the franchise was 543,734 .
gold_template: This graph depicts the total/average regular season templateTitle[5] templateTitle[6] of the templateTitle[3] templateTitle[4] franchise of the templateTitleSubject[0] League from the templateValue[0][last] season to the templateValue[0][0] season . In templateValue[0][0] , the templateLabel[1][0] regular season templateTitle[5] templateTitle[6] of the franchise was templateValue[1][0] .

generated_template: This graph depicts the total/average regular season templateTitle[5] templateTitle[6] of the templateTitleSubject[0] templateTitle[4] franchise of the templateTitleSubject[0] League from the templateValue[0][last] season to the templateValue[0][0] season . In templateValue[0][0] , the templateLabel[1][0] regular season templateTitle[5] templateTitle[6] of the franchise was templateValue[1][0] .
generated: This graph depicts the total/average regular season home attendance of the National Hockey League Panthers franchise of the National Hockey League League from the 2005/06 season to the 2018/19 season .  In 2018/19 , the Total regular season home attendance of the franchise was 543734 .

Example 1100:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2014', '2019']}
title: Monthly number of rain days in the United Kingdom ( UK ) 2014 to 2019
X_Axis['Month']: ["Dec_'19", "Nov_'91", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sep_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17", "Dec_'16", "Nov_'16", "Oct_'16", "Sep_'16", "Aug_'16", "Jul_'16", "Jun_'16", "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16", "Dec_'15", "Nov_'15", "Oct_'15", "Sep_'15", "Aug_'15", "Jul_'15", "Jun_'15", "May_'15", "Apr_'15", "Mar_'15", "Feb_'15", "Jan_'15", "Dec_'14", "Nov_'14", "Oct_'14", "Sep_'14", "Aug_'14", "Jul_'14", "Jun_'14", "May_'14", "Apr_'14", "Mar_'14", "Feb_'14", "Jan_'14"]
Y_Axis['Number', 'of', 'days']: ['17.6', '17.5', '17.4', '15.4', '15.7', '11.0', '14.2', '10.8', '8.0', '16.2', '10.5', '11.0', '15.9', '14.7', '11.9', '12.7', '13.2', '7.5', '5.0', '7.9', '13.5', '15.5', '11.6', '18.0', '16.9', '15.5', '13.9', '17.7', '14.5', '14.7', '12.9', '9.7', '7.4', '14.6', '13.9', '12.5', '11.3', '13.0', '8.5', '13.6', '11.5', '13.0', '14.3', '9.6', '15.2', '12.0', '14.0', '20.6', '22.3', '20.8', '10.3', '8.5', '14.2', '15.9', '9.2', '15.4', '8.9', '13.0', '11.3', '18.8', '15.7', '16.2', '17.9', '4.9', '16.6', '10.1', '9.4', '15.2', '11.2', '12.5', '20.1', '23.4']

gold: Between January 2014 and December 2019 , the lowest number of monthly raindays occurred in September 2014 , when just 5 days recorded 1mm or more of rain . The highest number of raindays was recorded in January of the same year , at 23.4 . Fourth quarter the wettest The wettest periods of the year tend to be the start and the end .
gold_template: Between 2014 and 2019 , the lowest templateYLabel[0] of templateTitle[0] raindays occurred in 2014 , when just templateYValue[18] templateYLabel[1] recorded 1mm or more of templateTitle[2] . The highest templateYLabel[0] of raindays was recorded in of the same year , at templateYValue[max] . Fourth quarter the wettest The wettest periods of the year tend to be the start and the end .

generated_template: This statistic displays the templateTitle[0] templateTitle[1] templateTitle[2] the average templateYLabel[0] of templateXLabel[0] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from October templateTitleDate[min] to 2019 . As of 2019 , the templateTitleSubject[1] had an average of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic displays the Monthly number rain the average Number of Month days in the United Kingdom ( UK ) from October 2014 to 2019 .  As of 2019 , the UK had an average of 23.4 million days .

Example 1101:
titleEntities: {'Subject': ['IKEA'], 'Date': ['2018']}
title: Leading 5 selling countries of IKEA products 2018
X_Axis['Country']: ['Germany', 'United_States', 'France', 'United_Kingdom', 'China']
Y_Axis['Sales', 'share']: ['15', '13', '8', '6', '6']

gold: This statistic depicts the leading five selling countries of IKEA products in 2018 . In that year , Germany was the leading selling country of IKEA products , with 15 percent of IKEA 's sales coming from Germany.IKEAIKEA is an internationally known home furnishing retailer . It has grown rapidly since it was founded in 1943 .
gold_template: This statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[2] templateXLabel[0] of templateTitleSubject[0] templateTitle[5] , with templateYValue[max] templateScale of templateTitleSubject[0] 's templateYLabel[0] coming from Germany.IKEAIKEA is an internationally known home furnishing retailer . It has grown rapidly since it was founded in 1943 .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] in templateTitleDate[0] , templateTitle[3] templateTitle[4] . In that year , the templateXValue[0] was the templateTitleSubject[0] people , with a total of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Sales share of the Leading in 2018 , countries IKEA .  In that year , the Germany was the IKEA people , with a total of 15 share .

Example 1102:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2019']}
title: Number of lobbyists in the U.S. 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Number', 'of', 'lobbyists']: ['11862', '11656', '11554', '11188', '11519', '11789', '12082', '12171', '12607', '12912', '13719', '14132', '14825', '14488', '14093', '13215', '12960', '12149', '11853', '12540']

gold: This statistic shows the number of registered active lobbyists in the United States from 2000 to 2019 . In 2019 , the total number of unique , registered lobbyists who have actively lobbied amounted to 11,862 .
gold_template: This statistic shows the templateYLabel[0] of registered active templateYLabel[1] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the total templateYLabel[0] of unique , registered templateYLabel[1] who have actively lobbied amounted to templateYValue[idxmax(X)] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of the templateTitle[0] of templateYLabel[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . According to the source , the templateTitle[0] of the templateTitleSubject[0] was templateYValue[idxmin(X)] templateYLabel[2] templateYLabel[3] . In templateXValue[max] , the templateYLabel[0] templateNegativeTrend to templateYValue[idxmax(X)] templateYValue[idxmax(X)] templateYLabel[3] .
generated: This statistic shows the Number of lobbyists the Number of lobbyists 2019 from 2000 to 2019 .  According to the source , the Number of the U.S. was 12540 lobbyists .  In 2019 , the Number dropped to 11862 lobbyists .

Example 1103:
titleEntities: {'Subject': ['Croatian'], 'Date': []}
title: Leading Croatian national team players at FIFA World Cup 2018 , by market value
X_Axis['Month']: ['Ivan_Rakitic', 'Ivan_Perisic', 'Mateo_Kovacic', 'Andrej_Kramaric', 'Marcelo_Brozovic', 'Luka_Modric', 'Sime_Vrsaljko', 'Dejan_Lovren', 'Mario_Mandzukic', 'Milan_Badelj', 'Marko_Pjaca', 'Nikola_Kalinic', 'Ante_Rebic', 'Duje_Caleta–Car', 'Domagoj_Vida', 'Lovre_Kalinic', 'Tin_Jedvaj', 'Danijel_Subasic', 'Vedran_Corluka', 'Ivan_Strinic', 'Filip_Bradaric', 'Josip_Pivaric', 'Dominik_Livakovic']
Y_Axis['Market', 'value', 'in', 'million', 'euros']: ['50.0', '40.0', '30.0', '27.0', '27.0', '25.0', '25.0', '20.0', '18.0', '15.0', '15.0', '14.0', '10.0', '10.0', '7.0', '6.5', '5.0', '4.5', '4.0', '4.0', '3.5', '2.0', '1.5']

gold: The statistic displays the leading players of the national football team of Croatia at FIFA World Cup as of June 2018 , by market value . The most valuable player was Ivan Rakitic , with a market value of 50 million euros .
gold_template: The statistic displays the templateTitle[0] templateTitle[4] of the templateTitle[2] football templateTitle[3] of Croatia at templateTitle[5] templateTitle[6] templateTitle[7] as of 2018 , templateTitle[9] templateYLabel[0] templateYLabel[1] . The most valuable player was templateXValue[0] , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] .

generated_template: As of 2019 , templateXValue[0] Amsterdam had the highest templateYLabel[0] of about templateYValue[max] templateScale templateYLabel[3] . templateYValue[1] templateScale templateYLabel[3] of the compared to the templateXValue[1] before templateXValue[2] with a total templateYLabel[0] templateYLabel[1] of templateYValue[1] templateScale templateYLabel[3] .
generated: As of 2019 , Ivan_Rakitic Amsterdam had the highest Market of about 50.0 million euros .  40.0 million euros of the compared to the Ivan_Perisic before Mateo_Kovacic with a total Market value of 40.0 million euros .

Example 1104:
titleEntities: {'Subject': ['Manchester'], 'Date': ['2009', '2019']}
title: Manchester United total revenue 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['796.4', '775.96', '764.36', '677.76', '519.75', '569.72', '477.65', '421.27', '435.89', '376.68', '366.24']

gold: The statistic depicts the revenue of the Premier League club Manchester United from 2009 to 2019 . In the fiscal year 2019 , Manchester United generated around 796.4 million U.S. dollars in revenue .
gold_template: The statistic depicts the templateYLabel[0] of the Premier League club templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In the fiscal templateXLabel[0] templateXValue[max] , templateTitleSubject[0] templateTitle[1] generated around templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] .

generated_template: The statistic presents the templateYLabel[0] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[4] templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic presents the Revenue of the Manchester worldwide from 2009 to 2019 .  In 2019 , the 2009 Revenue of the Manchester amounted to 796.4 million U.S. dollars .

Example 1105:
titleEntities: {'Subject': ['Fiji'], 'Date': ['2019']}
title: Unemployment rate in Fiji 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['4.15', '4.15', '4.14', '4.32', '4.55', '4.53', '4.36', '4.52', '4.41', '4.35', '4.16', '3.62', '3.76', '3.83', '3.89', '4.06', '4.07', '4.16', '4.15', '4.15', '4.12']

gold: This statistic shows the unemployment rate in Fiji from 1999 to 2019 . In 2019 , the unemployment rate in Fiji was at approximately 4.15 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Fiji from 1999 to 2019 .  In 2019 , the Unemployment rate in Fiji was at approximately 4.15 % .

Example 1106:
titleEntities: {'Subject': ['Syrian'], 'Date': ['2018']}
title: Syrian refugees - major hosting countries worldwide in 2018
X_Axis['Country']: ['Turkey', 'Lebanon', 'Jordan', 'Germany', 'Iraq', 'Egypt', 'Sweden', 'Sudan', 'Austria', 'Netherlands']
Y_Axis['Number', 'of', 'admitted', 'Syrian', 'refugees']: ['3622366', '944181', '676283', '532065', '252526', '132871', '109343', '93502', '49179', '32092']

gold: The statistics shows the ten largest Syrian refugee-hosting countries as of 2018 . The data refers to the total number of Syrian refugees in a given country , not considering the date of their application for asylum or the date of their flight . At the end of 2018 , about 3.62 million Syrian refugees were living in Turkey .
gold_template: The statistics shows the ten largest templateYLabel[2] refugee-hosting templateTitle[4] as of templateTitleDate[0] . The data refers to the total templateYLabel[0] of templateYLabel[2] templateYLabel[3] in a given templateXLabel[0] , not considering the date of their application for asylum or the date of their flight . At the end of templateTitleDate[0] , about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] were living in templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] in templateTitleDate[0] . templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] , with nearly templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number of the refugees major hosting countries worldwide 2018 in .  Turkey had the highest Number of admitted , with nearly 3622366 Syrian refugees .

Example 1107:
titleEntities: {'Subject': ['Major League Baseball'], 'Date': ['2010', '2019']}
title: Major League Baseball : average concession stand prices 2010 to 2019
Data:
               0     1     2     3     4     5     6     7     8
Year        2019  2018  2016  2015  2014  2013  2012  2011  2010
Beer        5.97  5.98   5.9  5.98  6.09  6.09  6.16  5.81  5.79
Soft drink   4.6  4.65  4.19  4.07  4.02  3.67  3.71  3.59  3.47
Hot dog     4.95  5.01  4.52  4.39  4.32  4.14  4.13  3.88  3.79 

gold: The timeline depicts the average price of soft drinks , beers and hot dogs in Major League Baseball from 2010 to 2019 . The average price of a hot dog was 4.95 U.S. dollars in 2019 . Major League Baseball concession prices – additional information Hot dogs , soft drink and beer are food and beverage staples at Major League Baseball games .
gold_template: The timeline depicts the templateTitle[3] price of templateLabel[2][0] drinks , beers and templateLabel[3][0] dogs in templateTitleSubject[0] Baseball from templateValue[0][last] to templateValue[0][0] . The templateTitle[3] price of a templateLabel[3][0] templateLabel[3][1] was templateValue[3][0] U.S. dollars in templateValue[0][0] . templateTitleSubject[0] Baseball templateTitle[4] templateTitle[6] – additional information templateLabel[3][0] dogs , templateLabel[2][0] templateLabel[2][1] and templateLabel[1][0] are food and beverage staples at templateTitleSubject[0] Baseball games .

generated_template: The statistic depicts the templateTitle[2] templateTitle[3] templateTitle[4] at games of the templateTitleSubject[0] ( NBA ) from templateValue[0][last] to templateValue[0][0] . In the templateValue[0][3] season , a 16 ounce templateTitle[5] cost templateValue[1][3] U.S. dollars .
generated: The statistic depicts the Baseball average concession at games of the Major League Baseball ( NBA ) from 2010 to 2019 .  In the 2015 season , a 16 ounce stand cost 5.98 U.S. dollars .

Example 1108:
titleEntities: {'Subject': ['Spain'], 'Date': ['2024']}
title: Government revenue and spending in Spain 2024
Data:
               0       1       2       3       4       5       6       7       8       9      10
Year        2024    2023    2022    2021    2020    2019    2018    2017    2016    2015    2014
Revenue   571.76  553.49  536.22  519.18  503.69  487.28  469.54  442.22  422.16  415.74  403.48
Spending  599.51  580.11  561.98  544.03   527.6  514.19  499.52  478.13  472.16  472.74  465.42 

gold: This statistic shows the government revenue and spending in Spain from 2014 to 2018 , with projections up until 2024 . In 2018 , government revenue in Spain amounted to around 469.54 billion euros , while government spending came to around 499.52 billion euros . The Spanish economy The Spanish economy not only ranked as one of the largest economies in Europe , but also in the world .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] and templateTitle[2] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][6] , with projections up until templateValue[0][0] . In templateValue[0][6] , templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to around templateValue[1][6] templateScale euros , while templateTitle[0] templateTitle[2] came to around templateValue[2][6] templateScale euros . The Spanish economy The Spanish economy not only ranked as one of the largest economies in Europe , but also in the world .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] and templateTitle[2] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][6] , with projections up until templateValue[0][0] . According to the source , templateTitle[0] templateTitle[1] in the templateTitleSubject[0] amounted to around templateValue[1][0] templateScale US dollars .
generated: This statistic shows the Government revenue and spending in the Spain from 2014 to 2018 , with projections up until 2024 .  According to the source , Government revenue in the Spain amounted to around 571.76 million US dollars .

Example 1109:
titleEntities: {'Subject': ['Twitch H1'], 'Date': ['2019']}
title: Leading gaming content on Twitch H1 2019 , by hours watched
X_Axis['Games']: ['League_of_Legends', 'Fortnite', 'Just_Chatting', 'Grand_Theft_Auto_V', 'Dota_2', 'Apex_Legends', 'Counter-Strike:_Global_Offensive', 'Overwatch', 'Hearthstone', 'World_of_Warcraft']
Y_Axis['Number', 'of', 'hours', 'watched']: ['512.3', '465.0', '372.5', '269.1', '237.1', '181.4', '178.0', '127.3', '120.7', '118.5']

gold: The statistic presents the leading gaming content on Twitch from January 1 , 2019 to June 30 , 2019 , ranked by number of hours watched . League of Legends topped the charts with a combined 512.3 million hours watched on Twitch in the first half of 2019 .
gold_template: The statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] on templateTitleSubject[0] from 1 , templateTitleDate[0] to 30 , templateTitleDate[0] , ranked templateTitle[6] templateYLabel[0] of templateYLabel[1] templateYLabel[2] . templateXValue[0] of templateXValue[0] topped the charts with a combined templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] on templateTitleSubject[0] in the first half of templateTitleDate[0] .

generated_template: The statistic presents the distribution of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , based on templateTitle[7] templateYLabel[0] templateYLabel[1] . According to the source , the templateTitle[0] of all time was templateXValue[0] , with a templateYValue[max] templateScale templateYLabel[3] .
generated: The statistic presents the distribution of Twitch H1 gaming content Twitch in 2019 , based on hours Number .  According to the source , the Leading of all time was League_of_Legends , with a 512.3 million watched .

Example 1110:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Upland spot cotton price in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1995', '1990']
Y_Axis['Price', 'per', 'pound', 'in', 'U.S.', 'cents']: ['94.55', '86.39', '64.17', '61.47', '73.37', '79.39', '74.4', '132.6', '87.93', '52.58', '57.97', '52.99', '48.51', '47.35', '52.47', '56.23', '36.99', '39.68', '57.47', '93.47', '71.24']

gold: This statistic shows the upland spot cotton price per pound in the United States from 1990 to 2018 . In calendar year 2017 , the average spot price per one pound of upland cotton was about 86.4 U.S. cents .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In calendar templateXLabel[0] templateXValue[1] , the average templateTitle[1] templateYLabel[0] templateYLabel[1] one templateYLabel[2] of templateTitle[0] templateTitle[2] was about templateYValue[1] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the average templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] as templateTitle[2] templateTitle[3] templateYLabel[3] templateTitle[5] from templateXValue[min] to templateXValue[max] . In the templateXValue[min] calendar templateXLabel[0] , the average templateTitle[0] farmer templateTitle[2] an average templateYLabel[0] of templateYValue[idxmin(X)] templateYLabel[4] templateYLabel[1] one templateYLabel[2] of upland templateTitle[0] .
generated: This statistic shows the average Upland Price per pound as cotton price U.S. 1990 from to 2018 .  In the 1990 calendar Year , the average Upland farmer cotton an average Price of 71.24 cents per one pound of upland .

Example 1111:
titleEntities: {'Subject': ['Countries'], 'Date': ['2010']}
title: Countries with the highest proportion of soccer players 2010
X_Axis['Country']: ['Germany', 'Chile', 'Paraguay', 'Slovakia', 'The_Netherlands']
Y_Axis['Proportion', 'of', 'soccer', 'players']: ['19.8', '15.7', '14.8', '11.4', '10.4']

gold: This table shows the countries with the highest proportion of soccer players in the population . 19.8 percent of the German population are soccer players .
gold_template: This table shows the templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the population . templateYValue[max] templateScale of the German population are templateYLabel[1] templateYLabel[2] .

generated_template: In templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[1] , with templateYValue[max] templateScale . templateXValue[1] was the second highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[1] , with templateYValue[1] templateScale . The UK templateYLabel[1] templateYLabel[2] are a popular social templateTitle[5] in the templateXValue[0] .
generated: In 2010 , Germany had the highest Proportion of soccer players in the Countries , with 19.8 % .  Chile was the second highest Proportion of soccer players in the Countries , with 15.7 % .  The UK soccer players are a popular social 2010 in the Germany .

Example 1112:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2008', '2018']}
title: Number of homicides in Sweden 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'homicides']: ['108', '113', '106', '112', '87', '87', '68', '81', '91', '93', '82']

gold: This statistic shows the number of confirmed homicides in Sweden from 2008 to 2018 . The number of homicides has fluctuated in this period . In 2017 , the number stood at 113 homicides which was relatively high considering the whole 10-year period .
gold_template: This statistic shows the templateYLabel[0] of confirmed templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] has fluctuated in this period . In templateXValue[1] , the templateYLabel[0] stood at templateYValue[max] templateYLabel[1] which was relatively high considering the whole 10-year period .

generated_template: In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[0] reported criminal offences fluctuated in recent years . In templateXValue[max] , the templateYLabel[0] stood at close to 499 thousand crimes , and among them there were templateYValue[1] templateYLabel[1] reported . According to the Danish Penal Code , the penalty for homicide ranges from a minimum of a five-year prison sentence to imprisonment for life .
generated: In 2018 , the Number of Sweden reported criminal offences fluctuated in recent years .  In 2018 , the Number stood at close to 499 thousand crimes , and among them there were 113 homicides reported .  According to the Danish Penal Code , the penalty for homicide ranges from a minimum of a five-year prison sentence to imprisonment for life .

Example 1113:
titleEntities: {'Subject': ['Tea'], 'Date': ['2018']}
title: Tea market : major importing countries worldwide 2018
X_Axis['Country']: ['Pakistan', 'Russian_Federation', 'USA', 'United_Kingdom', 'United_Arab_Emirates', 'Egypt', 'Saudi_Arabia', 'Germany', 'Morocco', 'Japan', 'France', 'China', 'China_Hong_Kong_SAR', 'Afghanistan', 'Canada']
Y_Axis['Trade', 'value', 'in', 'million', 'U.S.', 'dollars']: ['571.33', '497.07', '487.29', '397.68', '323.16', '318.11', '252.52', '237.39', '220.28', '182.18', '180.85', '177.82', '151.85', '146.11', '145.55']

gold: This statistic shows the leading tea importing countries worldwide in 2018 . That year , the Russian federation was the second leading importer of tea worldwide , with imports valued at approximately 497.07 million U.S. dollars .
gold_template: This statistic shows the leading templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . That year , the templateXValue[1] was the second leading importer of templateTitleSubject[0] templateTitle[5] , with imports valued at approximately templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[4] templateTitle[5] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In that year , the templateXValue[0] had the largest templateYLabel[0] of templateYLabel[3] with a total of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Trade of the market major Tea countries worldwide in 2018 , Country .  In that year , the Pakistan had the largest Trade of U.S. with a total of 571.33 million U.S. .

Example 1114:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014', '2017']}
title: U.S. online shopping frequency 2014 to 2017
X_Axis['Response']: ['At_least_once_a_week', 'Once_or_more_per_month', '7-12_times_per_year', '4-6_times_per_year', '1-3_times_per_year', 'Never']
Y_Axis['Share', 'of', 'respondents']: ['29', '32', '13', '12', '10', '4']

gold: This statistic presents the online shopping frequency of internet users in the United States as of March 2017 . During the most recent survey period , 29 percent of respondents stated they purchased goods online at least once a week .
gold_template: This statistic presents the templateTitle[1] templateTitle[2] templateTitle[3] of internet users in the templateTitle[0] as of 2017 . During the most recent survey period , templateYValue[0] templateScale of templateYLabel[1] stated they purchased goods templateTitle[1] at templateXValue[0] a templateXValue[0] .

generated_template: This statistic gives information on the templateTitle[1] of templateTitle[4] in the templateTitle[5] in templateTitleDate[0] . During the survey , it was found that templateYValue[1] templateScale of templateYLabel[1] stated that they ate templateTitle[2] templateTitle[3] templateXValue[2] templateXValue[2] .
generated: This statistic gives information on the online of 2014 in the 2017 in 2014 .  During the survey , it was found that 32 % of respondents stated that they ate shopping frequency 7-12_times_per_year .

