Example 1:
titleEntities: {'Subject': ['Croatia'], 'Date': ['2010', '2019']}
title: Electricity prices for households in Croatia 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']: ['13.21', '13.21', '13.11', '12.36', '11.96', '13.31', '13.11', '13.12', '13.17', '13.24', '13.12', '13.5', '13.72', '13.84', '12.08', '11.46', '11.37', '11.53', '11.51']

gold: This statistic shows the electricity prices for household end users in Croatia semi-annually from 2010 to 2018 . In the second half of 2017 , the average electricity price for households was 12.36 euro cents per kWh . This increased the following year to 13.21 euro cents .
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[1] . In the second half of templateXValue[3] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh . This templatePositiveTrend the following templateXLabel[0] to templateYValue[0] 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[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh . This was an templatePositiveTrend from the previous period .
generated: This statistic shows the Electricity prices for household end users in Croatia semi-annually from 2010_S2 to 2019_S1 .  In the second half of 2018_S2 , the average Electricity price for households was 13.84 Euro cents per kWh .  This was an increase from the previous period .

Example 2:
titleEntities: {'Subject': ['Dominican Republic'], 'Date': ['2016']}
title: Dominican Republic : poverty headcount ratio at 3.20 U.S. dollars a day 2005 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Percentage', 'of', 'population']: ['5.9', '6.4', '7.8', '10', '11.1', '10.4', '12', '12.2', '13.7', '13.3', '13.7', '16.4']

gold: The poverty rate in the Dominican Republic has been decreasing in the past few years . In 2016 , approximately 5.9 percent of the Dominicans were living on less than 3.20 U.S. dollars per day , almost three-times lower than 2005 , when 16.4 percent of the country 's population was estimated to live in poverty .
gold_template: The templateTitle[2] rate in the templateTitleSubject[0] has been templateNegativeTrend in the past few years . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale of the Dominicans were living on less than templateTitle[5] templateTitle[6] templateTitle[7] per templateTitle[8] , almost three-times lower than templateXValue[min] , when templateYValue[idxmin(X)] templateScale of the country 's templateYLabel[1] was estimated to live in templateTitle[2] .

generated_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: The Republic rate in Dominican Republic has been oscillating throughout recent years .  In 2016 , 5.9 percentage of the Argentine population were living on less than ratio 3.20 U.S. dollars day , down from 16.4 percentage of the population in 2005.In nominal terms , household income dollars capita in Dominican Republic has shown a significant improvement in 2016 .

Example 3:
titleEntities: {'Subject': ['Irish'], 'Date': ['2018']}
title: Voting intention in the Irish abortion referendum 2018 , by age group
Data:
                                                   0      1      2      3    4
Year                                           18-24  25-34  35-49  50-64  65+
Yes - In favor of repealing the 8th Amendment     52     53     49     37   30
No - against repealing the 8th Amendment          27     23     26     38   47
Not sure                                          12     17     18     18   17
Will not vote                                      8      6      5      2    5
Refused                                            1      1      2      5    1 

gold: Prior to the Irish Repeal the Eighth Amendment Referendum , which took place on Friday 25th May 2018 , support for repealing the amendment was highest among 25 to 34-year old 's , while those aged 65 and over were the most likely to oppose repealing the amendment.Yes wins with 66.4 % of the votes The eight amendment was repealed following the vote , with 66.4 percent voting to repeal the amendment . The eighth amendment itself limited abortion to cases where the mother 's life was at risk , as a result of this vote the Irish Parliament now has the authority to legalize abortion in other cases as well . Men and women supported repeal While most men and women supported repealing the amendment , there were higher levels of support among women .
gold_template: Prior to the templateTitleSubject[0] Repeal the Eighth templateLabel[1][4] templateTitle[4] , which took place on Friday 25th 2018 , support for templateLabel[1][2] the templateLabel[1][4] was highest among 25 to 34-year old 's , while those aged 65 and over were the most likely to oppose templateLabel[1][2] the amendment.Yes wins with 66.4 templateScale of the votes The templateValue[4][0] templateLabel[1][4] was repealed following the templateLabel[4][2] , with 66.4 templateScale templateTitle[0] to repeal the templateLabel[1][4] . The eighth templateLabel[1][4] itself limited templateTitle[3] to cases where the mother 's life was at risk , as a result of this templateLabel[4][2] the templateTitleSubject[0] Parliament now has the authority to legalize templateTitle[3] in other cases as well . Men and women supported repeal While most men and women supported templateLabel[1][2] the templateLabel[1][4] , there were higher levels of support among women .

generated_template: The statistic above presents the results of a survey among American adults regarding the templateTitle[0] of templateTitle[1] they templateTitle[2] within the last templateLabel[0][0] . In templateLabel[3][0] , templateValue[1][min] templateScale of respondents stated that they templateTitle[2] more than templateValue[1][0] templateTitle[1] in the past templateLabel[0][0] . Book purchasing in the templateTitle[5] – additional information A survey in 2013 asked its respondents to rate the most important features in printed templateTitle[1] which attracted them to buy .
generated: The statistic above presents the results of a survey among American adults regarding the Voting of intention they Irish within the last Year .  In Not , 30 % of respondents stated that they Irish more than 52 intention in the past Year .  Book purchasing in the 2018 – additional information A survey in 2013 asked its respondents to rate the most important features in printed intention which attracted them to buy .

Example 4:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2019']}
title: Mexico : Light vehicle sales by manufacturer 2019
X_Axis['Automaker']: ['Nissan', 'General_Motors', 'Volkswagen', 'Toyota', 'KIA', 'Honda', 'FCA_Mexico', 'Ford', 'Mazda', 'Hyundai', 'Baic', 'Renault', 'Suzuki', 'BMW_Group', 'Mercedes_Benz', 'Mitsubishi', 'Peugeot', 'Volvo', 'ISUZU', 'Acura', 'Lincoln', 'Infiniti', 'Land_Rover', 'Subaru', 'Jaguar', 'Smart']
Y_Axis['Number', 'of', 'units', 'sold']: ['174706', '133823', '117045', '67670', '62762', '48937', '41413', '39731', '39040', '29018', '21147', '20308', '19880', '15859', '13627', '11134', '7097', '1334', '1224', '1224', '1082', '951', '908', '790', '207', '30']

gold: The Japanese multinational Nissan was the automobile manufacturer with the highest number of light vehicles sold in Mexico , with more than 174 thousand units sold as of August 2019 . The American multinational General Motors ranked second with almost 134 thousand light vehicle units sold in 2019 .
gold_template: The Japanese multinational templateXValue[0] was the automobile templateTitle[5] with the highest templateYLabel[0] of templateTitle[1] vehicles templateYLabel[2] in templateXValue[6] , with more than 174 thousand templateYLabel[1] templateYLabel[2] as of 2019 . The American multinational templateXValue[1] ranked second with almost templateYValue[1] thousand templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , there were a total of templateYValue[max] templateYLabel[1] in the templateTitle[3] .
generated: This statistic shows the Number of units in the United Kingdom ( Mexico ) in 2019 , manufacturer Automaker .  In 2019 , there were a total of 174706 units in the sales .

Example 5:
titleEntities: {'Subject': ['New Year', 'Americans'], 'Date': ['2018']}
title: New Year 's resolution of Americans for 2018
X_Axis['Response']: ['Save_money', 'Lose_weight_or_get_in_shape', 'Have_more_sex', 'Travel_more', 'Read_more_books', 'Learn_a_new_skill_or_hobby', 'Buy_a_house', 'Quit_smoking', 'Find_love']
Y_Axis['Share', 'of', 'respondents']: ['53', '45', '25', '24', '23', '22', '21', '16', '15']

gold: More than half of Americans wanted to start 2018 by saving money and by getting in shape . The most popular New Year 's resolution ever – `` quit smoking '' – was not as high up on the list as it used to be , but at least 16 percent decided to finally give up smoking once and for all . Resolution makers , resolution keepers ? While some might say that they do not need New Year 's Eve to finally turn their life around , making resolutions on December 31 is a common , well-liked tradition , especially in the Western world .
gold_template: templateXValue[2] than half of templateTitleSubject[1] wanted to start templateTitleDate[0] by saving templateXValue[0] and by getting in templateXValue[1] . The most popular templateXValue[5] templateTitleSubject[0] templateTitle[2] templateTitle[3] ever – `` templateXValue[7] '' – was not as high up on the list as it used to be , but at least templateYValue[7] templateScale decided to finally give up templateXValue[7] once and templateTitle[5] all . templateTitle[3] makers , templateTitle[3] keepers ? While some might say that they do not need templateXValue[5] templateTitleSubject[0] templateTitle[2] Eve to finally turn their life around , making resolutions on 31 is a common , well-liked tradition , especially in the Western world .

generated_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: The statistic shows the New Year 's of resolution Save_money respondents from New Year in 2018 .  During the Deloitte survey , 53 % of respondents stated that Save_money the social or Save_money was their favorite source of resolution .

Example 6:
titleEntities: {'Subject': ['Finland'], 'Date': ['2018']}
title: Urbanization in Finland 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['85.38', '85.33', '85.28', '85.23', '85.18', '85.13', '84.84', '84.31', '83.77', '83.43', '83.3']

gold: This statistic shows the degree of urbanization in Finland from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 85.38 percent of Finland '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 Finland from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 85.38 % of Finland 's total population lived in urban areas and cities .

Example 7:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': []}
title: Leading pets owned by households in the United Kingdom ( UK ) 2018/19
X_Axis['Pet', 'Type']: ['Any_pet', 'Dogs', 'Cats', 'Rabbits', 'Indoor_Birds', 'Guinea_pigs', 'Hamsters', 'Tortoises_and_Turtles', 'Lizards', 'Domestic_fowl', 'Snakes', 'Horses_and_ponies', 'Rats', 'Ferrets', 'Gerbils', 'People_keep_insects', 'Pigeons', 'Frogs_and_toads']
Y_Axis['Share', 'of', 'households']: ['40', '25', '17', '1', '1', '1', '1', '0.7', '0.6', '0.4', '0.4', '0.3', '0.1', '0.1', '0.2', '0.1', '0.1', '0.1']

gold: This statistic shows a ranking of the leading pets owned by households in the United Kingdom ( UK ) in 2018 and 2019 , based on running totals sourced over two years . The most popular individual pet owned in the United Kingdom is the dog , with 25 percent of people owning one . In total , 40 percent of the UK population own a pet .
gold_template: This statistic shows a ranking of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in 2018 and 2019 , based on running totals sourced over two years . The most popular individual templateXValue[0] templateTitle[2] in the templateTitleSubject[0] is the dog , with templateYValue[1] templateScale of templateXValue[15] owning templateYValue[3] . In total , templateYValue[max] templateScale of the templateTitleSubject[1] population own a templateXValue[0] .

generated_template: This statistic shows the results of a survey among the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] . During the survey period , templateYValue[max] templateScale of templateYLabel[1] stated that they used templateXValue[0] or templateXValue[1] .
generated: This statistic shows the results of a survey among the United Kingdom pets owned by in .  During the survey period , 40 % of households stated that they used Any_pet or Dogs .

Example 8:
titleEntities: {'Subject': ['United States'], 'Date': ['2012', '2013']}
title: Expected numer of Valentine 's Day proposals in the United States 2012 to 2013
X_Axis['Year']: ['2013', '2012']
Y_Axis["Valentine's", 'Day', 'proposals', 'in', 'millions']: ['14', '6']

gold: This statistic presents the number of American couples who are planning to make or are expecting to receive a proposal on Valentine 's day . In 2013 , 14 million couples were expecting an engagement on that day , up from four million in 2012 .
gold_template: This statistic presents the number of American couples who are planning to make or are expecting to receive a proposal on templateTitle[2] templateTitle[3] templateYLabel[1] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale couples were expecting an engagement on that templateYLabel[1] , up from four templateScale in templateXValue[min] .

generated_template: This statistic shows the templateYLabel[0] of migrant worker templateYLabel[1] templatePositiveTrend up away from their parents in templateTitleSubject[0] in templateXValue[min] and templateXValue[max] . The 6th National Population Census of the Republic of templateTitleSubject[0] estimated that templateYValue[max] templateScale templateTitle[1] templateYLabel[1] until the age of 17 templatePositiveTrend up without their parents .
generated: This statistic shows the Valentine's of migrant worker Day growing up away from their parents in United States 2012 and 2013 .  The 6th National Population Census of the Republic of United States estimated that 14 millions numer Day until the age of 17 grew up without their parents .

Example 9:
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: This statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] of templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[2] templateTitle[3] templateYLabel[0] of templateYLabel[1] templateTitle[6] amounted to about templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] .
generated: This statistic depicts the Attendance Cineplex cinemas 2010 Attendance of 2018 from 2010 to 2018 .  In 2018 , Attendance cinemas 2010 Attendance of millions 2018 amounted to about 69.27 millions .

Example 10:
titleEntities: {'Subject': ['Eritrea'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Eritrea 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']: ['2.9', '2.72', '2.54', '2.33', '2.19', '2.11', '2.01', '1.92', '2.21', '2.01', '2.6', '3.52', '3.06', '2.61', '2.12', '1.86', '1.38', '1.32', '1.21', '1.1', '1.11', '0.87', '0.73', '0.75', '0.71', '0.79', '0.81', '0.77', '0.75', '0.62', '0.56']

gold: The statistic shows gross domestic product ( GDP ) in Eritrea from 1994 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 Eritrea 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 11:
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[6] of the templateTitleSubject[0] templateTitle[1] of templateTitle[2] templateTitle[3] and templateTitle[4] templateTitle[5] from templateValue[0][0] to templateValue[0][last] , by product , measured in templateScale templateTitleSubject[0] dollars . In templateValue[0][0] , the templateTitle[5] for templateLabel[2][0] templateLabel[2][1] templateTitle[4] was estimated to be around templateValue[8][1] templateScale templateTitleSubject[0] dollars . The forecast says that the templateTitle[5] templateTitle[6] will templatePositiveTrend to some templateValue[2][last] templateScale dollars until templateValue[0][last] .
generated: This statistic shows the segment of the Johnson Johnson Johnson of & Johnson and 's pharmaceuticals from 2012 to 2024 , by product , measured in million Johnson Johnson dollars .  In 2012 , the pharmaceuticals for Cardiovascular/Metabolism/Other 's was estimated to be around 3.55 billion Johnson Johnson dollars .  The forecast says that the pharmaceuticals segment will increase to some 6.79 billion dollars until 2024 .

Example 12:
titleEntities: {'Subject': ['Denver Broncos', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Denver Broncos ( 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']: ['3000', '2650', '2600', '2400', '1940', '1450', '1161', '1132', '1046', '1049', '1081', '1061', '994', '975', '907', '815', '683', '604']

gold: This graph depicts the franchise value of the Denver Broncos from the National Football League from 2002 to 2019 . In 2019 , the franchise value came to three billion U.S. dollars . The Denver Broncos are owned by the Pat Bowlen Trust , who bought the franchise for 78 million U.S. dollars in 1984 .
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] came to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by the Pat Bowlen Trust , who bought the templateYLabel[0] for 78 templateScale templateYLabel[3] templateYLabel[4] in 1984 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitleSubject[0] franchise 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 templateTitle[2] templateTitleSubject[0] are owned by William DeWitt Jr. , who bought the templateYLabel[0] for 150 templateScale templateYLabel[3] templateYLabel[4] in 1996 .
generated: This graph depicts the value of the Denver Denver Broncos franchise of Major League Baseball from 2002 to 2019 .  In 2019 , the Franchise had an estimated value of 3000 million U.S. dollars .  The Denver Denver Broncos are owned by William DeWitt Jr. , who bought the Franchise for 150 million U.S. dollars in 1996 .

Example 13:
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
Brand  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] templateLabel[0][0] templateTitle[2] templateTitle[3] within the last three to twelve months of templateTitleDate[max] .

generated_template: This statistic shows the templateTitle[0] of templateValue[0][9] templateValue[0][10] templateTitle[3] templateValue[0][10] , templateTitle[5] templateValue[0][10] templateTitle[7] in templateTitleDate[min] and templateTitleDate[max] , templateValue[0][9] templateLabel[0][0] . There were templateValue[2][6] templateValue[0][6] templateValue[0][10] within the global templateValue[0][9] templateValue[0][10] templateTitle[3] templateValue[0][10] , templateTitle[5] property portfolio in templateTitleDate[max] .
generated: This statistic shows the Penetration of Abant Aqua water , Turkey Aqua 2013 in 2012 and 2013 , Abant Brand .  There were 9 Sirma Aqua within the global Abant Aqua water , Turkey property portfolio in 2013 .

Example 14:
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: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of the Peruvian population accessed the templateYLabel[3] , up from nearly templateYValue[1] templateScale a templateXLabel[0] earlier .
generated: This statistic gives information on the internet penetration in Mexico from 2000 to 2018 .  In 2018 , about 65.8 percentage of the Peruvian population accessed the internet , up from nearly 63.9 percentage a Year earlier .

Example 15:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2017', '2018']}
title: Share of social media users in Denmark 2017 - 2018 , by age group
Data:
                0            1            2            3            4            5            6      7
Year  16-24_years  25-34_years  35-44_years  45-54_years  55-64_years  65-74_years  75-89_years  Total
2017           98           94           87           77           63           42           26     75
2018           97           91           90           82           69           50           32     77 

gold: Danes of ages between 16 and 24 were the most active social media users in Denmark in 2017 and 2018 . The share of social media users in that age group was 98 percent in 2017 and decreased slightly in 2018 to 97 percent . There was an increase of the share of social media users of older ages .
gold_template: Danes of ages between 16 and 24 were the most active templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . The templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in that templateTitle[8] templateTitle[9] was templateValue[1][max] templateScale in templateTitleDate[min] and templateNegativeTrend slightly in templateTitleDate[max] to templateValue[1][idxmax(1)] templateScale . There was an templatePositiveTrend of the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] of older ages .

generated_template: The statistic shows 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: The statistic shows the results of a survey on the proportion of people within different age groups who gamble social on a monthly basis in the Denmark ( UK ) from 2017 to 2018 .  As of 2018 , it was found that 97 % of respondents between the age of 25 and 34 years stated that they took part in a form of social media in the past four weeks .  This age group had the highest proportion of people who gamble social .

Example 16:
titleEntities: {'Subject': ['Los Angeles'], 'Date': ['2016']}
title: Quarterly average daily rate of hotels in Los Angeles 2016 to 2017
Data:
           0    1    2    3
Quarter   Q4   Q3   Q2   Q1
2016     212  221  221  224
2017       0    0    0  224 

gold: This statistic shows the quarterly average daily rate of hotels in Los Angeles in 2016 and 2017 . In the first quarter of 2017 , the average daily rate of hotels in Los Angeles in the United States was 224 U.S. dollars . In 2016 , there were 5.6 million international overnight visitors to Los Angeles .
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[1][idxmax(1)] U.S. dollars . In templateTitleDate[min] , there were 5.6 templateScale international overnight visitors to templateTitleSubject[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 Los Angeles 2016 and .  In the first Quarter of 2016 , the average daily rate of hotels in Los Angeles the country was 224 U.S. dollars .

Example 17:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: Average duration of unemployment 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['Duration', 'in', 'weeks']: ['21.6', '22.7', '25.0', '27.5', '29.2', '33.7', '36.5', '39.4', '39.3', '33.0', '24.4', '17.9', '16.8', '16.8', '18.4', '19.6', '19.2', '16.6', '13.1', '12.6', '13.4', '14.5', '15.8', '16.7', '16.6', '18.8', '18.0', '17.7', '13.7', '12.0']

gold: This statistic displays the average duration of unemployment in the United States from 1990 to 2019 , in weeks . In 1990 , the average duration of unemployment was 12 weeks . The duration increased to 21.6 weeks in 2019 .
gold_template: This statistic displays the templateTitle[0] templateYLabel[0] of templateTitle[2] in the templateTitle[3] from templateTitleDate[min] to templateTitleDate[max] , in templateYLabel[1] . In templateTitleDate[min] , the templateTitle[0] templateYLabel[0] of templateTitle[2] was templateYValue[min] templateYLabel[1] . The templateYLabel[0] templatePositiveTrend to templateYValue[0] templateYLabel[1] in templateTitleDate[max] .

generated_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: This statistic shows the Duration 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 , 21.6 refugees were admitted to the unemployment .