Example 1115:
titleEntities: {'Subject': ['Pharmaceutical'], 'Date': ['2017', '2030']}
title: Pharmaceutical sector growth worldwide 2017 - 2030 , by country
X_Axis['Country']: ['India', 'China', 'Indonesia', 'Saudi_Arabia', 'Brazil', 'World', 'Argentina', 'Mexico', 'Australia', 'Russia', 'South_Korea', 'Poland', 'United_States', 'Canada', 'Spain', 'France', 'United_Kingdom', 'Germany', 'Italy', 'Japan']
Y_Axis['Growth', 'in', 'percentage']: ['232', '230', '196', '171', '169', '160', '158', '154', '149', '147', '141', '140', '134', '131', '129', '129', '128', '118', '118', '117']

gold: This statistic shows the worldwide forecast of pharmaceutical sector growth between 2017 and 2030 , sorted by select country . The forecast predicts that between 2017 and 2030 the pharmaceutical sector worldwide will grow by 160 percent , with the biggest growth forecast given for India with 232 percent .
gold_template: This statistic shows the templateTitle[3] forecast of templateTitleSubject[0] templateTitle[1] templateYLabel[0] between templateTitleDate[min] and templateTitleDate[max] , sorted templateTitle[6] select templateXLabel[0] . The forecast predicts that between templateTitleDate[min] and templateTitleDate[max] the templateTitleSubject[0] templateTitle[1] templateTitle[3] will grow templateTitle[6] templateYValue[5] templateScale , with the biggest templateYLabel[0] forecast given for templateXValue[0] with templateYValue[max] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateYLabel[3] templateYLabel[4] templateTitle[7] templateTitleSubject[0] in templateTitleDate[0] , in templateXLabel[0] . In templateXValue[0] , the templateYLabel[0] templateYLabel[1] to templateTitleSubject[0] was templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Growth percentage of the Pharmaceutical percentage country Pharmaceutical in 2017 , in Country .  In India , the Growth percentage to Pharmaceutical was 232 percentage .

Example 1116:
titleEntities: {'Subject': ['New York'], 'Date': ['2000', '2018']}
title: Personal income in New York - income per capita 2000 to 2018
X_Axis['Fiscal', 'years']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00"]
Y_Axis['Income', 'in', 'U.S.', 'dollars']: ['68668', '65644', '61226', '59141', '56743', '54491', '53982', '51498', '48972', '47277', '48328', '47520', '44179', '40905', '39185', '37459', '37008', '37194', '36028']

gold: This statistic shows the per capita personal income in New York from 2000 to 2018 . In 2018 , the per capita personal income in New York was 68,668 U.S. dollars . Personal income is the income that is received by persons from all sources .
gold_template: This statistic shows the templateTitle[5] templateTitle[6] templateTitle[0] templateYLabel[0] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[5] templateTitle[6] templateTitle[0] templateYLabel[0] in templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] . templateTitle[0] templateYLabel[0] is the templateYLabel[0] that is received by persons from all sources .

generated_template: This statistic shows the templateTitle[4] of the templateTitle[0] templateTitle[1] templateTitle[2] ( templateTitleSubject[0] ) templateTitle[5] in templateTitleDate[min] and templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[4] templateTitle[5] of the templateTitle[0] templateTitle[1] templateTitle[2] stood at templateYValue[min] templateScale .
generated: This statistic shows the income of the Personal income New ( New York ) per in 2000 and 2018 .  In 2018 , the income per of the Personal income New stood at 36028 % .

Example 1117:
titleEntities: {'Subject': ['France'], 'Date': ['2014']}
title: Construction cost per kilometer of high-speed line in France 2014
X_Axis['High-speed', 'Line']: ['LGV_Paris-Lyon', 'LGV_Atlantic', 'LGV_North', 'LGV_Rhône-Alpes', 'North-South_interconnection', 'LGV_East', 'LGV_Rhine-Rhone', 'LGV_Mediterranean']
Y_Axis['Construction', 'cost', 'per', 'kilometer', 'in', 'millions', 'of', 'euros']: ['5.5', '10.4', '10.8', '13.4', '15.5', '15.7', '17.5', '19.7']

gold: Here is a statistic about the construction cost per kilometer of high-speed lines ( HSL ) in France in 2014 ( in millions of euros ) . It shows that the construction of the Mediterranean high-speed line , which links the Provence-Alpes-Côte d'Azur and Languedoc-Roussillon regions , cost around 20 million euros per kilometer .
gold_template: Here is a statistic about the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateXLabel[0] lines ( HSL ) in templateTitleSubject[0] in templateTitleDate[0] ( in templateScale of templateYLabel[5] ) . It shows that the templateYLabel[0] of the templateXValue[last] templateXLabel[0] templateXLabel[1] , which links the Provence-Alpes-Côte d'Azur and Languedoc-Roussillon regions , templateYLabel[1] around templateYValue[max] templateScale templateYLabel[5] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic illustrates the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] of templateTitle[4] templateTitle[5] in templateTitleDate[0] , templateTitle[7] templateXLabel[1] . It was found that the average templateYLabel[0] templateYLabel[1] of students at templateXValue[0] templateXValue[last] , at templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic illustrates the Construction cost of the kilometer of high-speed line in 2014 , Line .  It was found that the average Construction cost of students at LGV_Paris-Lyon LGV_Mediterranean , at 19.7 millions kilometer .

Example 1118:
titleEntities: {'Subject': ['United States'], 'Date': ['2010']}
title: Projected annual inflation rate in the United States 2010 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Inflation', 'rate']: ['2.24', '2.23', '2.23', '2.27', '2.73', '2', '2.44', '2.14', '1.27', '0.12', '1.62', '1.47', '2.07', '3.14', '1.64']

gold: This statistic shows the annual inflation rate in the U.S. from 2010 to 2018 with additional projections up to 2024 . The data represents U.S. city averages . The base period was 1982 - 84 .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in the templateTitle[5] from templateXValue[min] to templateXValue[6] with additional projections up to templateXValue[max] . The data represents templateTitle[5] city averages . The base period was 1982 - 84 .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price templatePositiveTrend of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: This statistic shows the average Inflation rate in United States from 2010 to 2018 , with projections up until 2024 .  The Inflation rate is calculated using the price increase of a defined product basket .  This product basket contains products and services , on which the average consumer spends money throughout the Year .

Example 1119:
titleEntities: {'Subject': ['Netflix'], 'Date': ['2017']}
title: Share of households with Netflix in the U.S. 2017
X_Axis['Year']: ['2017', '2011']
Y_Axis['Share', 'of', 'TV', 'households']: ['54', '28']

gold: The statistic shows the share of TV households with a Netflix subscription in the United States in 2011 and 2017 . In the presented time period , the share of TV households with Netflix rose from 28 percent to 54 percent .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] a templateTitleSubject[0] subscription in the templateTitle[4] in templateXValue[min] and templateXValue[max] . In the presented time period , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] templateTitleSubject[0] templatePositiveTrend from templateYValue[idxmin(X)] templateScale to templateYValue[idxmax(X)] templateScale .

generated_template: This statistic displays the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitle[2] templateTitle[3] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic displays the Share of the households Netflix U.S. in Netflix 2011 and 2017 .  In 2017 , the Share of Netflix U.S. in Netflix was at 54 TV households .

Example 1120:
titleEntities: {'Subject': ['Live Nation Entertainment'], 'Date': ['2008', '2016']}
title: Live Nation Entertainment 's artist management revenue from 2008 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['421.71', '434.2', '389.44', '352.95', '399.94', '393.13', '362.16', '251.5', '228.83']

gold: This statistic presents the revenue Live Nation generated with artist management business ( Artist Nation ) from 2008 to 2016 . In 2014 , the U.S. live event specialist generated a total revenue of 389.44 million US dollars managing its signed artists , up from 352.95 million a year earlier .
gold_template: This statistic presents the templateYLabel[0] templateTitleSubject[0] generated with templateTitle[4] templateTitle[5] business ( templateTitle[4] templateTitleSubject[0] ) templateTitle[7] templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[2] templateTitleSubject[0] event specialist generated a total templateYLabel[0] of templateYValue[2] templateScale US templateYLabel[3] managing its signed artists , up templateTitle[7] templateYValue[3] templateScale a templateXLabel[0] earlier .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Live Nation Entertainment , a franchise of the National Football League , from 2008 to 2016 .  In 2016 , the Revenue of the Live Nation Entertainment was 434.2 421.71 U.S. dollars .

Example 1121:
titleEntities: {'Subject': ['PV'], 'Date': ['2018']}
title: Solar PV capacity - new installations worldwide by country 2018
X_Axis['Country']: ['China', 'India', 'US', 'Japan', 'Australia', 'Germany', 'Mexico', 'Republic_of_Korea', 'Turkey', 'Netherlands']
Y_Axis['Percentage', 'of', 'newly', 'installed', 'capacity']: ['45', '11', '11', '7', '4', '3', '3', '2', '2', '1']

gold: This statistic shows the share of new installed solar PV capacity worldwide in 2018 , by country . In 2018 , new solar PV capacity installations in China accounted for around 45 percent of the world 's total new installed grid-connected PV capacity .
gold_template: This statistic shows the share of templateTitle[3] templateYLabel[2] templateTitle[0] templateTitleSubject[0] templateYLabel[3] templateTitle[5] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , templateTitle[3] templateTitle[0] templateTitleSubject[0] templateYLabel[3] templateTitle[4] in templateXValue[0] accounted for around templateYValue[max] templateScale of the world 's total templateTitle[3] templateYLabel[2] grid-connected templateTitleSubject[0] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitleSubject[0] worldwide in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In that year , the templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Percentage newly of the capacity new PV worldwide in 2018 , country .  In that year , the China had the highest Percentage newly of 45 installed capacity .

Example 1122:
titleEntities: {'Subject': ['Apple App Store'], 'Date': ['2014']}
title: Apple App Store : global category revenue share per business model 2014
Data:
                                           0          1              2        3             4      5                 6     7        8      9          10         11             12            13                 14      15       16
Category                            Catalogs  Education  Entertainment  Finance  Food_&_Drink  Games  Health_&_Fitness  Kids  Medical  Music  Navigation  Newsstand  Photo_&_Video  Productivity  Social_Networking  Sports  Weather
Free apps with in-app purchases         35.4       47.6           70.4     52.7          41.3     92              29.7  39.2     29.4   73.8          21       99.6           37.6          41.6               90.6    68.9     31.4
Paid apps without in-app purchases      63.7       50.6           27.8     43.1            55    6.1              66.6    59     68.1   23.3          51        0.2           57.4          56.5                4.4    27.9     59.9
Paid apps with in-app purchases          0.9        1.9            1.8      4.2           3.7      2               3.8   1.8      2.6    2.8          28        0.2              5             2                  5     3.1      8.7 

gold: This statistic presents the global revenue distribution of the ten most-grossing app categories in the Apple App Store , sorted by business model . In February 2014 , 92 percent of gaming app revenue was generated through freemium apps - free apps with in-app purchases . Only 6.1 percent of revenue came from paid-for gaming apps without in-app purchases .
gold_template: This statistic presents the templateTitle[3] templateTitle[5] distribution of the ten most-grossing templateTitleSubject[0] categories in the templateTitleSubject[0] Store , sorted by templateTitle[8] templateTitle[9] . In 2014 , templateValue[1][5] templateScale of gaming templateTitleSubject[0] templateTitle[5] was generated through freemium templateLabel[1][1] - templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] templateLabel[1][3] templateLabel[1][4] . Only templateValue[2][5] templateScale of templateTitle[5] came from paid-for gaming templateLabel[1][1] templateLabel[2][2] templateLabel[1][3] templateLabel[1][4] .

generated_template: This statistic shows the average templateTitle[0] templateTitle[1] in the templateTitleSubject[0] in templateTitleDate[0] , broken down templateTitle[7] templateTitle[8] . The templateTitle[0] of templateValue[0][0] templateValue[0][0] amounted to templateValue[2][0] templateScale U.S. dollars in templateLabel[1][0] templateLabel[1][1] .
generated: This statistic shows the average Apple App in the Apple App Store in 2014 , broken down per business .  The Apple of Catalogs amounted to 63.7 million U.S. dollars in Free apps .

Example 1123:
titleEntities: {'Subject': ['UK'], 'Date': ['1918', '2019']}
title: Voter turnout in UK elections 1918 to 2019
X_Axis['Year']: ['2019', '2017', 'Brexit_Referendum_2016', '2015', '2010', '2005', '2001', '1997', '1992', '1987', '1983', '1979', 'Oct_1974', 'Feb_1974', '1970', '1966', '1964', '1959', '1955', '1951', '1950', '1945', '1935', '1931', '1929', '1924', '1923', '1922', '1918']
Y_Axis['Voter', 'turnout']: ['67.3', '68.8', '72.2', '66.2', '65.1', '61.4', '59.4', '71.4', '77.7', '75.3', '72.7', '76', '72.8', '78.8', '72', '75.8', '77.1', '78.7', '76.8', '82.6', '83.9', '72.8', '71.1', '76.4', '76.3', '77', '71.1', '73', '57.2']

gold: In the 2019 United Kingdom General Election , voter turnout was 67.3 percent of eligible voters , a 1.5 percent drop compared with the previous general election in 2017 . Between 1922 and 1997 voter turnout never fell below 70 percent , but in 2001 it dropped to just 59.4 percent . Since that low point , voter turnout has gradually recovered and reached 72.2 percent in the Brexit Referendum of 2016 , which is still some way off the peak of 83.9 percent recorded in the 1950 General Election .
gold_template: In the templateXValue[0] United Kingdom General Election , templateYLabel[0] templateYLabel[1] was templateYValue[0] templateScale of eligible voters , a 1.5 templateScale drop compared with the previous general election in templateXValue[1] . Between templateXValue[27] and templateXValue[7] templateYLabel[0] templateYLabel[1] never templateNegativeTrend below 70 templateScale , but in templateXValue[6] it templateNegativeTrend to just templateYValue[6] templateScale . Since that low point , templateYLabel[0] templateYLabel[1] has gradually recovered and reached templateYValue[2] templateScale in the templateXValue[2] of templateXValue[2] , which is still some way off the peak of templateYValue[max] templateScale recorded in the templateXValue[idxmax(Y)] General Election .

generated_template: In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] . This figure is a slight templatePositiveTrend compared to the low templateYLabel[1] since templateXValue[5] , when there were templateYValue[1] templateScale in templateXValue[1] .
generated: In 2019 , the Voter turnout in UK amounted to 67.3 turnout .  This figure is a slight increase compared to the low turnout since 2005 , when there were 68.8 % in 2017 .

Example 1124:
titleEntities: {'Subject': ['Frequency', 'Spain'], 'Date': ['2017']}
title: Frequency of internet use in Spain 2017
X_Axis['Duration']: ['Once_a_week_(including_every_day)', 'Daily', 'At_least_once_a_week_(but_not_every_day)', 'Less_than_once_a_week']
Y_Axis['Share', 'of', 'individuals']: ['80', '69', '11', '5']

gold: This statistic shows the percentage of individuals using the internet by frequency in Spain in 2017 . In 2017 , 69 percent of individuals in Spain accessed the internet daily or more frequently .
gold_template: This statistic shows the templateScale of templateYLabel[1] using the templateTitle[1] by templateTitleSubject[0] in templateTitleSubject[1] in templateTitleDate[0] . In templateTitleDate[0] , templateYValue[1] templateScale of templateYLabel[1] in templateTitleSubject[1] accessed the templateTitle[1] templateXValue[1] or more frequently .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[7] templateXLabel[1] of templateTitle[9] . The templateTitle[0] of templateXValue[last] stood at templateYValue[min] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] .
generated: This statistic shows the Frequency internet use Spain in the United Kingdom ( Frequency ) in 2017 , Duration of 2017 .  The Frequency of Less_than_once_a_week stood at 5 individuals in 2017 .

Example 1125:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Crude oil imports to Canada by region of origin 2018
X_Axis['Country']: ['United_States', 'Saudi_Arabia', 'West_Africa', 'Europe', 'CIS_(excluding_Russia)', 'North_Africa', 'South_and_Central_America', 'Russia', 'Total']
Y_Axis['Imports', 'in', 'million', 'metric', 'tons']: ['18.8', '5.6', '1.1', '1.4', '1.1', '0.5', '0.3', '0.2', '29.1']

gold: In 2018 , Canada imported 18.8 million metric tons of crude oil from the United States , which was the largest quantity Canada imported from of any country . Canadian crude oil In 1858 , the first oil deposit in North America was found in Ontario , Canada . Today , however , Canada 's largest volume of crude oil is produced in Alberta , where the Athabasca oil sands are located .
gold_template: In templateTitleDate[0] , templateTitleSubject[0] imported templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] from the templateXValue[0] , which was the largest quantity templateTitleSubject[0] imported from of any templateXLabel[0] . Canadian templateTitle[0] templateTitle[1] In 1858 , the first templateTitle[1] deposit in templateXValue[5] templateXValue[6] was found in Ontario , templateTitleSubject[0] . Today , however , templateTitleSubject[0] 's largest volume of templateTitle[0] templateTitle[1] is produced in Alberta , where the Athabasca templateTitle[1] sands are located .

generated_template: In 2019 , templateXValue[0] was the leading producer of templateTitle[0] and templateTitle[1] in the templateTitleSubject[0] with templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . The amount of templateTitle[0] tourists came from templateXValue[2] . templateXValue[1] , with templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[1] .
generated: In 2019 , United_States was the leading producer of Crude and oil in the Canada with 29.1 million metric tons .  The amount of Crude tourists came from West_Africa .  Saudi_Arabia , with 5.6 million metric tons of oil .

Example 1126:
titleEntities: {'Subject': ['Online'], 'Date': ['2014', '2018']}
title: Online share of retail trade in selected countries 2014 to 2018
Data:
                      0        1       2       3            4               5            6        7        8      9      10     11
Country  United_Kingdom  Germany  France  Sweden  Netherlands  Europe_average  Switzerland  Austria  Belgium  Spain  Poland  Italy
2014               13.5       10     6.9     7.6          7.1             7.2            0        0        0      3     2.8    2.1
2015               15.2     11.6       8     7.7          7.4               7          6.3      6.1      5.7    3.5     3.3    2.5
2016               16.8     13.5     9.2     8.6          8.5               8          6.9      6.7      6.4    4.1     3.7      3
2017               17.8     15.1      10     9.5          9.5             8.8          7.5      7.4      7.3    4.8     4.3    3.4
2018               17.8     15.1    10.1     9.7          9.1             8.8          7.3      7.4      7.3    4.8     4.3    3.4 

gold: This statistic provides information on the online share of retail trade in selected European countries from 2014 to 2018 . In 2018 , approximately 17.8 percent of retail sales in the United Kingdom were generated online . The United Kingdom led the country comparison in each year .
gold_template: This statistic provides information on the templateTitleSubject[0] templateTitle[1] of templateTitle[2] templateTitle[3] in templateTitle[4] European templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , approximately templateValue[4][idxmax(4)] templateScale of templateTitle[2] sales in the templateValue[0][0] were generated templateTitleSubject[0] . The templateValue[0][0] led the templateLabel[0][0] comparison in each year .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[5] templateTitle[6] . In templateTitleDate[max] , the highest templateTitle[1] templateTitle[2] in the templateValue[0][6] was at templateValue[5][idxmax(5)] templateScale .
generated: This statistic shows the Online share in the Online from 2014 to 2018 , countries 2014 .  In 2018 , the highest share retail in the Switzerland was at 17.8 % .

Example 1127:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1999', '2019']}
title: U.S. advanced flat glass prices in 1999 and 2019 by glass type
Data:
                      0                1                 2                         3                4                     5                 6             7
Glass Type  Smart_glass  Ballistic_glass  Fire-rated_glass  Burglary_resistant_glass  Hurricane_glass  Low-emissivity_glass  Reflective_glass  Safety_glass
1999              38.05            27.65             11.55                       7.7             8.85                  5.89              5.81          4.08
2019               63.5             62.0              18.9                      18.6             17.1                 13.85              13.0           6.8 

gold: This statistic shows theadvanced flat glass prices in the United States in 1999 , and forecast figures for 2019 . In 1999 , safety glass cost 4.08 dollars per square foot .
gold_template: This statistic shows theadvanced templateTitle[2] templateValue[0][0] templateTitle[4] in the templateTitle[0] in templateTitleDate[min] , and forecast figures for templateTitleDate[max] . In templateTitleDate[min] , templateValue[0][7] templateValue[0][0] cost templateValue[1][min] dollars per square foot .

generated_template: In templateTitleDate[0] , templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[7] with a total of templateValue[1][0] templateScale U.S. dollars . The templateValue[0][0] is templateValue[2][0] templateScale of templateLabel[2][0] templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] , which was go the templateValue[0][0] .
generated: In 1999 , was the U.S. advanced flat glass prices 1999 2019 in the by with a total of 38.05 million U.S. dollars .  The Smart_glass is 63.5 % of 2019 1999 , which was go the Smart_glass .

Example 1128:
titleEntities: {'Subject': ['Jamaica'], 'Date': ['2018']}
title: Urbanization in Jamaica 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['55.67', '55.38', '55.1', '54.83', '54.58', '54.35', '54.13', '53.93', '53.74', '53.56', '53.37']

gold: This statistic shows the degree of urbanization in Jamaica from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 55.67 percent of Jamaica 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Jamaica from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 55.67 % of Jamaica 's total population lived in urban areas and cities .

Example 1129:
titleEntities: {'Subject': ['United States'], 'Date': ['2017', '2024']}
title: United States : retail e-commerce sales 2017 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['599186', '565696', '524091', '474540', '419879', '365207', '315629', '274755']

gold: In 2019 , U.S. online retail sales of physical goods amounted to 365.2 billion US dollars and are projected to reach close to 600 billion US dollars in 2024 . Apparel and accessories retail e-commerce in the U.S. is projected to generate 194.4 billion U.S. dollars in revenue by 2024 .
gold_template: In templateXValue[5] , templateYLabel[2] online templateTitle[2] templateTitle[4] of physical goods amounted to templateYValue[5] templateScale US templateYLabel[3] and are projected to reach close to 600 templateScale US templateYLabel[3] in templateXValue[max] . Apparel and accessories templateTitle[2] templateTitle[3] in the templateYLabel[2] is projected to generate 194.4 templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] by templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the States retail United States sales from 2017 to 2024 .  In 2017 , the United States States retail Revenue amounted to 274755 million U.S. dollars .

Example 1130:
titleEntities: {'Subject': ['Dominican Republic'], 'Date': ['2024']}
title: Inflation rate in Dominican Republic 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['3.97', '3.97', '3.97', '3.95', '4.12', '1.85', '3.56', '3.28', '1.61', '0.84', '3', '4.83', '3.7', '8.46', '6.33', '1.44', '10.64', '6.14', '7.57', '4.19', '51.46', '27.45', '5.22', '8.88', '7.22', '6.97', '4.83', '8.3', '5.4', '12.54', '8.26', '5.25', '4.26', '47.08', '50.46', '40.66', '43.86', '13.55', '7.64', '45.34', '20.15']

gold: This statistic shows the average inflation rate in Dominican Republic from1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Dominican Republic amounted to about 3.56 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from1984 to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Dominican Republic from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Dominican Republic amounted to about 3.56 % compared to the previous Year .

Example 1131:
titleEntities: {'Subject': ['Birkenstock'], 'Date': ['2013']}
title: Global revenue of Birkenstock 2012 to 2013
X_Axis['Year']: ['2013', '2012']
Y_Axis['Revenue', 'in', 'million', 'euros']: ['14525.69', '14109.4']

gold: Between 2012 and 2013 the global revenue of Birkenstock & Co. increased from 14.11 billion euros to 14.53 billion euros . Birkenstock & Co. Birkenstock & Co. is a shoe manufacturer founded and based in Germany which is known for producing comfortable and high-quality sandals for men , women , and children . Their product line also includes sneakers , boots , and clogs .
gold_template: Between templateXValue[min] and templateXValue[max] the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] & Co. templatePositiveTrend from templateYValue[min] templateScale templateYLabel[2] to templateYValue[max] templateScale templateYLabel[2] . templateTitleSubject[0] & Co. templateTitleSubject[0] & Co. is a shoe manufacturer founded and based in Germany which is known for producing comfortable and high-quality sandals for men , women , and children . Their product line also includes sneakers , boots , and clogs .

generated_template: This statistic presents the templateYLabel[2] templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[0] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the source , the templateYLabel[2] templateYLabel[0] for the templateTitle[1] templateTitle[2] is expected to templatePositiveTrend by templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: This statistic presents the euros Revenue of the revenue Birkenstock Global in the Birkenstock from 2012 to 2013 .  According to the source , the euros Revenue for the revenue Birkenstock is expected to increase by 14109.4 million euros in 2013 .

Example 1132:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2017']}
title: Number of physicians in Vietnam 2017 , by region
X_Axis['Vietnamese', 'Region']: ['Northern_Central_area_and_Central_coastal_area', 'Northern_midlands_and_mountain_areas', 'Mekong_River_Delta', 'Red_River_Delta', 'South_East', 'Thanh_Hoa', 'Ha_Noi', 'Central_Highlands', 'Ho_Chi_Minh_city', 'Nghe_An', 'Kien_Giang', 'Dong_Thap', 'Ha_Giang', 'Phu_Tho', 'Quang_Nam', 'Bac_Giang', 'An_Giang', 'Hai_Duong', 'Long_An', 'Lai_Chau', 'Tien_Giang', 'Hoa_Binh', 'Dien_Bien', 'Dong_Nai', 'Ha_Tinh', 'Son_La', 'Ben_Tre', 'Ca_Mau', 'Binh_Thuan', 'Lao_Cai', 'Dak_Lak', 'Hung_Yen', 'Binh_Duong', 'Thai_Binh', 'Lang_Son', 'Cao_Bang', 'Khanh_Hoa', 'Bac_Ninh', 'Binh_Dinh', 'Quang_Ngai', 'Soc_Trang', 'Tuyen_Quang', 'Vinh_Long', 'Tay_Ninh', 'Bac_Lieu', 'Gia_Lai', 'Binh_Phuoc', 'Vinh_Phuc', 'Hai_Phong', 'Can_Tho', 'Tra_Vinh', 'Yen_Bai', 'Lam_Dong', 'Nam_Dinh', 'Ninh_Binh', 'Quang_Ninh', 'Hau_Giang', 'Quang_Binh', 'Thai_Nguyen', 'Phu_Yen', 'Ba_Ria_-_Vung_Tau', 'Ninh_Thuan', 'Bac_Kan', 'Ha_Nam', 'Quang_Tri', 'Da_Nang', 'Kon_Tum', 'Thua_Thien-Hue', 'Dak_Nong']
Y_Axis['Number', 'of', 'physicians']: ['11747', '11511', '11307', '9406', '5090', '3016', '2721', '2656', '1660', '1443', '1413', '1189', '1174', '1162', '1110', '1075', '1071', '1061', '1055', '1004', '972', '924', '920', '916', '915', '900', '897', '873', '854', '797', '793', '777', '776', '775', '743', '722', '718', '697', '695', '695', '687', '687', '680', '671', '663', '662', '651', '647', '642', '641', '615', '602', '601', '596', '589', '554', '551', '502', '444', '420', '416', '414', '357', '347', '345', '340', '331', '280', '269']

gold: In 2017 , there were over three thousand physicians in Thanh Hoa . In that year , the total number of physicians amounted to approximately 51.72 thousand in the country .
gold_template: In templateTitleDate[0] , there were over templateYValue[5] thousand templateYLabel[1] in templateXValue[5] . In that year , the total templateYLabel[0] of templateYLabel[1] amounted to approximately 51.72 thousand in the country .

generated_template: The graph shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in the templateTitle[0] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateXValue[0] , there were templateYValue[max] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] .
generated: The graph shows the Number of physicians in the Number in 2017 , region Vietnamese .  In Northern_Central_area_and_Central_coastal_area , there were 11747 physicians in the Vietnam .

Example 1133:
titleEntities: {'Subject': ['Chemical'], 'Date': ['2000', '2018']}
title: Chemical fibers global production 2000 to 2018
Data:
                           0                 1      2
Fiber Type  Cellulose_fibers  Synthetic_fibers  Total
2000                    2640             28400  31040
2010                    4400             45200  49600
2011                    4743             47957  52700
2012                    5040             50960  56000
2013                    5900             54400  60300
2014                    6200             57100  63300
2015                    6100             60700  66800
2016                    6400             64800  71200
2017                    6700             64900  71600
2018                    6800             66600  73400 

gold: In 2018 , the total global production output of the chemical fiber industry amounted to 73.4 million metric tons . Chemical fibers are made from organic ( `` natural '' ) and synthetic polymers . Chemical fibers are manufactured industrially for various purposes .
gold_template: In templateTitleDate[max] , the templateValue[0][2] templateTitle[2] templateTitle[3] output of the templateTitleSubject[0] templateLabel[0][0] industry amounted to 73.4 templateScale metric tons . templateTitleSubject[0] templateValue[0][0] are made from organic ( `` natural '' ) and templateValue[0][1] polymers . templateTitleSubject[0] templateValue[0][0] are manufactured industrially for various purposes .

generated_template: This statistic shows the templateTitle[4] of the templateTitle[0] templateTitle[1] templateTitle[2] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[min] to templateTitleDate[max] , templateTitle[7] templateTitle[8] . As of 2019 , templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[4] was templateValue[3][last] templateScale of templateTitle[4] .
generated: This statistic shows the 2000 of the Chemical fibers global in the United Kingdom ( Chemical ) from 2000 to 2018 , .  As of 2019 , Chemical fibers global in the 2000 was 52700 % of 2000 .

Example 1134:
titleEntities: {'Subject': ['Hang Seng'], 'Date': ['1986', '2019']}
title: Annual Hang Seng index performance 1986 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90", "'89", "'88", "'87", "'86"]
Y_Axis['Index', 'points']: ['28189.75', '25845.7', '29919.15', '22000.56', '21914.4', '23605.04', '23306.4', '22656.9', '18434.4', '23035.4', '21872.5', '14387.5', '27812.7', '19964.7', '14876.4', '14230.1', '12575.9', '9321.29', '11397.2', '15095.5', '16962.1', '10048.6', '10722.8', '13451.5', '10073.0', '8191.04', '11888.4', '5512.39', '4297.33', '3024.55', '2836.57', '2687.44', '2302.75', '2568.3']

gold: The statistic shows the annual development of the Hang Seng index from 1986 to 2019 . The Hang Seng index reflects the performance of the largest stocks traded on the Hong Kong Stock Exchange . The year value of the Hang Seng index amounted to 28,189.75 by the end of 2019 .
gold_template: The statistic shows the templateTitle[0] development of the templateTitleSubject[0] index from templateTitleDate[min] to templateTitleDate[max] . The templateTitleSubject[0] index reflects the templateTitle[4] of the largest stocks traded on the Hong Kong Stock Exchange . The templateXLabel[0] value of the templateTitleSubject[0] index amounted to templateYValue[0] by the end of templateTitleDate[max] .

generated_template: This statistic shows the value of the templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] . The templateYLabel[0] from templateYLabel[1] templateNegativeTrend from templateYValue[last] templateScale in templateTitleDate[max] .
generated: This statistic shows the value of the Hang Seng Index performance from 1986 to 2019 .  The Index from points dropped from 2568.3 % in 2019 .

Example 1135:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Burglary rate , by state U.S. 2018
X_Axis['State']: ['New_Mexico', 'Mississippi', 'Oklahoma', 'Louisiana', 'Arkansas', 'North_Carolina', 'Alabama', 'Nevada', 'South_Carolina', 'Alaska', 'Washington', 'Tennessee', 'Missouri', 'Arizona', 'Georgia', 'Kansas', 'California', 'Ohio', 'Texas', 'Hawaii', 'Oregon', 'Kentucky', 'Indiana', 'United_States', 'Colorado', 'North_Dakota', 'Iowa', 'Florida', 'Delaware', 'Michigan', 'Utah', 'Maryland', 'Illinois', 'Montana', 'West_Virginia', 'South_Dakota', 'Minnesota', 'Idaho', 'Nebraska', 'Rhode_Island', 'Wyoming', 'District_of_Columbia', 'Wisconsin', 'Vermont', 'Connecticut', 'New_Jersey', 'Pennsylvania', 'Maine', 'Massachusetts', 'Virginia', 'New_York', 'New_Hampshire']
Y_Axis['Burglary', 'rate', 'per', '100,000', 'inhabitants']: ['767.8', '697.8', '681.1', '668.1', '636.8', '599.9', '590.1', '584.7', '579.7', '539.6', '533.5', '489.4', '444.9', '439.7', '431.3', '430.6', '416.2', '412.2', '410.8', '396.4', '389.1', '384.7', '377.6', '376.0', '375.2', '358.4', '352.6', '337.7', '326.5', '316.6', '315.3', '312.6', '306.7', '306.6', '296.5', '291.4', '288.4', '281.6', '271.9', '265.8', '264.0', '254.5', '242.5', '234.2', '222.5', '215.9', '211.6', '202.7', '200.8', '182.8', '159.3', '136.2']

gold: In 2017 , New Mexico had the highest burglary rate in the United States . That year , they had 767.8 occurrences per 100,000 residents . Mississippi followed with 697.8 incidents per 100,000 residents .
gold_template: In 2017 , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] in the templateXValue[23] . That year , they had templateYValue[max] occurrences templateYLabel[2] 100,000 residents . templateXValue[1] followed with templateYValue[1] incidents templateYLabel[2] 100,000 residents .

generated_template: This statistic shows the templateScale of templateYLabel[1] of the templateTitle[0] in the templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateTitle[0] of templateXValue[0] was ranked first with a templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the percentage of rate the Burglary in the by in 2018 , U.S. State .  In 2018 , the Burglary of New_Mexico was ranked first with a Burglary of 767.8 per 100,000 .

Example 1136:
titleEntities: {'Subject': ['Facebook'], 'Date': []}
title: Facebook : number of followers of popular beauty brands 2020
X_Axis['Month']: ['Bath_&_Body_Works', 'Risqué', 'Mary_Kay_Brasil', 'Eudora', 'Pantene_Brasil', 'Doce_Película', 'Nature_Republic_Myanmar', 'COVERGIRL', 'Dailus', 'Embelleze']
Y_Axis['Brand', 'fans', 'in', 'millions']: ['9.8', '7.23', '6.26', '5.56', '4.92', '4.88', '4.87', '4.64', '4.21', '4.01']

gold: This statistic gives information on the most popular beauty brands on Facebook , ranked by number of followers on the social network . As of February 2020 , personal care brand Bath & Body Works was ranked first with close to 9.8 million Facebook followers .
gold_template: This statistic gives information on the most templateTitle[3] templateTitle[4] templateTitle[5] on templateTitleSubject[0] , ranked by templateTitle[1] of templateTitle[2] on the social network . As of 2020 , personal care templateYLabel[0] templateXValue[0] Body templateXValue[0] was ranked first with close to templateYValue[max] templateScale templateTitleSubject[0] templateTitle[2] .

generated_template: This statistic gives information on the most popular templateTitle[1] templateTitle[2] on templateTitleSubject[0] , ranked by number of followers on the social network . As of 2018 , Sony 's templateXValue[0] was ranked first with templateYValue[max] templateScale templateTitleSubject[0] on the social network .
generated: This statistic gives information on the most popular number followers on Facebook , ranked by number of followers on the social network .  As of 2018 , Sony 's Bath_&_Body_Works was ranked first with 9.8 millions Facebook on the social network .

Example 1137:
titleEntities: {'Subject': ['Germany'], 'Date': ['2018']}
title: Distribution of gross domestic product ( GDP ) across economic sectors Germany 2018
Data:
                 0      1      2      3      4      5      6      7      8      9     10
Year          2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
Agriculture   0.77   0.83    0.7   0.68    0.9   0.94   0.85   0.91    0.8    0.7   0.84
Industry     27.46  27.44   27.5  27.06  27.01  26.78  27.28  27.07  26.85  24.74  26.85
Services     61.84  61.78  61.84  62.21  62.11  62.19  61.69  61.79  62.26  64.23  62.22 

gold: The services sector is the largest in Germany and has been generating a steady share of around 60 percent of gross domestic product since 2007 . Following the financial crisis , the services sector grew to just over 64 percent in 2009 , but otherwise has made up a consistent share of GDP . Industry was hit the hardest in 2009 , dropping just below a quarter of total GDP , while agriculture has been below one percent since the early 2000s .
gold_template: The templateLabel[3][0] sector is the largest in templateTitleSubject[0] and has been generating a steady share of around 60 templateScale of templateTitle[1] templateTitle[2] templateTitle[3] since 2007 . Following the financial crisis , the templateLabel[3][0] sector templatePositiveTrend to just over templateValue[3][9] templateScale in templateValue[0][9] , but otherwise has made up a consistent share of templateTitle[4] . templateLabel[2][0] was hit the hardest in templateValue[0][9] , templateNegativeTrend just below a quarter of total templateTitle[4] , while templateLabel[1][0] has been below templateValue[1][0] templateScale since the early 2000s .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] contributed around templateValue[1][0] templateScale to the templateTitle[4] of templateTitleSubject[0] , templateValue[2][0] templateScale came from the templateLabel[2][0] and templateValue[3][0] templateScale from the templateLabel[3][0] sector .
generated: This statistic shows the Distribution of the gross domestic product ( GDP ) across economic sectors in Germany from 2008 to 2018 .  In 2018 , Agriculture contributed around 0.77 % to the GDP of Germany , 27.46 % came from the Industry and 61.84 % from the Services sector .

Example 1138:
titleEntities: {'Subject': ['Pakistan'], 'Date': ['2014', '2020']}
title: Smartphone penetration as share of connections in Pakistan 2014 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Percentage', 'of', 'connections']: ['51', '43', '34', '26', '19', '14', '10']

gold: The smartphone penetration rate as share of population in Pakistan was forecasted at 51 percent for 2020 , five times higher than in 2014 . This prediction was based on the assumption that the continuing declining of mobile device prices will support the increase of smartphones . Telecommunications market As of 2017 , the cellular subscription growth rate in Pakistan was 3.8 percent , ranking it in the middle field of Asia Pacific countries for this development .
gold_template: The templateTitle[0] templateTitle[1] rate as templateTitle[2] of population in templateTitleSubject[0] was forecasted at templateYValue[max] templateScale for templateXValue[idxmax(Y)] , five times higher than in templateXValue[min] . This prediction was based on the assumption that the continuing declining of mobile device prices will support the templatePositiveTrend of smartphones . Telecommunications market As of templateXValue[3] , the cellular subscription growth rate in templateTitleSubject[0] was 3.8 templateScale , ranking it in the middle field of Asia Pacific countries for this development .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The average templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[3] templateTitle[4] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .
generated: This statistic shows the Percentage of connections the penetration share connections Pakistan in from 2014 to 2020 .  The average Smartphone penetration share connections Pakistan was 51 connections in 2020 .

Example 1139:
titleEntities: {'Subject': ['United States'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in the United States 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['76252.11', '73953.43', '71760.35', '69643.84', '67426.84', '65111.6', '62868.92', '59999.51', '57901.07', '56787.46', '55010.07', '53061.24', '51556.17', '49825.5', '48402.58', '47007.67', '48283.41', '47869.24', '46213.51', '44025.56', '41629.86', '39411.55', '37971.28', '37101.1', '36317.74', '34494.54', '32833.67', '31440.09', '29946.97', '28671.48', '27674.02', '26364.19', '25392.93', '24302.78', '23847.98', '22814.08', '21376.0', '20000.97', '19034.77', '18199.32', '17086.44']

gold: The statistic shows the gross domestic product ( GDP ) per capita in the United States from 1984 to 2018 , with projections up until 2024 . In 2018 , the gross domestic product per capita in the United States amounted to around 62,868 U.S. dollars . Thus , the United States is one of the countries with the largest GDP per capita worldwide .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] amounted to around 62,868 templateYLabel[3] templateYLabel[4] . Thus , the templateTitleSubject[0] is one of the countries with the largest templateYLabel[0] templateYLabel[1] templateYLabel[2] worldwide .

generated_template: The statistic shows the templateYLabel[0] of the total templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . According to the total market value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows the GDP of the total capita in the United States from 1984 to 2017 , with projections up until 2024 .  According to the total market value of all goods and services produced in a country in a Year .  It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

Example 1140:
titleEntities: {'Subject': ['Amazon'], 'Date': ['2017', '2017']}
title: U.S. consumer perception of Amazon as a company 2017
X_Axis['Response']: ['I_like_using_Amazon', 'With_Amazon_I_get_what_I_need', 'I_trust_Amazon_with_my_payment_information', 'Amazon_takes_the_interests_of_its_customers_seriously', 'Amazon_assumes_a_pioneering_role_in_this_day_and_age', 'Amazon_is_an_integral_part_of_my_life', 'Amazon_respects_my_privacy', 'I_would_like_to_see_more_products_and_services_from_Amazon', 'There_are_no_alternatives_to_Amazon', 'None_of_the_above']
Y_Axis['Share', 'of', 'respondents']: ['67', '57', '53', '38', '35', '32', '32', '27', '18', '12']

gold: The statistic shows U.S. consumer opinions on Amazon in general as of March 2017 . As of the Statista survey , 67 percent of survey respondents stated that they liked using Amazon . A third of respondents also stated that Amazon was an integral part of their life .
gold_template: The statistic shows templateTitle[0] templateTitle[1] opinions on templateXValue[0] in general as of 2017 . As of the Statista survey , templateYValue[max] templateScale of survey templateYLabel[1] stated that they liked templateXValue[0] . A third of templateYLabel[1] also stated that templateXValue[0] was an templateXValue[5] of their templateXValue[5] .

generated_template: This statistic presents the distribution of templateTitle[0] templateTitle[1] users in the templateTitle[4] as of 2019 . During the survey , templateYValue[max] templateScale of the templateYLabel[1] stated that they used templateXValue[0] on the social network .
generated: This statistic presents the distribution of U.S. consumer users in the company as of 2019 .  During the survey , 67 % of the respondents stated that they used I_like_using_Amazon on the social network .

Example 1141:
titleEntities: {'Subject': ['Gate'], 'Date': ['2015']}
title: Gate revenues in sports worldwide by region 2015
Data:
                   0      1      2      3      4      5      6      7      8      9
Year            2015   2014   2013   2012   2011   2010   2009   2008   2007   2006
North America  17.84  17.21  16.63  15.86  15.33  15.71  15.55  15.98   15.6  14.51
EMEA           18.31  17.78  16.96  17.26   16.1  16.54  16.13  16.54  16.03  15.95
Asia Pacific    6.59   6.19   5.91   5.74   5.89   5.68   5.56   5.81   5.06   4.97
Latin America   2.01   2.36   1.81   1.75   1.72   1.64   1.63   1.67    1.7   1.62 

gold: This graph depicts the total worldwide gate revenue earned in sports from 2006 to 2010 and forecasts for the years 2011 to 2015 by region . In 2010 , a revenue of 15.71 billion U.S. dollars was generated through gate revenue in North America . Game day revenue in sports Revenue from ticket sales ( gate receipts ) and other game-day related revenue is an important stream of revenue in sports .
gold_template: This graph depicts the total templateTitle[3] templateTitleSubject[0] revenue earned in templateTitle[2] from templateValue[0][last] to templateValue[0][5] and forecasts for the years templateValue[0][4] to templateValue[0][0] templateTitle[4] templateTitle[5] . In templateValue[0][5] , a revenue of templateValue[1][5] templateScale U.S. dollars was generated through templateTitleSubject[0] revenue in templateLabel[1][0] templateLabel[1][1] . Game day revenue in templateTitle[2] Revenue from ticket sales ( templateTitleSubject[0] receipts ) and other game-day related revenue is an important stream of revenue in templateTitle[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitle[0] templateTitle[1] templateTitle[2] of the templateLabel[1][0] templateLabel[1][1] templateTitle[4] in the templateTitle[5] was templateValue[1][0] U.S. dollars .
generated: This statistic shows the Gate revenues sports worldwide by region in the 2015 from 2006 to 2015 .  In 2015 , the Gate revenues sports of the North America by in the region was 17.84 U.S. dollars .

Example 1142:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2014']}
title: Wearable technology market share in the United Kingdom ( UK ) 2014 , by product
X_Axis['Wearable', 'technology', 'products']: ['Health_and_fitness_trackers', 'Wrist_sport_computers', 'Action_cameras_headsets_and_glasses', 'Smart_watches']
Y_Axis['Share', 'of', 'market']: ['39', '26', '24', '11']

gold: This statistic shows the market share of wearable technology products in the United Kingdom ( UK ) between January and September 2014 . Health and fitness trackers held the largest share of the wearables market at 39 percent , following by wrist sport computers with a 26 percent share .
gold_template: This statistic shows the templateYLabel[1] templateYLabel[0] of templateXLabel[0] templateXLabel[1] templateXLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) between and 2014 . templateXValue[0] and templateXValue[0] held the largest templateYLabel[0] of the wearables templateYLabel[1] at templateYValue[max] templateScale , following templateTitle[8] templateXValue[1] computers with a templateYValue[1] templateScale templateYLabel[0] .

generated_template: This statistic provides information on the templateYLabel[0] of templateTitle[2] in the templateTitle[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[1] . During the survey period , it was found that templateYValue[3] templateScale of the templateXValue[0] were templateTitle[1] templateTitle[2] .
generated: This statistic provides information on the Share of market in the Wearable in 2014 , United technology .  During the survey period , it was found that 11 % of the Health_and_fitness_trackers were technology market .

Example 1143:
titleEntities: {'Subject': ['Number'], 'Date': ['1900', '2016']}
title: Number of earthquakes , by country 1900 to 2016
X_Axis['Country']: ['China', 'Indonesia', 'Iran_(Islamic_Republic_of)', 'Turkey', 'Japan', 'Peru', 'United_States_of_America', 'Italy', 'Afghanistan', 'India', 'Greece', 'Mexico']
Y_Axis['Number', 'of', 'earthquakes']: ['157', '113', '106', '77', '61', '44', '41', '33', '32', '32', '32', '32']

gold: From 1990 to 2016 , China was the country with highest amount of earthquakes . Between that time period , they reported 157 earthquakes . Indonesia , Iran , Turkey , and Japan rounded out the top five countries with the most earthquakes .
gold_template: From 1990 to templateTitleDate[max] , templateXValue[0] was the templateXLabel[0] with highest amount of templateYLabel[1] . Between that time period , they reported templateYValue[max] templateYLabel[1] . templateXValue[1] , templateXValue[2] , templateXValue[3] , and templateXValue[4] rounded out the top five countries with the most templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] and templateTitle[1] templateYLabel[1] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . From 1974 to the source , templateYValue[max] templateTitle[1] templateYLabel[1] were recorded in templateXValue[0] .
generated: This statistic shows the Number of earthquakes in the Number and earthquakes in 1900 , 2016 Country .  From 1974 to the source , 157 earthquakes were recorded in China .