Example 18:
titleEntities: {'Subject': ['Trump'], 'Date': ['2018']}
title: Trump budget proposal 2018 : changes by department
X_Axis['Department', 'Name']: ['Environmental_Protection_Agency', 'State_Department', 'Agriculture_Department', 'Labor_Department', 'Department_of_Health_and_Human_Services', 'Commerce_Department', 'Education_Department', 'Department_of_Housing_and_Urban_Development', 'Transportation_Department', 'Interior_Department', 'Energy_Department', 'Small_Business_Administration', 'Treasury_Department', 'Justice_Department', 'NASA', 'Department_of_Veterans_Affairs', 'Department_of_Homeland_Security', 'Defense_Department']
Y_Axis['Percentage', 'change', 'from', '2017']: ['-31', '-29', '-21', '-21', '-18', '-16', '-14', '-13', '-13', '-12', '-6', '-5', '-4', '-4', '-1', '6', '7', '9']

gold: This statistic shows the percentage change in spending on departments and services funded by the federal budget of the United States from the 2017 budget to the proposed 2018 budget . The level of funding for the Environmental Protection Agency in the 2018 federal budget represents a 31 percent decrease from the agency 's funding in 2017 .
gold_template: This statistic shows the templateScale templateYLabel[1] in spending on departments and templateXValue[4] funded templateTitle[5] the federal templateTitle[1] of the country templateYLabel[2] the templateYLabel[3] templateTitle[1] to the proposed templateTitleDate[0] templateTitle[1] . The level of funding for the templateXValue[0] Agency in the templateTitleDate[0] federal templateTitle[1] represents a 31 templateScale templateNegativeTrend templateYLabel[2] the templateXValue[0] 's funding in templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[4] templateYLabel[1] templateYLabel[2] worldwide as of 2019 , by templateXLabel[0] templateXLabel[1] . There were templateYValue[last] templateYLabel[1] templateYLabel[2] templateTitle[4] in templateXValue[8] . The Bushehr templateYLabel[1] templateTitle[1] Plant is the first templateYLabel[1] templateTitle[1] plant operating here .
generated: This statistic shows the Percentage of changes change from worldwide as of 2019 , by Department Name .  There were 9 change from changes in Transportation_Department .  The Bushehr change budget Plant is the first change budget plant operating here .

Example 19:
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 templateTitle[0] templateTitle[1] templateYLabel[0] in the NFL ( templateTitleSubject[0] League ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Global mine Production in the NFL ( Global League ) from 2007 to 2019 .  In 2019 , the Global mine Production amounted to approximately 1100 thousand metric tons .

Example 20:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Popular topics subject to online trolling in the U.S. 2017
X_Axis['Response']: ['Politics', 'Celebrity', 'Religion', 'News/current_events', 'Emotional_(e.g._a_traumatic_experience_death_etc.)', "Personal_(e.g._a_person's_day_a_vacation_photo_etc.)", 'Sports', 'Gaming', 'Health', 'Science', 'Other', 'Not_applicable_-_I_have_never_seen_internet_trolling_before']
Y_Axis['Share', 'of', 'respondents']: ['63', '52', '48', '46', '38', '33', '29', '27', '24', '22', '1', '12']

gold: This statistic presents a ranking of the topics most commonly subjected to online trolling according to internet users in the United States . During the August 2017 , 63 percent of respondents stated to having seen trolling behavior directed towards political content online .
gold_template: This statistic presents a ranking of the templateTitle[1] most commonly subjected to templateTitle[3] templateXValue[last] according to templateXValue[last] users in the templateTitle[5] . During the 2017 , templateYValue[max] templateScale of templateYLabel[1] stated to having templateXValue[last] behavior directed towards political content templateTitle[3] .

generated_template: This statistic presents the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded online by Harris Interactive in 2014 , asking the templateYLabel[1] where they usually templateTitle[3] their shampoo and/or templateTitle[6] . Some templateYValue[3] templateScale of templateTitleSubject[0] adults indicated that they buy their shampoo/conditioner templateXValue[3] .
generated: This statistic presents the results of a survey among U.S. adult topics .  The survey was fielded online by Harris Interactive in 2014 , asking the respondents where they usually online their shampoo and/or 2017 .  Some 46 % of U.S. adults indicated that they buy their shampoo/conditioner News/current_events .

Example 21:
titleEntities: {'Subject': ['Android'], 'Date': ['2019']}
title: Leading Android music apps worldwide 2019 , by revenue
X_Axis['Music', 'Platform']: ['Pandora_-_Streaming_Music_Radio_&_Podcasts', 'LINE_MUSIC（ラインミュージック）_音楽なら音楽無料お試し聴き放題の人気音楽アプリ', 'iHeartRadio_-_Free_Music_Radio_&_Podcasts', 'Smule_-_The_#1_Singing_App', 'TuneIn_-_NFL_Radio_Free_Music_Sports_&_Podcasts', 'AWA_-_音楽ストリーミングサービス', 'SPOON_RADIO', 'StarMaker:_Sing_with_50M+_Music_Lovers', 'Ultimate_Guitar:_Chords_&_Tabs', 'BOOM:_музыкальный_плеер']
Y_Axis['Revenue', 'in', 'U.S.', 'dollars']: ['13235511', '996686', '861926', '810233', '799077', '546508', '496082', '322789', '229905', '209267']

gold: The graph represents the leading music and android apps in Google Play Store worldwide in November 2019 , ranked by the total revenue . As of that month , Pandora Radio was ranked first in the list with the total revenue worth of 13.2 million U.S. dollars .
gold_template: The graph represents the templateTitle[0] templateXValue[0] and templateTitleSubject[0] templateTitle[3] in Google Play Store templateTitle[4] in 2019 , ranked templateTitle[6] the total templateYLabel[0] . As of that month , templateXValue[0] was ranked first in the list templateXValue[7] the total templateYLabel[0] worth of templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the ten most popular television templateTitle[5] in the templateTitle[0] based on their templateTitle[2] of templateYLabel[1] . In 2016 , templateXValue[0] was ranked first with a templateTitle[1] templateTitle[2] of templateYValue[max] templateScale of total templateYLabel[1] .
generated: The statistic shows the ten most popular television 2019 in the Leading based on their music of U.S. In 2016 , Pandora_-_Streaming_Music_Radio_&_Podcasts was ranked first with a Android music of 13235511 % of total U.S. .

Example 22:
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 23:
titleEntities: {'Subject': ['Instagram'], 'Date': ['2013', '2018']}
title: Number of monthly active Instagram users 2013 to 2018
X_Axis['Month']: ["Jun_'18", "Sep_'17", "Apr_'17", "Dec_'16", "Jun_'16", "Sep_'15", "Dec_'14", "Mar_'14", "Sep_'13", "Jun_'13", "Feb_'13", "Jan_'13"]
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['1000', '800', '700', '600', '500', '400', '300', '200', '150', '130', '100', '90']

gold: How many people use Instagram ? In June 2018 , Instagram had reached one billion monthly active users , up from 800 million in September 2017 . The app is one of the most popular social networks worldwide . Global Instagram audiences Instagram is a mobile social network that allows users to edit and share photos and videos .
gold_template: How many people use templateTitleSubject[0] ? In 2018 , templateTitleSubject[0] had reached templateYValue[max] templateScale templateTitle[1] templateTitle[2] templateYLabel[1] , up from templateYValue[1] templateScale in 2017 . The app is one of the most popular social networks worldwide . Global templateTitleSubject[0] audiences templateTitleSubject[0] is a mobile social network that allows templateYLabel[1] to edit and share photos and videos .

generated_template: This statistic shows templateTitleSubject[0] 's monthly templateTitle[1] traffic based upon templateTitle[0] visitor numbers . As of 2017 , templateTitleSubject[0] had templateYValue[max] templateScale templateTitle[0] templateYLabel[1] from the templateTitle[1] across its app and web presence . The social photo sharing website managed to garner great attention by templatePositiveTrend more than 11 templateScale US templateYLabel[1] in 2012 .
generated: This statistic shows Instagram 's monthly traffic based upon Number visitor numbers .  As of 2017 , Instagram had 1000 millions Number users from the monthly across its app and web presence .  The social photo sharing website managed to garner great attention by gaining more than 11 millions US users in 2012 .

Example 24:
titleEntities: {'Subject': ['Pakistan'], 'Date': ['2024']}
title: Ratio of government expenditure to gross domestic product ( GDP ) in Pakistan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'government', 'expenditure', 'to', 'GDP']: ['22.15', '22.42', '22.94', '23.31', '23.64', '21.6', '21.63', '21.3', '19.94', '19.77', '20.1']

gold: The statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in Pakistan from 2014 to 2018 , with forecasts up to 2024 . In 2018 , government expenditure in Pakistan amounted to about 21.63 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 forecasts 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[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 Pakistan from 2014 to 2018 , with projections up until 2024 .  In 2018 , government expenditure in Pakistan amounted to about 19.77 % of the gross domestic product .

Example 25:
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: In templateTitleDate[0] , it appears that the majority of French teenagers were in middle school when they had templateTitle[5] templateTitle[6] templateTitle[7] . Love appears to be an important area of life at a templateTitle[1] templateTitle[4] , with more than 50 templateScale of templateTitle[1] French templateTitle[2] stating that love relationships were important for them . templateTitle[6] love experiences Even though new technologies and smartphones may have changed the way teenagers live templateTitle[5] love life , it seems that the templateTitle[4] for templateTitle[6] love and sex experiences has templateXValue[last] really changed templateXValue[5] the templateXValue[0] .
generated: In 2019 , it appears that the majority of French teenagers were in middle school when they had 2019 .  Love appears to be an important area of life at a age users , with more than 50 % of age French distribution stating that love relationships were important for them .  2019 love experiences Even though new technologies and smartphones may have changed the way teenagers live 2019 love life , it seems that the users for 2019 love and sex experiences has Over_55_years really changed 45-54_years the 6-11_years .

Example 26:
titleEntities: {'Subject': ['NHL Stanley Cup'], 'Date': ['2006', '2019']}
title: Average U.S. TV ratings NHL Stanley Cup games 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Average', 'TV', 'rating']: ['3.0', '2.7', '2.7', '2.3', '3.2', '3.0', '3.3', '1.8', '2.7', '2.9', '2.7', '2.6', '1.2', '1.8']

gold: The statistic depicts the average TV ratings of Stanley Cup games in the United States from 2006 to 2019 . In 2019 , the average TV rating was at 3 in the United States .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] templateTitle[3] of templateTitleSubject[0] games in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[0] in the templateTitle[1] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[idxmax(X)] directory listings for templateYLabel[1] templateYLabel[2] throughout the templateTitleSubject[0] .
generated: This statistic shows the Average of TV rating in the NHL Stanley Cup from 2006 to 2019 .  In 2019 , there were a total of 3.0 directory listings for TV rating throughout the NHL Stanley Cup .

Example 27:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2013', '2019']}
title: Internet retail sales value trend monthly in the United Kingdom ( UK ) 2013 to 2019
X_Axis['Month']: ['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']: ['12.4', '11.8', '11.6', '13.3', '13', '12.9', '12', '14.2', '16.1', '15.8', '21', '13', '16.5', '15.3', '13.6', '9', '10.1', '9.6', '13.6', '19', '20.1', '20.1', '17.1', '22.1', '24.3', '22.2', '16.1', '30.1', '28.4', '30', '25.8', '23.6', '17.8', '17.2', '19.6', '12.9', '11.6', '12.4', '13.8', '8.4', '14.9', '11.6', '14.4', '8.7', '13.2', '13.9', '9.4', '14.3', '12.5', '11.4', '19.3', '11.1', '19.7', '12', '10.1', '13.7', '14.8', '15', '16.6', '15', '9.1', '11.9', '11', '13.5', '17.5', '17.8', '17.4', '21.7', '10.2', '18.1', '10.7', '13.9', '18.8', '12.9', '12.8']

gold: This statistic displays the monthly trend of the value of retail internet sales in the United Kingdom ( UK ) from January 2013 to March 2019 . In March 2019 , retail sales increased by 12.4 percent .
gold_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_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: In the period of consideration , the total Internet change of sales in the United Kingdom followed a similar pattern each year .  The most notable change occurred in 2018 , when the change of sales shot up in Aug_18 , and to 30.1 , 13.6 and 9.6 change respectively .  Unsurprisingly it was the end of each year when sales change were lowest .

Example 28:
titleEntities: {'Subject': ['Germany'], 'Date': ['2018']}
title: Age structure in 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
0-14 years          13.62  13.45  13.32  13.22  13.25  13.33  13.43  13.51  13.57  13.71  13.84
15-64 years         64.92  65.18   65.4  65.56  65.68  65.73  65.75  65.79  65.88  65.99  66.14
65 years and older  21.46  21.37  21.29  21.22  21.06  20.94  20.82  20.71  20.55   20.3  20.02 

gold: This statistic shows the age structure in Germany from 2008 to 2018 . In 2018 , about 13.62 percent of Germany '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][0] templateScale of templateTitleSubject[0] 's total population were aged 0 to templateValue[1][0] 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 Germany from 2008 to 2018 .  In 2018 , about 13.22 % of Germany 's total population were aged 0 to 14 years .

Example 29:
titleEntities: {'Subject': ['Messenger'], 'Date': ['2014', '2017']}
title: Facebook Messenger : number of monthly active users 2014 to 2017
X_Axis['Month']: ["Sep_'17", "Apr_'17", "Jul_'16", "Apr_'16", "Dec_'15", "Jun_'15", "Mar_'15", "Nov_'14", "Apr_'14"]
Y_Axis['Number', 'of', 'monthly', 'active', 'users', 'in', 'millions']: ['1300', '1200', '1000', '900', '800', '700', '600', '500', '200']

gold: This statistic presents the number of monthly active Facebook Messenger chat app users from April 2014 to September 2017 . As of the last reported period , the mobile messenger had 1.3 billion monthly active users worldwide , ranking second among mobile chat apps worldwide . Facebook messenger users – additional information Mobile messenger apps are on the rise , with optimistic projections for this market in the coming years .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[0] templateTitleSubject[0] chat app templateYLabel[3] from 2014 to 2017 . As of the last reported period , the mobile templateTitleSubject[0] had templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide , ranking second among mobile chat apps worldwide . templateTitle[0] templateTitleSubject[0] templateYLabel[3] – additional information Mobile templateTitleSubject[0] apps are on the rise , with optimistic projections for this market in the coming years .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] as of October templateTitleDate[0] . As of that templateXLabel[0] , the social messaging app community had accumulated over templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] worldwide .
generated: This statistic gives information on the Number of monthly active in Messenger as of October 2014 .  As of that Month , the social messaging app community had accumulated over 1300 millions monthly active worldwide .

Example 30:
titleEntities: {'Subject': ['Economic'], 'Date': ['2016']}
title: Economic damage caused by major landslides worldwide as of 2016
X_Axis['Mudslide']: ['Peru_(January_1983)', 'China_(May_1_1998)', 'China_(August_7_2010)', 'Italy_(December_14_1982)', 'Switzerland_(February_21_1999)', 'Italy_(July_28_1987)', 'Ecuador_(March_28_1993)', 'Guatemala_(September_4_2010)', 'Russia_(March_10_1989)', 'China_(May_6_2016)']
Y_Axis['Economic', 'damage', 'in', 'thousand', 'U.S.', 'dollars']: ['988800', '890000', '759000', '700000', '685000', '625000', '500000', '500000', '423000', '820000']

gold: The statistic shows the economic damage caused by mudslides in the period from 1900 to 2016  . The mudslide in Peru in 1983 caused an economic loss of almost 989 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] mudslides in the period from 1900 to templateTitleDate[0] . The templateXLabel[0] in templateXValue[0] in 1983 templateTitle[2] an templateYLabel[0] loss of almost templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the ten templateTitle[0] templateTitle[4] templateTitle[3] , other than English , in templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] , by templateYLabel[0] of templateYLabel[1] . The templateTitle[0] commonly templateTitle[4] templateXLabel[0] was templateXValue[0] with almost templateYValue[max] thousand native templateYLabel[1] , followed by templateXValue[1] and templateTitleSubject[0] .
generated: This statistic shows the ten Economic major by , other than English , in Economic worldwide in 2016 , by Economic of damage .  The Economic commonly major Mudslide was Peru_(January_1983) with almost 988800 thousand native damage , followed by China_(May_1_1998) and Economic .

Example 31:
titleEntities: {'Subject': ['Development'], 'Date': ['2050']}
title: Development of the world population until 2050
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['World', 'population', 'in', 'billions']: ['9.74', '9.48', '9.2', '8.89', '8.55', '8.18', '7.79', '7.38', '6.96', '6.54', '6.14', '5.74', '5.33', '4.87', '4.46', '4.08', '3.7', '3.34', '3.03', '2.77', '2.54']

gold: The statistic shows the development of the world population from 1950 to 2050 . The world population was around 7.38 billion people in 2015 . The global population As shown above , the total number of people living on Earth has more than doubled since the 1950s , and continues to increase .
gold_template: The statistic shows the templateTitleSubject[0] of the templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] was around templateYValue[7] templateScale people in templateXValue[7] . The global templateYLabel[1] As shown above , the total number of people living on Earth has more than doubled since the 1950s , and continues to templatePositiveTrend .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . The templateYLabel[0] templateYLabel[1] of a templateTitle[3] is an index that divides the templateTitle[3] into two equal groups : half of the templateTitle[3] 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[3] was templateYValue[7] years .
generated: The statistic shows the World population in the Development between 1950 and 2050 .  The World population of a until is an index that divides the until into two equal groups : half of the until is older than the World population and the other half younger .  In 2015 , the World population of Development 's until was 7.38 years .

Example 32:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2001', '2018']}
title: Average wind speed in the United Kingdom ( UK ) 2001 to 2018
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['wind', 'speed', 'in', 'knots']: ['8.2', '8.5', '8.7', '8.4', '9.4', '8.7', '8.6', '8.2', '9.0', '7.8', '9.0', '9.3', '9.0', '9.0', '8.9', '9.0', '8.9', '9.1', '8.6']

gold: Over the past two decades , the average wind speed in the United Kingdom has remained relatively stable . In 2019 , the average wind speed in the UK was 8.2 knots . Speeds peaked during this period in 2015 at 9.4 knots , before falling to 8.4 knots the following year .
gold_template: Over the past two decades , the templateTitle[0] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] has remained relatively stable . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[1] was templateYValue[idxmax(X)] templateYLabel[2] . Speeds peaked during this period in templateXValue[4] at templateYValue[max] templateYLabel[2] , before templateNegativeTrend to templateYValue[3] templateYLabel[2] the following templateXLabel[0] .

generated_template: This statistic 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: This statistic shows the wind of knots in the wind knots permanent via knots from 2001 to 2019 .  In the most recently reported period , close to 8.2 % speed knots had fixed knots , up from close to 7.8 % in 2010 .  The wind are one of the biggest online markets worldwide .

Example 33:
titleEntities: {'Subject': ['OPEC'], 'Date': ['2016', '2040']}
title: OPEC reference basket - oil price 2016 to 2040
Data:
                      0     1
Year               2040  2016
Nominal price       155    40
Real price (2015)    92    40 

gold: In 2040 , the nominal price of the OPEC reference basket oil is expected to reach 155 U.S. dollars . The nominal price is an unadjusted number , without taking elements such as inflation , seasonality , loan fees , interest compounding into account . Prices rising and recovering The `` real price '' ( in 2015 U.S. dollars ) of oils in the OPEC reference basket is projected to more than double from 2016 to 2040 .
gold_template: In templateValue[0][0] , the templateLabel[1][0] templateTitle[4] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] is expected to reach templateValue[1][0] U.S. dollars . The templateLabel[1][0] templateTitle[4] is an unadjusted number , without taking elements such as inflation , seasonality , loan fees , interest compounding into account . Prices templatePositiveTrend and recovering The `` templateLabel[2][0] templateTitle[4] '' ( in 2015 U.S. dollars ) of oils in the templateTitleSubject[0] templateTitle[1] templateTitle[2] is projected to more than double from templateValue[0][last] to templateValue[0][0] .

generated_template: This statistic shows the templateLabel[1][0] templateTitle[2] of the templateTitleSubject[1] templateTitle[0] templateTitle[1] to the templateTitleSubject[1] templateTitle[3] in templateValue[0][last] and templateValue[0][0] , alongside the total contribution of the templateTitle[1] to the templateTitle[3] including templateLabel[2][1] demand , such as via the supply chain industries and induced spending of employee 's wages . In templateValue[0][0] , the templateLabel[1][0] templateTitle[2] of the templateTitle[0] templateTitle[1] was measured at templateValue[1][0] templateScale British pounds ( GBP ) , with a total contribution of templateValue[2][0] templateScale .
generated: This statistic shows the Nominal basket of the OPEC reference to the OPEC oil in 2016 and 2040 , alongside the total contribution of the reference to the oil including price demand , such as via the supply chain industries and induced spending of employee 's wages .  In 2040 , the Nominal basket of the OPEC reference was measured at 155 million British pounds ( GBP ) , with a total contribution of 92 % .