Example 1144:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016', '2016']}
title: Most googled online dating keywords in the United Kingdom ( UK ) in February 2016
X_Axis['Online', 'Dating', 'Keyword']: ['first_dates', 'dating_sites', 'free_dating_sites', 'dating', 'free_dating', 'polish_dating', 'dating_websites', 'online_dating', 'speed_dating', 'free_online_dating', 'ohter_keywords']
Y_Axis['Searches', 'in', 'thousands']: ['110.0', '74.0', '60.5', '33.1', '27.1', '14.8', '12.1', '12.1', '9.9', '8.1', '379.03']

gold: This statistic displays the most googled online dating keywords on Google UK during February 2016 . Most googled was the term `` first dates '' , which was searched 110 thousand times in the United Kingdom that month .
gold_template: This statistic displays the templateTitle[0] templateTitle[1] templateXValue[7] templateXValue[1] templateXValue[last] on Google templateTitleSubject[1] during templateTitle[8] templateTitleDate[0] . templateTitle[0] templateTitle[1] was the term `` templateXValue[0] '' , which was searched templateYValue[0] thousand times in the templateTitleSubject[0] that month .

generated_template: This statistic displays the templateTitle[0] templateTitle[1] templateXValue[last] related to templateXValue[0] templateYLabel[3] templateYLabel[0] in the templateTitleSubject[0] in templateTitleDate[0] . The templateTitle[0] templateTitle[1] templateTitle[2] the templateTitle[4] stood at templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] of templateYLabel[0] .
generated: This statistic displays the Most googled ohter_keywords related to first_dates thousands Searches in the United Kingdom in 2016 .  The Most googled online the keywords stood at 60.5 thousands of Searches .

Example 1145:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Leading mobile messengers in the U.S. 2019 , by users
X_Axis['Platform']: ['Facebook_Messenger', 'Snapchat', 'WhatsApp', 'Messenger_by_Google', 'Discord_-_Chat_for_Games', 'Google_Hangouts_(main)', 'GroupMe', 'Kik', 'Skype_(main)', 'Telegram', 'LINE', 'WeChat']
Y_Axis['Number', 'of', 'monthly', 'unique', 'users', 'in', 'millions']: ['106.4', '45.98', '25.58', '17.46', '15.01', '14.19', '10.75', '7.72', '5.53', '3.47', '3.15', '1.48']

gold: As of September 2019 , Facebook Messenger was the most popular mobile messenger app in the United States with 106.4 million unique U.S. users . Snapchat ranked second with an audience of almost 45.98 million users . As of that period , Facebook Messenger had a 57 percent mobile audience reach in the United States .
gold_template: As of 2019 , templateXValue[0] was the most popular templateTitle[1] templateXValue[0] app in the templateTitle[3] with templateYValue[max] templateScale templateYLabel[2] templateTitleSubject[0] templateYLabel[3] . templateXValue[1] ranked second with an audience of almost templateYValue[1] templateScale templateYLabel[3] . As of that period , templateXValue[0] had a 57 templateScale templateTitle[1] audience reach in the templateTitle[3] .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] in templateTitleSubject[0] as of 2019 . During the survey period , templateXValue[0] was ranked first with an average of templateYValue[max] templateScale copies .
generated: This statistic gives information on the Leading mobile messengers Platform in U.S. as of 2019 .  During the survey period , Facebook_Messenger was ranked first with an average of 106.4 millions copies .

Example 1146:
titleEntities: {'Subject': ['Latin'], 'Date': ['2019']}
title: YouTube : most watched Latin artists ' music videos 2019
X_Axis['Music', 'Video']: ['Luis_Fonsi_-_Despacito_ft._Daddy_Yankee', 'J_Balvin_Willy_William_-_Mi_Gente_(Official_Video)', 'Shakira_-_Chantaje_(Official_Video)_ft._Maluma', 'Shakira_-_Waka_Waka_(This_Time_for_Africa)_(The_Official_2010_FIFA_World_Cup™_Song)', 'Te_Bote_Remix_-_Casper_Nio_García_Darell_Nicky_Jam_Bad_Bunny_Ozuna__Video_Oficial', 'Luis_Fonsi_Demi_Lovato_-_Échame_La_Culpa_(Video_Oficial)', 'Natti_Natasha_❌_Ozuna_-_Criminal_[Official_Video]', 'Nicky_Jam_x_J._Balvin_-_X_(EQUIS)__Video_Oficial__Prod._Afro_Bros_&_Jeon']
Y_Axis['Number', 'of', 'views', 'in', 'billions']: ['6.33', '2.44', '2.41', '2.28', '1.88', '1.87', '1.84', '1.68']

gold: This year , the music video of the song from Luis Fonsi and Daddy Yankee `` Despacito '' became the first video in the history of YouTube to surpass six billion views . Despacito set a global record as the most-watched music video from Latin American artists on the platform , followed by `` Mi Gente '' , another worldwide Reggaeton hit , this time by Colombian artist J Balvin featuring French producer Willy William , with approximately 2.44 billion views .
gold_template: This year , the templateXLabel[0] templateXValue[4] of the song from templateXValue[0] and templateXValue[0] `` templateXValue[0] '' became the first templateXValue[4] in the history of templateTitle[0] to surpass templateYValue[max] templateScale templateYLabel[1] . templateXValue[0] set a global record as the most-watched templateXLabel[0] templateXValue[4] from templateTitleSubject[0] American templateTitle[4] on the platform , followed by `` templateXValue[1] '' , another worldwide Reggaeton hit , this templateXValue[3] by Colombian artist templateXValue[1] featuring French producer templateXValue[1] , with approximately templateYValue[1] templateScale templateYLabel[1] .

generated_template: templateXValue[0] had the highest templateYLabel[0] of templateYLabel[2] templateYLabel[3] of any templateXLabel[0] in the world in templateTitleDate[0] with a total of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . This was followed by templateXValue[1] templateXValue[2] with templateYValue[1] templateScale of templateYValue[2] templateScale templateYLabel[3] templateYLabel[4] .
generated: Luis_Fonsi_-_Despacito_ft._Daddy_Yankee had the highest Number of billions any Music in the world in 2019 with a total of 6.33 billions .  This was followed by J_Balvin_Willy_William_-_Mi_Gente_(Official_Video) Shakira_-_Chantaje_(Official_Video)_ft._Maluma with 2.44 billions of 2.41 billions .

Example 1147:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Sexism in advertising in the U.S. 2016
X_Axis['Share', 'of...']: ['...respondents_who_have_seen_ads_that_portay_women_as_dumb_helpless_or_incompetent', '...female_respondents_who_have_been_offended_by_sexism_in_advertising', '...respondents_who_have_seen_ads_they_find_ridiculous_in_how_they_portray_women', '...respondents_who_have_seen_advertising_they_deem_"offensive"_to_women']
Y_Axis['Unnamed:', '1']: ['85', '84', '82', '76']

gold: The statistic shows data on sexism in advertising in the United States as of July 2016 . During a survey , 76 percent of respondents stated they had seen advertising they deemed `` offensive '' to women .
gold_template: The statistic shows data on templateXValue[1] in templateXValue[1] in the templateTitle[2] as of 2016 . During a survey , templateYValue[min] templateScale of templateXValue[1] stated templateXValue[2] had templateXValue[0] templateXValue[1] templateXValue[2] deemed `` offensive '' to templateXValue[0] .

generated_template: This statistic gives information on the templateScale of the total templateYLabel[0] of people living in the templateTitle[2] as of 2018 . During the survey period , it was found that templateYValue[7] templateScale of all templateXValue[last] were living in templateXValue[0] .
generated: This statistic gives information on the percentage of the total Unnamed: of people living in the U.S. as of 2018 .  During the survey period , it was found that 76 % of all ...respondents_who_have_seen_advertising_they_deem_"offensive"_to_women were living in ...respondents_who_have_seen_ads_that_portay_women_as_dumb_helpless_or_incompetent .

Example 1148:
titleEntities: {'Subject': ['Global'], 'Date': ['2007', '2019']}
title: Global mine production of asbestos 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Production', 'in', 'thousand', 'metric', 'tons']: ['1100', '1150', '1170', '1280', '2000', '2020', '2020', '1970', '2030', '2010', '2070', '2090', '2200']

gold: This statistic depicts the total world mine production of asbestos from 2007 to 2019 . The world mine production of asbestos amounted to approximately 2.2 million metric tons in 2007 . In 2019 , total production dropped just above 1.1 million metric tons .
gold_template: This statistic depicts the total world templateTitle[1] templateYLabel[0] of templateTitle[3] from templateXValue[min] to templateXValue[max] . The world templateTitle[1] templateYLabel[0] of templateTitle[3] amounted to approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . In templateXValue[max] , total templateYLabel[0] templateNegativeTrend just above templateYValue[min] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the total amount of templateTitle[2] mined in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the total amount of production mined in the Global from 2007 to 2019 .  In 2007 , mine production amounted to 2200 thousand metric tons .

Example 1149:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2019']}
title: Population of the Netherlands 2019 , by age
X_Axis['Year']: ['0_-_20_years', '20_-_40_years', '40_-_65_years', '65_-_80_years', '80_years_and_older']
Y_Axis['Number', 'of', 'inhabitants']: ['3791838', '4309987', '5866334', '2515184', '798820']

gold: This statistic shows the population of the Netherlands in 2019 , by age . On January 1 , 2019 there were approximately 3.8 million people younger than 20 years living in the Netherlands . The largest age group was formed by those between 40 and 65 years old .
gold_template: This statistic shows the templateTitle[0] of the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[3] templateTitle[4] . On 1 , templateTitleDate[0] there were approximately templateYValue[0] templateScale people younger than templateXValue[0] living in the templateTitleSubject[0] . The largest templateTitle[4] group was formed templateTitle[3] those between templateXValue[1] and templateXValue[2] templateXValue[0] old .

generated_template: This statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the source , there were over templateYValue[max] templateScale templateYLabel[1] living in the templateTitleSubject[0] .
generated: This statistic shows the Population of the Netherlands 2019 in the Netherlands from 0_-_20_years to 65_-_80_years .  According to the source , there were over 5866334 million inhabitants living in the Netherlands .

Example 1150:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2020']}
title: Public opinion on the most important problem facing the U.S. 2020
X_Axis['Response']: ['Dissatisfaction_with_government/Poor_leadership', 'Immigration', 'Healthcare', 'Ethics/moral/religious/family_decline', 'Unifying_the_country', 'Poverty/Hunger/Homelessness', 'Lack_of_respect_for_each_other', 'Environment/Pollution/Climate_change', 'Race_relations/Racism', 'Situation_in_Iraq/ISIS', 'Foreign_policy/Foreign_aid/Focus_overseas', 'Economy_in_general', 'Guns/Gun_control', 'Gap_between_rich_and_poor', 'Education', 'Wars/War_(nonspecific)/Fear_of_war']
Y_Axis['Share', 'of', 'respondents']: ['28', '6', '6', '5', '5', '5', '4', '4', '3', '2', '2', '2', '2', '2', '2', '2']

gold: This statistic represents American adults ' view of the most important problem facing the United States . In January 2020 , 28 percent of the participants stated that poor leadership and a general dissatisfaction with the government were the most important problems facing the U.S .
gold_template: This statistic represents American adults ' view of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] the templateTitle[6] . In 2020 , templateYValue[max] templateScale of the participants stated that templateXValue[13] templateXValue[0] and a templateXValue[11] templateXValue[0] the government were the templateTitle[2] templateTitle[3] problems templateTitle[5] the templateTitle[6] .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] adult Americans on the templateTitle[0] of templateTitle[1] . During the survey , it was found that templateYValue[max] templateScale of the templateYLabel[1] stated that they had templateTitle[0] templateTitle[1] at templateXValue[0] .
generated: This statistic shows the results of a survey among U.S. adult Americans on the Public of opinion .  During the survey , it was found that 28 % of the respondents stated that they had Public opinion at Dissatisfaction_with_government/Poor_leadership .

Example 1151:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Youth unemployment rate in Italy 2018 , by region
X_Axis['Month']: ['Sicily', 'Campania', 'Calabria', 'Apulia', 'Molise', 'Basilicata', 'Liguria', 'Sardinia', 'Lazio', 'Umbria', 'Piedmont', 'Abruzzo', 'Friuli-Venezia_Giulia', 'Tuscany', 'Marche', 'Aosta_Valley', 'Veneto', 'Lombardy', 'Emilia-Romagna', 'Trentino-South_Tyrol']
Y_Axis['Unemployment', 'rate']: ['53.6', '53.6', '52.7', '43.6', '40.3', '38.7', '36.3', '35.7', '34.5', '31.1', '30', '29.7', '23.7', '22.9', '22.1', '21.7', '21', '20.8', '17.8', '11.9']

gold: The financial crisis that burst out in 2008 , alongside with the consequential problems on the labor market and high unemployment rates , affected in particular the south of Italy . Despite an overall improvement of the country 's economic situation over the last years , some significant differences between the regions still can be observed . When it comes to the unemployment rates , in 2018 Calabria , Sicily and Campania were the three regions where the share of inhabitants without a job exceeded 20 percent .
gold_template: The financial crisis that burst out in 2008 , alongside with the consequential problems on the labor market and high templateYLabel[0] rates , affected in particular the south of templateTitleSubject[0] . Despite an overall improvement of the country 's economic situation over the last years , some significant differences between the regions still can be observed . When it comes to the templateYLabel[0] rates , in templateTitleDate[0] templateXValue[2] , templateXValue[0] and templateXValue[1] were the three regions where the share of inhabitants without a job exceeded 20 templateScale .

generated_template: In templateTitleDate[0] , the highest templateYLabel[0] templateYLabel[1] was registered in the South of templateTitleSubject[0] . templateXValue[0] , templateXValue[1] , and templateXValue[2] , the three regions where the shares of citizens without a job exceeded templateYValue[2] templateScale , led in the ranking of Italian regions with the highest templateYLabel[0] rates . The disparities in templateYLabel[0] indicators can be observed not only on the regional level , but also among genders .
generated: In 2018 , the highest Unemployment rate was registered in the South of Italy .  Sicily , Campania and Calabria , the three regions where the shares of citizens without a job exceeded 52.7 % , led in the ranking of Italian regions with the highest Unemployment rates .  The disparities in Unemployment indicators can be observed not only on the regional level , but also among genders .

Example 1152:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Italy : largest rivers in 2018 , by length
X_Axis['Rivers', 'In', 'Italy']: ['Po', 'Adige', 'Tevere', 'Adda', 'Oglio', 'Tanaro', 'Ticino', 'Arno', 'Piave', 'Reno']
Y_Axis['Lenght', 'in', 'kilometers']: ['652', '410', '405', '313', '280', '276', '248', '241', '220', '211']

gold: The graph shows the largest rivers in Italy as of 2018 , by length ( in kilometers ) . According to data , the largest river in Italy is Po which is 652 kilometers long .
gold_template: The graph shows the templateTitle[1] templateXLabel[0] in templateXLabel[1] as of templateTitleDate[0] , templateTitle[4] templateTitle[5] ( in templateYLabel[1] ) . According to data , the templateTitle[1] river in templateXLabel[1] is templateXValue[0] which is templateYValue[max] templateYLabel[1] long .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[4] templateXValue[0] . The templateYLabel[0] of templateTitleSubject[0] amounted to over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: This statistic gives information on the Italy largest Lenght of the by Po .  The Lenght of Italy amounted to over 652 million kilometers in 2018 .

Example 1153:
titleEntities: {'Subject': ['Digital'], 'Date': ['2003', '2014']}
title: Digital and print advertising revenue of U.S. newspapers 2003 to 2014
Data:
             0     1      2      3      4      5      6     7      8     9    10    11
Year      2003  2004   2005   2006   2007   2008   2009  2010   2011  2012  2013  2014
Print    44.94  46.7  47.41  46.61  42.21  34.74  24.82  22.8  20.69  18.9  17.3  16.4
Digital   1.22  1.54   2.03   2.66   3.17   3.11   2.74  3.04   3.25  3.34  3.42   3.5 

gold: The statistic shows annual digital and print advertising revenue of U.S. newspapers from 2003 to 2014 . In 2014 , newspapers generated 3.5 billion U.S. dollars selling online advertising space and 16.4 billion U.S. dollars selling print ad space . In 2015 , newspaper publishers in the United States generated an estimated revenue of 14.87 billion U.S. dollars from advertising space .
gold_template: The statistic shows annual templateTitleSubject[0] and templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] templateTitle[5] from templateValue[0][0] to templateValue[0][last] . In templateValue[0][last] , templateTitle[5] generated templateValue[2][last] templateScale templateTitle[4] dollars selling online templateTitle[2] space and templateValue[1][last] templateScale templateTitle[4] dollars selling templateTitle[1] ad space . In 2015 , newspaper publishers in the templateTitle[4] generated an estimated templateTitle[3] of 14.87 templateScale templateTitle[4] dollars from templateTitle[2] space .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . In templateValue[0][0] , the templateTitle[0] of templateTitleSubject[0] employees in the templateTitleSubject[0] was templateValue[1][0] U.S. dollars .
generated: This statistic shows the Digital of print in the Digital from 2014 to 2003 , 2014 .  In 2003 , the Digital of employees in the Digital was 44.94 U.S. dollars .

Example 1154:
titleEntities: {'Subject': ['Chicago Cubs'], 'Date': ['2001', '2018']}
title: Chicago Cubs revenue 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['452', '457', '434', '340', '302', '266', '274', '266', '258', '246', '239', '214', '197', '179', '170', '156', '143', '131']

gold: The statistic depicts the revenue of the Chicago Cubs from 2001 to 2018 . In 2018 , the revenue of the Major League Baseball franchise amounted to 452 million U.S. dollars.The Chicago Cubs are owned by the Ricketts family , who bought the franchise for 700 million U.S. dollars in 2009 .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] are owned by the Ricketts family , who bought the franchise for 700 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[9] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Chicago Cubs , a franchise of the National Football League , from 2001 to 2018 .  In 2018 , the Revenue of the Chicago Cubs was 457 452 U.S. dollars .

Example 1155:
titleEntities: {'Subject': ['Value'], 'Date': ['2018']}
title: Value of the leading merchandise exporters worldwide 2018 , by exporting nation
X_Axis['Exporting', 'nation']: ['China', 'Extra-EU_(28)', 'United_States', 'Japan', 'Republic_of_Korea', 'Hong_Kong_China', 'Mexico', 'Canada', 'Russian_Federation', 'Singapore', 'United_Arab_Emirates', 'Chinese_Taipei', 'India', 'Switzerland', 'Kingdom_of_Saudi_Arabia']
Y_Axis['Export', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['2487', '2309', '1664', '738', '605', '569', '451', '450', '444', '413', '346', '336', '326', '311', '299']

gold: This statistic shows the value of the leading merchandise exporters worldwide in 2018 , by exporting nation . In that year , the United States was the third largest merchandise exporting nation in the world , with a value amounting to about 1.66 trillion U.S. dollars .
gold_template: This statistic shows the templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] templateXLabel[1] . In that year , the templateXValue[2] was the third largest templateTitle[2] templateXLabel[0] templateXLabel[1] in the world , with a templateYLabel[1] amounting to about templateYValue[2] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This graph shows the estimated templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[3] in templateTitleDate[0] , by templateXLabel[0] . In this year , the templateXValue[0] generated approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] of templateYLabel[1] templateYLabel[2] .
generated: This graph shows the estimated Export of value billion in the exporters in 2018 , by Exporting .  In this year , the China generated approximately 2487 billion U.S. dollars of value billion .

Example 1156:
titleEntities: {'Subject': ['Forecast Google Glass'], 'Date': ['2014', '2018']}
title: Forecast Google Glass unit sales 2014 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014']
Y_Axis['Unit', 'sales', 'in', 'millions']: ['21.15', '10.62', '3.74', '2.47', '0.83']

gold: Here you can see a forecast for the sale of Google Glasses from 2014 to 2018 . The source predicts that Google 's product will sell well and that by 2018 will sell more than 21 million units .
gold_template: Here you can see a templateTitleSubject[0] for the sale of templateTitleSubject[0] Glasses from templateXValue[min] to templateXValue[max] . The source predicts that templateTitleSubject[0] 's product will sell well and that by templateXValue[max] will sell more than templateYValue[max] templateScale units .

generated_template: In templateXValue[max] , the templateYLabel[1] for smartwatches are expected to reach around templateYValue[idxmax(X)] templateScale units templateTitle[3] , nearly doubling the templateYLabel[1] total templateYLabel[2] templateYLabel[3] templateYLabel[4] . Apple produces one of the most popular popular devices in the world , the Apple Watch , which gives the company one of the highest market shares among all templateTitleSubject[0] manufacturers . Wearables The wearable electronic device subsector of the broader consumer electronics industry consists of everything from fitness trackers , to smart watches , to smart fabrics .
generated: In 2018 , the sales for smartwatches are expected to reach around 21.15 millions units unit , nearly doubling the sales total millions .  Apple produces one of the most popular devices in the world , the Apple Watch , which gives the company one of the highest market shares among all Forecast Google Glass manufacturers .  Wearables The wearable electronic device subsector of the broader consumer electronics industry consists of everything from fitness trackers , to smart watches , to smart fabrics .

Example 1157:
titleEntities: {'Subject': ['Monsanto'], 'Date': ['2013', '2017']}
title: Monsanto 's number of regular and temporary workers worldwide 2013 to 2017
Data:
                           0                  1
Worker Type  Regular_workers  Temporary_workers
2013                   21900               4300
2014                   22400               4600
2015                   22500               3000
2016                   20800               3300
2017                   20500               2800 

gold: This statistic depicts the number of regular and temporary employees working for Monsanto as of August 2013 to August 2017 . The agrochemical company employed 2,800 temporary workers worldwide as of August 2017 . Monsanto was an agricultural company specialized on genetically engineered seeds .
gold_template: This statistic depicts the templateTitle[2] of templateValue[0][0] and templateValue[0][1] employees working for templateTitleSubject[0] as of 2013 to 2017 . The agrochemical company employed templateValue[5][min] templateValue[0][1] templateValue[0][0] templateTitle[6] as of 2017 . templateTitleSubject[0] was an agricultural company specialized on genetically engineered seeds .

generated_template: The statistic shows the templateTitleSubject[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] , templateTitle[7] templateTitle[8] . According to the source , templateValue[1][0] templateScale of those who were sold in templateTitleSubject[0] .
generated: The statistic shows the Monsanto of 's number regular temporary , 2013 2017 .  According to the source , 21900 % of those who were sold in Monsanto .

Example 1158:
titleEntities: {'Subject': ['Golden State Warriors', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Golden State Warriors ( NBA ) games 2015/16
X_Axis['Year']: ['2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['79.84', '47.38', '44.27', '35.7', '34.13', '34.13', '37.5', '39.0', '31.13', '26.63']

gold: This graph depicts the average ticket price for Golden State Warriors games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 26.63 U.S. dollars . Golden State Warriors The Golden State Warriors are a professional basketball team that is part of the Western Conference in the National Basketball Association ( NBA ) .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] Warriors templateTitle[7] of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] Warriors The templateTitleSubject[0] Warriors are a professional basketball team that is part of the Western Conference in the National Basketball Association ( templateTitleSubject[1] ) .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Golden State Warriors games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Average ticket price was 26.63 U.S. dollars .