Example 34:
titleEntities: {'Subject': ['Iraq'], 'Date': ['2017']}
title: Age structure in Iraq 2017
X_Axis['Year']: ['0-14_years', '15-24_years', '25-54_years', '55-64_years', '65_years_and_over']
Y_Axis['Share', 'of', 'population']: ['39.01', '19.42', '33.97', '4.05', '3.55']

gold: This statistic shows the estimated age structure in Iraq in 2017 . In 2017 , about 39.01 percent of Iraq 's total population was estimated to be aged between 0 and 14 years .
gold_template: This statistic shows the estimated templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , about templateYValue[max] templateScale of templateTitleSubject[0] 's total templateYLabel[1] was estimated to be aged between 0 and 14 templateXValue[0] .

generated_template: The statistic shows templateYLabel[0] of templateTitle[1] users in the templateTitle[0] in templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] templateTitle[7] . During the survey period , it was found that templateYValue[max] templateScale of templateXValue[0] to 29-year olds in the templateTitle[0] were templateTitle[1] users . Overall , 90 templateScale of the adult templateTitleSubject[0] templateYLabel[1] accessed the templateTitle[1] .
generated: The statistic shows Share of structure users in the Age in 2017 , sorted 2017 .  During the survey period , it was found that 39.01 % of 0-14_years to 29-year olds in the Age were structure users .  Overall , 90 % of the adult Iraq population accessed the structure .

Example 35:
titleEntities: {'Subject': ['Manpower Group'], 'Date': ['2006', '2019']}
title: Revenue of Manpower Group worldwide 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['20.86', '21.99', '21.03', '19.65', '19.33', '20.76', '20.25', '20.68', '22.01', '18.87', '16.04', '21.54', '20.49', '17.56']

gold: This statistic shows the revenue of Manpower Group worldwide from 2006 to 20119 . Global human resources consulting firm Manpower Group generated approximately 20.86 billion U.S. dollars in revenue in 2019 . In 2018 , Manpower ranked as the third largest staffing and temporary employment company in the world , behind the Swiss firm Adecco and Randstad .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] worldwide from templateXValue[min] to 20119 . Global human resources consulting firm templateTitleSubject[0] generated approximately templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] in templateXValue[max] . In templateXValue[1] , templateTitleSubject[0] ranked as the third largest staffing and temporary employment company in the world , behind the Swiss firm Adecco and Randstad .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of PricewaterhouseCoopers from templateXValue[min] to templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , templateTitleSubject[0] generated approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateTitle[0] templateTitle[1] templateTitle[2] . templateTitleSubject[0] - additional information templateTitleSubject[0] is one of the four largest accounting and audit firms in the world .
generated: This statistic shows the Revenue Manpower Group of PricewaterhouseCoopers from 2006 to 2019 .  In the fiscal Year of 2019 , Manpower Group generated approximately 20.86 billion U.S. dollars in Revenue Manpower Group .  Manpower Group - additional information Manpower Group is one of the four largest accounting and audit firms in the world .

Example 36:
titleEntities: {'Subject': ['West Virginia'], 'Date': ['2009', '2018']}
title: Coal-mining employment in West Virginia 2009 to 2018
Data:
                   0        1      2
unknown  Underground  Surface  Total
2009           14842     6829  21671
2011           16963     6344  23307
2013           15694     4587  20281
2015           12555     2935  15490
2017           10507     2715  13222
2018           10767     3195  13962 

gold: This statistic shows the coal-mining employment in West Virginia from 2009 to 2018 , by mine type . In 2018 , 13,962 people were working in West Virginia 's coal industry in total . Of that number , almost 10,800 of the employees worked in underground coal mines .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , by mine type . In templateTitleDate[max] , templateValue[6][max] people were working in templateTitleSubject[0] 's coal industry in templateValue[0][2] . Of that number , almost 10,800 of the employees worked in templateValue[0][0] coal mines .

generated_template: templateValue[0][0] and templateValue[0][1] ( Outlook.com ) were still the most used templateTitle[1] services in the templateTitleSubject[0] , according to survey respondents in templateTitleDate[max] . Compared to earlier years , the use of both Google 's as well as Microsoft 's free e-mail service saw an templatePositiveTrend in their usage among Dutch consumers , whilst e-mail services provided by domestic templateTitle[2] ( such as KPN and Ziggo ) saw much less use . Sending and receiving e-mails is a popular online communication method across all age groups in the country .
generated: Underground and Surface ( Outlook.com ) were still the most used employment services in the West Virginia , according to survey respondents in 2018 .  Compared to earlier years , the use of both Google 's as well Microsoft 's free e-mail service saw an increase in their usage among Dutch consumers , whilst e-mail services provided by domestic West ( such as KPN and Ziggo ) saw much less use .  Sending and receiving e-mails is a popular online communication method across all age groups in the country .

Example 37:
titleEntities: {'Subject': ['U.S. GDP'], 'Date': ['2018']}
title: Percentage added to U.S. GDP 2018 , by industry
X_Axis['Industry']: ['Finance_insurance_real_estate_rental_and_leasing', 'Professional_and_business_services', 'Government', 'Manufacturing', 'Educational_services_health_care_and_social_assistance', 'Wholesale_trade', 'Retail_trade', 'Information', 'Construction', 'Arts_entertainment_recreation_accommodation_and_food_services', 'Transportation_and_warehousing', 'Other_services_except_government', 'Mining', 'Utilities', 'Agriculture_forestry_fishing_and_hunting']
Y_Axis['Percentage', 'of', 'GDP']: ['20.7', '12.6', '12.2', '11.4', '8.7', '6', '5.5', '5.5', '4.1', '4.1', '3.2', '2.1', '1.6', '1.6', '0.8']

gold: This graph shows the value added to the Gross Domestic Product ( GDP ) of the United States of America as a percentage of GDP in 2018 , by industry . In 2018 , the mining industry added 1.6 percent of value to U.S. GDP .
gold_template: This graph shows the value templateTitle[1] to the Gross Domestic Product ( templateYLabel[1] ) of the templateTitle[2] of America as a templateScale of templateYLabel[1] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[12] templateXLabel[0] templateTitle[1] templateYValue[12] templateScale of value to templateTitleSubject[0] templateYLabel[1] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of the average templateTitleSubject[0] templateTitle[5] templateTitle[6] user as of templateTitleSubject[0] templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of templateYLabel[1] stated they would choose templateXValue[1] templateTitle[1] if they could only one of templateYLabel[1] templateYLabel[2] for the last two years .
generated: The statistic shows the Percentage added U.S. of the average U.S. GDP by industry user as of U.S. GDP 2018 .  The survey revealed that 20.7 percentage of GDP stated they would choose Professional_and_business_services added if they could only one of GDP for the last two years .

Example 38:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2011', '2019']}
title: Public expenditure on national roads in the United Kingdom ( UK ) 2011 to 2019
X_Axis['Year']: ['2018/18', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12']
Y_Axis['Expenditure', 'in', 'million', 'GBP']: ['4826', '4270', '4160', '3987', '3713', '3151', '2851', '3081']

gold: This statistic shows public sector expenditure on national roads in the United Kingdom from 2011/12 to 2018/19 . Public sector spending on national roads generally increased during this period , reaching 4.8 billion British pounds in 2018/19 .
gold_template: This statistic shows templateTitle[0] sector templateYLabel[0] on templateTitle[2] templateTitle[3] in the templateTitleSubject[0] from templateXValue[last] to 2018/19 . templateTitle[0] sector spending on templateTitle[2] templateTitle[3] generally templatePositiveTrend during this period , reaching templateYValue[max] templateScale British pounds in 2018/19 .

generated_template: This statistic shows the total annual templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[last] to templateXValue[0] . Over this period the survey period templateXValue[0] , total templateYLabel[0] of templateTitle[0] templateTitle[1] templatePositiveTrend templateTitle[3] amounted to templateYValue[1] templateScale British pounds . This was more than templateXValue[1] templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[1] .
generated: This statistic shows the total annual Expenditure of Public expenditure million in the United Kingdom ( UK ) from 2011/12 to 2018/18 .  Over this period the survey period 2018/18 , total Expenditure of Public expenditure increasing roads amounted to 4270 million British pounds .  This was more than 2017/18 Expenditure of Public expenditure million in the UK .

Example 39:
titleEntities: {'Subject': ['Pfizer'], 'Date': ['2010', '2018']}
title: Pfizer 's revenues in global submarkets 2010 to 2018
Data:
                             0      1      2      3      4      5      6      7      8
Year                      2010   2011   2012   2013   2014   2015   2016   2017   2018
United States            28855  26993  23086  20274  19073  21704  26369  26026  25329
Developed Europe         16156  16099  13375  11739  11719   9714   9306   8508   9116
Developed Rest of World   9891  10975  10554   8346   7314   6298   6729   6612   6551
Emerging Markets         10263  11252  11971  11225  11499  11136  10420  11399  12651 

gold: This statistic shows Pfizer 's revenues in global submarkets from 2010 to 2018 . Pfizer Inc. is a multinational pharmaceutical corporation . The company is headquartered in Midtown Manhattan , New York City .
gold_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateTitle[2] in templateTitle[3] templateTitle[4] from templateValue[0][0] to templateValue[0][last] . templateTitleSubject[0] Inc. is a multinational pharmaceutical corporation . The company is headquartered in Midtown Manhattan , New York City .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] inhabitants from templateValue[0][0] to templateValue[0][last] . In templateValue[0][last] , about templateValue[1][last] templateScale of inhabitants were aged 0 to 14 years , while approximately templateValue[2][0] templateScale were aged templateLabel[3][0] templateLabel[1][1] and templateLabel[3][2] .
generated: This statistic shows the Pfizer 's of Pfizer inhabitants from 2010 to 2018 .  In 2018 , about 25329 % of inhabitants were aged 0 to 14 years , while approximately 16156 % were aged Developed States and of .

Example 40:
titleEntities: {'Subject': ['Haiti'], 'Date': ['2019']}
title: Unemployment rate in Haiti 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']: ['13.48', '13.53', '13.41', '13.65', '13.72', '13.92', '14.14', '14.1', '14.93', '15.64', '16.24', '15.94', '16.8', '15.81', '14.88', '13.54', '12.16', '11', '9.55', '8.34', '7.2']

gold: This statistic shows the unemployment rate in Haiti from 1999 to 2019 . In 2019 , the unemployment rate in Haiti was 13.48 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: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale , a slight templateNegativeTrend from templateYValue[1] templateScale the previous templateXLabel[0] . templateYLabel[0] as an economic key indicator The templateYLabel[0] templateYLabel[1] of a country represents the share of people without a job in the country 's labor force , i.e . unemployed persons among those who are able and/or willing to work .
generated: In 2019 , the Unemployment rate in Haiti was at approximately 13.48 % , a slight decrease from 13.53 % the previous Year .  Unemployment as an economic key indicator The Unemployment rate of a country represents the share of people without a job in the country 's labor force , i.e unemployed persons among those who are able and/or willing to work .

Example 41:
titleEntities: {'Subject': ['Bulgaria'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) 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['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['89.96', '84.59', '79.67', '74.9', '70.13', '66.25', '65.2', '58.34', '53.24', '50.2', '56.82', '55.56', '53.9', '57.42', '50.61', '51.89', '54.41', '44.41', '34.13', '29.64', '25.96', '20.98', '16.28', '14.08', '13.15', '13.5', '13.23', '10.02', '9.49', '12.56', '7.5', '4.27', '7.86', '1.94', '19.77', '44.84', '44.02', '26.94', '23.24', '26.26', '30.67']

gold: The statistic shows the gross domestic product ( GDP ) in Bulgaria 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 Bulgaria 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 42:
titleEntities: {'Subject': ['Europe'], 'Date': ['2017']}
title: Retail price of premium cigarettes in Europe 2017 , by country
X_Axis['Country']: ['United_Kingdom', 'Ireland', 'France', 'Netherlands', 'Belgium', 'Finland', 'Denmark', 'Germany', 'Malta', 'Italy', 'Luxembourg', 'Austria', 'Spain', 'Portugal', 'Cyprus', 'Greece', 'Slovenia', 'Estonia', 'Romania', 'Hungary', 'Slovakia', 'Czech_Republic', 'Latvia', 'Poland', 'Croatia', 'Lithuania', 'Bulgaria']
Y_Axis['Retail', 'price', 'in', 'GBP']: ['9.91', '9.15', '5.67', '5.37', '5.12', '5.02', '5.0', '4.86', '4.46', '4.37', '4.21', '4.05', '3.93', '3.89', '3.89', '3.4', '3.16', '3.09', '3.09', '3.06', '3.0', '3.0', '2.92', '2.81', '2.72', '2.67', '2.32']

gold: This statistic depicts the price of a premium pack of 20 cigarettes across selected European countries in 2017 . The price of cigarettes is substantially different across Europe . As of 2017 , the most expensive country in which to purchase cigarettes was the United Kingdom ( UK ) with a premium pack of 20 cigarettes retailing at 9.91 British pounds , followed by Ireland at 9.15 British pounds .
gold_template: This statistic depicts the templateYLabel[1] of a templateTitle[2] pack of 20 templateTitle[3] across selected European countries in templateTitleDate[0] . The templateYLabel[1] of templateTitle[3] is substantially different across templateTitleSubject[0] . As of templateTitleDate[0] , the most expensive templateXLabel[0] in which to purchase templateTitle[3] was the templateXValue[0] ( UK ) with a templateTitle[2] pack of 20 templateTitle[3] retailing at templateYValue[max] British pounds , followed templateTitle[6] templateXValue[1] at templateYValue[1] British pounds .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of households in the templateTitleSubject[0] on templateTitle[2] and templateTitle[3] in templateTitleDate[0] , split templateTitle[7] templateXLabel[0] . In the templateXValue[0] , households spent a total of 76.29 templateScale templateYLabel[4] on templateTitle[2] and templateTitle[3] in templateTitleDate[0] .
generated: This statistic shows the Retail price GBP of households in the Europe on premium and cigarettes in 2017 , split country .  In the United_Kingdom , households spent a total of 76.29 million GBP on premium and cigarettes in 2017 .

Example 43:
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 between 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 between 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 44:
titleEntities: {'Subject': ['Mexican Americans'], 'Date': ['1990', '2019']}
title: Mexican Americans - unemployment rate 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['Unemployment', 'rate']: ['4.4', '4.7', '5.1', '5.8', '6.4', '7.2', '9', '10.4', '11.6', '12.4', '12.2', '7.7', '5.6', '5.2', '6.2', '7.2', '7.7', '7.5', '6.7', '5.9', '6.6', '7.3', '7.7', '9.2', '9.7', '10.1', '10.7', '11.8', '10.5', '8.4']

gold: This statistic displays the unemployment rate of Mexican Americans from 1990 to 2019 . The unemployment rate for Mexican Americans decreased to 4.4 percent in 2019 . The overall national unemployment rate was at 3.7 percent in 2019 and can be found here .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . The templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] templateNegativeTrend to templateYValue[min] templateScale in templateTitleDate[max] . The overall national templateYLabel[0] templateYLabel[1] was at 3.7 templateScale in templateTitleDate[max] and can be found here .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] of templateTitle[0] Americans from templateTitleDate[min] to templateTitleDate[max] . templateYLabel[0] templateYLabel[1] of the templateTitle[0] labor force has templateNegativeTrend to templateYValue[min] templateScale in templateTitleDate[max] , compared to templateYValue[max] templateScale in 2010 . The national templateYLabel[0] templateYLabel[1] can be found here .
generated: This statistic displays the Unemployment rate of Mexican Americans from 1990 to 2019 .  Unemployment rate of the Mexican labor force has decreased to 4.4 % in 2019 , compared to 12.4 % in 2010 .  The national Unemployment rate can be found here .

Example 45:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2018']}
title: Urbanization in the Netherlands 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Proportion', 'of', 'urban', 'population']: ['91.49', '91.08', '90.64', '90.17', '89.69', '89.19', '88.59', '87.88', '87.13', '86.29', '85.4']

gold: This statistic shows the degree of urbanization in the Netherlands from 2008 to 2018 and details the percentage of the entire population , living in urban areas . In 2018 , 91.49 percent of the total population of the Netherlands lived in cities . Population of the Netherlands The Netherlands is a developed country with a high standard of living .
gold_template: This statistic shows the degree of templateTitle[0] in the 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] of the templateTitleSubject[0] lived in cities . templateYLabel[2] of the templateTitleSubject[0] The templateTitleSubject[0] is a developed country with a high standard of living .

generated_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: This statistic shows the Proportion of urban population in Netherlands from 2008 to 2018 .  The Proportion of population has declined during the period , from the peak of roughly 91.49 thousand in 2018 to around 85.4 thousand in 2008 .

Example 46:
titleEntities: {'Subject': ['Europe'], 'Date': ['2006', '2018']}
title: Market size of professional football in Europe 2006 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']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['28.4', '25.5', '24.6', '21.8', '21.3', '19.9', '19.4', '16.9', '16.3', '15.7', '14.6', '13.6']

gold: The statistic shows the total revenue of the European professional soccer market from 2006/07 to 2017/18 . In the 2017/18 season , the total revenue of the European professional soccer market was estimated at 28.4 billion euros .
gold_template: The statistic shows the total templateYLabel[0] of the European templateTitle[2] soccer templateTitle[0] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , the total templateYLabel[0] of the European templateTitle[2] soccer templateTitle[0] was estimated at templateYValue[max] templateScale templateYLabel[2] .

generated_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: The statistic illustrates the Revenue of Europe & Cie. from 2006/07 to 2017/18 .  In its fiscal Year 2016/17 , Europe made total Revenue of 28.4 billion euros worldwide , a record high .  Europe 's annual sales have witnessed continuous growth during the measured period .

Example 47:
titleEntities: {'Subject': ['American', 'Burger King'], 'Date': ['2000', '2019']}
title: American customer satisfaction index : Burger King restaurants 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', '2003', '2002', '2001', '2000']
Y_Axis['ACSI', 'score']: ['76', '76', '77', '76', '72', '76', '76', '75', '75', '74', '69', '71', '69', '70', '71', '68', '68', '65', '67']

gold: This statistic shows the American customer satisfaction index scores of Burger King restaurants in the United States from 2000 to 2019 . Burger King 's ACSI score was 76 in 2019.The limited-service restaurant industry was not measured in 2004 . Burger King Burger King is a quick-service restaurant chain headquartered in Miami-Dade County , Florida .
gold_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] scores of templateTitleSubject[1] restaurants in the templateTitle[7] from templateXValue[min] to templateXValue[max] . templateTitleSubject[1] 's templateYLabel[0] templateYLabel[1] was templateYValue[0] templateYValue[idxmax(X)] 2019.The limited-service restaurant industry was not measured in 2004 . templateTitleSubject[1] Burger templateTitleSubject[1] is a quick-service restaurant chain headquartered in Miami-Dade County , Florida .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] scores of templateTitleSubject[1] restaurants in the templateTitle[7] from templateXValue[min] to templateXValue[max] . templateTitleSubject[1] 's templateYLabel[0] templateYLabel[1] was templateYValue[0] templateYValue[idxmax(X)] 2019.The limited-service restaurant industry was not measured in 2004 .
generated: This statistic shows the American customer satisfaction index scores of Burger King restaurants in the U.S. from 2000 to 2019 .  Burger King 's ACSI score was 76 2019.The limited-service restaurant industry was not measured in 2004 .

Example 48:
titleEntities: {'Subject': ['European'], 'Date': ['2020']}
title: Select European cities ranked by overall prospects in real estate 2020
X_Axis['Country']: ['Paris', 'Berlin', 'Frankfurt', 'London', 'Madrid', 'Amsterdam', 'Munich', 'Hamburg', 'Barcelona', 'Lisbon', 'Milan', 'Dublin', 'Brussels', 'Warsaw', 'Vienna', 'Luxembourg', 'Zurich', 'Stockholm', 'Copenhagen', 'Prague', 'Helsinki', 'Rome', 'Manchester', 'Birmingham', 'Edinburgh', 'Lyon', 'Budapest', 'Athens', 'Oslo', 'Istanbul', 'Moscow']
Y_Axis['Rating', 'points', 'average']: ['2.16', '2.13', '2.07', '2.03', '1.89', '1.85', '1.82', '1.68', '1.54', '1.52', '1.36', '1.35', '1.32', '1.14', '1.09', '1.06', '0.95', '0.92', '0.87', '0.85', '0.79', '0.78', '0.72', '0.64', '0.6', '0.59', '0.56', '0.55', '0.37', '0.35', '0.17']

gold: Multiple factors can play a role in the decision to invest in real estate . To calculate how prosperous European cities are on the estate market , the Urban Land Institute considered transport connectivity , forecasted real estate returns , a city 's economic performance , availability of assets/opportunities for new development , market size and liquidity , regulatory environment , digital connectivity , attractiveness to talent , city leadership , housing affordability , and affordability of space for new/small/growing businesses . Leading European cities for development and investment According to the ranking , the leading five European cities by overall prospects for 2020 are Paris , Berlin , Frankfurt , London and Madrid .
gold_template: Multiple factors can play a role in the decision to invest in templateTitle[7] templateTitle[8] . To calculate how prosperous templateTitleSubject[0] templateTitle[2] are on the templateTitle[8] market , the Urban Land Institute considered transport connectivity , forecasted templateTitle[7] templateTitle[8] returns , a city 's economic performance , availability of assets/opportunities for new development , market size and liquidity , regulatory environment , digital connectivity , attractiveness to talent , city leadership , housing affordability , and affordability of space for new/small/growing businesses . Leading templateTitleSubject[0] templateTitle[2] for development and investment According to the ranking , the leading five templateTitleSubject[0] templateTitle[2] templateTitle[4] templateTitle[5] templateTitle[6] for templateTitleDate[0] are templateXValue[0] , templateXValue[1] , templateXValue[2] , templateXValue[3] and templateXValue[4] .

generated_template: This statistic shows the templateXValue[0] templateYLabel[1] templateTitle[3] templateYLabel[0] of templateTitleSubject[0] as of 2017 in leading online markets . During the measured period , templateTitleSubject[0] accounted for templateYValue[3] templateScale of templateYLabel[1] templateYLabel[2] in templateXValue[2] . The Microsoft-owned platform accounted for templateYValue[0] templateScale of templateYLabel[1] templateYLabel[2] templateXValue[0] .
generated: This statistic shows the Paris points ranked Rating of European as of 2017 in leading online markets .  During the measured period , European accounted for 2.03 % of points average in Frankfurt .  The Microsoft-owned platform accounted for 2.16 % of points average Paris .

Example 49:
titleEntities: {'Subject': ['Spain'], 'Date': ['2018']}
title: Population density in Spain 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Inhabitants', 'per', 'square', 'kilometer']: ['93.53', '93.27', '93.05', '92.95', '92.92', '93.2', '93.51', '93.51', '93.15', '92.98', '92.13']

gold: The statistic shows the population density in Spain from 2008 to 2018 . In 2018 , the population density in Spain amounted to about 93.53 inhabitants per square kilometer .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . See the templateTitle[0] of templateTitleSubject[0] for comparison .
generated: The statistic shows the Population density in Spain from 2008 to 2018 .  In 2018 , the Population density in Spain amounted to about 93.53 Inhabitants per square kilometer .  See the Population of Spain for comparison .

Example 50:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2018']}
title: United Kingdom : largest import commodities 2018
X_Axis['Month']: ['Fuels', 'Animal_and_vegetable_oils_and_fats', 'Crude_materials', 'Marieral_manufactures', 'Chemicals', 'Beverages_and_tobacoo', 'Machinery_and_transport_equipment', 'Food_and_live_animals', 'Miscellaneous_manufactures', 'Unspecified_goods']
Y_Axis['Value', 'of', 'imports', 'in', 'million', 'GBP']: ['152.9', '112.9', '112.6', '107.2', '106.9', '105.9', '104.8', '104.3', '103.8', '95.8']

gold: This statistic shows the 30 largest import commodities of the United Kingdom ( UK ) in 2018 in million GBP . This ranges from machinery and oil to food and chemicals . In this year , the largest imported commodity was fuel , with approximately 152 million British pounds , followed by animal and vegetable oils and crude materials with almost 113 million British pounds each .
gold_template: This statistic shows the 30 templateTitle[2] templateTitle[3] templateTitle[4] of the templateTitleSubject[0] ( UK ) in templateTitleDate[0] in templateScale templateYLabel[3] . This ranges from templateXValue[6] and oil to templateXValue[7] and templateXValue[4] . In this year , the templateTitle[2] imported commodity was fuel , with approximately 152 templateScale British pounds , followed by templateXValue[1] and templateXValue[1] and templateXValue[2] with almost templateYValue[1] templateScale British pounds each .

generated_template: This statistic shows the templateTitle[0] templateTitle[2] templateTitle[3] in the templateTitle[1] in templateTitleDate[0] , templateTitle[4] on templateYLabel[1] templateYLabel[2] . templateXValue[0] ranked the highest with a templateTitle[5] templateTitle[6] of templateYValue[max] templateScale templateYLabel[1] , followed by templateXValue[1] with templateYValue[1] templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the United largest import in the Kingdom in 2018 , commodities on imports million .  Fuels ranked the highest with a 2018 of 152.9 million imports , followed by Animal_and_vegetable_oils_and_fats with 112.9 million imports .

Example 51:
titleEntities: {'Subject': ['Molson Coors Brewing Company', 'Canada'], 'Date': ['2013', '2019']}
title: Molson Coors Brewing Company sales in Canada 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Sales', 'in', 'million', 'Canadian', 'dollars']: ['2034.2', '2041.4', '1993.7', '1706.8', '1228.2', '1287.2', '1313.8']

gold: Sales of the Molson Coors Brewing Company reached around 2.03 billion Canadian dollars in Canada in 2019 , an increase of just over 0.7 billion Canadian dollars over the six-year survey period . In comparison , their worldwide net sales amounted to around 10.8 billion dollars in 2018 . Molson Coors Brewing Company origins The company was formed in 2005 through a merger between Molson of Canada and Coors of the United States .
gold_template: templateYLabel[0] of the templateTitleSubject[0] Brewing templateTitleSubject[0] reached around templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateTitleSubject[1] in templateXValue[max] , an templatePositiveTrend of just over 0.7 templateScale templateYLabel[2] templateYLabel[3] over the six-year survey period . In comparison , their worldwide net templateYLabel[0] amounted to around 10.8 templateScale templateYLabel[3] in templateXValue[1] . templateTitleSubject[0] Brewing templateTitleSubject[0] origins The templateTitleSubject[0] was formed in 2005 through a merger between templateTitleSubject[0] of templateTitleSubject[1] and templateTitleSubject[0] of the country .

generated_template: This statistic displays the total templateTitle[0] templateYLabel[0] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , about 5.91million metric tons of templateTitle[0] were produced in the templateTitle[2] , up from templateYValue[2] templateScale metric tons in templateXValue[2] .
generated: This statistic displays the total Molson Sales in the Brewing from 2013 to 2019 .  In 2018 , about 5.91million metric tons of Molson were produced in the Brewing , up from 1993.7 million metric tons in 2017 .

Example 52:
titleEntities: {'Subject': ['Italy'], 'Date': ['2017']}
title: Potential reasons to leave Italy for another country 2017
X_Axis['Response']: ['To_go_to_a_place_where_taxes_are_lower', 'To_find_a_job/a_better_job/_a_better_paid_job', 'To_have_new_experiences', 'To_live_in_a_country_where_services_are_more_efficient', 'To_live_in_a_country_less_oppress_by_bureaucracy_and_where_there_are_rules', 'To_live_in_a_cleaner_and_more_tidier_country', 'Other', 'I_don´t_know/No_answer']
Y_Axis['Share', 'of', 'respondents']: ['42.9', '36.8', '12.4', '9.4', '6.8', '6.2', '3.9', '3.6']

gold: Almost 43 percent of Italian respondents would move to another country in which the taxes were lower than in Italy . In a survey conducted in 2017 , this was the most popular factor Italians indicated as a reason to leave their country . Furthermore , the possibility to find a better job , or a job at all , was a good reason for 36.8 percent of respondents .
gold_template: Almost templateYValue[max] templateScale of Italian templateYLabel[1] would move to templateTitle[5] templateXValue[3] in which the templateXValue[0] were templateXValue[0] than in templateTitleSubject[0] . In a survey conducted in templateTitleDate[0] , this was the most popular factor Italians indicated as a reason to templateTitle[2] their templateXValue[3] . Furthermore , the possibility to templateXValue[1] a templateXValue[1] , or a templateXValue[1] at all , was a good reason templateTitle[4] templateYValue[1] templateScale of templateYLabel[1] .

generated_template: This statistic shows the results of a survey question designed to find out what is templateTitle[0] templateTitle[1] to templateTitle[4] templateTitle[5] ( templateTitle[6] - templateTitle[7] ) in templateTitleSubject[0] , as of 2013 . The majority of templateYLabel[1] said that their templateXValue[0] is the templateTitle[0] templateTitle[1] thing to them .
generated: This statistic shows the results of a survey question designed to find out what is Potential reasons to for another ( country - 2017 ) in Italy , as of 2013 .  The majority of respondents said that their To_go_to_a_place_where_taxes_are_lower is the Potential reasons thing to them .

Example 53:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: U.S. per capita consumption of fresh broccoli 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']: ['5.9', '7.1', '7.5', '7.4', '6.6', '6.9', '6.3', '5.9', '5.6', '6.2', '6.0', '5.6', '5.8', '5.3', '5.3', '5.3', '5.4', '5.4', '5.9']

gold: The timeline presents the per capita consumption of fresh broccoli in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh broccoli amounted to approximately 5.9 pounds in 2018 .
gold_template: The timeline presents 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_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 broccoli in the U.S. from 2000 to 2018 .  According to the report , the U.S. Per capita consumption of fresh broccoli amounted to approximately 5.9 pounds in 2018 .

Example 54:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2019']}
title: Cinema box office revenue in the United Kingdom ( 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['Revenue', 'in', 'million', 'GBP']: ['1251.84', '1277.12', '1277.88', '1246.56', '1240.38', '1057.68', '1082.1', '1099.1', '1038.0', '988.0', '944.0', '854.4', '821.0', '762.1', '776.3', '769.6', '742.0', '755.0', '645.0', '627.5']

gold: This statistic shows a timeline of cinema box office revenue generated in the United Kingdom ( UK ) from 2000 to 2019 . In 2010 , box office revenue was 988 million British pounds . In 2019 , box office revenue exceeded 1.25 billion British pounds .
gold_template: This statistic shows a timeline of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] generated in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . In templateXValue[9] , templateTitle[1] templateTitle[2] templateYLabel[0] was templateYValue[9] templateScale British pounds . In templateXValue[max] , templateTitle[1] templateTitle[2] templateYLabel[0] exceeded templateYValue[0] templateScale British pounds .

generated_template: In templateXValue[max] , a total of templateYValue[idxmax(X)] templateScale templateTitle[0] CDs were shipped in the templateTitle[3] . This figure marks the lowest total in decades – templateTitle[0] CD templateYLabel[2] have nearly halved over the past two years . More than 900 templateScale CDs were shipped in the templateTitle[3] in templateXValue[19] , but the product has experienced a relatively decline in popularity in each templateXLabel[0] since .
generated: In 2019 , a total of 1251.84 million Cinema CDs were shipped in the revenue .  This figure marks the lowest total in decades – Cinema CD GBP have nearly halved over the past two years .  More than 900 million CDs were shipped in the revenue in 2000 , but the product has experienced a relatively decline in popularity each Year since .

Example 55:
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 15 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . templateTitle[1] defense templateYLabel[1] totaling USD templateYValue[max] templateScale , the templateXValue[0] ranked first . Worldwide leaders in templateYLabel[0] templateYLabel[1] The templateXValue[6] States lead the globe in templateYLabel[0] templateYLabel[1] in templateTitleDate[0] .
generated: This statistic shows the 15 Tea market the major Trade value in 2018 .  market defense value totaling USD 571.33 million , the Pakistan ranked first .  Worldwide leaders in Trade value The Saudi_Arabia States lead the globe in Trade value in 2018 .

Example 56:
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 timeline shows jewelry , templateTitle[0] , and templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] templateTitle[6] templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[1] jewelry , templateTitle[0] , and templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] .
generated: This timeline shows jewelry , Clothing and accessories store sales in the United States 1992 to 2017 .  In 2017 , U.S. jewelry , Clothing and accessories store sales amounted to about 258.74 billion U.S. dollars .

Example 57:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019']}
title: Number of grocery stores in Canada by employment size 2019
X_Axis['Year']: ['1-4_employees', '5-9_employees', '10-19_employees', '20-49_employees', '50-99_employees', '100-199_employees', '200-499_employees', '500+_employees']
Y_Axis['Number', 'of', 'stores']: ['5648', '3302', '2312', '1290', '1595', '1165', '261', '13']

gold: As of December 2019 , there were over 15,500 grocery stores in Canada . Micro grocery stores , which employ between one and four employees , were the most widespread type of grocery store in Canada . There were 5,648 micro grocery stores located across Canada at this time .
gold_template: As of 2019 , there were over 15,500 templateTitle[1] templateYLabel[1] in templateTitleSubject[0] . Micro templateTitle[1] templateYLabel[1] , which employ between one and four templateXValue[0] , were the most widespread type of templateTitle[1] store in templateTitleSubject[0] . There were templateYValue[max] micro templateTitle[1] templateYLabel[1] located across templateTitleSubject[0] at this time .

generated_template: This statistic shows the total templateYLabel[0] of templateTitle[1] templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] in templateTitleSubject[1] from templateXValue[last] to templateXValue[0] . There were approximately templateYValue[min] thousand templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] in templateTitleSubject[0] in templateXValue[0] .
generated: This statistic shows the total Number of grocery stores Canada to by employment in Canada from 500+_employees to 1-4_employees .  There were approximately 13 thousand stores Canada to by employment in Canada 1-4_employees .

Example 58:
titleEntities: {'Subject': ['Sunflower'], 'Date': []}
title: Sunflower seed production in major countries 2018/19
X_Axis['Region']: ['Ukraine', 'Russia', 'European_Union', 'Argentina', 'Turkey', 'Other']
Y_Axis['Production', 'in', 'million', 'metric', 'tons']: ['15.0', '12.71', '9.9', '3.5', '1.8', '8.55']

gold: Ukraine had the highest production volume of sunflower seeds of any country in the world in the 2018/2019 crop year . During that time period , Ukraine produced around 15 million metric tons of sunflower seeds . Russia is also a major producer of sunflower seeds worldwide .
gold_template: templateXValue[0] had the highest templateYLabel[0] volume of templateTitleSubject[0] seeds of any country in the world in the 2018/2019 crop year . During that time period , templateXValue[0] produced around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] seeds . templateXValue[1] is also a templateTitle[3] producer of templateTitleSubject[0] seeds worldwide .

generated_template: This statistic shows the world 's templateTitleSubject[0] templateYLabel[0] in templateTitleDate[0] , broken down templateTitle[3] templateTitle[4] . In templateXValue[0] , over 370 templateScale templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] were produced in that year .
generated: This statistic shows the world 's Sunflower Production in , broken down major countries .  In Ukraine , over 370 million metric tons of Sunflower were produced in that year .

Example 59:
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-kilogram templateTitle[4] templateTitle[5] templateTitle[6] was templateValue[5][max] Canadian dollars in 2019 in templateTitleSubject[0] . This templateTitle[2] is an all-time high templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . templateTitle[4] templateTitle[5] templateTitle[6] is a relatively expensive option when compared to other cuts of beef , such as ground beef , which retailed at a templateTitle[2] of 11.3 Canadian dollars per kilogram in templateValue[0][7] templateTitleDate[max] .
generated: The Average retail price for one-kilogram butter Canada 2015 was 5.16 Canadian dollars in 2019 Canada .  This price is an all-time high for butter Canada 2015 .  butter Canada 2015 is a relatively expensive option when compared to other cuts of beef , such as ground beef , which retailed at a price of 11.3 Canadian dollars per kilogram in May 2019 .

Example 60:
titleEntities: {'Subject': ['Net'], 'Date': ['2006']}
title: Net profit of airlines worldwide 2006 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Net', 'profit', 'in', 'billion', 'U.S.', 'dollars']: ['29.3', '25.9', '27.3', '37.6', '34.2', '36.0', '13.8', '10.7', '9.2', '8.3', '17.3', '-4.6', '-26.1', '14.7', '5.0']

gold: This statistic shows the net profit of commercial airlines worldwide from 2006 to 2019 . In 2019 , the net profit of commercial airlines is projected to reach around 28 billion U.S. dollars . Airline industry In 2018 , the U.S. airline industry generated total operating revenue of almost 240 billion U.S. dollars , making the United States one of the largest markets for the airline industry worldwide .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of commercial templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[1] . In templateXValue[1] , the templateYLabel[0] templateYLabel[1] of commercial templateTitle[2] is projected to reach around 28 templateScale templateYLabel[3] templateYLabel[4] . Airline industry In templateXValue[2] , the templateYLabel[3] airline industry generated total operating revenue of almost 240 templateScale templateYLabel[3] templateYLabel[4] , making the country one of the largest markets for the airline industry templateTitle[3] .

generated_template: This statistic shows the templateScale of templateYLabel[2] that were templateTitle[1] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , some templateYValue[last] templateScale of templateYLabel[1] templateYLabel[2] in the templateTitle[0] were templateTitle[1] templateTitle[2] .
generated: This statistic shows the billion of that were profit billion in the Net from 2006 to 2020 .  In 2019 , some 5.0 billion of profit billion in the Net were profit airlines .

Example 61:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: U.S. labor force 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['Civilian', 'labor', 'force', 'in', 'millions']: ['163.54', '162.07', '160.32', '159.19', '157.13', '155.92', '155.39', '154.98', '153.62', '153.89', '154.14', '154.29', '153.12', '151.43', '149.32', '147.4', '146.51', '144.86', '143.73', '142.58', '139.37', '137.67', '136.3', '133.94', '132.3', '131.06', '129.2', '128.11', '126.35', '125.84']

gold: This graph shows the civilian labor force in the United States from 1990 to 2019 . In 2019 , the number of people who had jobs or were seeking employment amounted to about 163.54 million .
gold_template: This graph shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the number of people who had jobs or were seeking employment amounted to about templateYValue[max] templateScale .

generated_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: This statistic shows the Civilian 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 , 163.54 refugees were admitted to the force .

Example 62:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2008']}
title: Exercise rates in the U.S. , by age group and education level 2008
Data:
                                0         1         2         3             4
unknown                  25_to_34  35_to_44  45_to_54  55_to_64  65_and_older
No high school degree          45        43        36        32            30
High school graduate           54        51        52        48            42
Some college, no degree        71        64        62        56            52
Associate degree               68        71        67        61            61
Bachelor's degree              81        79        78        75            67 

gold: This statistic shows the exercise habits of individuals aged 25 years and over in the United States in 2008 , as differentiated by their age group and also level of education . In 2008 , 81 percent of 25 to 34 year olds with a bachelor 's degree also reported doing either moderate or vigorous exercise on a regular basis .
gold_template: This statistic shows the templateTitle[0] habits of individuals aged templateValue[0][0] years and over in the templateTitle[2] in templateTitleDate[0] , as differentiated templateTitle[3] their templateTitle[4] templateTitle[5] and also templateTitle[7] of templateTitle[6] . In templateTitleDate[0] , templateValue[5][0] templateScale of templateValue[0][0] to templateValue[0][0] year olds with a bachelor 's templateLabel[1][3] also reported doing either moderate or vigorous templateTitle[0] on a regular basis .

generated_template: This statistic shows the results of a survey concerning incidents of computer templateTitle[2] in German templateTitle[3] in templateTitleDate[0] . During the survey period it was found that templateValue[1][0] templateScale of respondents stated that their company templateNegativeTrend victim to templateValue[0][0] of IT or templateValue[0][0] within the two years prior to the survey . By comparison , a further templateValue[1][4] templateScale of respondents reported they were templateLabel[2][0] templateLabel[1][0] by such templateValue[0][0] .
generated: This statistic shows the results of a survey concerning incidents of computer U.S. in German by in 2008 .  During the survey period it was found that 45 % of respondents stated that their company fell victim to 25_to_34 of IT or 25_to_34 within the two years prior to the survey .  By comparison , a further 30 % of respondents reported they were High No by such 25_to_34 .

Example 63:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018', '2018']}
title: Leading car rental companies in the U.S. ranked by index ranking 2018
X_Axis['Brand']: ['Enterprise', 'Hertz', 'Budget', 'Avis', 'Alamo']
Y_Axis['Index', 'ranking']: ['20.9', '14.4', '11.6', '10.2', '7.8']

gold: This statistic shows leading car rental companies in the United States ranked by average index ranking in 2018 . YouGov 's brand Index 2018 index results show that Enterprise ranked highest with a score of 20.9 , followed by Hertz at 14.4 .
gold_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] templateTitle[5] templateTitle[6] average templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . YouGov 's templateXLabel[0] templateYLabel[0] templateTitleDate[0] templateYLabel[0] results show that templateXValue[0] templateTitle[5] highest with a score of templateYValue[max] , followed templateTitle[6] templateXValue[1] at templateYValue[1] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] paid hourly rates at or below the prevailing federal templateTitle[2] templateTitle[3] in the templateTitle[1] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , around templateYValue[0] people without a templateXValue[0] diploma earned the templateTitle[2] templateTitle[3] or below .
generated: This statistic shows the Index of ranking paid hourly rates at or below the prevailing federal rental companies in the car in 2018 , ranked Brand .  In 2018 , around 20.9 people without a Enterprise diploma earned the rental companies or below .

Example 64:
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[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 New York Knicks franchise of the National Basketball Association from 2003 to 2020 .  In 2020 , the Franchise had an estimated value of 4600 million U.S. dollars .