Example 1159:
titleEntities: {'Subject': ['Sweden'], 'Date': ['1993', '2018']}
title: World ranking of Sweden 's national football team 1993 to 2018
X_Axis['Year']: ['1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['FIFA', 'World', 'Ranking', 'position']: ['9', '3', '13', '17', '18', '18', '16', '23', '16', '25', '19', '13', '14', '14', '24', '32', '42', '33', '18', '20', '27', '44', '35', '41', '18', '23']

gold: The highest position of Sweden 's national football team in the FIFA World Ranking ever reached was in 1994 , when they came at fourth place . In this year , Sweden was in the play-off for the third place in the 1994 FIFA World Cup . The teams lowest rank was 44 , which was reached in 2014 .
gold_template: The highest templateYLabel[3] of templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateYLabel[0] templateYLabel[1] templateYLabel[2] ever reached was in templateXValue[1] , when they came at fourth place . In this templateXLabel[0] , templateTitleSubject[0] was in the play-off for the third place in the templateXValue[1] templateYLabel[0] templateYLabel[1] Cup . The teams lowest rank was templateYValue[max] , which was reached in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the Norwegian templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . The highest templateYLabel[3] ever reached was templateYValue[min] in templateXValue[idxmin(Y)] . Rank templateYValue[max] was the lowest result of the templateTitle[6] , which was reached in templateXValue[idxmax(Y)] .
generated: This statistic shows the FIFA World Ranking of the Norwegian national football team from 1993 to 2018 .  The highest position ever reached was 3 in 1994 .  Rank 44 was the lowest result of the team , which was reached in 2014 .

Example 1160:
titleEntities: {'Subject': ['Celanese'], 'Date': ['2007', '2018']}
title: Celanese 's revenue 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['7155', '6140', '5389', '5674', '6802', '6510', '6418', '6763', '5918', '5082', '6823', '6444']

gold: This statistic shows Celanese 's revenue between 2007 and 2018 . Celanese Corporation is a U.S.-based technology and specialty materials company . In 2018 , the corporation made some 7.15 billion U.S. dollars of revenue .
gold_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] between templateXValue[min] and templateXValue[max] . templateTitleSubject[0] Corporation is a U.S.-based technology and specialty materials company . In templateXValue[max] , the corporation made some 7.15 templateScale templateYLabel[2] templateYLabel[3] of templateYLabel[0] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Celanese , a franchise of the National Football League , from 2007 to 2018 .  In 2018 , the Revenue of the Celanese was 7155 U.S. dollars .

Example 1161:
titleEntities: {'Subject': ['Major'], 'Date': []}
title: IT services : Major vendors , by revenue per employee
X_Axis['It', 'Services', 'Vendor']: ['IBM', 'ACN', 'Dell', 'CSC', 'HP', 'Atos_Origin', 'XRX/ACS', 'Capgemini', 'Wipro', 'INFY', 'CTSH', 'TCS']
Y_Axis['Revenue', 'per', 'employee', 'in', 'U.S.', 'dollars']: ['289474', '185784', '177953', '171628', '165205', '136838', '130230', '120911', '54702', '43946', '41485', '36350']

gold: The ranking shows the revenue per employee of major IT services vendors worldwide . As an example , Wipro had a revenue per employee of 54,702 U.S. dollars .
gold_template: The ranking shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] IT templateXLabel[0] templateTitle[2] worldwide . As an example , templateXValue[8] had a templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[8] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the results of a survey among the templateTitle[0] employees in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[last] to templateXValue[0] . The templateTitle[0] of templateYLabel[2] templateYLabel[3] at templateXValue[0] , however , with a day of templateYValue[7] templateScale templateYLabel[3] .
generated: This statistic shows the results of a survey among the services employees in the United Kingdom ( Major ) from TCS to IBM .  The services of employee U.S. at IBM , however with a day of 120911 % U.S. .

Example 1162:
titleEntities: {'Subject': ['World Cup'], 'Date': ['2018']}
title: FIFA World Cup winner 's prize money 2018
X_Axis['Year']: ['2018_(Russia)', '2014_(Brazil)', '2010_(South_Africa)', '2006_(Germany)', '2002_(Korea_&_Japan)']
Y_Axis['Prize', 'money', 'in', 'million', 'U.S.', 'dollars']: ['38.0', '35.0', '30.0', '19.56', '7.99']

gold: This statistic shows the prize money awarded to the winner of the FIFA World Cup from 2002 to 2018 . The winner of the 2014 World Cup was given 35 million U.S. dollars in prize money .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] awarded to the templateTitle[3] of the templateTitle[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . The templateTitle[3] of the templateXValue[1] templateTitleSubject[0] was given templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in templateYLabel[0] templateYLabel[1] .

generated_template: The statistic shows the total templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateYLabel[0] templateYLabel[1] generated templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the total Prize money of the FIFA World Cup worldwide from 2002_(Korea_&_Japan) to 2018_(Russia) .  In 2018_(Russia) , the global Prize money generated 38.0 U.S. dollars .

Example 1163:
titleEntities: {'Subject': ['Latin America'], 'Date': ['2019']}
title: Latin America : minimum monthly wages in 2019 , by country
X_Axis['Country']: ['Uruguay', 'Chile', 'Ecuador', 'Paraguay', 'Bolivia', 'Argentina', 'Peru', 'Brazil', 'Colombia', 'Mexico', 'Venezuela']
Y_Axis['Monthly', 'minimum', 'wage', 'in', 'U.S.', 'dollars']: ['462.3', '431.7', '394.0', '355.6', '298.0', '297.9', '275.7', '257.3', '254.7', '156.9', '7.0']

gold: This statistic displays the minimum monthly wage in selected Latin American countries for 2019 . According to the source , Uruguay is the country with the highest basic salary in the region , with a minimum wage of 462.3 U.S. dollars per month . On the other side of the spectrum is Venezuela , with a minimum monthly salary of only seven dollars .
gold_template: This statistic displays the templateYLabel[1] templateYLabel[0] templateYLabel[2] in selected templateTitleSubject[0] American countries for templateTitleDate[0] . According to the source , templateXValue[0] is the templateXLabel[0] with the highest basic salary in the region , with a templateYLabel[1] templateYLabel[2] of templateYValue[max] templateYLabel[3] templateYLabel[4] per month . On the other side of the spectrum is templateXValue[last] , with a templateYLabel[1] templateYLabel[0] salary of only templateYValue[min] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[7] templateTitleDate[0] , templateTitle[7] templateTitle[8] . In that year , templateXValue[0] had the largest templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Monthly of the America minimum monthly wages 2019 in country 2019 , country .  In that year , Uruguay had the largest Monthly of 462.3 wage U.S. .

Example 1164:
titleEntities: {'Subject': ['Turkey'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Turkey 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['12489.9', '11901.69', '11373.64', '10635.82', '9683.57', '8957.89', '9405.32', '10551.14', '10817.39', '10914.89', '12022.18', '12395.37', '11552.69', '11140.95', '10475.57', '8881.76', '10691.76', '9562.94', '7898.99', '7277.94', '5952.83', '4642.9', '3589.39', '3053.28', '4218.86', '4019.0', '4387.31', '4220.6', '4095.58', '3877.1', '3024.16', '4259.48', '3817.29', '3686.18', '3735.77', '2707.19', '2335.49', '2259.89', '1982.41', '1833.86', '1667.26']

gold: The statistic shows the gross domestic product ( GDP ) per capita in Turkey from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a change in it is a sign of economic growth .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a change in it is a sign of economic growth .

generated_template: The statistic shows the templateYLabel[0] of the total templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . According to the total market value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows the GDP of the total capita in the Turkey from 1984 to 2017 , with projections up until 2024 .  According to the total market value of all goods and services produced in a country in a Year .  It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

Example 1165:
titleEntities: {'Subject': ['Brexit', 'United Kingdom'], 'Date': ['2016']}
title: Brexit votes in the United Kingdom by social class 2016
Data:
                                 0                         1                        2                        3
Response  Lower_Working-Class_(DE)  Upper_Working-Class_(C2)  Lower_Middle-Class_(C1)  Upper_Middle-Class_(AB)
Remain                          36                        36                       51                       57
Leave                           64                        64                       49                       43 

gold: This statistic shows the result of a survey asking respondents how they voted in the Brexit referendum of 2016 , by social class . Of respondents , 57 percent of those in the AB social class advised they had voted to remain , while just 36 percent of C2 and DE respondents said they had voted to remain .
gold_template: This statistic shows the result of a survey asking respondents how they voted in the templateTitleSubject[0] referendum of templateTitleDate[0] , templateTitle[4] templateTitle[5] templateTitle[6] . Of respondents , templateValue[1][last] templateScale of those in the AB templateTitle[5] templateTitle[6] advised they had voted to templateLabel[1][0] , while just templateValue[1][0] templateScale of C2 and DE respondents said they had voted to templateLabel[1][0] .

generated_template: In the templateTitleSubject[0] referendum that took place in the templateTitleSubject[1] on 23 , templateTitleDate[0] , templateValue[1][0] templateScale of men voted to templateLabel[2][0] the European Union , compared with templateValue[2][last] templateScale of women . The referendum itself was won templateTitle[4] the templateLabel[2][0] side , after they won 51.9 templateScale of the vote overall .
generated: In the Brexit referendum that took place in the United Kingdom on 23 , 2016 36 % of men voted to Leave the European Union , compared with 43 % of women .  The referendum itself was won by the Leave side , after they won 51.9 % of the vote overall .

Example 1166:
titleEntities: {'Subject': ['American Airlines', 'US Airways'], 'Date': ['2015', '2015']}
title: American Airlines and US Airways - key figures 2015
X_Axis['Key', 'area']: ['Number_of_countries_served', 'Number_of_daily_departures', 'Number_of_aircraft_in_fleet', 'Number_of_passengers_(in_millions)', 'Revenue_passenger_kilometers_(in_millions)', 'Available_seat_kilometres_(in_millions)', 'Passenger_load_factor_(in_percent)']
Y_Axis['Unnamed:', '1']: ['55.0', '6985.0', '1549.0', '197.3', '350627.0', '427533.0', '82.0']

gold: This statistic represents combined key figures for American Airlines and US Airways ( as of June 2015 ) . American Airlines and US Airways had passenger traffic amounting to around 351 billion revenue passenger kilometers .
gold_template: This statistic represents combined templateXLabel[0] templateTitle[5] for templateTitleSubject[0] and templateTitleSubject[1] ( as of 2015 ) . templateTitleSubject[0] and templateTitleSubject[1] had templateXValue[4] traffic amounting to around 351 templateScale templateXValue[4] kilometers .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the United Kingdom ( templateTitleSubject[0] ) as of 2019 , sorted templateTitle[5] templateXLabel[0] . During that period of time , it was found that templateYValue[min] templateScale of all templateXValue[last] were templateXValue[last] .
generated: This statistic shows the American Airlines US in the United Kingdom ( American Airlines ) as of 2019 , sorted figures Key .  During that period of time , it was found that 55.0 % of all Passenger_load_factor_(in_percent) were .

Example 1167:
titleEntities: {'Subject': ['Obama'], 'Date': ['2012']}
title: 2012 election : support for Romney and Obama , by ethnic group
Data:
                           0         1      2         3
Race    White_(non-Hispanic)  Nonwhite  Black  Hispanic
Obama                     37        79     90        69
Romney                    59        15      5        25 

gold: This survey shows the voter support for Barack Obama and Mitt Romney in the 2012 election as of October 28 , by ethnic group . If the elections were held that day , about 90 percent of Black or African American voters would vote for Barack Obama .
gold_template: This survey shows the voter templateTitle[2] templateTitle[3] Barack templateTitleSubject[0] and Mitt templateTitle[4] in the templateTitleDate[0] templateTitle[1] as of October 28 , templateTitle[6] templateTitle[7] templateTitle[8] . If the elections were held that day , about templateValue[1][2] templateScale of templateValue[0][2] or African American voters would vote templateTitle[3] Barack templateTitleSubject[0] .

generated_template: The statistic shows the results of a survey concerning the country in templateTitleDate[0] , templateTitle[5] templateTitle[6] . According to the survey findings , templateValue[1][0] templateScale of templateLabel[2][0] respondents stated that they were templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] old .
generated: The statistic shows the results of a survey concerning the country in 2012 , Obama by .  According to the survey findings , 37 % of Romney respondents stated that they were Obama old .

Example 1168:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2008', '2018']}
title: Poverty risk rate in Belgium 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['At-risk-of-poverty', 'rate']: ['16.4', '15.9', '15.5', '14.9', '15.5', '15.1', '15.3', '15.3', '14.6', '14.6', '14.7']

gold: Between 2008 and 2018 , roughly 15 percent of the Belgium population was at risk of poverty , defined by Statistics Belgium as 60 percent of the average disposable income in the country . People with an income lower than this are considered poor . In 2018 , this definition matched nearly 16 percent of the Belgians .
gold_template: Between templateXValue[min] and templateXValue[max] , roughly templateYValue[3] templateScale of the templateTitleSubject[0] population was at templateTitle[1] of templateTitle[0] , defined by Statistics templateTitleSubject[0] as 60 templateScale of the average disposable income in the country . People with an income lower than this are considered poor . In templateXValue[max] , this definition matched nearly templateYValue[max] templateScale of the Belgians .

generated_template: This statistic shows the templateTitle[0] of templateYLabel[3] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .
generated: This statistic shows the Poverty of rate Belgium 2008 of Belgium from 2008 to 2018 .  The Poverty risk rate Belgium 2008 was 16.4 rate in 2018 .

Example 1169:
titleEntities: {'Subject': ['South Africa'], 'Date': ['2018']}
title: Age structure in South Africa 2018
Data:
                        0      1      2      3      4      5      6      7      8      9     10
Year                 2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
0-14 years          29.08  29.15  29.21  29.29  29.35  29.43  29.53  29.62  29.67  29.82     30
15-64 years          65.6  65.63  65.66  65.68   65.7  65.68  65.63  65.58  65.58  65.43  65.27
65 years and older   5.32   5.22   5.12   5.03   4.95   4.89   4.84    4.8   4.75   4.75   4.73 

gold: This statistic shows the age structure in South Africa from 2008 to 2018 . In 2018 , about 29.08 percent of South Africa 's total population were aged 0 to 14 years .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][min] templateScale of templateTitleSubject[0] 's total population were aged 0 to 14 templateLabel[1][1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][min] templateScale of templateTitleSubject[0] 's total population were aged 0 to 14 templateLabel[1][1] .
generated: This statistic shows the Age structure in South Africa from 2008 to 2018 .  In 2018 , about 29.08 % of South Africa 's total population were aged 0 to 14 years .

Example 1170:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019', '2019']}
title: Canada - average working hours per week 2019
X_Axis['Average', 'usual', 'weekly', 'hours']: ['1_to_14_hours', '15_to_29_hours', '30_to_34_hours', '35_to_39_hours', '40_hours', '41_to_49_hours', '50_hours_or_more']
Y_Axis['Number', 'of', 'employed', 'person', 'in', 'thousands']: ['1151.0', '2459.6', '1469.2', '4287.8', '7282.5', '1044.2', '1361.3']

gold: This statistic shows the total number of employed persons in Canada in 2019 , distinguished by average usual weekly working hours . In 2019 , about 7.28 million Canadians usually worked 40 hours per week .
gold_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] persons in templateTitleSubject[0] in templateTitleDate[0] , distinguished by templateXLabel[0] templateXLabel[1] templateXLabel[2] templateTitle[2] templateXValue[0] . In templateTitleDate[0] , about templateYValue[max] templateScale Canadians usually worked templateXValue[4] templateXValue[0] templateTitle[4] templateTitle[5] .

generated_template: The statistic shows the templateScale of the employees of the templateYLabel[1] who were using templateTitle[0] as of 2019 , sorted templateTitle[8] templateTitle[9] . According to the source , templateValue[1][0] templateScale of people who had templateTitle[4] on that year .
generated: The statistic shows the thousands of the employees of the employed who were using Canada as of 2019 , sorted 2019 .  According to the source , thousands of people who had per on that year .

Example 1171:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Top 10 causes of infant mortality in Canada in 2018
X_Axis['Cause']: ['Congenital_malformations_deformations_and_chromosomal_abnormalities', 'Disorders_related_to_short_gestation_and_low_birth_weight_not_elsewhere_classified', 'Newborn_affected_by_maternal_complications_of_pregnancy', 'Newborn_affected_by_complications_of_placenta_cord_and_membranes', 'Complications_of_labor_and_delivery', 'Intrauterine_hypoxia_and_birth_asphyxia', 'Neonatal_haemorrhages', 'Bacterial_sepsis_of_newborn', 'Respiratory_distress_of_newborn', 'Accidents_(unintentional_injuries)', 'Diseases_of_the_circulatory_system', 'Sudden_infant_death_syndrome']
Y_Axis['Number', 'of', 'deaths']: ['412', '171', '167', '93', '65', '51', '49', '47', '19', '17', '17', '10']

gold: This statistic shows the leading causes of infant mortality in Canada in 2018 . In 2018 , the leading cause of death of infants less than one year of age , at 412 deaths , was congenital malformations , deformations and chromosomal abnormalities .
gold_template: This statistic shows the leading templateTitle[2] of templateXValue[last] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the leading templateXLabel[0] of templateXValue[last] of infants less than one year of age , at templateYValue[max] templateYLabel[1] , was templateXValue[0] , templateXValue[0] and templateXValue[0] .

generated_template: This statistic gives information on the most popular templateXLabel[0] templateXLabel[1] of templateTitle[1] in the templateTitle[0] in templateTitleDate[0] . During the survey period , it was found that templateYValue[1] templateScale of templateYLabel[1] were templateXValue[0] , whereas templateXValue[2] were templateXValue[last] templateXValue[2] .
generated: This statistic gives information on the most popular Cause of 10 in the Top in 2018 .  During the survey period , it was found that 171 % of deaths were Congenital_malformations_deformations_and_chromosomal_abnormalities , whereas Newborn_affected_by_maternal_complications_of_pregnancy were Sudden_infant_death_syndrome Newborn_affected_by_maternal_complications_of_pregnancy .

Example 1172:
titleEntities: {'Subject': ['FedEx'], 'Date': ['2009', '2019']}
title: FedEx 's revenue 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['69.69', '65.45', '60.32', '50.37', '47.45', '45.57', '44.29', '42.68', '39.3', '34.73', '35.5']

gold: The timeline depicts the FedEx Corporation 's revenue from the fiscal year of 2008 to the fiscal year of 2018 . In the fiscal year of 2018 , the company generated some 69.69 billion U.S. dollars in revenue . FedEx is ranked among the leading logistics companies in the United States .
gold_template: The timeline depicts the templateTitleSubject[0] Corporation templateTitle[1] templateYLabel[0] from the fiscal templateXLabel[0] of 2008 to the fiscal templateXLabel[0] of templateXValue[1] . In the fiscal templateXLabel[0] of templateXValue[1] , the company generated some templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] . templateTitleSubject[0] is ranked among the leading logistics companies in the country .

generated_template: The statistic shows the total templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateTitleSubject[0] generated approximately templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] .
generated: The statistic shows the total Revenue of FedEx from 2009 to 2019 .  In 2018 , FedEx generated approximately 65.45 billion U.S. dollars in Revenue .

Example 1173:
titleEntities: {'Subject': ['England'], 'Date': ['2016', '2019']}
title: Football participation England 2016 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016']
Y_Axis['Number', 'of', 'participants']: ['2056900', '2095900', '2299700', '2299000']

gold: Football is not only the most popular sport to watch and spectate in the United Kingdom ( UK ) and England but also the most popular team sport to participate in , with roughly 2.1 million people in England playing at least twice per month . 44.7 percent of 11 to 15 year old and 31.4 percent of 5 to 10 year old children play football on a monthly basis . Football nation Being home to not only the biggest football league but the biggest and most successful sports league in the world , the Premier League , England has many football fans who support the sport with famous clubs such as Manchester United , Liverpool FC , Arsenal FC or Manchester City .
gold_template: templateTitle[0] is not only the most popular sport to watch and spectate in the United Kingdom ( UK ) and templateTitleSubject[0] but also the most popular team sport to participate in , with roughly templateYValue[min] templateScale people in templateTitleSubject[0] playing at least twice per month . 44.7 templateScale of 11 to 15 templateXLabel[0] old and 31.4 templateScale of 5 to 10 templateXLabel[0] old children play templateTitle[0] on a monthly basis . templateTitle[0] nation Being home to not only the biggest templateTitle[0] league but the biggest and most successful sports league in the world , the Premier League , templateTitleSubject[0] has many templateTitle[0] fans who support the sport with famous clubs such as Manchester United , Liverpool FC , Arsenal FC or Manchester City .

generated_template: The statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[7] templateTitle[8] . During that period of time , it was found that half of respondents stated that they had a total of templateYValue[1] templateScale .
generated: The statistic shows the Number of adults in the England who were using Football as of 2019 , sorted 2019 .  During that period of time , it was found that half of respondents stated that they had a total of 2095900 % .

Example 1174:
titleEntities: {'Subject': ['Saint Lucia'], 'Date': ['2024']}
title: Inflation rate in Saint Lucia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2', '2', '2', '2.08', '2.28', '2.09', '1.99', '0.11', '-3.08', '-0.98', '3.52', '1.47', '4.18', '2.77', '3.25', '-0.16', '5.55', '2.82', '3.56', '3.91', '1.46', '1.03', '-0.26', '5.27', '4.85', '2.42', '3.2', '-0.01', '0.93', '5.86', '2.66', '0.78', '5.66', '5.54', '4.33', '4.4', '0.83', '7.04', '2.17', '0.5', '2.05']

gold: This statistic shows the average inflation rate in Saint Lucia from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Saint Lucia amounted to about 1.99 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the average Inflation rate in Saint Lucia from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Saint Lucia amounted to about 1.99 % compared to the previous Year .

Example 1175:
titleEntities: {'Subject': ['Norwegian'], 'Date': ['2011', '2015']}
title: Companies on the Norwegian insurance market 2011 to 2015
Data:
                    0     1     2     3     4
Year             2015  2014  2013  2012  2011
Domestic market   119   119   119   118   117
Total market      119   119   119   118   117 

gold: The statistic shows the number of companies operating on the insurance market in Norway from 2011 to 2015 . Throughout the period the number of insurance companies on the domestic market remained stable at 119 companies between 2013 and 2015 .
gold_template: The statistic shows the number of templateTitle[0] operating on the templateTitle[2] templateTitle[3] in Norway from templateValue[0][last] to templateValue[0][0] . Throughout the period the number of templateTitle[2] templateTitle[0] on the templateLabel[1][0] templateTitle[3] remained stable at templateValue[1][0] templateTitle[0] between templateValue[0][2] and templateValue[0][0] .

generated_template: In templateValue[0][0] , there was a templateLabel[2][0] of templateValue[1][3] templateLabel[1][0] templateTitle[2] templateTitle[0] operating in the templateTitleSubject[0] templateTitle[2] templateTitle[3] . The templateTitleSubject[0] templateTitle[2] templateTitle[3] employs over 29 thousand staff and paid out more than seven templateScale euros in life templateTitle[2] benefits in templateValue[0][1] . The latest figures show that the largest templateTitle[2] group in Austria by templateTitle[3] share was the Vien templateTitle[2] Group .
generated: In 2015 , there was a Total of 118 Domestic insurance Companies operating in the Norwegian insurance market .  The Norwegian insurance market employs over 29 thousand staff and paid out more than seven billion euros in life insurance benefits in 2014 .  The latest figures show that the largest insurance group in Austria by market share was the Vien insurance Group .