Example 65:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2018']}
title: Survey on the most important social issues in Sweden 2018
X_Axis['Response']: ['Healthcare', 'School_and_education', 'Crime/Law_enforcement', 'Immigration', 'Integration', 'Environment', 'Elderly_care', 'Swedish_economy', 'Unemployment', 'Social_differences']
Y_Axis['Share', 'of', 'respondents']: ['44', '33', '31', '25', '19', '15', '14', '13', '10', '10']

gold: This statistic displays a survey on the most important social issues in Sweden as of March 2018 . During the survey period , 44 percent of the respondents considered healthcare to be the most important social issue in Sweden .
gold_template: This statistic displays a templateTitle[0] on the templateTitle[1] templateTitle[2] templateXValue[last] templateTitle[4] in templateTitleSubject[0] as of 2018 . During the templateTitle[0] period , templateYValue[max] templateScale of the templateYLabel[1] considered templateXValue[0] to be the templateTitle[1] templateTitle[2] templateXValue[last] issue in templateTitleSubject[0] .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] problems templateTitle[4] templateTitle[5] templateXValue[4] in the templateTitleSubject[0] in 2020 . During the survey , about templateYValue[max] templateScale of the templateYLabel[1] stated that the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateXValue[4] was templateXValue[0] .
generated: This statistic shows the most important problems issues Sweden Integration in the Sweden in 2020 .  During the survey , about 44 % of the respondents stated that the most important social issues Sweden Integration was Healthcare .

Example 66:
titleEntities: {'Subject': ['Japan'], 'Date': ['2017', '2023']}
title: Japan : number of social media users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['61.8', '59.9', '58.1', '55.9', '53.5', '51.3', '48.9']

gold: This statistic shows the number of social network users in Japan from 2017 to 2023 . In 2018 , approximately 51.3 million people actively used social media . In 2023 , this figure is projected to reach about 61.8 million social network users .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] network templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , approximately templateYValue[5] templateScale people actively used templateTitle[2] templateTitle[3] . In templateXValue[max] , this figure is projected to reach about templateYValue[idxmax(X)] templateScale templateTitle[2] network templateYLabel[1] .

generated_template: In templateXValue[5] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[5] templateScale and is projected to grow to templateYValue[max] templateScale in templateXValue[idxmax(Y)] . The current templateTitleSubject[1] usage penetration in templateTitleSubject[0] is about 64 templateScale of the population . Social media in templateTitleSubject[0] Social networking is a popular online activity in templateTitleSubject[0] .
generated: In 2018 , the Number of Japan users in Japan amounted to 51.3 millions and is projected to grow 61.8 millions in 2023 .  The current Japan usage penetration in Japan is about 64 millions of the population .  Social media in Japan Social networking is a popular online activity in Japan .

Example 67:
titleEntities: {'Subject': ['Q1'], 'Date': ['2015']}
title: King quarterly gross bookings and revenue Q1 2012-Q4 2015
Data:
                     0       1       2       3       4       5       6       7       8       9      10      11      12      13      14      15
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  Q3_'12  Q2_'12  Q1_'12
Gross bookings     509     502     529     604     586     544     611     641     632     648     481     219      71      43      38      29
Revenue            461     480     490     570     546     514     594     607     602     621     455     206      70      41      32      22 

gold: This statistic shows a timeline with the global revenues as well as the gross bookings of King Digital Entertainment as of the fourth quarter of 2015 . During the most recently reported quarter the gaming company had generated 461 million U.S. dollars in revenues . Popular titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga , all of which are monetized through in-game item purchases .
gold_template: This statistic shows a timeline with the global revenues as well as the templateTitle[2] templateTitle[3] of templateTitle[0] Digital Entertainment as of the fourth templateLabel[0][0] of templateTitleDate[0] . During the most recently reported templateLabel[0][0] the gaming company had generated templateValue[2][0] templateScale U.S. dollars in revenues . Popular titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga , all of which are monetized through in-game item purchases .

generated_template: The statistic shows the market templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] templateTitle[5] from the fourth templateLabel[0][0] of templateValue[0][7] to the fourth 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: The statistic shows the market Q1 quarterly gross bookings of revenue Q1 from the fourth Quarter of Q1_'14 to the fourth Quarter of Q4_'15 , 2015 .  In the third Quarter of Q4_'15 , there were 461 Q1 gross A deals in China and Hong Revenue .

Example 68:
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 templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , according to the templateYLabel[0] Finance valuation methodology . The ranking , provided by The Banker Magazine , is an independent , publicly reported measure of the strength of a templateYLabel[0] and its impact across all business lines and customer groups . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was valued at templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] and the bank was ranked third in its peer group .
generated: The statistic depicts the Market value of PV U.S. from 2011 to 2016 , according to the Market Finance valuation methodology .  The ranking , provided by The Banker Magazine , is an independent , publicly reported measure of the strength of a Market and its impact across all business lines and customer groups .  In 2016 , the Market value of PV U.S. was valued at 4.7 billion U.S. dollars and the bank was ranked third in its peer group .

Example 69:
titleEntities: {'Subject': ['Canada'], 'Date': []}
title: Car sales by brand in Canada - December 2018/19
Data:
                   0       1        2      3     4       5          6     7       8
unknown         Ford  Toyota  Hyundai  Honda   Ram  Nissan  Chevrolet   GMC  Subaru
December 2018  15503   14153     6760   9011  5325    8518       7692  4517    3998
December 2019  14614   11500     7807   7467  7018    6767       6639  5561    4671 

gold: Ford has been the best-selling car brand in Canada for a decade now despite a recent decline in sales . In December 2019 , Canadian car shoppers bought between 14,000 and 15,000 Ford vehicles . Serial winner : Ford Quite remarkably , Ford 's F-Series was Canada 's best-selling light vehicle for the 53rd consecutive year in 2018 .
gold_template: templateValue[0][0] has been the best-selling templateTitle[0] templateTitle[3] in templateTitleSubject[0] for a decade now despite a recent decline in templateTitle[1] . In templateTitle[5] templateLabel[2][1] , Canadian templateTitle[0] shoppers bought between 14,000 and 15,000 templateValue[0][0] vehicles . Serial winner : templateValue[0][0] Quite remarkably , templateValue[0][0] 's F-Series was templateTitleSubject[0] 's best-selling light vehicle for the 53rd consecutive year in templateLabel[1][1] .

generated_template: This graph shows the templateTitle[2] budget of the templateTitle[0] templateTitle[1] for discretionary and mandatory programs for fiscal years templateTitleDate[min] , templateLabel[2][0] and templateTitleDate[max] . In templateTitleSubject[0] templateTitleDate[max] , mandatory templateTitle[3] is predicted to sum up to about 3.01 templateScale templateTitle[0] dollars . It consists primarily of benefit programs such as : templateValue[0][0] , templateValue[0][2] , templateValue[0][5] , as well as templateValue[0][4] programs .
generated: This graph shows the by budget of the Car sales for discretionary and mandatory programs for fiscal years , December and .  In Canada , mandatory brand is predicted to sum up to about 3.01 billion Car dollars .  It consists primarily of benefit programs such as : Ford , Hyundai Nissan , as well Ram programs .

Example 70:
titleEntities: {'Subject': ['BHP'], 'Date': ['2008', '2019']}
title: BHP 's production of selected metals 2008 to 2019
Data:
               0       1       2       3       4       5       6       7       8       9      10      11
Year        2019    2018    2017    2016    2015    2014    2013    2012    2011    2010    2009    2008
Copper    1689.3  1753.3  1326.0  1579.8  1708.0  1727.0  1689.0  1468.0  1139.4  1075.2  1207.1  1375.5
Nickel      87.4    93.0    85.8    80.7    90.0    99.0   154.1   157.9   152.7   176.2   173.1   167.9
Iron ore  237694  238421  231352  226958  232508  203564  169856  159478  134406  124962  114415  112260 

gold: This statistic depicts BHP 's production of selected metals from 2008 to 2019 . In 2019 , BHP produced some 1.7 million metric tons of copper , 87,400 metric tons of nickel , and 237.7 million metric tons of iron ore .
gold_template: This statistic depicts templateTitleSubject[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateTitleSubject[0] produced some 1.7 templateScale metric tons of templateLabel[1][0] , 87,400 metric tons of templateLabel[2][0] , and 237.7 templateScale metric tons of templateLabel[3][0] templateLabel[3][1] .

generated_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] templateTitle[2] in templateTitleSubject[1] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , there were templateValue[2][0] templateLabel[2][0] templateTitle[2] in templateTitleSubject[1] and templateValue[1][0] templateLabel[1][0] templateTitle[2] .
generated: This statistic shows the BHP of production in BHP from 2008 to 2019 .  In 2019 , there were 87.4 Nickel production in BHP and 1689.3 Copper production .

Example 71:
titleEntities: {'Subject': ['Scotland'], 'Date': ['2014', '2039']}
title: Scotland : forecasted median age of population 2014 to 2039
X_Axis['Year']: ['2039', '2034', '2029', '2024', '2019', '2014']
Y_Axis['Age']: ['45.2', '44.3', '43.5', '42.9', '42.4', '41.9']

gold: This statistic shows the forecasted median age of the population of Scotland from 2014 to 2039 . The average age of the population is predicted to rise continuously over this 25 year period , with the sharpest rise between 2034 and 2039 , of 0.9 years .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] of the templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The average templateYLabel[0] of the templateTitle[4] is predicted to rise continuously over this 25 templateXLabel[0] period , with the sharpest rise between templateXValue[1] and templateXValue[max] , of 0.9 years .

generated_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: This statistic provides information on the level of Scotland forecasted median Age from 2014 to 2039 .  In 2019 , Scotland forecasted median Age amounted to 42.4 petabytes Age and is expected to multiply 45.2 Age in 2039 .

Example 72:
titleEntities: {'Subject': ['Malaysia'], 'Date': []}
title: Fertility rate in Malaysia
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'born', 'children', 'per', 'woman']: ['2.02', '2.04', '2.06', '2.07', '2.09', '2.11', '2.13', '2.15', '2.17', '2.19', '2.22']

gold: This statistic shows the fertility rate in Malaysia 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 in Malaysia amounted to 2.02 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[2] templateYLabel[1] by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateScale of the templateYLabel[1] templateYLabel[2] living in urban areas in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] was living in cities and urban areas .
generated: This statistic shows the percentage of the born children living in urban areas in Malaysia from 2007 to 2017 .  In 2017 , 2.02 % of the born children of Malaysia was living in cities and urban areas .

Example 73:
titleEntities: {'Subject': ['Canada'], 'Date': ['1960', '2018']}
title: GDP of Canada 1960 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['1709.33', '1646.87', '1526.71', '1552.9', '1801.48', '1842.02', '1823.97', '1789.14', '1613.54', '1169.36', '742.3', '604.03', '593.93', '364.76', '273.85', '173.83', '87.9', '53.91', '41.09']

gold: This statistic shows the gross domestic product ( GDP ) of Canada from 1960 to 2018 . In 2018 , the GDP of Canada was 1.71 trillion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[0] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] was templateYValue[0] templateScale templateYLabel[4] templateYLabel[5] .

generated_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 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 ( 2018 ) in Canada from 1960 to 2018 .  Gross domestic product ( 2018 ) denotes the value of all services and goods produced within a country in any given Year .  2018 is an important indicator of a country 's economic power .

Example 74:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015', '2015']}
title: U.S. metropolitan area interstates - average truck speed 2015
X_Axis['Selcted', 'areas']: ['Phoenix', 'Dallas', 'Miami', 'Atlanta', 'Houston', 'Detroit', 'Washington', 'Boston', 'Philadelphia', 'Seattle', 'New_York', 'Chicago', 'Los_Angeles', 'San_Francisco']
Y_Axis['Average', 'speed', 'in', 'miles', 'per', 'hour']: ['60.36', '59.46', '59.04', '58.79', '58.06', '56.54', '56.34', '54.79', '54.54', '54.07', '53.9', '53.26', '47.93', '47.61']

gold: The statistic shows the average operating truck speed on selected U.S. metropolitan area interstates in 2015 . In that year , trucks had an average operating speed of just over 59 miles per hour in Miami , Florida .
gold_template: The statistic shows the templateYLabel[0] operating templateTitle[5] templateYLabel[1] on selected templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] . In that year , trucks had an templateYLabel[0] operating templateYLabel[1] of just over templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[2] , Florida .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in selected templateTitleSubject[0] Eastern countries as of 2019 . As of that month , templateXValue[0] had templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] , followed templateTitle[6] templateXValue[1] with templateYValue[1] templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Average of speed miles in selected U.S. Eastern countries as of 2019 .  As of that month , Phoenix had 60.36 million speed miles , followed speed Dallas with 59.46 million speed miles .

Example 75:
titleEntities: {'Subject': ['Europe'], 'Date': ['2018']}
title: Cocaine use in the past 12 months in Europe as of 2018  , by country
X_Axis['Country']: ['United_Kingdom', 'Netherlands', 'Spain', 'France', 'Denmark', 'Ireland', 'Italy', 'Sweden', 'Norway', 'Croatia', 'Estonia', 'Germany', 'Slovenia', 'Finland', 'Belgium', 'Latvia', 'Luxembourg', 'Greece', 'Austria', 'Hungary', 'Bulgaria', 'Romania', 'Cyprus', 'Poland', 'Portugal', 'Turkey', 'Lithuania', 'Czechia', 'Slovakia']
Y_Axis['Share', 'of', 'population']: ['2.7', '2.2', '2.2', '1.6', '1.6', '1.5', '1.2', '1.2', '1.1', '0.8', '0.7', '0.6', '0.5', '0.5', '0.5', '0.5', '0.4', '0.4', '0.4', '0.3', '0.3', '0.2', '0.2', '0.2', '0.2', '0.1', '0.1', '0.1', '0.1']

gold: As of 2018 , the United Kingdom had the highest prevalence of cocaine use in Europe at 2.7 percent of the population having used in the previous year . This was followed by Spain and the Netherlands which both had 2.2 percent of individuals using cocaine in this period . Breakdown of cocaine users in the UK When broken down by age , the highest prevalence of cocaine users in the UK is found in the youngest age groups .
gold_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_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: The United_Kingdom had the highest prevalence of Cocaine use among adults in Europe as of 2018 , with approximately 2.7 % of the population reported to using Cocaine .  This was followed by Netherlands with 2.2 % of the population and then the Spain at 2.2 % .  Only 0.1 % of respondents in Slovakia , Czechia Lithuania and Turkey reported using Cocaine in this period .

Example 76:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. - job growth 2018 , by state
X_Axis['State']: ['Nevada', 'Utah', 'Idaho', 'Arizona', 'Washington', 'Colorado', 'Florida', 'South_Carolina', 'Texas', 'California', 'Georgia', 'Oregon', 'North_Carolina', 'Tennessee', 'Oklahoma', 'West_Virginia', 'New_Mexico', 'Alabama', 'New_York', 'Pennsylvania', 'Michigan', 'Virginia', 'Arkansas', 'Montana', 'South_Dakota', 'District_of_Columbia', 'Indiana', 'Delaware', 'New_Jersey', 'Illinois', 'Kansas', 'Massachussets', 'New_Hampshire', 'Wisconsin', 'Minnesota', 'Maine', 'Iowa', 'Maryland', 'Wyoming', 'North_Dakota', 'Ohio', 'Louisiana', 'Hawaii', 'Kentucky', 'Missouri', 'Rhode_Island', 'Nebraska', 'Mississippi', 'Vermont', 'Connecticut', 'Alaska']
Y_Axis['Growth', 'rate']: ['3.4', '3.2', '3.1', '2.8', '2.6', '2.4', '2.4', '2.4', '2.3', '2', '1.9', '1.8', '1.7', '1.6', '1.5', '1.5', '1.3', '1.2', '1.1', '1.1', '1.1', '1.1', '1.1', '1.1', '1.1', '1', '1', '1', '0.9', '0.9', '0.9', '0.9', '0.8', '0.8', '0.8', '0.7', '0.7', '0.7', '0.7', '0.6', '0.6', '0.5', '0.5', '0.5', '0.5', '0.5', '0.4', '0.3', '0.2', '0.1', '-0.6']

gold: This statistic shows the growth in jobs in the United States from 2017 to 2018 , by state . In 2018 , the state of Nevada was ranked first with the job growth of 3.4 percent .
gold_template: This statistic shows the templateYLabel[0] in jobs in the templateTitle[0] from 2017 to templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXLabel[0] of templateXValue[0] was ranked first with the templateTitle[1] templateYLabel[0] of templateYValue[max] templateScale .

generated_template: This statistic shows the templateScale of templateTitle[0] that involved templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[max] templateScale of templateYLabel[1] were committed with use of templateTitle[2] in templateXValue[1] . A ranking of the total number of templateTitle[2] templateTitle[5] templateTitleSubject[0] templateXLabel[0] can be found here .
generated: This statistic shows the percentage of U.S. that involved growth in the 2018 in , state .  In 2018 , about 3.4 % of rate were committed with use of growth in Utah .  A ranking of the total number of growth state U.S. can be found here .

Example 77:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009', '2017']}
title: Employment in the U.S. media industry 2009 - 2017 , by sector
Data:
                                  0                                                            1                               2                                    3                    4                                                   5
unknown  Sound_recording_industries  Internet_publishing_and_broadcasting_and_web_search_portals  Broadcasting_(except_internet)  Motion_picture_and_video_industries  Software_publishers  Newspaper_periodical_book_and_directory_publishers
2009                          23644                                                        91134                          272772                               316409               370019                                              593253
2010                          23419                                                        90892                          263009                               295179               364463                                              542689
2011                          22629                                                       135554                          263623                               287341               362410                                              494250
2012                          22897                                                       159673                          274226                               292904               397145                                              450258
2013                          22957                                                       176292                          278647                               321463               428030                                              437385
2014                          27477                                                       199656                          267130                               377987               442246                                              431427
2015                          28519                                                       212902                          259527                               403035               469845                                              407500
2016                          22940                                                       236464                          267072                               424638               546191                                              370408
2017                          22602                                                            0                          261295                               338250               643794                                              365529 

gold: Employment data from key sectors of the U.S. media industry in 2017 revealed that software publishers employed the most people by far , with over 643 thousand employees in total . Motion picture and video industries ranked second in 2016 but by 2017 the number of employees in this sector had dropped by almost one hundred thousand .
gold_template: templateTitle[0] data from key sectors of the templateTitleSubject[0] templateTitle[2] templateTitle[3] in templateTitleDate[max] revealed that templateValue[0][4] employed the most people templateTitle[6] far , with over 643 thousand employees in total . templateValue[0][3] and templateValue[0][3] templateValue[0][0] ranked second in templateLabel[8][0] but templateTitle[6] templateTitleDate[max] the number of employees in this templateTitle[7] had templateNegativeTrend templateTitle[6] almost one hundred thousand .

generated_template: templateValue[0][0] was the templateTitle[0] often used web-shop in templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . Over 14 thousand Danish people were asked which web-shop they did their last purchase from and templateValue[2][max] templateScale of them answered Zalando in templateTitleDate[max] . templateValue[1][1] templateScale of the respondents did their last purchase on Amazon , the second templateTitle[0] used web-shop , and templateValue[1][4] templateScale did it on H & M , the web-shop ranked third .
generated: Sound_recording_industries was the Employment often used web-shop in U.S. 2009 and 2017 .  Over 14 thousand Danish people were asked which web-shop they did their last purchase from and 542689 % of them answered Zalando in 2017 .  91134 % of the respondents did their last purchase on Amazon , the second Employment used web-shop , and 370019 % did it on H & M , the web-shop ranked third .

Example 78:
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 templateYLabel[0] of harvested tomato templateTitle[0] in templateTitleSubject[0] in templateTitleDate[0] , by templateXLabel[0] . In this year , templateXValue[0] produced the largest templateYLabel[0] of at over templateYValue[max] templateScale .
generated: This statistic shows the Percentage of harvested tomato Forecast in Ibex Spain 2019 , by Company .  In this year , Mediaset produced the largest Percentage of at over 8.46 percentage .

Example 79:
titleEntities: {'Subject': ['National Football'], 'Date': ['2006', '2019']}
title: Average revenue multiple of National Football league teams 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Revenue', 'multiple']: ['6.31', '6.01', '6.14', '6.16', '5.67', '4.77', '4.07', '4.01', '3.97', '4.08', '4.41', '4.68', '4.69', '4.66']

gold: This graph depicts the average revenue multiple per franchise in the National Football League from 2006 to 2019 . In 2019 , the average revenue multiple of an NFL franchise was 6.31 .
gold_template: This graph depicts the templateTitle[0] templateYLabel[0] templateYLabel[1] per franchise in the templateTitleSubject[0] League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of an NFL franchise was templateYValue[idxmax(X)] .

generated_template: This statistic shows the total templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the total templateTitle[0] templateTitle[1] templateYLabel[0] amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the total Average revenue in the National Football from 2006 to 2019 .  In 2019 , the total Average revenue amounted to approximately 6.31 million multiple .

Example 80:
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 12 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] in the templateTitle[4] and templateTitle[5] templateTitle[6] worldwide in templateTitleDate[0] . According to the World templateTitle[4] & templateTitle[5] Council , around templateYValue[max] templateScale people were employed in the U.S. templateTitle[4] and templateTitle[5] templateTitle[6] .
generated: This statistic shows the 12 Instagram hashtagged the cities Instagram in the worldwide and 2017 worldwide in 2017 .  According to the World worldwide & 2017 Council , around 88.78 millions people were employed in the U.S. worldwide and 2017 .

Example 81:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Top 50 metropolitan areas with the highest violent crime rate in the U.S. 2018
X_Axis['Metropolitan', 'Statistical', 'Area']: ['Anchorage_AK', 'Farmington_NM', 'Memphis_TN-MS-AR', 'Albuquerque_NM', 'Detroit-Dearborn-Livonia_MI', 'Danville_IL', 'Pine_Bluff_AR', 'Hammond_LA', 'Lubbock_TX', 'Wichita_KS', 'Stockton_CA', 'Alexandria_LA', 'Philadelphia_PA', 'Odessa_TX', 'Florence_SC', 'Rockford_IL', 'Little_Rock-North_Little_Rock-Conway_AR', 'Albany_GA', 'Anniston-Oxford_AL', 'Monroe_LA', 'Baltimore-Columbia-Towson_MD', 'Gainesville_FL', 'Corpus_Christi_TX', 'Lawton_OK', 'Pueblo_CO', 'Flint_MI', 'Sumter_SC', 'Battle_Creek_MI', 'Saginaw_MI', 'Amarillo_TX2', 'Redding_CA', 'Indianapolis-Carmel-Anderson_IN', 'Fairbanks_AK', 'Milwaukee-Waukesha_WI', 'Jackson_TN', 'Shreveport-Bossier_City_LA', 'Dothan_AL', 'Nashville-Davidson–Murfreesboro–Franklin_TN', 'Springfield_IL', 'Fresno_CA', 'Kalamazoo-Portage_MI', 'Bakersfield_CA', 'Los_Angeles-Long_Beach-Glendale_CA', 'Modesto_CA', 'Madera_CA', 'Columbia_SC', 'Merced_CA', 'Tulsa_OK', 'New_Orleans-Metairie_LA', 'Springfield_MO']
Y_Axis['Violent', 'crime', 'rate', 'per', '100,000', 'inhabitnats']: ['1263.6', '1199.8', '1142.5', '1084.7', '1001.8', '931.9', '880.4', '849.1', '829.3', '826.0', '795.1', '775.3', '763.7', '760.6', '748.2', '742.6', '738.1', '728.5', '726.0', '724.6', '720.8', '713.3', '699.3', '695.1', '693.4', '683.2', '679.0', '666.5', '661.0', '660.4', '650.1', '641.5', '625.5', '609.7', '608.6', '602.8', '602.0', '599.1', '596.0', '595.3', '592.5', '591.7', '577.9', '576.2', '572.4', '567.9', '567.2', '564.8', '563.8', '562.6']

gold: This statistic shows the top 50 metropolitan areas with the highest violent crime rate in the United States in 2018 . In 2018 , Anchorage in Alaska reported 1,263.6 violent crimes per 100,000 inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateXLabel[0] templateTitle[3] templateTitle[4] the templateTitle[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[9] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] in Alaska reported templateYValue[max] templateYLabel[0] crimes templateYLabel[3] 100,000 inhabitants .

generated_template: This statistic shows the templateScale of templateTitle[1] in the templateTitle[0] with templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in 2017 , templateTitle[7] templateXLabel[0] . During the survey period , it was found that templateYValue[1] templateScale of the templateTitle[1] population in templateXValue[1] used a templateTitle[2] templateTitle[4] to access the templateTitle[3] at home .
generated: This statistic shows the percentage of 50 in the Top with metropolitan areas highest violent in 2017 , rate Metropolitan .  During the survey period , it was found that 1199.8 % of the 50 population in Farmington_NM used a metropolitan highest to access the areas at home .

Example 82:
titleEntities: {'Subject': ['General Motors'], 'Date': ['2013', '2019']}
title: General Motors - revenue 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['137.2', '147.05', '145.59', '149.18', '135.73', '137.96', '138.79']

gold: General Motors generated some 137 billion U.S. dollars in revenue during the 2019 fiscal year , a drop of 6.7 percent year-on-year . Revenue from GM 's automotive segments contracted by eight percent overall and 16 percent in markets outside North America . Investments , unions , and weak demand in Asia Automotive revenue suffered due to various factors , including a labor union strike in the U.S. , stalling vehicle sales , and high investments targeted at autonomous driving .
gold_template: templateTitleSubject[0] generated some templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] during the templateXValue[max] fiscal templateXLabel[0] , a drop of 6.7 templateScale year-on-year . templateYLabel[0] from GM 's automotive segments contracted by eight templateScale overall and 16 templateScale in markets outside North America . Investments , unions , and weak demand in Asia Automotive templateYLabel[0] suffered due to various factors , including a labor union strike in the templateYLabel[2] , stalling vehicle sales , and high investments targeted at autonomous driving .

generated_template: The timeline displays templateTitleSubject[0] Company templateTitle[1] revenues from templateXValue[min] to templateXValue[max] . The home goods e-retailer generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in revenues in templateXValue[1] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the preceding templateXLabel[0] . templateTitleSubject[0] 's online templateYLabel[0] ( net sales ) reached more than templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[min] .
generated: The timeline displays General Motors Company Motors revenues from 2013 to 2019 .  The home goods e-retailer generated 149.18 billion U.S. dollars in revenues 2018 , up from 147.05 billion U.S. dollars in the preceding Year .  General Motors 's online Revenue ( net sales ) reached more than 145.59 billion U.S. dollars in 2013 .

Example 83:
titleEntities: {'Subject': ['India'], 'Date': ['2013', '2019']}
title: Consumption of pork in India 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Consumption', 'in', 'thousand', 'metric', 'tons']: ['294.39', '300.9', '306.35', '316.52', '344.16', '350.44', '354.1']

gold: Nearly 295 thousand metric tons of pork was consumed across India in 2019 . This was a decrease compared to the previous year . According to OECD , consuming meat was equated with living standards , diet , livestock production and consumer prices .
gold_template: Nearly 295 thousand templateYLabel[2] templateYLabel[3] of templateTitle[1] was consumed across templateTitleSubject[0] in templateXValue[max] . This was a templateNegativeTrend compared to the previous templateXLabel[0] . According to OECD , consuming meat was equated with living standards , diet , livestock production and consumer prices .

generated_template: The templateYLabel[0] of the Italian high-end jewelry company templateTitleSubject[0] templatePositiveTrend almost twofold from templateXValue[min] to templateXValue[max] . The peak was reached in templateXValue[1] , when templateTitleSubject[0] 's annual templateYLabel[0] amounted to approximately templateYValue[max] templateScale templateYLabel[2] . In templateXValue[max] the templateYLabel[0] templateNegativeTrend to templateYValue[0] templateScale templateYLabel[2] .
generated: The Consumption of the Italian high-end jewelry company India increased almost twofold from 2013 to 2019 .  The peak was reached in 2018 , when India 's annual Consumption amounted to approximately 354.1 thousand metric .  In 2019 the Consumption decreased to 294.39 thousand metric .

Example 84:
titleEntities: {'Subject': ['Bank China'], 'Date': ['2018']}
title: Total assets of the Bank of China 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Total', 'assets', 'in', 'billion', 'yuan']: ['21267.28', '19467.42', '18148.89', '16815.6', '15251.4', '13874.3', '12680.6', '11829.8', '10459.7']

gold: This statistic shows the total assets of the Bank of China from 2010 to 2018 . In 2018 , the Bank of China generated total assets worth approximately 21.27 trillion yuan .
gold_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 templateTitleSubject[0] of templateTitleSubject[0] generated templateYLabel[0] templateYLabel[1] worth approximately templateYValue[max] templateScale templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] at 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: This statistic shows the Total of billion at universities in the Bank China ( ) from 2010 to 2018 .  The Total of billion 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 .

Example 85:
titleEntities: {'Subject': ['Apple'], 'Date': ['2006', '2014']}
title: Apple : iPod sales worldwide 2006 to 2014
X_Axis['fiscal', 'years']: ['2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Sales', 'in', 'million', 'units']: ['14.38', '26.38', '35.17', '42.62', '50.31', '54.13', '54.83', '51.63', '39.41']

gold: This statistic shows the number of iPod units sold worldwide by Apple from 2006 to 2014 ( according to the company 's fiscal year ) . Sales have been decreasing in recent years after peaking at 54.83 million in 2008 . Global Apple iPod sales – additional information The first line of iPods was introduced to the market in 2001 , Apple 's first foray into consumer electronics .
gold_template: This statistic shows the number of templateTitle[1] templateYLabel[2] sold templateTitle[3] by templateTitleSubject[0] from templateXValue[last] to templateXValue[0] ( according to the company 's templateXLabel[0] year ) . templateYLabel[0] have been templateNegativeTrend in recent templateXLabel[1] after peaking at templateYValue[max] templateScale in templateXValue[idxmax(Y)] . Global templateTitleSubject[0] templateTitle[1] templateYLabel[0] – additional information The first line of iPods was introduced to the market in 2001 , templateTitleSubject[0] 's first foray into consumer electronics .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] titles templateTitle[5] as of 2019 . With templateYValue[max] templateScale templateYLabel[2] sold templateTitle[5] , templateXValue[0] 7 was the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] game as of 2019 .
generated: This statistic shows the Apple iPod worldwide titles 2014 as of 2019 .  With 54.83 million units sold 2014 , 7 was the Apple iPod worldwide game as of 2019 .

Example 86:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. chemical imports by business sector 2018
X_Axis['Business', 'sector']: ['Pharmaceuticals', 'Basic_Chemicals', 'Specialties', 'Consumer_products', 'Agricultural_Chemicals']
Y_Axis['Imports', 'in', 'billion', 'U.S.', 'dollars']: ['136.4', '65.75', '22.67', '11.51', '9.17']

gold: This statistic shows the value of chemical imports into the United States in 2018 , sorted by business sector . In that year , the pharmaceuticals sector was the leading U.S. chemical importer . Imported products from this sector were worth some 136 billion U.S. dollars .
gold_template: This statistic shows the value of templateTitle[1] templateYLabel[0] into the templateTitle[0] in templateTitleDate[0] , sorted templateTitle[3] templateXLabel[0] templateXLabel[1] . In that year , the templateXValue[0] templateXLabel[1] was the leading templateYLabel[2] templateTitle[1] importer . Imported templateXValue[3] from this templateXLabel[1] were worth some templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] paid hourly rates at or below the prevailing federal templateTitle[2] templateTitle[3] in the templateTitle[1] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , around templateYValue[0] people without a templateXValue[0] diploma earned the templateTitle[2] templateTitle[3] or below .
generated: This statistic shows the Imports of billion paid hourly rates at or below the prevailing federal imports by in the chemical in 2018 , sector Business .  In 2018 , around 136.4 people without a Pharmaceuticals diploma earned the imports by or below .

Example 87:
titleEntities: {'Subject': ['Juventus FC'], 'Date': []}
title: Juventus FC revenue by stream 2018/19
Data:
                    0        1        2        3        4        5        6        7        8        9       10
Season        2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09
Matchday         65.6     51.2     57.8     43.7     51.4     41.0     38.0     31.8     11.6     16.9     16.7
Broadcasting    208.5    200.4    233.5    195.7    199.0    153.4    166.0     90.6     88.7    132.5    132.2
Commercial      185.6    143.3    114.4    101.7     73.5     85.0     68.4     73.0     53.6     55.6     54.3 

gold: The broadcasting revenue stream is the largest source of revenue for Juventus FC . In 2018/2019 , the football club earned approximately 208.5 million euros from domestic and international competitions broadcasting , more than twice of what they earned in 2011/2012 . The second biggest revenue stream was commercial – sponsorships and merchandising .
gold_template: The templateLabel[2][0] templateTitle[2] templateTitle[4] is the largest source of templateTitle[2] for templateTitleSubject[0] . In 2018/2019 , the football club earned approximately templateValue[2][0] templateScale euros from domestic and international competitions templateLabel[2][0] , more than twice of what they earned in 2011/2012 . The second biggest templateTitle[2] templateTitle[4] was templateLabel[3][0] – sponsorships and merchandising .

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 triple than in 2010/2011 . The second biggest templateTitle[2] templateTitle[4] is the templateLabel[3][0] one .
generated: Broadcasting is the largest source of revenue for Juventus FC .  In 2018/2019 , the club earned approximately 208.5 million euros from Broadcasting , more than triple in 2010/2011 .  The second biggest revenue stream is the Commercial one .

Example 88:
titleEntities: {'Subject': ['Tokyo Airport'], 'Date': ['2018']}
title: Passenger traffic at Tokyo Airport 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', 'passengers', 'in', 'millions']: ['87.13', '85.41', '79.7', '75.32', '72.83', '68.91', '66.8', '62.58', '64.21', '61.9', '66.75', '66.82', '66.09', '63.28', '62.29', '62.88', '61.08', '58.69', '56.4']

gold: The statistic shows the passenger traffic at Tokyo Airport ( HND ) between 2000 and 2018 . In 2018 , passenger traffic at Haneda Airport in Tokyo increased to approximately 87 million passengers from about 85 million passengers boarding or exiting planes in the previous year , making it the world 's fifth largest airport in terms of passenger traffic .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] at templateTitleSubject[0] ( HND ) between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] at Haneda templateTitleSubject[0] in templateTitleSubject[0] templatePositiveTrend to approximately templateYValue[max] templateScale templateYLabel[1] from about templateYValue[1] templateScale templateYLabel[1] boarding or exiting planes in the previous templateXLabel[0] , making it the world 's fifth largest templateTitleSubject[0] in terms of templateTitle[0] templateTitle[1] .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . The total templateYLabel[0] templateTitle[2] of the templateTitle[3] abroad amounted to 5.95 templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] templateXValue[idxmax(Y)]
generated: This statistic shows the Number traffic Tokyo of the Airport in Tokyo Airport from 2000 to 2018 , on a historical-cost basis .  In 2018 , the millions passengers made in Tokyo Airport were valued at approximately 87.13 millions .  The total Number Tokyo of the Airport abroad amounted to 5.95 millions in 2018 . 

Example 89:
titleEntities: {'Subject': ['Royal Dutch Shell'], 'Date': ['2010', '2018']}
title: Royal Dutch Shell 's spending on research and development 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Spending', 'in', 'million', 'U.S.', 'dollars']: ['986', '922', '1014', '1093', '1222', '1318', '1307', '1123', '1019']

gold: This statistic shows Royal Dutch Shell 's spending on research and development from 2010 to 2018 . In 2010 , the company spent some 1.02 billion U.S. dollars on R & D . Royal Dutch Shell is one of the leading oil and gas companies worldwide , operating in every segment of the oil and gas industry .
gold_template: This statistic shows templateTitleSubject[0] Shell templateTitle[3] templateYLabel[0] on templateTitle[5] and templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the company spent some templateYValue[last] templateScale templateYLabel[2] templateYLabel[3] on R & D . templateTitleSubject[0] Shell is one of the leading oil and gas companies worldwide , operating in every segment of the oil and gas industry .

generated_template: This statistic shows the templateTitle[0] and templateTitle[1] templateYLabel[0] of templateTitle[3] , templateTitle[4] and templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitle[0] and templateTitle[1] templateYLabel[0] of templateTitle[3] , templateTitle[4] and templateTitle[5] in the templateYLabel[2] were forecasted to reach templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] .
generated: This statistic shows the Royal and Dutch Spending of 's , spending and research in the development from 2010 to 2018 .  Royal and Dutch Spending of 's , spending and research in the U.S. were forecasted to reach 986 million U.S. dollars in 2017 .

Example 90:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015', '2019']}
title: Iron ore mine production - U.S. 2015 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015']
Y_Axis['Production', 'in', 'million', 'metric', 'tons']: ['48.0', '49.5', '47.9', '41.8', '46.1']

gold: Iron ore production in the United States has undergone a period of decline in recent years . As of 2019 estimates suggest that there were just 48 metric tons of or produced in the U.S. Iron ore is an important mineral substance that is used in the iron and steel industry . Iron Ore Iron ore is mined in about 50 countries globally and is used almost exclusively to create steel .
gold_template: templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[4] has undergone a period of decline in recent years . As of templateXValue[max] estimates suggest that there were just templateYValue[0] templateYLabel[2] templateYLabel[3] of or produced in the templateTitleSubject[0] templateTitle[0] templateTitle[1] is an important mineral substance that is used in the templateTitle[0] and steel industry . templateTitle[0] templateTitle[1] templateTitle[0] templateTitle[1] is mined in about templateYValue[max] countries globally and is used almost exclusively to create steel .

generated_template: This statistic depicts the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateYLabel[0] templatePositiveTrend to some templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic depicts the Iron ore Production in the mine from 2015 to 2019 .  In 2016 , the Iron ore Production increased to some 41.8 million metric tons .

Example 91:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Most popular summer vacation activities for U.S. travelers in 2014
X_Axis['Response']: ['Shopping', 'Swimming_/_watersports', 'Visiting_a_historical_event_or_sight', 'Going_to_a_park_or_national_park', 'Sightseeing_tours']
Y_Axis['Share', 'of', 'respondents']: ['54', '49', '49', '46', '46']

gold: This statistic shows the most popular summer vacation activities for travelers in the United States in 2014 . During the survey , 49 percent of the respondents stated that they planned to visit a historical event or sight during their summer vacation in 2014 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[7] in the templateTitle[6] in templateTitleDate[0] . During the survey , templateYValue[1] templateScale of the templateYLabel[1] stated that they planned to visit a templateXValue[2] or templateXValue[2] during their templateTitle[2] templateTitle[3] in templateTitleDate[0] .

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 Most popular an active U.S. or subscription in the U.S. as of 2017 , sorted 2014 .  According to the source , 54 % of Swimming_/_watersports who subscribe to online video or music subscriptions had a U.S. or subscription as of 2017 .

Example 92:
titleEntities: {'Subject': ['Countries'], 'Date': []}
title: Countries ranked by number of ice hockey players 2018/19
X_Axis['Country']: ['Canada', 'United_States', 'Czech_Republic', 'Russia', 'Finland', 'Sweden', 'Switzerland', 'France', 'Germany', 'Japan', 'Slovakia', 'Norway', 'Great_Britain', 'Austria', 'Hungary', 'Latvia', 'Kazakhstan', 'Ukraine', 'Italy', 'Belarus']
Y_Axis['Number', 'of', 'players']: ['621026', '567908', '121613', '112236', '64641', '55431', '27867', '21667', '21340', '18837', '11394', '10353', '8162', '7670', '7106', '7000', '6915', '5384', '5210', '4580']

gold: The statistics ranks countries by the number of registered ice hockey players in 2018/19 . In the 2018/19 season , Canada had the most registered ice hockey players with 621 thousand according to the International Ice Hockey Federation .
gold_template: The statistics ranks templateTitleSubject[0] templateTitle[2] the templateYLabel[0] of registered templateTitle[4] templateTitle[5] templateYLabel[1] in templateTitle[7] . In the templateTitle[7] season , templateXValue[0] had the most registered templateTitle[4] templateTitle[5] templateYLabel[1] with templateYValue[max] thousand according to the International templateTitle[4] templateTitle[5] Federation .

generated_template: templateXValue[0] is the templateXLabel[0] with the largest templateYLabel[0] templateYLabel[1] in the world . The templateTitle[2] has a templateYLabel[0] templateYLabel[1] of around templateYValue[max] templateYLabel[2] , which is around six templateYLabel[2] more than in templateXValue[1] and templateXValue[2] – the other templateTitle[5] that make up the templateTitle[3] three . Southern European templateTitle[5] make up a large part of the templateTitle[3] templateTitle[4] , with templateXValue[4] , templateXValue[2] , templateXValue[2] , templateXValue[8] , templateXValue[9] , and templateXValue[18] all making the list .
generated: Canada is the Country with the largest Number players in the world .  The by has a Number players of around 621026 players , which is around six players more than in United_States and Czech_Republic – the other hockey that make up the number three .  Southern European hockey make up a large part of the number ice , with Finland , Czech_Republic Germany , Japan and Italy all making the list .