Example 1176:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Percentage of childless women , by age U.S. 2018
X_Axis['Year']: ['15_to_19_years_old', '20_to_24_years_old', '25_to_29_years_old', '30_to_34_years_old', '35_to_39_years_old', '40_to_44_years_old', '45_to_50_years_old']
Y_Axis['Percentage', 'of', 'childless', 'women']: ['96.9', '78.6', '54.2', '33.6', '20', '15', '15.4']

gold: In 2018 , 96.9 percent of women between the ages of 15 and 19 years old in the United States were childless -- the most out of any age group . In the same year , 15 percent of women between the ages of 40 and 44 years old were childless . Demographics of women without children As of 2018 , a little less than half of all women in the U.S. were childless .
gold_template: In templateTitleDate[0] , templateYValue[max] templateScale of templateYLabel[2] between the ages of templateXValue[0] and templateXValue[0] old in the templateTitle[5] were templateYLabel[1] -- the most out of any templateTitle[4] group . In the same templateXLabel[0] , templateXValue[0] templateScale of templateYLabel[2] between the ages of templateXValue[5] and templateXValue[5] templateXValue[0] were templateYLabel[1] . Demographics of templateYLabel[2] without children As of templateTitleDate[0] , a little less than half of all templateYLabel[2] in the templateTitleSubject[0] were templateYLabel[1] .

generated_template: This graph shows the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[5] in templateTitleDate[0] . In templateXValue[0] , there were a total of templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] templateXValue[last] templateXValue[0] .
generated: This graph shows the average Percentage of childless women in the U.S. in 2018 .  In 15_to_19_years_old , there were a total of 96.9 percentage childless women 45_to_50_years_old 15_to_19_years_old .

Example 1177:
titleEntities: {'Subject': ['MLB'], 'Date': ['2019']}
title: MLB all-time RBI leaders 2019
X_Axis['Players']: ['Hank_Aaron', 'Babe_Ruth', 'Alex_Rodriguez', 'Cap_Anson', 'Albert_Pujols', 'Barry_Bonds', 'Lou_Gehrig', 'Stan_Musial', 'Ty_Cobb', 'Jimmie_Foxx', 'Eddie_Murray', 'Willie_Mays', 'Mel_Ott', 'Carl_Yastrzemski', 'Ted_Williams', 'Ken_Griffey', 'Rafael_Palmeiro', 'Dave_Winfield', 'Manny_Ramirez', 'Al_Simmons', 'Frank_Robinson', 'David_Ortiz', 'Honus_Wagner', 'Adrian_Beltre', 'Frank_Thomas', 'Reggie_Jackson', 'Jim_Thome', 'Cal_Ripken', 'Miguel_Cabrera', 'Gary_Sheffield']
Y_Axis['Number', 'of', 'runs', 'batted', 'in']: ['2297', '2214', '2086', '2075', '2075', '1996', '1995', '1951', '1944', '1922', '1917', '1903', '1860', '1844', '1839', '1836', '1835', '1833', '1831', '1828', '1812', '1768', '1732', '1707', '1704', '1702', '1699', '1695', '1694', '1676']

gold: This statistic shows the MLB all-time RBI leaders as of October 9 , 2019 . Hank Aaron has batted in the most runs in Major League Baseball history with 2,297 RBI .
gold_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] as of October 9 , templateTitleDate[0] . templateXValue[0] has templateYLabel[2] in the most templateYLabel[1] in Major League Baseball history with templateYValue[max] templateTitle[2] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In templateXValue[0] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the average Number runs of the RBI leaders MLB in 2019 , Players .  In Hank_Aaron , the MLB Number runs of MLB amounted to 2297 batted .

Example 1178:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2009', '2019']}
title: Laptop penetration in the United Kingdom ( UK ) 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Percentage', 'of', 'population']: ['60', '63', '64', '64', '65', '63', '66', '62', '59', '52', '47']

gold: Laptop computing has come a long way in the past decade , with increased power , battery life , portability and display quality all enhancing the attractiveness of a portable computer over a desktop computer . The penetration rate of the laptop in the United Kingdom reflects this , increasing from 47 percent in 2009 , to 60 percent in 2019 . Laptops penetration highest among 35 - 54 year olds Laptop penetration rates are above 50 percent across each of the main age groups , and highest among those aged 35 - 54 years .
gold_template: templateTitle[0] computing has come a long way in the past decade , with templatePositiveTrend power , battery life , portability and display quality all enhancing the attractiveness of a portable computer over a desktop computer . The templateTitle[1] rate of the templateTitle[0] in the templateTitleSubject[0] reflects this , templatePositiveTrend from templateYValue[min] templateScale in templateXValue[idxmin(Y)] , to templateYValue[0] templateScale in templateXValue[max] . Laptops templateTitle[1] highest among 35 - 54 templateXLabel[0] olds templateTitle[0] templateTitle[1] rates are above 50 templateScale across each of the main age groups , and highest among those aged 35 - 54 years .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the penetration United in United Kingdom from 2009 to 2019 .  In 2019 , about 60 percentage of United Kingdom 's population lived below the penetration line .

Example 1179:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2005', '2017']}
title: Argentina : poverty headcount ratio at 3.20 U.S. dollars per day 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Percentage', 'of', 'population']: ['2', '2.4', '2.8', '2.4', '2.7', '2.6', '3.7', '6', '6.2', '6.9', '8', '10']

gold: The poverty rate in Argentina has been oscillating throughout recent years . In 2017 , two percent of the Argentine population were living on less than 3.20 U.S. dollars per day , down from ten percent of the population in 2005.In nominal terms , household income per capita in Argentina has shown a significant improvement in 2017 .
gold_template: The templateTitle[1] rate in templateTitleSubject[0] has been oscillating throughout recent years . In templateXValue[max] , templateYValue[min] templateScale of the Argentine templateYLabel[1] were living on less than templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] , down from templateYValue[max] templateScale of the templateYLabel[1] in 2005.In nominal terms , household income templateTitle[7] capita in templateTitleSubject[0] has shown a significant improvement in templateXValue[idxmin(Y)] .

generated_template: The templateTitle[1] rate in templateTitleSubject[0] has been templateNegativeTrend lately . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale of the templateYLabel[1] of the South American country was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] templateScale observed in 2006.Furthermore , the percentages of Chileans with credit card have templatePositiveTrend throughout recent years .
generated: The poverty rate in Argentina has been decreasing lately .  In 2017 , approximately 2 percentage of the population of the South American country was living on less than 3.20 U.S. dollars per , down from 10 percentage observed in 2006.Furthermore , the percentages of Chileans with credit card have increased throughout recent years .

Example 1180:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Number of missing persons files in the U.S. by race 2019
X_Axis['Race']: ['White', 'Black', 'Unknown', 'Asian', 'Indian']
Y_Axis['Number', 'of', 'missing', 'persons']: ['359768', '205802', '20587', '12671', '10447']

gold: In 2019 , there were 359,768 cases filed by the NCIC where the race of the reported missing was white . In the same year , 20,587 people were missing whose race was unknown . What is the NCIC ? The National Crime Information Center ( NCIC ) is a digital database that stores crime data for the United States , so criminal justice agencies can access it .
gold_template: In templateTitleDate[0] , there were templateYValue[max] cases filed templateTitle[5] the NCIC where the templateXLabel[0] of the reported templateYLabel[1] was templateXValue[0] . In the same year , templateYValue[2] people were templateYLabel[1] whose templateXLabel[0] was templateXValue[2] . What is the NCIC ? The National Crime Information Center ( NCIC ) is a digital database that stores crime data for the templateTitle[4] , so criminal justice agencies can access it .

generated_template: This statistic shows the time templateYLabel[0] of the templateYLabel[1] templateYLabel[2] in the country in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , some templateYValue[max] templateScale of the templateYLabel[1] templateYLabel[2] were templateXValue[0] .
generated: This statistic shows the time Number of the missing persons in the country in 2019 , by Race .  In 2019 , some 359768 % of the missing persons were White .

Example 1181:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Metropolitan areas with the highest birth rate in the U.S. in 2018
X_Axis['City']: ['Hinesville_GA', 'Jacksonville_NC', 'Provo-Orem_UT', 'Laredo_TX', 'Logan_UT-ID', 'Clarksville_TN-KY', 'McAllen-Edinburg-Mission_TX', 'Odessa_TX', 'Watertown-Fort_Drum_NY', 'Midland_TX', 'Idaho_Falls_ID', 'Fayetteville_NC', 'Fairbanks_AK', 'Killeen-Temple_TX', 'El_Centro_CA', 'Ogden-Clearfield_UT', 'Brownsville-Harlingen_TX', 'Yakima_WA', 'Visalia-Porterville_CA', 'Bakersfield_CA', 'Salt_Lake_City_UT', 'Fresno_CA', 'Elkhart-Goshen_IN', 'Madera_CA', 'Yuba_City_CA', 'El_Paso_TX', 'Merced_CA', 'Hanford-Corcoran_CA', 'Twin_Falls_ID', 'Kennewick-Richland_WA', 'Sioux_Falls_SD', 'Enid_OK', 'Hammond_LA', 'Anchorage_AK', 'Lake_Charles_LA', 'Fargo_ND-MN', 'Grand_Island_NE', 'Houston-The_Woodlands-Sugar_Land_TX', 'Greeley_CO', 'Yuma_AZ', 'Modesto_CA', 'Omaha-Council_Bluffs_NE-IA', 'Lawton_OK', 'Bismarck_ND', 'Abilene_TX', 'Grand_Forks_ND-MN', 'Sioux_City_IA-NE-SD', 'Salinas_CA', 'Des_Moines-West_Des_Moines_IA', 'Fayetteville-Springdale-Rogers_AR-MO']
Y_Axis['Birth', 'rate', '(births', 'per', '1,000', 'population)']: ['20.36', '19.77', '19.43', '18.22', '17.42', '17.05', '16.82', '16.73', '16.63', '16.46', '16.16', '15.99', '15.96', '15.92', '15.68', '15.57', '15.44', '15.38', '14.96', '14.96', '14.96', '14.9', '14.86', '14.82', '14.78', '14.74', '14.71', '14.7', '14.68', '14.66', '14.49', '14.48', '14.45', '14.4', '14.38', '14.3', '14.2', '14.15', '14.09', '14.05', '14.04', '14.02', '14.01', '13.91', '13.89', '13.87', '13.86', '13.79', '13.77', '13.73']

gold: This statistic shows the top 50 metropolitan areas with the highest birth rate in the United States in 2018 . Birth rate is the total number of live births per 1,000 of a population in a particular year . Hinesville metro area in Georgia was ranked first with 20.36 births per 1,000 population in 2018 .
gold_template: This statistic shows the top 50 templateTitle[0] templateTitle[1] templateTitle[2] the templateTitle[3] templateYLabel[0] templateYLabel[1] in the templateTitle[6] in templateTitleDate[0] . templateYLabel[0] templateYLabel[1] is the total number of live births templateYLabel[3] 1,000 of a population in a particular year . templateXValue[0] metro area in Georgia was ranked first templateTitle[2] templateYValue[max] births templateYLabel[3] 1,000 population in templateTitleDate[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateXValue[0] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , ranked by the templateYLabel[0] templateYLabel[1] of templateXValue[0] with over templateYValue[max] templateScale templateYLabel[3] . The ranking ranking ranking was followed by templateXValue[1] with templateYValue[1] templateScale templateYLabel[3] .
generated: This statistic shows the Metropolitan areas highest birth of Hinesville_GA in the United Kingdom ( U.S. ) in 2018 , ranked by the Birth rate of Hinesville_GA with over 20.36 % per .  The ranking ranking was followed by Jacksonville_NC with 19.77 % per .

Example 1182:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': ['2002', '2019']}
title: Number of homicide offences in England and Wales 2002 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2004/04', '2002/03']
Y_Axis['Number', 'of', 'homicides', 'offences']: ['701', '728', '721', '577', '539', '533', '558', '553', '639', '620', '664', '775', '758', '764', '868', '904', '1047']

gold: How many homicides are registered in England and Wales ? There were 701 homicides recorded in England and Wales in 2018/19 , slightly fewer than in the previous reporting year of 2017/18 , when there were 728 homicides . Between 2002/03 and 2013/14 the number of homicides in England and Wales almost halved from over 1 thousand to just over 530 . The recent rise in the number of homicides has occurred alongside an increase in the overall number of crimes , with police officials linking this to long-term cuts to their manpower and resources .
gold_template: How many templateYLabel[1] are registered in templateTitleSubject[0] and templateTitleSubject[1] ? There were templateYValue[0] templateYLabel[1] recorded in templateTitleSubject[0] and templateTitleSubject[1] in templateXValue[0] , slightly fewer than in the previous reporting templateXLabel[0] of templateXValue[1] , when there were templateYValue[1] templateYLabel[1] . Between templateXValue[last] and templateXValue[5] the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] and templateTitleSubject[1] almost halved from over templateYValue[0] thousand to just over 530 . The recent rise in the templateYLabel[0] of templateYLabel[1] has occurred alongside an templatePositiveTrend in the overall templateYLabel[0] of crimes , with police officials linking this to long-term cuts to their manpower and resources .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in the templateTitle[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Number homicides of the offences in the Number from 2002/03 to 2018/19 .  In 2018/19 , the Number homicides of the England amounted to 1047 offences .

Example 1183:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Growth of U.S. beauty product face mask online searches 2016
X_Axis['Beauty', 'Product']: ['carbonated_clay_mask', 'carbonated_bubble_clay_mask', 'bubble_clay_mask', 'blackhead_mask', 'dead_sea_mud_mask', 'charcoal_mask', 'sheet_mask', 'korean_face_mask', 'charcoal_face_mask', 'peel-off_face_mask', 'black_face_mask', 'turmeric_mask', 'indian_clay_mask', 'aztec_clay_mask', 'egg_white_mask', 'best_face_masks', 'best_face_mask', 'DIY_face_mask', 'mud_mask', 'bentonite_clay_mask', 'face_mask_for_acne', 'clay_mask', 'face_mask', 'turmeric_face_mask', 'baking_soda_face_mask', 'DIY_hair_mask', 'face_masks_for_acne', 'avocado_mask', 'avocado_face_mask', 'honey_face_mask', 'homemade_face_mask', 'oatmeal_face_mask', 'winter_face_mask']
Y_Axis['Year-on-year', 'growth', 'of', 'search', 'phrase', 'volume']: ['5030', '2188', '1899', '756', '329', '274', '184', '173', '151', '117', '100', '80', '79', '72', '67', '66', '62', '48', '45', '35', '33', '28', '26', '16', '15', '4', '1', '-6', '-14', '-15', '-19', '-20', '-36']

gold: This statistic shows the year-on-year growth of the top volume U.S. beauty product search queries related to face masks on Google as of September 2016 . During the measured period , the search query `` homemade face mask '' decreased by 26 percent in over-year search volume . The fastest-growing face mask trends search query was `` carbonated clay mask '' with a 5,030 percent year-on-year growth in volume .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the top templateYLabel[4] templateTitleSubject[0] templateXLabel[0] templateXLabel[1] templateYLabel[2] queries related to templateXValue[7] templateXValue[15] on Google as of 2016 . During the measured period , the templateYLabel[2] query `` templateXValue[30] templateXValue[7] templateXValue[0] '' templateNegativeTrend by templateYValue[22] templateScale in over-year templateYLabel[2] templateYLabel[4] . The fastest-growing templateXValue[7] templateXValue[0] trends templateYLabel[2] query was `` templateXValue[0] mask '' with a templateYValue[max] templateScale templateYLabel[0] templateYLabel[1] in templateYLabel[4] .

generated_template: This statistic shows the results of a survey among the templateTitle[0] templateXLabel[1] templateTitle[7] templateTitle[8] in templateTitleSubject[0] in templateTitleDate[0] . According to the source , templateXValue[0] generated templateYValue[1] templateScale templateYLabel[3] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the results of a survey among the Growth Product searches 2016 in U.S. 2016 .  According to the source , carbonated_clay_mask generated 2188 million phrase of growth search phrase volume .

Example 1184:
titleEntities: {'Subject': ['Japan'], 'Date': ['2017', '2023']}
title: Japan : number of internet users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'internet', 'users', 'in', 'millions']: ['117.5', '117.7', '117.9', '117.9', '117.9', '117.6', '115.8']

gold: This statistic provides information on the number of internet users in Japan from 2017 to 2023 . In 2018 , Japan had approximately 117.6 million internet users . This figure is projected to decrease to about 117.5 million internet users in 2023 .
gold_template: This statistic provides information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateTitleSubject[0] had approximately templateYValue[5] templateScale templateYLabel[1] templateYLabel[2] . This figure is projected to templateNegativeTrend to about templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[max] .

generated_template: In templateXValue[5] , templateTitleSubject[0] had templateYValue[5] templateScale templateYLabel[1] templateYLabel[2] . This figure is projected to grow to templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[idxmax(Y)] . The templateYLabel[1] penetration amounted to 47.1 templateScale of the population in templateTitleSubject[0] and is set to reach 84.5 templateScale in templateXValue[max] .
generated: In 2018 , Japan had 117.6 millions internet users .  This figure is projected to grow 117.9 millions internet users in 2021 .  The internet penetration amounted to 47.1 millions of the population in Japan and is set to reach 84.5 millions in 2023 .

Example 1185:
titleEntities: {'Subject': ['World'], 'Date': ['2020', '2020']}
title: World ranking of professional golf players 2020
X_Axis['Golfer']: ['Rory_McIlroy_(NIR)', 'Jon_Rahm_(ESP)', 'Brooks_Koepka_(USA)', 'Justin_Thomas_(USA)', 'Dustin_Johnson_(USA)', 'Adam_Scott_(AUS)', 'Patrick_Cantley_(USA)', 'Patrick_Reed_(USA)', 'Webb_Simpson_(USA)', 'Tiger_Woods_(USA)', 'Xander_Schauffele_(USA)', 'Tommy_Fleetwood_(ENG)', 'Justin_Rose_(ENG)', 'Bryson_DeChambeau_(USA)', 'Tony_Finau_(USA)', 'Matt_Kuchar_(USA)', 'Louis_Oosthuizen_(RSA)', 'Gary_Woodland_(USA)', 'Shane_Lowry_(IRE)', 'Hideki_Matsuyama_(JPN)']
Y_Axis['Average', 'points']: ['9.48', '8.68', '8.62', '7.62', '6.72', '6.06', '6.04', '5.93', '5.88', '5.81', '5.62', '5.52', '5.3', '5.1', '4.88', '4.63', '4.38', '4.36', '4.33', '4.31']

gold: This graph depicts the top golf players in the official world ranking as of February 2020 , by average points . Top of the list was the British golfer , Rory McIlroy , who had an average of 9.48 points as of February 2020 .
gold_template: This graph depicts the top templateTitle[3] templateTitle[4] in the official templateTitleSubject[0] templateTitle[1] as of 2020 , by templateYLabel[0] templateYLabel[1] . Top of the list was the British templateXLabel[0] , templateXValue[0] , who had an templateYLabel[0] of templateYValue[max] templateYLabel[1] as of 2020 .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] in the templateTitle[7] as of 2019 . It was templateXValue[0] , the templateTitle[0] templateTitle[1] templateXValue[1] with a templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the World ranking professional golf players World in the 2020 as of 2019 .  It was Rory_McIlroy_(NIR) , the World ranking Jon_Rahm_(ESP) with a points of 9.48 million points .

Example 1186:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['1931', '1960']}
title: United Kingdom ( UK ) : Live births 1931 to 1960
X_Axis['Years', '1931-1960']: ["'60", "'59", "'58", "'57", "'56", "'55", "'54", "'53", "'52", "'51", "'50", "'49", "'48", "'47", "'46", "'45", "'44", "'43", "'42", "'41", "'40", "'39", "'38", "'37", "'36", "'35", "'34", "'33", "'32", "'31"]
Y_Axis['Number', 'of', 'live', 'births']: ['918286', '878561', '870497', '851466', '825137', '789315', '794769', '804269', '792917', '796645', '818421', '855298', '905182', '1025427', '955266', '795868', '878298', '810524', '771851', '695726', '701875', '726632', '735573', '723779', '720129', '711426', '711843', '691560', '730079', '749974']

gold: During the period between 1931 and 1960 , the number of live births in the United Kingdom peaked in 1947 , where there were estimated to have been over one million births . By contrast , the year with the least births was 1941 when there were just 695 thousand births . These two figures are undoubtedly linked to the Second World War , with 1941 coming just after the Battle of Britain and 1947 being two years after the end of the war .
gold_template: During the period between templateTitleDate[min] and templateTitleDate[max] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] peaked in 1947 , where there were estimated to have been over templateYValue[0] templateScale templateYLabel[2] . By contrast , the year with the least templateYLabel[2] was 1941 when there were just 695 thousand templateYLabel[2] . These two figures are undoubtedly linked to the Second World War , with 1941 coming just after the Battle of Britain and 1947 being two templateXLabel[0] after the end of the war .

generated_template: This statistic shows the results of a survey among the templateTitle[0] employees making it the largest group of employees employees templateYLabel[3] templateTitle[4] employees templateYLabel[3] . According to the source , a education employees employees .
generated: This statistic shows the results of a survey among the United employees making it the largest group of employees births .  According to the source , a education employees .

Example 1187:
titleEntities: {'Subject': ['Total'], 'Date': []}
title: Total player salaries in the sports industry by league 2018/19
X_Axis['Sports', 'League']: ['NFL', 'MLB', 'NBA', 'EPL', 'NHL', 'La_Liga', 'Serie_A', 'Bundesliga', 'Ligue_1', 'CSL', 'NPB', 'MLS', 'AFL', 'J-League', 'IPL', 'SPFL', 'CFL']
Y_Axis['Total', 'player', 'salaries', 'in', 'billion', 'U.S.', 'dollars']: ['4.94', '3.96', '3.41', '2.14', '1.98', '1.38', '1.09', '0.94', '0.72', '0.51', '0.32', '0.26', '0.18', '0.16', '0.09', '0.07', '0.04']

gold: The statistic shows total professional sports player salaries by league in 2018/19 . Total salaries paid to NFL players amounted to 4.94 billion U.S. dollars for the 2018/19 season .
gold_template: The statistic shows templateYLabel[0] professional templateXLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[5] templateXLabel[1] in templateTitle[7] . templateYLabel[0] templateYLabel[2] paid to templateXValue[0] players amounted to templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] for the templateTitle[7] season .

generated_template: With each templateYLabel[1] taking home a handsome templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] every year , the templateXValue[0] is the professional templateXLabel[0] templateXLabel[1] with the highest templateYLabel[1] wages worldwide . In second place is the Indian Premier templateXLabel[1] , there were over templateYValue[max] thousand templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[0] .
generated: With each player taking home a handsome 4.94 billion U.S. every year , the NFL is the professional Sports League with the highest player wages worldwide .  In second place is the Indian Premier League , there were over 4.94 thousand billion U.S. dollars in NFL .

Example 1188:
titleEntities: {'Subject': ['Atlanta Hawks', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Atlanta Hawks ( NBA ) games 2015/16
X_Axis['Year']: ['2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['36.82', '35.26', '35.26', '34.75', '36.13', '36.13', '51.78', '36.9', '38.0', '42.18']

gold: This graph depicts the average ticket price for Atlanta Hawks games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 42.18 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[min] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Atlanta Hawks games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Average ticket price was at 34.75 U.S. dollars .