Example 93:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2011', '2015']}
title: Leading grocery retailers ranked by market share in the United Kingdom ( UK ) 2011 to 2015
Data:
                      0     1     2     3     4
Year               2011  2012  2013  2014  2015
Tesco                25    24    22    21    19
Sainsburys           15    13    13    15    13
Asda                 14    13    13    13    13
Morrisons            12    12    10    11    10
Co-op                 5     4     5     6     4
Waitrose              5     4     5     6     5
Marks and Spencer     4     4     4     4     4
Aldi                  3     3     5     6     6
Iceland               3     3     3     4     2
Lidl                  2     2     3     4     4
Farmfoods             1     0     0     0     0
Internet              4     4     5     3     6
Other multiples       5   4.7     5     4     6
Other                 7     8     8     2     1 

gold: This statistic displays the distribution of the grocery market among leading grocery retailers in the United Kingdom ( UK ) from 2011 to 2015 . Tesco held the largest share at 25 percent in 2011 , dropping by six percentage points to a 19 percent share of the grocery retail market by 2015 . Prior to the popularity of the discounters , the grocery retail market was dominated by the 'big four ' supermarkets : Tesco , Sainsbury 's , Asda and Morrisons .
gold_template: This statistic displays the distribution of the templateTitle[1] templateTitle[5] among templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateValue[0][0] to templateValue[0][last] . templateLabel[1][0] held the largest templateTitle[6] at templateValue[1][0] templateScale in templateValue[0][0] , templateNegativeTrend templateTitle[4] templateValue[5][3] templateScale points to a templateValue[1][last] templateScale templateTitle[6] of the templateTitle[1] retail templateTitle[5] templateTitle[4] templateValue[0][last] . Prior to the popularity of the discounters , the templateTitle[1] retail templateTitle[5] was dominated templateTitle[4] the 'big templateValue[5][last] ' supermarkets : templateLabel[1][0] , Sainsbury 's , templateLabel[3][0] and templateLabel[4][0] .

generated_template: The templateTitle[0] of commercial templateTitle[2] templateTitle[3] in the templateTitle[1] has been steadily templateNegativeTrend , with the templateTitle[0] of templateLabel[1][0] templateTitle[3] templateNegativeTrend from templateValue[1][11] in 2008 to templateValue[0][1] in templateTitleDate[max] . For smaller templateTitle[2] templateTitle[3] the decline has been even stronger , with the templateValue[2][11] templateTitle[3] counted in 2008 templateNegativeTrend to templateValue[2][0] by 2018 . templateLabel[1][0] templateTitle[2] templateTitle[3] According to the templateTitleSubject[0] Bureau of Transportation , templateLabel[1][0] templateTitle[2] templateTitle[3] are commercial airlines generating over templateValue[0][18] templateScale templateTitleSubject[0] dollars in operating revenue per templateLabel[0][0] .
generated: The Leading of commercial retailers ranked in the grocery has been steadily decreasing , with the Leading of Tesco ranked falling from 19 in 2008 to 2012 in 2015 .  For smaller retailers ranked the decline has been even stronger , with the 13 ranked counted in 2008 falling to 15 by 2018 .  Tesco retailers ranked According to the United Kingdom Bureau of Transportation , Tesco retailers ranked are commercial airlines generating over 2015 billion United Kingdom dollars in operating revenue per Year .

Example 94:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: U.S. cotton production 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', 'bales']: ['20102.0', '18367.0', '20922.5', '16601.0', '12888.0', '16319.4', '12909.2', '17313.8', '15573.2', '18101.8', '12183.0', '12825.4', '19206.9', '21587.8', '23890.2', '23250.7', '18255.2', '17208.6', '20302.8', '17188.3']

gold: Cotton production in the United States fluctuates significantly from year to year . In 2019 , around 20.1 million bales of cotton were produced in the United States , a drop from 18.4 million bales in the previous year . Leading States for cotton production Historically , much of the cotton in the United States has been grown in the Southern states , and this trend continues to this day .
gold_template: templateTitle[1] templateYLabel[0] in the templateTitle[0] fluctuates significantly from templateXLabel[0] to templateXLabel[0] . In templateXValue[max] , around templateYValue[0] templateScale templateYLabel[2] of templateTitle[1] were produced in the templateTitle[0] , a drop from templateYValue[1] templateScale templateYLabel[2] in the previous templateXLabel[0] . Leading States for templateTitle[1] templateYLabel[0] Historically , much of the templateTitle[1] in the templateTitle[0] has been grown in the Southern states , and this trend continues to this day .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitleSubject[0] templateTitle[2] templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[idxmax(X)] people emigrated templateTitle[2] templateTitleSubject[0] to another country . At this time , the majority of templateYLabel[1] templateTitle[2] templateTitleSubject[0] came templateTitle[2] the province of Ontario , accounting for around 27,070 templateYLabel[1] , whereas 12,478 templateYLabel[1] came templateTitle[2] British Columbia , the second largest templateYLabel[0] of any province .
generated: This statistic shows the total Production of thousand production U.S. 2000 to 2019 .  In 2019 , approximately 20102.0 people emigrated production U.S. to another country .  At this time , the majority of thousand production U.S. came production the province of Ontario , accounting for around 27,070 thousand , whereas 12,478 thousand came production British Columbia , the second largest Production of any province .

Example 95:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010']}
title: Coffee makers : U.S. market share by product type 2010
X_Axis['Coffee', 'Type']: ['Automatic_Drip', 'Single-Serve', 'Espresso']
Y_Axis['Market', 'share']: ['75', '19', '6']

gold: The statistic illustrates the market share of coffee makers in the United States in 2010 . In 2010 , espresso machines hold a market share of 6 percent of the coffee maker retail sales in the United States . In 2010 , the retail sales of espresso machines amounted to 1.4 million units in the United States .
gold_template: The statistic illustrates the templateYLabel[0] templateYLabel[1] of templateXLabel[0] templateTitle[1] in the templateTitle[2] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[last] machines hold a templateYLabel[0] templateYLabel[1] of templateYValue[min] templateScale of the templateXLabel[0] maker retail sales in the templateTitle[2] . In templateTitleDate[0] , the retail sales of templateXValue[last] machines amounted to 1.4 templateScale units in the templateTitle[2] .

generated_template: In templateTitleDate[0] , templateYLabel[1] templateTitle[4] retail e-commerce templateYLabel[0] in the templateXValue[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] , more than double the amount of templateYLabel[1] templateTitle[4] templateTitle[1] templateYLabel[0] in the templateTitleSubject[0] .
generated: In 2010 , share retail e-commerce Market in the Automatic_Drip amounted to 75 share , more than double the amount of share makers Market in the U.S. .

Example 96:
titleEntities: {'Subject': ['Japan', 'Facebook'], 'Date': ['2017', '2023']}
title: Japan : number of Facebook users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['26.9', '26.1', '25.2', '24.3', '23.5', '22.6', '21.7']

gold: This statistic shows the number of Facebook users in Japan from 2017 to 2023 . In 2018 , approximately 22.6 million people were using Facebook in Japan . In 2023 , this figure is projected to reach about 26.9 million Facebook users .
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] , approximately templateYValue[5] templateScale people were using templateTitleSubject[1] in templateTitleSubject[0] . In templateXValue[max] , this figure is projected to reach about templateYValue[idxmax(X)] templateScale templateTitleSubject[1] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[4] templateScale , up from templateYValue[min] templateScale in templateXValue[idxmin(Y)] .
generated: This statistic shows the Number of Facebook users in Japan from 2017 to 2023 .  In 2019 , the Number of Facebook users in Japan is expected to reach 23.5 millions , up from 21.7 millions in 2017 .

Example 97:
titleEntities: {'Subject': ['Singapore'], 'Date': ['2017', '2023']}
title: Singapore : internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['97', '96', '94', '93', '91', '89', '84']

gold: This statistic gives information on the internet penetration rate in Singapore from 2017 to 2023 . In 2017 , 84 percent of the Singaporean population were using the internet . In 2023 , this figure is projected to grow to 97 percent .
gold_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_template: The statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the templateYLabel[1] users accessed the templateTitle[3] through their templateTitle[1] device . This figure is projected to grow to 59percent in templateXValue[max] .
generated: The statistic shows the internet user penetration 2017 2023 in Singapore from 2017 to 2023 .  In 2017 , 84 % of the population users accessed the penetration through their internet device .  This figure is projected to grow 59percent in 2023 .

Example 98:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2018']}
title: Kitchen and household appliances imports in the U.S. 2002 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Imports', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['31.21', '29.04', '26.91', '27.03', '25.24', '23.4', '22.27', '20.89', '19.85', '17.13', '19.36', '19.79', '18.44', '15.83', '13.8', '11.82', '10.43']

gold: This statistic shows the value of imports of the household and kitchen appliances into the United States from 2002 to 2018 . In 2018 , imports of such products came to a total of about 31.21 billion U.S. dollars . U.S. imports of domestic appliances – additional information Electrical devices used within a household are part of a billion-dollar home appliance industry , which is projected to grow in the coming years .
gold_template: This statistic shows the templateYLabel[1] of templateYLabel[0] of the templateTitle[1] and templateTitle[0] templateTitle[2] into the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] of such products came to a total of about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] of domestic templateTitle[2] – additional information Electrical devices used within a templateTitle[1] are part of a billion-dollar home appliance industry , which is projected to grow in the coming years .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of the global templateTitle[4] industry represented by the templateTitle[3] forty templateTitle[4] templateTitle[5] worldwide , from templateXValue[min] to templateXValue[max] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[4] industry 's leading templateTitle[5] was approximately templateYValue[7] templateScale . templateYValue[4] years later , in templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] stood at templateYValue[0] templateScale .
generated: This statistic shows the average Imports value billion of the global U.S. industry represented by the imports forty U.S. 2002 worldwide , from 2002 to 2018 .  In 2011 , the Imports value billion of the U.S. industry 's leading 2002 was approximately 20.89 billion .  25.24 years later , in 2018 , the Imports value billion stood at 31.21 billion .

Example 99:
titleEntities: {'Subject': ['Monsanto'], 'Date': ['2011', '2017']}
title: Monsanto 's seed and genomics segment net sales 2011 to 2017
Data:
                            0     1     2     3     4     5     6
Year                     2017  2016  2015  2014  2013  2012  2011
Corn seed and traits     6270  5825  5953  6401  6596  5814  4805
Soybean seed and traits  2662  2162  2276  2102  1653  1771  1542
Vegetable seeds           815   801   816   867   821   851   895
Cotton seed and traits    615   440   523   665   695   779   847 

gold: This statistic depicts agrochemical company Monsanto 's net sales in the seed and genomics segment from 2011 to 2017 , by specialty . In 2017 , the corn seed and traits specialty came to approximately 6.27 billion U.S. dollars of net sales . Monsanto was an agricultural company specialized on genetically engineered seeds .
gold_template: This statistic depicts agrochemical company templateTitleSubject[0] templateTitle[1] templateTitle[5] templateTitle[6] in the templateTitle[2] and templateTitle[3] templateTitle[4] from templateValue[0][last] to templateValue[0][0] , by specialty . In templateValue[0][0] , the templateLabel[1][0] templateTitle[2] and templateLabel[1][2] specialty came to approximately 6.27 templateScale U.S. dollars of templateTitle[5] templateTitle[6] . templateTitleSubject[0] was an agricultural company specialized on genetically engineered templateLabel[3][1] .

generated_template: This statistic shows the templateTitle[0] of directly operated templateTitleSubject[0] stores templateTitle[5] from templateValue[0][last] to templateValue[0][0] , templateTitle[8] templateTitle[9] . In templateValue[0][0] , templateTitleSubject[0] operated templateValue[1][0] templateTitle[1] throughout templateLabel[1][0] templateLabel[1][1] .
generated: This statistic shows the Monsanto of directly operated Monsanto stores net from 2011 to 2017 , .  In 2017 , Monsanto operated 6270 's throughout Corn seed .

Example 100:
titleEntities: {'Subject': ['U.S. UHNW'], 'Date': ['2014']}
title: Wealth in the U.S. - UHNW ( super rich ) population in 2014 , by city
X_Axis['State']: ['New_York', 'San_Francisco', 'Los_Angeles', 'Chicago', 'Washington', 'Houston', 'Dallas', 'Toronto', 'Atlanta', 'Seattle']
Y_Axis['Number', 'of', 'Ultra-High-Net-Worth', 'people']: ['8655', '5460', '5135', '2885', '2730', '2545', '2330', '1840', '1230', '1095']

gold: This statistic shows the number of the super-rich , or Ultra-High-Net-Worth , persons in the United States in 2014 , sorted by city . New York has the largest concentration of super-rich individuals ; about 8,655 UHNW ( Ultra High Net Worth ) people are living in the metro area .
gold_template: This statistic shows the templateYLabel[0] of the super-rich , or templateYLabel[1] , persons in the templateTitle[1] in templateTitleDate[0] , sorted templateTitle[7] templateTitle[8] . templateXValue[0] has the largest concentration of super-rich individuals ; about templateYValue[max] templateTitleSubject[0] ( Ultra High Net Worth ) templateYLabel[2] are living in the metro area .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the largest templateYLabel[0] of templateTitle[3] templateTitle[4] templateYLabel[2] templateTitle[6] in the templateTitle[7] in templateTitleDate[0] . According to the source , Connecticut was the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[2] templateTitle[6] in templateTitleDate[0] templateTitle[1] templateYValue[max] templateYLabel[1] to every templateYLabel[3] thousand templateYLabel[5] .
generated: This statistic shows the Wealth U.S. the largest Number of super rich people 2014 in the by in 2014 .  According to the source , Connecticut was the State U.S. the UHNW super rich people 2014 in U.S. 8655 Ultra-High-Net-Worth to every people thousand .

Example 101:
titleEntities: {'Subject': ['United States'], 'Date': ['2010']}
title: Projected Consumer Price Index 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['Consumer', 'price', 'index', '(100=1982-1984)']: ['287.51', '281.23', '275.09', '269.1', '263.12', '256.12', '251.1', '245.13', '240.01', '237.0', '236.72', '232.95', '229.59', '224.92', '218.08']

gold: This statistic shows the consumer price index in the U.S. from 2010 to 2018 with additional projections up to 2024 . The base period was 1982 - 84 . In 2019 , the annual Consumer Price Index is projected to be at about 256.12 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] from templateXValue[min] to templateXValue[6] with additional projections up to templateXValue[max] . The base period was 1982 - 84 . In templateXValue[5] , the annual templateYLabel[0] templateYLabel[1] templateYLabel[2] is templateTitle[0] to be at about templateYValue[5] .

generated_template: This statistic shows templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , revenues from templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to templateYValue[5] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows Projected Consumer price in United States from 2010 to 2024 .  In 2019 , revenues from Projected Consumer in United States amounted to 256.12 billion (100=1982-1984) .

Example 102:
titleEntities: {'Subject': ['European'], 'Date': ['2017']}
title: Share of individuals who wear spectacles in selected European countries 2017
X_Axis['Country']: ['Slovakia', 'Serbia', 'United_Kingdom', 'Denmark', 'Finland', 'Norway', 'Germany', 'France', 'Netherlands', 'Austria', 'Portugal', 'Switzerland', 'Italy', 'Latvia', 'Bulgaria', 'Spain', 'Czech_Republic', 'Romania', 'Greece', 'Ireland', 'Hungary', 'Belgium', 'Estonia', 'Ukraine', 'Slovenia', 'Poland', 'Cyprus', 'Turkey', 'Croatia']
Y_Axis['Individuals', 'who', 'wear', 'spectacles']: ['85', '70', '68', '65', '65', '63', '63', '58', '57', '52', '52', '52', '51', '50', '50', '49', '45', '45', '45', '44', '40', '40', '38', '35', '30', '30', '25', '20', '15']

gold: This statistic displays the proportion of individuals who wear eye spectacles in selected European countries in 2017 . In this year , Slovakia had the highest proportion of individuals wearing eyeglasses with approximately 85 percent doing so . This was followed by Serbia at 70 percent and the United Kingdom at 68 percent .
gold_template: This statistic displays the proportion of templateYLabel[0] templateYLabel[1] templateYLabel[2] eye templateYLabel[3] in templateTitle[5] templateTitleSubject[0] templateTitle[7] in templateTitleDate[0] . In this year , templateXValue[0] had the highest proportion of templateYLabel[0] wearing eyeglasses with approximately templateYValue[max] templateScale doing so . This was followed by templateXValue[1] at templateYValue[1] templateScale and the templateXValue[2] at templateYValue[2] templateScale .

generated_template: This statistic displays the proportion of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] only ( excludes templateYLabel[1] templateYLabel[2] templateYLabel[3] both templateYLabel[4] templateYLabel[5] and eyeglasses ) in templateTitle[6] templateTitleSubject[0] templateTitle[8] in templateTitleDate[0] . In this year , templateXValue[0] , templateXValue[1] and templateXValue[0] had the highest proportion of templateYLabel[1] wearing templateYLabel[4] templateYLabel[5] with approximately templateYValue[max] templateScale doing so . This was followed by templateXValue[3] and templateXValue[4] with templateYValue[3] templateScale of the respective populations wearing templateYLabel[4] templateYLabel[5] .
generated: This statistic displays the proportion of who wear spectacles only ( excludes who wear spectacles both and eyeglasses ) in European 2017 in .  In this year , Slovakia Serbia and Slovakia had the highest proportion of who wearing spectacles with approximately 85 % doing so .  This was followed by Denmark and Finland with 65 % of the respective populations wearing spectacles .

Example 103:
titleEntities: {'Subject': ['Major League Baseball'], 'Date': ['2018']}
title: Annual amount of luxury tax paid in Major League Baseball 2003 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Luxury', 'tax', 'paid', 'in', 'million', 'U.S.', 'dollars']: ['14.34', '61.15', '74.0', '72.8', '44.92', '39.4', '19.3', '17.3', '19.5', '25.7', '28.2', '29.9', '26.5', '38.1', '30.0', '11.8']

gold: The statistic depicts the luxury tax  paid by Major League Baseball teams from 2003 to 2018 . The luxury tax is officially known as Competitive Balance Tax ( CBT ) . In 2018 , a total of 14.34 million U.S. dollars were paid as luxury taxes in Major League Baseball .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] by templateTitleSubject[0] Baseball teams from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is officially known as Competitive Balance templateYLabel[1] ( CBT ) . In templateXValue[max] , a total of templateYValue[idxmax(X)] templateScale templateYLabel[4] templateYLabel[5] were templateYLabel[2] as templateYLabel[0] taxes in templateTitleSubject[0] Baseball .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] of the templateTitleSubject[0] Yankees from templateXValue[min] to templateXValue[max] . For the templateXValue[1] season the templateTitleSubject[0] templateYLabel[2] templateYValue[1] templateScale templateYLabel[4] templateYLabel[5] in templateYLabel[0] templateYLabel[1] .
generated: The statistic shows the Luxury tax of the Major League Baseball Yankees from 2003 to 2018 .  For the 2017 season the Major League Baseball paid 61.15 million U.S. dollars in Luxury tax .

Example 104:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Dating website or app usage among U.S. online users 2019 , by age group
Data:
                                  0                             1         2           3
unknown  Yes_I'm_doing_so_currently  Yes_I've_done_so_in_the_past  No_never  Don't_know
18-34                            14                            29        51           6
35-54                             9                            31        58           3
55+                               2                            16        81           1 

gold: This statistic presents the percentage of adult internet users in the United States who have used a dating website or app as of January 2019 , by age group . According to the findings , 14 percent of respondents between the ages of 18 to 34 years stated that they were currently using a dating website or app , while in comparison only nine percent of respondents between the ages of 35 to 54 years reported similar responses .
gold_template: This statistic presents the templateScale of adult internet templateTitle[7] in the templateTitle[5] who have used a templateTitle[0] templateTitle[1] or templateTitle[2] as of 2019 , templateTitle[9] templateTitle[10] templateTitle[11] . According to the findings , templateValue[1][0] templateScale of respondents between the ages of 18 to 34 years stated that they were templateValue[0][0] using a templateTitle[0] templateTitle[1] or templateTitle[2] , while in comparison only templateValue[2][0] templateScale of respondents between the ages of 35 to 54 years reported similar responses .

generated_template: This statistic shows the results of a survey in templateTitleDate[0] among templateTitleSubject[0] adults by gender on the most templateTitle[0] issues to them in templateTitle[2] a templateTitle[3] or templateTitle[4] . During the survey , templateValue[1][1] templateScale of templateLabel[1][0] were of the opinion that finding someone with a templateValue[0][1] would be very templateTitle[0] to them in templateTitle[2] a templateTitle[3] or templateTitle[4] while templateValue[2][1] templateScale of templateLabel[2][0] were of the opinion that finding someone with a templateValue[0][1] would be very templateTitle[0] to them .
generated: This statistic shows the results of a survey in 2019 among U.S. adults by gender on the most Dating issues to them in app a usage or among .  During the survey , 29 percent of 18-34 were of the opinion that finding someone with a Yes_I've_done_so_in_the_past would be very Dating to them in app a usage or among while 31 percent of 35-54 were of the opinion that finding someone with a Yes_I've_done_so_in_the_past would be very Dating to them .