Example 1189:
titleEntities: {'Subject': ['Country Index'], 'Date': ['2017', '2017']}
title: Made-In Country Index : country ranking 2017
X_Axis['Index', 'rate']: ['Germany', 'Switzerland', 'European_Union', 'United_Kingdom', 'Sweden', 'Canada', 'Italy', 'Japan', 'France', 'United_States', 'Finland', 'Norway', 'Netherlands', 'Australia', 'New_Zealand', 'Denmark', 'Austria', 'Belgium', 'Ireland', 'Spain', 'South_Korea', 'Singapore', 'Portugal', 'Poland', 'Greece', 'Russia', 'Taiwan', 'Hungary', 'Czech_Republic', 'Argentina', 'Brazil', 'Malaysia', 'Slovakia', 'Thailand', 'Chile', 'Colombia', 'Turkey', 'Peru', 'South_Africa', 'Mexico', 'Romania', 'Israel', 'India', 'Indonesia', 'Ukraine', 'Vietnam', 'Philippines', 'Bangladesh', 'China', 'Iran']
Y_Axis['Unnamed:', '1']: ['100', '98', '92', '91', '90', '85', '84', '81', '81', '81', '77', '77', '76', '75', '73', '73', '72', '71', '65', '64', '56', '56', '54', '51', '48', '46', '46', '45', '43', '42', '42', '41', '40', '40', '39', '39', '37', '37', '37', '37', '37', '36', '36', '36', '35', '34', '32', '29', '28', '27']

gold: Quality of products is always an important factor when it comes to consumption and spending , and many consumers define a certain product 's quality standard by applying specific characteristics - one of those being its origin . Nowadays , Germany is synonymous with high-quality cars , for example , while Switzerland would be known for high-quality clocks or chocolate , and Italy for fine wine . Quality measured In order to make these preferences among consumers visible , the global Made-In-Country Index was conducted - to show how positively products `` made in ... '' are perceived in various countries all over the world .
gold_template: Quality of products is always an important factor when it comes to consumption and spending , and many consumers define a certain product 's quality standard by applying specific characteristics - templateYLabel[1] of those being its origin . Nowadays , templateXValue[0] is synonymous with high-quality cars , for example , while templateXValue[1] would be known for high-quality clocks or chocolate , and templateXValue[6] for fine wine . Quality measured In order to make these preferences among consumers visible , the global Made-In-Country templateXLabel[0] was conducted - to show how positively products `` made in ... '' are perceived in various countries all over the world .

generated_template: This statistic shows the templateScale of the total templateXLabel[1] living in templateXLabel[0] with the highest templateYLabel[0] in templateTitleDate[0] . In this year , templateYValue[max] templateScale of the total of the templateXValue[0] was living in templateXLabel[0] with .
generated: This statistic shows the percentage of the total rate living in Index with the highest Unnamed: in 2017 .  In this year , 100 % of the total of the Germany was living in Index with .

Example 1190:
titleEntities: {'Subject': ['Market'], 'Date': ['2016', '2019']}
title: Market capitalization of leading 100 banks worldwide 2016 to 2019
X_Axis['Quarter']: ["Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16"]
Y_Axis['Market', 'capitalization', 'in', 'trillion', 'Euros']: ['5.3', '5.2', '5.2', '4.8', '5.3', '5.2', '5.4', '5.6', '5.4', '5.3', '5.4', '5.2', '4.4', '4.1', '4.2']

gold: This statistic presents the market capitalization of leading 100 banks worldwide from the first quarter 2016 to the third quarter 2019 . The market cap of top 100 global banks amounted to 5.3 trillion Euros in the third quarter of 2019 .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from the first templateXLabel[0] templateTitleDate[min] to the third templateXLabel[0] templateTitleDate[max] . The templateYLabel[0] cap of top templateTitle[3] global templateTitle[4] amounted to templateYValue[0] templateScale templateYLabel[3] in the third templateXLabel[0] of templateTitleDate[max] .

generated_template: This statistic shows the templateYLabel[1] of the templateTitle[0] templateYLabel[0] templateYLabel[3] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the first templateXLabel[0] of templateTitleDate[max] . As of the fourth templateXLabel[0] of templateTitleDate[max] , the templateTitle[0] of templateTitleSubject[0] templateYLabel[3] was templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the capitalization of the Market Euros of Market from the first Quarter of 2016 to the first Quarter of 2019 .  As of the fourth Quarter of 2019 , the Market of Euros was 5.6 trillion Euros .

Example 1191:
titleEntities: {'Subject': ['Pakistan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Pakistan 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['5.02', '5.02', '4.53', '3', '2.35', '3.29', '5.53', '5.22', '4.56', '4.06', '4.05']

gold: Pakistan 's gross domestic product ( GDP ) growth was 5.53 percent in 2018 after adjusting for inflation . However , projections for the coming years predict that GDP will grow at a rate around 5 percent in the coming years . GDP in developing nations Gross domestic product measures value of all final goods and services produced within a country 's borders during a certain period of time .
gold_template: templateTitleSubject[0] 's templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] was templateYValue[max] templateScale in templateXValue[idxmax(Y)] after adjusting for inflation . However , projections for the coming years predict that templateYLabel[0] will grow at a templateTitle[5] around templateYValue[0] templateScale in the coming years . templateYLabel[0] in developing nations templateTitle[0] templateTitle[1] templateTitle[2] measures value of all final goods and services produced within a country 's borders during a certain period of time .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Pakistan from 2014 to 2018 , with projections up until 2024 .  In 2018 , Pakistan 's real Gross domestic product increased by around 5.53 % compared to the previous Year .

Example 1192:
titleEntities: {'Subject': ['Detroit Tigers'], 'Date': ['2006', '2019']}
title: Detroit Tigers average ticket price 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['28.31', '28.15', '28.88', '29.01', '28.22', '26.36', '31.0', '29.32', '23.48', '27.38', '25.28', '19.45', '18.48']

gold: This graph depicts the average ticket price for Detroit Tigers games in Major League Baseball from 2006 to 2019 . In 2019 , the average ticket price was at 28.31 U.S. dollars . Detroit Tigers The Detroit Tigers are a franchise of Major League Baseball founded in 1894 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] The templateTitleSubject[0] are a franchise of Major League Baseball founded in 1894 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Detroit Tigers games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 28.31 U.S. dollars .

Example 1193:
titleEntities: {'Subject': ['Christmas'], 'Date': []}
title: U.S. Christmas lights energy costs
X_Axis['Lighting', 'Type']: ['Standard_incandescent_lights_(125_lights_4_watts_each)', 'Mini-incandescent_lights_(300_lights_0.4_watts_each)', 'LED_lights_(280_lights_0.04_watts_each)']
Y_Axis['Energy', 'cost', 'in', 'U.S.', 'dollars']: ['27.21', '6.53', '0.06']

gold: This statistic represents the energy costs of lighting a six-foot Christmas tree , lit 12 hours a day for 40 days , decorated with various light types . Americans choosing not to burn down their house during Christmas season should stay away from real candles anyway , and if they also prefer spending their money on Christmas presents instead of the Christmas tree decoration , LED lights are the best choice - decorating the tree with 280 LED lights for 40 days costs only 0.61 U.S. dollars .
gold_template: This statistic represents the templateYLabel[0] templateTitle[4] of templateXLabel[0] a six-foot templateTitleSubject[0] tree , lit 12 hours a day for 40 days , decorated with various light types . Americans choosing not to burn down their house during templateTitleSubject[0] season should stay away from real candles anyway , and if they also prefer spending their money on templateTitleSubject[0] presents instead of the templateTitleSubject[0] tree decoration , templateXValue[last] templateXValue[0] are the best choice - decorating the tree with 280 templateXValue[last] templateXValue[0] for 40 days templateTitle[4] only 0.61 templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateTitle[6] templateYLabel[0] . According to the source , the templateTitle[0] templateYLabel[0] of people who participated in templateXValue[0] amounted to approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: The statistic shows the U.S. Christmas lights in the energy in , costs Energy .  According to the source , the U.S. Energy of people who participated in Standard_incandescent_lights_(125_lights_4_watts_each) amounted to approximately 27.21 million dollars in .

Example 1194:
titleEntities: {'Subject': ['Nicaragua'], 'Date': ['2024']}
title: Inflation rate in Nicaragua 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['5.04', '4.94', '4.79', '4.67', '4.19', '5.63', '4.97', '3.85', '3.52', '4', '6.04', '7.14', '7.19', '8.08', '5.46', '3.69', '19.83', '11.13', '9.14', '9.6', '8.47', '5.3', '3.75', '7.36', '11.55', '11.21', '13.05', '9.19', '11.65', '11.12', '3.7', '13.5', '21.9', '116.6', '3004.1', '7428.7', '4775.2', '13109.5', '885.2', '571.4', '141.3']

gold: This statistic shows the average inflation rate in Nicaragua from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Nicaragua amounted to about 4.97 percent compared to the previous year . Nicaragua 's economy Nicaragua 's inflation rate has been on the decline since 2011 , but it is expected to rise again in 2016 .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . templateTitleSubject[0] 's economy templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] has been on the decline since templateXValue[13] , but it is expected to rise again in templateXValue[8] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Nicaragua from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Nicaragua amounted to about 4.97 % compared to the previous Year .

Example 1195:
titleEntities: {'Subject': ['France'], 'Date': ['2018']}
title: Best selling books in France 2018
X_Axis['Title']: ['Un_appartement_à_ParisGuillaume_Musso', "Ta_deuxième_vie_commence_quand_tu_comprends_que_tu_n'en_as_qu'uneR._Giordano", 'La_jeune_fille_et_la_nuitGuillaume_Musso', 'La_disparition_de_Stephanie_MailerJoël_Dicker', 'La_tresseLaetitia_Colomban', 'Famille_parfaiteLisa_Gardner', 'La_dernière_des_StanfieldMarc_Levy', 'Lucky_Luke_8_:_Un_cow-boy_à_ParisJul_Achdé', "L'amie_prodigieuse_3_:_Celle_qui_fuit_et_celle_qui_resteElena_Ferrante", "Couleurs_de_l'incendiePierre_Lemaitre"]
Y_Axis['Units', 'sold']: ['590000', '541000', '500500', '438300', '412600', '347400', '328000', '319300', '303600', '302300']

gold: In 2018 , 'Un appartement à Paris ' from Guillaume Musso was the bestselling book title in France . Musso needs no introduction in France , a country where 24 percent of the inhabitants considered themselves as fervent readers . Guillaume Musso and Elena Ferrante : best sellers `` Astérix et la Transitalique '' was published in both print and digital formats and sold close to 1.6 million copies in 2017 in France .
gold_template: In templateTitleDate[0] , 'Un templateXValue[0] Paris ' from Guillaume templateXValue[0] was the bestselling book templateXLabel[0] in templateTitleSubject[0] . templateXValue[0] needs no introduction in templateTitleSubject[0] , a country where 24 templateScale of the inhabitants considered themselves as fervent readers . Guillaume templateXValue[0] and Elena templateXValue[8] : templateTitle[0] sellers `` Astérix templateXValue[2] Transitalique '' was published in both print and digital formats and templateYLabel[1] close to 1.6 templateScale copies in 2017 in templateTitleSubject[0] .

generated_template: This statistic presents the ranking of the templateTitle[0] templateYLabel[2] in the templateTitleSubject[0] as of 2019 . According to the source , templateXValue[0] accounted for templateYValue[max] templateScale of all templateYLabel[2] during the statistic .
generated: This statistic presents the ranking of the Best sold in the France as of 2019 .  According to the source , Un_appartement_à_ParisGuillaume_Musso accounted for 590000 % of all sold during the statistic .

Example 1196:
titleEntities: {'Subject': ['India'], 'Date': ['2017']}
title: Streaming service subscribers in India 2017
X_Axis['Video-on-demand', 'Service']: ['Hotstar', 'Voot', 'Amazon_Video', 'Sony_LIV', 'Netflix', 'Ozee_TV']
Y_Axis['Number', 'of', 'subscribers', 'in', 'millions']: ['63.0', '13.2', '9.5', '4.6', '4.2', '2.4']

gold: The graph shows the number of subscribers to selected video streaming services in India as of January 2017 . According to the source , Netflix had 4.2 million subscribers in India as of January 2017 .
gold_template: The graph shows the templateYLabel[0] of templateYLabel[1] to selected templateXValue[2] templateTitle[0] services in templateTitleSubject[0] as of 2017 . According to the source , templateXValue[4] had templateYValue[4] templateScale templateYLabel[1] in templateTitleSubject[0] as of 2017 .

generated_template: The statistic shows the distribution of templateTitle[1] templateTitle[2] of templateTitle[4] on templateTitleSubject[0] in the fiscal year of templateTitleDate[0] . According to the source , there were just over templateYValue[max] templateScale templateYLabel[1] in templateTitleSubject[0] .
generated: The statistic shows the distribution of service subscribers of 2017 on India in the fiscal year of 2017 .  According to the source , there were just over 63.0 millions subscribers in India .

Example 1197:
titleEntities: {'Subject': ['Uganda'], 'Date': ['2018']}
title: Age structure in Uganda 2018
Data:
                        0      1      2      3      4      5      6      7      8      9     10
Year                 2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
0-14 years          46.93  47.32  47.67  47.99  48.26  48.51  48.73  48.93  49.09  49.25   49.4
15-64 years         51.13  50.76  50.42  50.13  49.86  49.61  49.39  49.19  49.04  48.85  48.69
65 years and older   1.94   1.92    1.9   1.88   1.88   1.88   1.88   1.87   1.87   1.89   1.91 

gold: This statistic shows the age structure in Uganda from 2008 to 2018 . In 2018 , about 46.93 percent of Uganda 's total population were aged 0 to 14 years , 51.13 percent were aged 15 to 64 years and 1.94 percent were 65 years old and older .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][min] templateScale of templateTitleSubject[0] 's total population were aged 0 to 14 templateLabel[1][1] , templateValue[1][idxmin(1)] templateScale were aged 15 to 64 templateLabel[1][1] and templateValue[2][idxmax(2)] templateScale were templateLabel[3][0] templateLabel[1][1] old and templateLabel[3][2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][min] templateScale of templateTitleSubject[0] 's total population were aged 0 to 14 templateLabel[1][1] .
generated: This statistic shows the Age structure in Uganda from 2008 to 2018 .  In 2018 , about 46.93 % of Uganda 's total population were aged 0 to 14 years .

Example 1198:
titleEntities: {'Subject': ['Edmonton Oilers'], 'Date': ['2005', '2019']}
title: Edmonton Oilers ' revenue 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['154', '145', '151', '117', '119', '119', '80', '106', '96', '87', '83', '85', '71', '75']

gold: This graph depicts the annual National Hockey League revenue of the Edmonton Oilers from the 2005/06 season to the 2018/19 season . The revenue of the Edmonton Oilers amounted to 154 million U.S. dollars in the 2018/19 season .
gold_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .
generated: This graph depicts the annual National Hockey League Revenue of the Edmonton Oilers from the 2005/06 season to the 2018/19 season .  The Revenue of the Edmonton Oilers amounted to 154 million U.S. dollars in the 2018/19 season .

Example 1199:
titleEntities: {'Subject': ['Leading'], 'Date': ['2019']}
title: Leading eSports games worldwide 2019 , by tournament prize pool
X_Axis['Game', 'Name']: ['Fortnite', 'DOTA_2', 'Counter-Strike:_Global_Offensive', 'PlayerUnknown’s_Battlegrounds', 'Overwatch', 'League_of_Legends', 'Call_of_Duty:_Black_Ops_4', 'Arena_of_Valor', 'Hearthstone', 'Rainbow_Six_Siege']
Y_Axis['Total', 'prize', 'pool', 'in', 'million', 'U.S.', 'dollars']: ['64.37', '46.15', '18.22', '12.41', '9.09', '8.95', '6.52', '5.8', '4.07', '3.88']

gold: The statistic presents the leading eSports games worldwide in 2019 , ranked by cumulative tournament prize pool . According to the source , League of Legends took sixth spot on the list , with a prize pool worth 8.95 million U.S. dollars that year . Fortnite ranked first , owing to a prize pool valued at 64.37 million U.S. dollars in 2019 .
gold_template: The statistic presents the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , ranked templateTitle[5] cumulative templateTitle[6] templateYLabel[1] templateYLabel[2] . According to the source , templateXValue[5] of templateXValue[5] took sixth spot on the list , with a templateYLabel[1] templateYLabel[2] worth templateYValue[5] templateScale templateYLabel[4] templateYLabel[5] that year . templateXValue[0] ranked first , owing to a templateYLabel[1] templateYLabel[2] valued at templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] in templateTitleDate[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[7] templateTitle[8] . The templateTitle[0] templateYLabel[0] of templateTitleSubject[0] amounted to over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] that year .
generated: This statistic shows the Leading eSports games worldwide of the United Kingdom ( Leading ) in 2019 , prize pool .  The Leading Total of Leading amounted to over 64.37 million U.S. that year .

Example 1200:
titleEntities: {'Subject': ['Bolivia'], 'Date': ['2000']}
title: Bolivia : internet penetration 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'individuals', 'using', 'internet']: ['43.83', '39.7', '35.56', '34.6', '36.99', '35.34', '30', '22.4', '16.8', '12.5', '10.5', '6.2', '5.23', '4.44', '3.51', '3.12', '2.12', '1.44']

gold: This statistic gives information on the internet penetration in Bolivia from 2000 to 2017 . In 2017 , 43.83 percent of the Bolivian population accessed the internet , up from 6.2 percent in 2006 .
gold_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the Bolivian population accessed the templateYLabel[3] , up from templateYValue[11] templateScale in templateXValue[11] .

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the Brazilian population accessed the templateYLabel[3] , up from templateYValue[15] templateScale in templateXValue[15] . templateTitleSubject[0] is one of the biggest online markets worldwide .
generated: This statistic gives information on the internet penetration in Bolivia from 2000 to 2017 .  In 2017 , 43.83 percentage of the Brazilian population accessed the internet , up from 3.12 percentage in 2002 .  Bolivia is one of the biggest online markets worldwide .

Example 1201:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Facebook usage reach in the United States 2019 , by gender
X_Axis['Response']: ['Men', 'Women']
Y_Axis['Share', 'of', 'respondents']: ['63', '75']

gold: This statistic shows the share of adults in the United States who were using Facebook as of February 2019 , sorted by gender . During that period of time , 75 percent of female respondents stated that they used the social networking site .
gold_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] . During that period of time , templateYValue[max] templateScale of female templateYLabel[1] stated that they used the social networking site .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitle[1] templateTitle[3] were templateTitleSubject[0] users as of 2019 , sorted templateTitle[7] templateTitle[8] and templateTitle[9] group . During that period of time , templateYValue[max] templateScale of the templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the Share of adults in the usage United were United States users as of 2019 , sorted gender and group .  During that period of time , 75 % of the respondents stated that they used the social networking site .

Example 1202:
titleEntities: {'Subject': ['Germany'], 'Date': ['2011', '2015']}
title: Market share of smartphone brands in Germany 2011 to 2015
Data:
                  0        1        2        3        4        5        6        7        8        9       10       11       12       13       14       15       16       17       18       19       20       21       22       23       24       25       26       27       28       29       30       31       32
Month       Jul'_15  Mar'_15  Feb_'15  Jun_'14  Sep_'13  Jul_'13  Jun_'13  May_'13  Mar_'13  Feb_'13  Jan_'13  Dec_'12  Nov_'12  Oct_'12  Sep_'12  Aug_'12  Jul_'12  Jun_'12  Mai_'12  Apr_'12  May_'12  Feb_'12  Jan_'12  Dec_'11  Nov_'11  Oct_'11  Sep_'11  Aug_'11  Jul_'11  Jun_'11  May_'11  Apr_'11  Mar_'11
Samsung        43.1     44.9     44.2       43       41     40.2     39.9     39.6     36.7     35.3     34.3     33.7     32.7       32     30.7       30       28       26       26       25       24     22.3     20.7     20.3     19.1     18.2     16.3     15.3     13.9     12.9     11.9       11       11
Apple          20.8     20.2     20.6       20     19.4     19.3     19.5       20     21.5     22.1     21.3     21.8     21.4     21.2     20.9       21       22       22       22       22     22.4     22.6     22.7     22.2     21.6     21.3     21.8     21.7       22     21.3       21     20.5     20.8
Sony              8      7.8      8.2        8      8.3      8.2      8.4      8.6      8.6      8.1      8.2      7.9      8.2      8.1      8.3        8        8        8        8        8      8.2        8      8.2      7.9      7.8      7.8      7.3      7.1        7      7.6      7.6      7.3      6.6
Nokia             6      7.1        7        8     10.4     11.4     11.4     11.6     12.7     13.3     14.2       15     16.2     16.9       17       18       19       20       21       22     21.6       23     24.3     25.1     26.6     27.6     29.6       31     32.1     33.1     34.4       36     37.1
HTC             5.9      6.2      6.4        0        9      8.7      8.9      9.1      9.1      9.5      9.7      9.8      9.8      9.6       10       10       10       10       11       11     10.5     10.7     10.3     10.9     10.8     11.1     10.7     10.9       11     11.4       11     11.1     10.4
Blackberry        0        0        0        0      1.6      1.5      1.7      1.7      1.6      1.8        2      1.9      2.1      2.2      2.4        2        2        2        3        3      2.8        3        3      3.3      3.3      3.4      3.5      3.5      3.7      3.6      4.2      4.2      4.5 

gold: This statistic shows the market share of the most popular smartphone brands in Germany from June 2011 to July 2015 . In July 2015 , roughly 43 percent of smartphone owners used a Samsung phone , making it the brand with the highest market share in this ranking . In the same month , Apple 's iPhone had a market share of 20.8 percent .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of the most popular templateTitle[2] templateTitle[3] in templateTitleSubject[0] from 2011 to 2015 . In 2015 , roughly templateValue[1][0] templateScale of templateTitle[2] owners used a templateLabel[1][0] phone , making it the brand with the highest templateTitle[0] templateTitle[1] in this ranking . In the same templateLabel[0][0] , templateLabel[2][0] 's iPhone had a templateTitle[0] templateTitle[1] of templateValue[2][last] templateScale .

generated_template: This statistic presents the templateScale of online consumers in the templateTitle[0] who are templateTitleSubject[0] templateTitle[2] members . As of 2019 , it was found that templateValue[1][0] templateScale of the templateTitle[1] templateTitle[2] templateTitle[3] were templateLabel[1][0] , an templatePositiveTrend over templateValue[3][0] templateScale from the previous year . The next few years As of 2019 , templateTitle[3] templateValue[0][0] was the most popular social networks worldwide .
generated: This statistic presents the percentage of online consumers in the Market who are Germany smartphone members .  As of 2019 , it was found that 43.1 % of the share smartphone brands were Samsung , an increase over 8 % from the previous year .  The next few years As of 2019 , brands Jul'_15 was the most popular social networks worldwide .

Example 1203:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2019']}
title: Average weekly working hours in Sweden 2019 , by type of employment
X_Axis['Form', 'of', 'Employment']: ['Self-employed', 'Permanently_employed', 'Temporary_employed', 'Employed_total']
Y_Axis['Average', 'number', 'of', 'working', 'hours', 'per', 'week']: ['35.4', '30.4', '25.5', '30.2']

gold: The ordinary weekly working hours in Sweden are stated to equal 40 hours . However , the actual average reported weekly working hours in Sweden fluctuated between 30 and 30.9 hours per week during the period from 2009 to 2019 . In 2019 , an actual working week amounted to 30.2 hours on average .
gold_template: The ordinary templateTitle[1] templateYLabel[2] templateYLabel[3] in templateTitleSubject[0] are stated to equal 40 templateYLabel[3] . However , the actual templateYLabel[0] reported templateTitle[1] templateYLabel[2] templateYLabel[3] in templateTitleSubject[0] fluctuated between templateYValue[1] and 30.9 templateYLabel[3] templateYLabel[4] templateYLabel[5] during the period from 2009 to templateTitleDate[0] . In templateTitleDate[0] , an actual templateYLabel[2] templateYLabel[5] amounted to templateYValue[last] templateYLabel[3] on templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of templateTitleSubject[0] in templateTitleDate[0] . Of the total templateTitle[0] templateTitle[1] in templateTitleSubject[0] was templateXValue[0] , with a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Average weekly working hours Sweden Average of Sweden in 2019 .  Of the total Average weekly in Sweden was Self-employed , with a Average of 35.4 million hours per .

Example 1204:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010', '2018']}
title: Textbook publishing revenue in the U.S. 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['8.79', '9.87', '10.17', '11.7', '8.62', '8.58', '8.16', '8.46', '8.6']

gold: The timeline presents data on textbook publishing revenue generated in the United States from 2010 to 2018 . In 2018 , the revenue amounted to 8.79 billion U.S. dollars .
gold_template: The timeline presents data on templateTitle[0] templateTitle[1] templateYLabel[0] generated in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: templateTitle[0] templateTitle[1] templateYLabel[0] has templatePositiveTrend astronomically in the last five years alone – templatePositiveTrend from templateYValue[5] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[5] to templateYValue[max] templateScale in templateXValue[idxmax(Y)] . templateTitle[1] has become a popular pastime for templateYLabel[2] templateTitle[0] fans and a major source of templateYLabel[0] for the industry , though many traditional consumers lament the resulting decline of physical templateTitle[0] formats . Physical CD shipments have dwindled , whilst digital templateTitle[0] platforms are flourishing .
generated: Textbook publishing Revenue has increased astronomically in the last five years alone – rising from 8.58 billion U.S. dollars in 2013 to 11.7 billion in 2015 .  publishing has become a popular pastime for U.S. Textbook fans and a major source of Revenue for the industry , though many traditional consumers lament the resulting decline of physical Textbook formats .  Physical CD shipments have dwindled , whilst digital Textbook platforms are flourishing .

Example 1205:
titleEntities: {'Subject': ['World Cup'], 'Date': ['1930', '2018']}
title: FIFA World Cup : players with most matches 1930 to 2018
X_Axis['Player']: ['Lothar_Matthäus_(Germany)', 'Miroslav_Klose_(Germany)', 'Paolo_Maldini_(Italy)', 'Uwe_Seeler_(Germany)', 'Diege_Maradona_(Argentina)', 'Władysław_Żmuda_(Poland)', 'Cafu_(Brazil)', 'Bastian_Schweinsteiger_(Germany)', 'Philipp_Lahm_(Germany)', 'Grzegorz_Lato_(Poland)']
Y_Axis['Number', 'of', 'appearances']: ['25', '24', '23', '21', '21', '21', '20', '20', '20', '20']

gold: This graph depicts the soccer players with the most appearances at the FIFA World Cup from 1930 to 2018 . The German midfielder , Lothar Matthäus , tops the list , having made 25 appearances at the World Cup between 1982 and 1998 .
gold_template: This graph depicts the soccer templateTitle[3] templateTitle[4] the templateTitle[5] templateYLabel[1] at the templateTitle[0] templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . The German midfielder , templateXValue[0] , tops the list , having made templateYValue[max] templateYLabel[1] at the templateTitleSubject[0] between 1982 and 1998 .

generated_template: The statistic presents the templateTitle[1] templateTitle[2] the most popular templateXLabel[0] templateTitle[4] templateYLabel[1] in templateTitleDate[0] . templateYValue[max] templateScale of templateYLabel[1] were templateXValue[0] .
generated: The statistic presents the World Cup the most popular Player most appearances in 1930 .  25 % of appearances were Lothar_Matthäus_(Germany) .

Example 1206:
titleEntities: {'Subject': ['Men World Handball Championships'], 'Date': ['2013']}
title: World rankings of Men 's World Handball Championships 1938 to 2013
Data:
                     0       1        2            3            4      5        6               7           8        9      10       11       12
Year            Sweden  France  Romania  Germany/GDR  Russia/USSR  Spain  Croatia  Czechoslovakia  Yugoslavia  Denmark  Poland  Hungary  Austria
World Champion       4       4        4            3            3      2        1               1           1        0       0        0        0
2nd                  3       1        0            4            3      0        3               2           1        3       1        1        1
3rd                  4       3        2            3            0      1        1               2           4        1       2        0        0 

gold: This statistic diplays world rankings of Men 's World Handball Championships from 1938 to 2013 , sorted by first three places . Croatia ranked sixth , with one World Championship won and three second places .
gold_template: This statistic diplays templateTitleSubject[0] templateTitle[1] of templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] Championships from templateTitleDate[min] to templateTitleDate[max] , sorted by first templateValue[1][3] places . templateValue[0][6] ranked sixth , with templateValue[1][6] templateTitleSubject[0] Championship won and templateValue[1][3] second places .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateValue[0][0] , templateValue[1][0] templateScale of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of the templateLabel[1][0] templateLabel[1][1] were templateLabel[1][0] templateLabel[1][1] .
generated: This statistic shows the World of rankings Men 's World in Men World Handball Championships from 2013 to .  In Sweden , 4 % of the rankings Men 's World of the World Champion were World Champion .

Example 1207:
titleEntities: {'Subject': ['China'], 'Date': ['2010', '2025']}
title: Share of automobile sales in China in 2010 and 2025 , by segment
Data:
                                                   0     1
Year                                            2025  2010
Premium class passenger cars/ limousines (E/F)     7     6
medium-sized vehicles (C/D)                       56    58
Compact and mini cars (A/B)                       37    36 

gold: This statistic shows the share of automobile sales in China in 2010 and 2025 , by segment . In 2010 , premium class automobiles and limousines generated a share of 6 percent in the total sales in China .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateValue[0][last] and templateValue[0][0] , templateTitle[6] templateTitle[7] . In templateValue[0][last] , templateLabel[1][0] templateLabel[1][1] automobiles and templateLabel[1][4] generated a templateTitle[0] of templateValue[1][last] templateScale in the total templateTitle[2] in templateTitleSubject[0] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] revenues in the templateTitleSubject[0] from templateValue[0][0] to templateValue[0][last] , broken down templateTitle[7] templateTitle[8] . The templateTitle[0] of the templateTitleSubject[0] in the templateTitleSubject[1] stood at templateValue[1][last] U.S. dollars in templateValue[0][last] .
generated: This statistic shows the Share of automobile revenues in the China from 2025 to 2010 , broken down segment .  The Share of the China in the China stood at 6 U.S. dollars in 2010 .

Example 1208:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2014']}
title: Symbol groups ranked by number of convenience stores in the United Kingdom ( UK ) 2014
X_Axis['Symbol', 'Groups']: ['Premier', 'Best-one', 'Costcutter', 'Spar', 'Londis', 'Lifestyle_express', 'The_Co-operative', 'Tesco_Express', 'Nisa', "McColl's"]
Y_Axis['Number', 'of', 'stores']: ['2950', '2904', '2547', '2312', '2018', '1976', '1785', '1463', '1002', '719']

gold: This statistic shows the leading ten symbol groups in the United Kingdom ( UK ) retail sector , ranked by number of convenience stores in 2014 . Premier ranked highest , with 2950 convenience stores , followed by Best-one , Costcutter , Spar and Londis .
gold_template: This statistic shows the leading ten templateXLabel[0] templateXLabel[1] in the templateTitleSubject[0] ( templateTitleSubject[1] ) retail sector , templateTitle[2] templateTitle[3] templateYLabel[0] of templateTitle[5] templateYLabel[1] in templateTitleDate[0] . templateXValue[0] templateTitle[2] highest , with templateYValue[max] templateTitle[5] templateYLabel[1] , followed templateTitle[3] templateXValue[1] , templateXValue[2] , templateXValue[3] and templateXValue[4] .

generated_template: This statistic displays the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] in the templateTitleSubject[1] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , there were a total of templateYValue[min] templateYLabel[1] living in templateTitleSubject[0] .
generated: This statistic displays the Number of United Kingdom stores in the UK in 2014 , stores Symbol .  In 2014 , there were a total of 719 stores living in United Kingdom .

Example 1209:
titleEntities: {'Subject': ['Turkey'], 'Date': ['2012', '2013']}
title: Penetration rate of bottled water brands in Turkey 2012 to 2013
Data:
               0      1      2      3      4     5      6           7          8      9    10    11        12      13      14     15       16   17      18      19
Response  Erikli  Hayat  Pinar  Damla  Aytaç  Saka  Sirma  Güzelpinar  Pure_Life  Abant  Aqua  NAYA  Aquafina  Buxton  Dasani  Evian  Perrier  SPA  Vittel  Volvic
2012          33     48     26     18      9    10     20           1          0      2     1     0         1       0       0      1        1    2       1       0
2013          28     27     17     14     11    10      9           2          2      1     1     1         1       0       0      0        0    0       0       0 

gold: This statistic illustrates the average penetration rate of bottled water brands in Turkey in 2012 and 2013 . The survey revealed that 14 percent of respondents purchased Damla brand bottled water within the last three to twelve months of 2013 .
gold_template: This statistic illustrates the average templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . The survey revealed that templateValue[2][3] templateScale of respondents purchased templateValue[0][3] brand templateTitle[2] templateTitle[3] within the last three to twelve months of templateTitleDate[max] .

generated_template: This statistic illustrates the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . The survey revealed that templateValue[1][0] templateScale of respondents purchased templateValue[0][0] compared with a templateValue[0][0] compared to templateValue[2][0] templateScale of templateValue[1][2] templateScale in templateTitleDate[max] .
generated: This statistic illustrates the Penetration rate bottled of water brands in Turkey 2012 and 2013 .  The survey revealed that 33 % of respondents purchased Erikli compared with a Erikli compared to 28 % of 26 % in 2013 .

Example 1210:
titleEntities: {'Subject': ['England'], 'Date': ['2019']}
title: Share of the population who gave to charity in England 2019 , by age
X_Axis['Year']: ['16_to_24', '25_to_34', '35_to_49', '50_to_64', '65_to_74', '75_and_over']
Y_Axis['Share', 'of', 'respondents']: ['59', '69', '76', '79', '82', '83']

gold: This statistic shows the share of the population who said they gave to charity in the last four weeks in 2018/19 , by age group . Proportionally , those aged 75 and more gave most to charity . At 59 percent , 16 to 24 year olds had the smallest proportion of charitable givers .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] said they templateTitle[3] to templateTitle[4] in the last four weeks in 2018/19 , templateTitle[7] templateTitle[8] group . Proportionally , those aged templateXValue[last] and more templateTitle[3] most to templateTitle[4] . At templateYValue[min] templateScale , templateXValue[0] to templateXValue[0] templateXLabel[0] olds had the smallest proportion of charitable givers .

generated_template: This statistic displays the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] as of 2015 . During the survey , it was found that templateYValue[max] templateScale of templateYLabel[1] stated that they had used the largest templateTitle[1] in templateTitleSubject[0] templateTitle[2] templateTitle[3] .
generated: This statistic displays the Share population who gave charity England in as of 2015 .  During the survey , it was found that 83 % of respondents stated that they had used the largest population in England who gave .

Example 1211:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2017']}
title: Most important export partner countries for Brazil in 2017
X_Axis['Country']: ['China', 'United_States', 'Argentina', 'Netherlands']
Y_Axis['Share', 'in', 'total', 'export']: ['21.8', '12.5', '8.1', '4.3']

gold: This statistic shows the most important export partner countries for Brazil in 2017 . In 20167 the main export partner country of Brazil was China with a share of 21.8 percent in exports .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitleSubject[0] in templateTitleDate[0] . In 20167 the main templateYLabel[2] templateTitle[3] templateXLabel[0] of templateTitleSubject[0] was templateXValue[0] with a templateYLabel[0] of templateYValue[max] templateScale in exports .

generated_template: This statistic shows the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateXValue[0] , the largest number of templateTitle[3] templateTitleSubject[0] stood at templateYValue[max] templateYLabel[2] templateYLabel[3] of templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the 20 Most important the export partner in 2017 , countries Country .  In China , the largest number of partner Brazil stood at 21.8 export of .

Example 1212:
titleEntities: {'Subject': ['Bangladesh'], 'Date': ['2017']}
title: Most important export partner countries for Bangladesh in 2017
X_Axis['Country']: ['Germany', 'United_States', 'United_Kingdom', 'Spain', 'France', 'Italy']
Y_Axis['Share', 'in', 'total', 'export']: ['12.9', '12.2', '8.7', '5.3', '5.1', '4.1']

gold: This statistic shows the most important export partner countries for Bangladesh in 2017 . In 2017 , the most important export partner of Bangladesh was Germany , with a share of 12.9 percent in exports .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the templateTitle[0] templateTitle[1] templateYLabel[2] templateTitle[3] of templateTitleSubject[0] was templateXValue[0] , with a templateYLabel[0] of templateYValue[max] templateScale in exports .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Share total of the Most important Bangladesh in 2017 , Bangladesh Country .  In 2017 , the Germany Share total of Bangladesh was 12.9 export .

Example 1213:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1980', '2016']}
title: U.S. public schools - average expenditure per pupil 1980 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1980']
Y_Axis['Expenditures', 'per', 'pupil', 'in', 'U.S.', 'dollars']: ['12617', '12224', '11819', '11509', '11362', '11433', '11427', '11239', '10982', '10336', '9778', '9316', '8900', '8610', '8259', '7904', '7394', '7013', '6676', '6393', '6147', '5989', '5767', '5584', '5421', '5258', '4980', '4645', '4240', '3970', '3756', '3470', '2272']

gold: This graph shows the average expenditure per pupil in daily attendance in public elementary and secondary schools in the United States from the academic year of 1980 to 2016 . An average of 12,617 U.S. dollars was spent on each pupil in public elementary and secondary schools in the academic year of 2016 .
gold_template: This graph shows the templateTitle[3] templateTitle[4] templateYLabel[1] templateYLabel[2] in daily attendance in templateTitle[1] elementary and secondary templateTitle[2] in the templateTitle[0] from the academic templateXLabel[0] of templateXValue[min] to templateXValue[max] . An templateTitle[3] of templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] was spent on each templateYLabel[2] in templateTitle[1] elementary and secondary templateTitle[2] in the academic templateXLabel[0] of templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] from templateXValue[min] to templateXValue[max] . According to the source , the templateTitle[0] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] amounted to over templateYValue[2] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: This statistic shows the Expenditures per of the expenditure from 1980 to 2016 .  According to the source , the U.S. Expenditures per of the U.S. amounted to over 11819 million U.S. dollars in 2016 .

Example 1214:
titleEntities: {'Subject': ['Japan'], 'Date': ['2018']}
title: Urbanization in Japan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population']: ['91.62', '91.54', '91.46', '91.38', '91.3', '91.23', '91.15', '91.07', '90.81', '89.99', '89.1']

gold: In the past decade , Japan 's degree of urbanization has leveled off at around 91 percent . This means that less than 10 percent of Japan 's population of 126 million inhabitants do not live in an urban setting . Japan is well above the degree of urbanization worldwide , which is 55 percent .
gold_template: In the past decade , templateTitleSubject[0] 's degree of templateTitle[0] has leveled off at around templateYValue[2] templateScale . This means that less than 10 templateScale of templateTitleSubject[0] 's templateYLabel[2] of 126 templateScale inhabitants do not live in an templateYLabel[1] setting . templateTitleSubject[0] is well above the degree of templateTitle[0] worldwide , which is 55 templateScale .

generated_template: The statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in the templateTitleSubject[0] lived in cities .
generated: The statistic shows the degree of Urbanization in Japan from 2008 to 2018 .  In 2018 , about 91.62 % of the total population in the Japan lived in cities .

Example 1215:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1992', '2018']}
title: Book store sales in the U.S. 1992 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992']
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['10.28', '10.11', '11.14', '11.01', '11.33', '11.49', '12.27', '13.72', '15.24', '15.8', '16.8', '17.17', '16.98', '16.99', '16.88', '16.22', '15.44', '15.1', '14.88', '14.17', '13.28', '12.74', '11.91', '11.2', '10.11', '9.11', '8.33']

gold: This timeline depicts book store sales in the United States from 1992 to 2018 . In 2018 , U.S. book store sales came to about 10.28 billion U.S. dollars down from 15.24 billion U.S. dollars in 2010 .
gold_template: This timeline depicts templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[2] templateTitle[0] templateTitle[1] templateYLabel[0] came to about templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] down from templateYValue[8] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[8] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , total templateYLabel[2] templateTitle[1] templateYLabel[0] amounted to about templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] .
generated: This statistic shows the store Sales in the Book from 1992 to 2018 .  According to the report , total U.S. store Sales amounted to about 8.33 billion U.S. dollars in 1992 .

Example 1216:
titleEntities: {'Subject': ['Yemen'], 'Date': ['2018']}
title: Infant mortality rate in Yemen 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['42.9', '42.9', '42.9', '42.9', '42.9', '42.9', '42.9', '43.0', '43.6', '45.0', '47.0']

gold: The statistic shows the infant mortality rate in Yemen from 2008 to 2018 . In 2018 , the infant mortality rate in Yemen was at about 42.9 deaths per 1,000 live births .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality rate in Yemen from 2008 to 2018 .  In 2018 , the Infant mortality rate in Yemen was at about 42.9 Deaths per 1,000 live births .

Example 1217:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: Value of consumer credit outstanding in the U.S. 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Consumer', 'credit', 'in', 'billion', 'U.S.', 'dollars']: ['3918.3', '3841.32', '3644.2', '3413.61', '3314.57', '3093.39', '2918.26', '2757.07', '2646.81', '2555.02', '2643.79', '2609.48', '2456.72', '2290.93', '2192.25', '2077.36', '1972.11', '1867.85', '1716.97']

gold: This statistic presents the total consumer credit outstanding in the United States from 2000 to 2018 . In 2018 , the consumer credit outstanding in the U.S. amounted to approximately 3.92 trillion U.S. dollars .
gold_template: This statistic presents the total templateYLabel[0] templateYLabel[1] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateTitle[3] in the templateYLabel[3] amounted to approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[2] templateYLabel[3] templateTitle[4] templateYLabel[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the data from the templateTitle[0] of the templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Consumer of the billion U.S. between 2000 and 2018 .  In 2018 , the data from the Value of the U.S. was 3918.3 billion U.S. .

Example 1218:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2007', '2018']}
title: Foster care in the U.S. - number of children waiting for adoption 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children']: ['125422', '123754', '116654', '109951', '106636', '102808', '101945', '106440', '108746', '113798', '125712', '133682']

gold: This statistic shows the number of children waiting for adoption in the United States from 2007 to 2018 . In 2018 , about 125,422 children in the U.S. were waiting to be adopted .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateYLabel[1] in the templateTitleSubject[0] were templateTitle[5] to be adopted .

generated_template: The statistic presents the templateYLabel[0] of the templateTitle[1] templateTitle[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[4] globally .
generated: The statistic presents the Number of the care U.S. in the Foster from 2007 to 2018 .  In 2018 , there were 125422 million children of globally .

Example 1219:
titleEntities: {'Subject': ['Global'], 'Date': []}
title: Global automotive industry : capacity and assembly volume 2015/2022
Data:
                         0     1
Country               2022  2015
North America         22.8  19.9
EU                    23.5  22.2
Eastern Europe         7.0   5.9
Asia-Pacific          77.0  68.1
Middle East & Africa   4.4   3.7
South America          6.4   5.8 

gold: This statistic represents the capacity of the global automobile production industry in 2015 and 2022 , by region . In 2022 , the North American industry is projected to have capacity of about 22.8 million units .
gold_template: This statistic represents the templateTitle[3] of the templateTitleSubject[0] automobile production templateTitle[2] in templateValue[0][last] and templateValue[0][0] , by region . In templateValue[0][0] , the templateLabel[1][0] American templateTitle[2] is projected to have templateTitle[3] of about templateValue[1][0] templateScale units .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . In templateValue[0][0] , the templateTitleSubject[0] templateTitle[1] for templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] templateLabel[1][2] amounted to templateValue[4][1] templateScale U.S. dollars .
generated: This statistic shows the Global automotive industry capacity assembly volume from 2015 to 2022 , 2015/2022 .  In 2022 , the Global automotive for North America amounted to 68.1 billion U.S. dollars .

Example 1220:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1975', '2018']}
title: Capacity factor of nuclear power plants in the U.S. 1975 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975']
Y_Axis['Capacity', 'factor']: ['92.6', '92.3', '92.3', '92.3', '91.7', '89.9', '86.1', '89.1', '91.1', '89.3', '88.1', '77.4', '66', '58', '56.3', '55.9']

gold: This statistic represents the capacity factor of nuclear power plants in the United States between 1975 and 2018 . In 2018 , the capacity factor of U.S. nuclear power plants stood at around 92.6 percent .
gold_template: This statistic represents the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] stood at around templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Capacity of factor nuclear Capacity factor power plants U.S. from 1975 to 2018 .  The Capacity of U.S. nuclear power plants was 92.6 factor .

Example 1221:
titleEntities: {'Subject': ['U.S. Eisenhower Obama'], 'Date': []}
title: Job creation by U.S. president from Eisenhower to Obama
X_Axis['American', 'Presidents']: ['Bill_Clinton', 'Jimmy_Carter', 'Ronald_Reagan', 'JFK_/_LBJ', 'Richard_Nixon_/_Gerald_Ford', 'Barack_Obama_(thru_June_2012)', 'George_H.W._Bush', 'Dwight_Eisenhower', 'George_W._Bush']
Y_Axis['Monthly', 'average', 'change', 'in', 'private', 'sector', 'jobs', 'in', 'thousand']: ['214', '188', '151', '125', '86', '76', '25', '16', '6']

gold: This statistic shows the monthly average change in private sector jobs by presidential tenure , from the month preceding their first full month in office to last month in office in the United States in the post-war era . The monthly average change in jobs was highest when Bill Clinton was U.S. president . An average of 214,000 jobs were created each month of his presidency .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateYLabel[3] templateYLabel[4] templateYLabel[5] templateTitle[2] presidential tenure , templateTitle[5] the month preceding their first full month in office to last month in office in the templateTitle[3] in the post-war era . The templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateYLabel[5] was highest when templateXValue[0] was templateTitleSubject[0] templateTitle[4] . An templateYLabel[1] of templateYValue[max] templateYLabel[5] were created each month of his presidency .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) as of 2019 . The templateTitle[0] templateYLabel[0] of the templateXValue[0] a templateTitle[2] templateTitle[3] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Job creation by U.S. in the United Kingdom ( U.S. Eisenhower Obama ) as of 2019 .  The Job Monthly of the Bill_Clinton a by U.S. of 214 thousand private sector .

Example 1222:
titleEntities: {'Subject': ['Canada'], 'Date': ['1995', '2018']}
title: CO2 emissions : Canada 1995 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['Emissions', 'in', 'million', 'metric', 'tons']: ['568.41', '571.14', '564.07', '576.76', '577.36', '577.35', '570.16', '566.67', '556.42', '543.07', '575.45', '594.73', '570.81', '576.86', '586.47', '587.49', '570.55', '564.23', '571.51', '548.94', '533.1', '524.43', '509.69', '494.24']

gold: Canada 's carbon dioxide emissions from fossil fuel and cement production reached about 568 million metric tons in 2018 , compared to 494 million metric tons in 1995 . A large percentage of global greenhouse gas emissions ( GHG ) are derived from the production and consumption of energy . In Canada , energy-related emissions account for an even higher share of total emissions due to extreme temperatures , large land area , and dispersed populations .
gold_template: templateTitleSubject[0] 's carbon dioxide templateYLabel[0] from fossil fuel and cement production reached about templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] , compared to templateYValue[min] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] . A large templateScale of global greenhouse gas templateYLabel[0] ( GHG ) are derived from the production and consumption of energy . In templateTitleSubject[0] , energy-related templateYLabel[0] account for an even higher share of total templateYLabel[0] due to extreme temperatures , large land area , and dispersed populations .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[2] templateYLabel[3] templateTitle[4] templateYLabel[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the data of the templateTitle[0] of the templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Emissions of the metric tons 2018 from 1995 to 2018 .  In 2018 , the data of the CO2 of the Canada was 568.41 metric tons .