Example 105:
titleEntities: {'Subject': ['United States'], 'Date': ['2024']}
title: Total population of the United States 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['338.27', '336.45', '334.64', '332.84', '331.05', '329.27', '327.35', '325.33', '323.23', '320.93', '318.62']

gold: The statistic shows the total population in the United States from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population of the U.S. amounted to approximately 327.35 million inhabitants . The United States ' economy over the last decade The United States of America is the world 's largest national economy and the second most prominent trader globally , trailing just behind China .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in 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 templateTitle[3] amounted to approximately templateYValue[6] templateScale templateYLabel[0] . The templateTitleSubject[0] ' economy over the last decade The templateTitleSubject[0] of America is the world 's largest national economy and the second most prominent trader globally , trailing just behind China .

generated_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: The population in United States grew to 327.35 millions 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 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 .

Example 106:
titleEntities: {'Subject': ['Unilever Group'], 'Date': ['2007', '2018']}
title: Revenue of the Unilever Group worldwide 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'euros']: ['50982', '53715', '52713', '53272', '48436', '49797', '51324', '46467', '44262', '39823', '40523', '40187']

gold: This statistic depicts the revenue of the Unilever Group worldwide from 2007 to 2018 . In 2018 , the global revenue generated by the Unilever Group was approximately 50.98 billion euros.UnileverUnilever operates in nearly 190 countries around the world and has been a traditional paragon of excellence and quality in the Fast Moving Consumer Goods sector . The company derives its competitive advantage from its global footprint and its track record of enhancing value for the consumers around the world .
gold_template: This statistic depicts the templateYLabel[0] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateYLabel[0] generated by the templateTitleSubject[0] was approximately templateYValue[0] templateScale euros.UnileverUnilever operates in nearly 190 countries around the world and has been a traditional paragon of excellence and quality in the Fast Moving Consumer Goods sector . The company derives its competitive advantage from its global footprint and its track record of enhancing value for the consumers around the world .

generated_template: This timeline depicts the templateTitle[0] templateYLabel[0] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] of the templateTitleSubject[0] worldwide was about templateYValue[max] templateScale templateYLabel[2] . The templateTitleSubject[0] is a French luxury goods corporation , which owns around 50 luxury brands templateTitle[4] , including Louis Vuitton and Bulgari .
generated: This timeline depicts the Revenue of the Unilever Group worldwide from 2007 to 2018 .  In 2018 , the Revenue of the Unilever Group worldwide was about 53715 million euros .  The Unilever Group is a French luxury goods corporation , which owns around 50 luxury brands 2007 , including Louis Vuitton and Bulgari .

Example 107:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2000', '2018']}
title: Average annual wage in Mexico 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', 'wage', 'in', 'U.S.', 'dollars']: ['16297.71', '16110.18', '16135.65', '16231.23', '16028.6', '16050.28', '16050.3', '16328.24', '16183.11', '17268.96', '17150.17', '17283.74', '17217.98', '17244.57', '17139.21', '17039.28', '16747.59', '16811.85', '15904.75']

gold: In 2018 , the average wage in Mexico achieved its highest level within the period presented , amounting to 16,298 U.S. dollars per year . In contrast , this value was equal to15,905 U.S. dollars in 2000 , making it the year with the lowest average wage of the period .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] achieved its highest level within the period presented , amounting to templateYValue[0] templateYLabel[2] templateYLabel[3] per templateXLabel[0] . In contrast , this value was equal to15,905 templateYLabel[2] templateYLabel[3] in templateXValue[min] , making it the templateXLabel[0] with the lowest templateYLabel[0] templateYLabel[1] of the period .

generated_template: This statistic 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: This statistic shows the Average of U.S. in the annual dollars permanent via dollars from 2000 to 2018 .  In the most recently reported period , close to 16297.71 % wage U.S. had fixed dollars , up from close to 17268.96 % in 2009 .  The annual are one of the biggest online markets worldwide .

Example 108:
titleEntities: {'Subject': ['Fastest'], 'Date': ['2018']}
title: Fastest growing mobile app categories 2018
X_Axis['Month']: ['Games_-_Mid-core', 'Ride-Hail_&_Taxi', 'Games_-_Sports', 'Video_&_Streaming', 'Dating', 'Games_-_Casual', 'Shopping', 'Games_-_Casino', 'Communication', 'Food_Delivery', 'Travel_Booking', 'Marketplace_&_Classifieds', 'Weather', 'Health', 'Comics', 'Music', 'Banking', 'Social_Network', 'News']
Y_Axis['Growth', 'Index', 'score']: ['18.53', '17.63', '15.07', '12.34', '11.28', '10.5', '10.1', '7.72', '7.64', '6.13', '4.76', '4.52', '4.11', '3.99', '3.82', '3.81', '3.8', '2.29', '2.11']

gold: This statistic presents a ranking of the mobile app verticals with the highest install and user base growth in 2018 . In 2018 , mid-core games had the highest Growth Index score with rating of 18.53 . This , according to the source , is due to the growth of battle royale and MOBA games such as Fortnite .
gold_template: This statistic presents a ranking of the templateTitle[2] templateTitle[3] verticals with the highest install and user base templateYLabel[0] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] with rating of templateYValue[max] . This , according to the source , is due to the templateYLabel[0] of battle royale and MOBA templateXValue[0] such as Fortnite .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] to templateTitle[2] templateTitle[3] templateTitle[4] worldwide in 2014 . templateXValue[0] had the most templateYLabel[2] in 2014 , with an estimated templateYLabel[0] of templateYLabel[2] of templateYValue[max] templateScale .
generated: This statistic shows the Growth of score to mobile app categories worldwide in 2014 .  Games_-_Mid-core had the most score in 2014 , with an estimated Growth of score 18.53 % .

Example 109:
titleEntities: {'Subject': ['Africa'], 'Date': ['2014']}
title: Pharmaceutical imports and exports in Africa by major country 2014
Data:
                    0        1        2       3        4        5       6        7              8         9        10       11      12      13      14
Country  South_Africa    Egypt  Morocco   Kenya  Tunisia  Senegal  Uganda  Nigeria  Cote_d'Ivoire  Tanzania  Ethiopia  Algeria   Ghana   Sudan  Malawi
Exports        380.04    300.8   123.33   86.95    42.65    13.76     9.7        8           5.25      2.75      2.02     1.79    1.36    0.23       0
Imports       1901.71  1838.35   585.33  475.54   570.21   193.49  354.36   318.54         375.88    310.87    309.77   2600.0  206.22  378.36  222.81 

gold: Africa is a new and growing market for international pharmaceutical companies . As of 2014 , the top African country for pharmaceutical imports was Algeria with over 2.6 billion U.S. dollars in pharmaceuticals being imported during that year . South Africa , however , was the country with the greatest value of pharmaceutical exports with 380 million U.S. dollars in pharmaceutical exports during that year .
gold_template: templateValue[0][0] is a new and templatePositiveTrend market for international templateTitle[0] companies . As of templateTitleDate[0] , the top African templateLabel[0][0] for templateTitle[0] templateTitle[1] was templateValue[0][11] with over 2.6 templateScale U.S. dollars in pharmaceuticals being imported during that year . templateValue[0][0] , however , was the templateLabel[0][0] with the greatest value of templateTitle[0] templateTitle[2] with templateValue[1][0] templateScale U.S. dollars in templateTitle[0] templateTitle[2] during that year .

generated_template: This statistic shows templateTitle[0] templateTitle[1] and templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] templateLabel[0][0] . During that year , templateValue[0][5] imported templateTitle[0] goods worth templateValue[2][5] templateScale U.S. dollars . On the other hand , templateTitle[0] templateTitle[2] from the same templateLabel[0][0] were worth templateValue[1][5] templateScale U.S. dollars .
generated: This statistic shows Pharmaceutical imports and exports in Africa 2014 , major country .  During that year , Senegal imported Pharmaceutical goods worth 193.49 million U.S. dollars .  On the other hand , Pharmaceutical exports from the same Country were worth 13.76 billion U.S. dollars .

Example 110:
titleEntities: {'Subject': ['MMR'], 'Date': ['19', '35']}
title: MMR vaccination rate among U.S. children aged 19 - 35 months 1994 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']
Y_Axis['Immunization', 'rate']: ['91.5', '91.1', '91.9', '91.5', '91.9', '90.8', '91.6', '91.5', '90', '92.1', '92.3', '92.3', '91.5', '93', '93', '91.6', '91.4', '91', '92', '86', '88.5', '91', '90', '89']

gold: As of 2017 , around 91.5 percent of children in the U.S. aged 19 to 35 months had been vaccinated against measles , mumps and rubella ( MMR ) . The MMR vaccine is widely used around the world and is recommended by the Centers for Disease Control and Prevention ( CDC ) for adults who do n't have immunity and all children . Measles The number of cases of measles in the United States has dropped significantly since the introduction of the MMR vaccination .
gold_template: As of templateXValue[max] , around templateYValue[idxmax(X)] templateScale of templateTitle[5] in the templateTitle[4] templateTitle[6] templateTitle[7] to templateTitle[8] templateTitle[9] had been vaccinated against measles , mumps and rubella ( templateTitleSubject[0] ) . The templateTitleSubject[0] vaccine is widely used around the world and is recommended by the Centers for Disease Control and Prevention ( CDC ) for adults who do n't have immunity and all templateTitle[5] . Measles The number of cases of measles in the templateTitle[4] has templateNegativeTrend significantly since the introduction of the templateTitleSubject[0] templateTitle[1] .

generated_template: This statistic shows the templateTitleSubject[0] templateYLabel[0] quantity of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the total templateYLabel[0] of templateTitle[2] templateTitleSubject[0] was around templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the MMR Immunization quantity of rate from 1994 to 2017 .  In 2017 , the total Immunization of rate MMR was around 91.5 rate .

Example 111:
titleEntities: {'Subject': ['European Union', 'EU'], 'Date': ['2018']}
title: Populist vote share in European Union ( EU ) countries in 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              24          25       26
Country  Hungary  Poland  Cyprus  Lithuania  Austria  Estonia  Bulgaria  Netherlands  Greece  Belgium  Denmark  France  Finland  Czechia  Slovakia  Germany  Sweden  Portugal  Italy  Ireland  Latvia  Slovenia  Spain  Romania  United_Kingdom  Luxembourg  Croatia
2018       65.09   51.18   35.38      32.83       26     32.9     32.65         22.2    54.6     4.09     21.1   27.09    24.78     49.6     34.34    21.98   18.58      9.38  50.03     17.7    6.85       8.2  21.15     9.98             1.8        4.94     6.49
2008        43.7    32.1    31.1      29.04    28.24     26.1        26        22.49      17    16.02     13.9   13.82     12.9    12.81      11.7     10.3    8.78      8.34    8.3     8.03       7       5.4   3.77     3.15             2.2         1.9      1.8 

gold: This statistic displays the national vote share of populist parties in selected European Union ( EU ) countries as of March 2018 . Hungary has the highest share of populist votes , with 65.09 percent of it 's population voting for populist parties . In Greece , Poland and Italy the populist vote share is over 50 percent , while in the United Kingdom , populist parties only accounted for 1.8 percent of votes .
gold_template: This statistic displays the national templateTitle[1] templateTitle[2] of templateTitle[0] parties in selected templateTitleSubject[0] ( templateTitleSubject[0] ) templateTitle[6] as of 2018 . templateValue[0][0] has the highest templateTitle[2] of templateTitle[0] votes , with templateValue[1][max] templateScale of it 's population voting for templateTitle[0] parties . In templateValue[0][8] , templateValue[0][1] and templateValue[0][18] the templateTitle[0] templateTitle[1] templateTitle[2] is over templateValue[1][13] templateScale , while in the templateValue[0][24] , templateTitle[0] parties only accounted for templateValue[1][min] templateScale of votes .

generated_template: templateTitle[0] transplants are required when an individual 's templateTitle[0] has stopped working effectively and brings a risk to the person 's life . A person who has recently died but with a healthy templateTitle[0] may be a suitable donor for a templateTitle[0] templateTitle[1] . In templateTitleSubject[0] in templateTitleDate[max] , templateValue[0][0] had the highest templateTitle[2] of templateTitle[0] transplants with templateValue[1][max] templateScale population .
generated: Populist transplants are required when an individual 's Populist has stopped working effectively and brings a risk to the person 's life .  A person who has recently died but with a healthy Populist may be a suitable donor for a Populist vote .  In European Union in 2018 , Hungary had the highest share of Populist transplants with 65.09 % population .

Example 112:
titleEntities: {'Subject': ['Europe'], 'Date': ['2019']}
title: Trend in new passenger car registrations in Europe 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['Percentage', 'change']: ['21.7', '4.9', '8.7', '14.5', '-8.4', '1.4', '-7.8', '0.1', '-0.4', '-3.9', '-1', '-4.6', '-8.4', '-8', '-7.3', '-23.5', '31.2']

gold: The number of new car registrations in the European Union increased by 21.7 percent from December 2018 to December 2019 . This is primarily due to a low baseline from December 2018 which saw low sales . In August 2018 , European new-car intenders flocked to dealerships ahead of price increases tied to tougher emissions tests , and EU auto sales increased 31 percent over August 2017 .
gold_template: The number of templateTitle[1] templateTitle[3] templateTitle[4] in the European Union templatePositiveTrend by templateYValue[0] templateScale from 2018 to 2019 . This is primarily due to a low baseline from 2018 which saw low sales . In 2018 , European new-car intenders flocked to dealerships ahead of price increases tied to tougher emissions tests , and EU auto sales templatePositiveTrend templateYValue[max] templateScale over 2017 .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[1] of templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitleSubject[1] between 2018 and 2019 . In 2019 , some templateYValue[max] templateScale of templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitleSubject[1] templateNegativeTrend .
generated: The statistic shows Europe new change of registrations Europe 2019 in the Europe between 2018 and 2019 .  In 2019 , some 31.2 percentage of registrations Europe 2019 in the Europe falling .

Example 113:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2019']}
title: Unemployment rate in Brazil 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']: ['12.22', '12.54', '12.83', '11.61', '8.44', '6.67', '6.99', '7.19', '6.69', '8.36', '8.28', '7.34', '8.33', '8.64', '9.57', '9.11', '9.99', '9.37', '9.61', '9.89', '10.21']

gold: This statistic shows the unemployment rate in Brazil from 1999 to 2019 . In 2019 , the unemployment rate among Brazil 's population ranged at approximately 12.22 percent . Employment and economic upswing in Brazil Recent growth in the Brazilian economy was the cause of the considerable improvement of the country 's unemployment rate .
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] among templateTitleSubject[0] 's population ranged at approximately templateYValue[idxmax(X)] templateScale . Employment and economic upswing in templateTitleSubject[0] Recent growth in the Brazilian economy was the cause of the considerable improvement of the country 's templateYLabel[0] templateYLabel[1] .

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 Brazil from 1999 to 2019 .  In 2019 , the Unemployment rate in Brazil was at approximately 12.22 % .

Example 114:
titleEntities: {'Subject': ['Thrones'], 'Date': ['2019', '2019']}
title: `` Game of Thrones '' deadliest seasons 2019 , by number of deaths
X_Axis['Season']: ['Season_1', 'Season_2', 'Season_3', 'Season_4', 'Season_5', 'Season_6', 'Season_7', 'Season_8']
Y_Axis['Number', 'of', 'deaths']: ['59', '130', '87', '182', '246', '540', '1096', '3523']

gold: Season 8 of HBO 's ‘ Game of Thrones ' was its deadliest season of all time , with a total of around 3,523 deaths compared to just 59 in the first season . Season 8 , the show 's final season , accounted for significantly more deaths than the first seven seasons combined.The deadliest location in the Game of Thrones series was ‘ Winterfell ' _ , which hosted one of the show 's final battles . Game of Thrones ‘ Game of Thrones ' is arguably one of the most successful television shows of all time , with millions of viewers from around the world tuning in to view each of its eight seasons and dozens of Primetime Emmy Awards recognizing its success .
gold_template: templateXValue[0] templateXValue[last] of HBO 's ‘ templateTitle[1] of templateTitleSubject[0] ' was its templateTitle[4] templateXValue[0] of all time , with a total of around templateYValue[max] templateYLabel[1] compared to just templateYValue[min] in the first templateXValue[0] . templateXValue[0] templateXValue[last] , the show 's final templateXValue[0] , accounted for significantly more templateYLabel[1] than the first templateXValue[6] templateTitle[5] combined.The templateTitle[4] location in the templateTitle[1] of templateTitleSubject[0] series was ‘ Winterfell ' _ , which hosted templateXValue[0] of the show 's final battles . templateTitle[1] of templateTitleSubject[0] ‘ templateTitle[1] of templateTitleSubject[0] ' is arguably templateXValue[0] of the most successful television shows of all time , with templateScale of viewers from around the world tuning in to view each of its templateXValue[last] templateTitle[5] and dozens of Primetime Emmy Awards recognizing its success .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] in templateTitleDate[0] , distinguished templateTitle[3] templateTitle[4] . That year , it was estimated that there were over templateYValue[2] thousand templateYLabel[1] in templateTitleSubject[0] .
generated: This statistic displays the Number of deaths in Thrones 2019 , distinguished '' deadliest .  That year , it was estimated that there were over 87 thousand deaths in Thrones .

Example 115:
titleEntities: {'Subject': ['American', 'Olive Garden'], 'Date': ['2007', '2019']}
title: American customer satisfaction index : Olive Garden restaurants in the U.S. 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['ACSI', 'score']: ['79', '80', '81', '81', '79', '80', '83', '80', '82', '84', '81', '82', '80']

gold: This statistic shows the American customer satisfaction index scores for Olive Garden restaurants in the United States from 2007 to 2019 . In 2019 , the ACSI for Olive Garden restaurants in the U.S. was 79 , down from 80 the previous year .
gold_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] scores for templateTitleSubject[1] restaurants in the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] for templateTitleSubject[1] restaurants in the templateTitle[7] was templateYValue[min] templateYValue[idxmax(X)] down from templateYValue[last] templateYValue[idxmin(X)] previous templateXLabel[0] .

generated_template: This statistic shows the templateTitleSubject[0] Satisfaction templateTitle[3] scores for templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] for templateTitle[4] templateTitle[5] in the templateTitle[6] was templateYValue[idxmax(X)] .
generated: This statistic shows the American Satisfaction index scores for Olive Garden in the restaurants from 2007 to 2019 .  In 2019 , the ACSI score for Olive Garden in the restaurants was 79 .

Example 116:
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: This statistic shows the Ukrainian 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 Ukrainian Inflation rate in Ukraine from 1994 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Ukraine amounted to about 10.95 % change to the compared Year .

Example 117:
titleEntities: {'Subject': ['Engie'], 'Date': ['2018']}
title: Engie - revenue by region 2018
X_Axis['Country']: ['France', 'Other_EU_countries', 'Belgium', 'Asia_Middle_East_and_Oceania', 'South_America', 'North_America', 'Other_European_countries', 'Africa']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['24.98', '15.45', '5.96', '4.94', '4.2', '3.87', '0.82', '0.39']

gold: This statistic represents Engie 's revenue in the fiscal year of 2018 , by region . The French multinational energy company generated a revenue of around six billion euros in its Belgium segment . The company was formed by the merger of Gaz de France and Suez to GDF Suez and officially changed its name to Engie in April 2015 .
gold_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] in the fiscal year of templateTitleDate[0] , templateTitle[2] templateTitle[3] . The French multinational energy company generated a templateYLabel[0] of around templateYValue[2] templateScale templateYLabel[2] in its templateXValue[2] segment . The company was formed templateTitle[2] the merger of Gaz de templateXValue[0] and Suez to GDF Suez and officially changed its name to templateTitleSubject[0] in 2015 .

generated_template: This statistic shows the countries with the largest templateTitle[1] templateYLabel[0] worldwide as estimated in templateTitleDate[0] . In that year , it was estimated that the templateXValue[0] had total templateTitle[1] templateYLabel[0] of approximately templateYValue[max] templateScale templateYLabel[2] . templateTitle[1] templateYLabel[0] templateTitle[3] templateXLabel[0] templateTitle[1] is a soft , silver-white metal within the alkali metal group on the periodic table .
generated: This statistic shows the countries with the largest revenue worldwide as estimated in 2018 .  In that year , it was estimated that the France had total revenue of approximately 24.98 billion euros .  revenue Revenue region Country revenue is a soft , silver-white metal within the alkali metal group on the periodic table .

Example 118:
titleEntities: {'Subject': ['Arizona Diamondbacks'], 'Date': ['2006', '2019']}
title: Arizona Diamondbacks 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']: ['20.86', '19.65', '18.53', '17.98', '17.98', '16.89', '15.74', '15.74', '14.31', '14.31', '15.96', '13.79', '19.68']

gold: This graph depicts the average ticket price for Arizona Diamondbacks games in Major League Baseball from 2006 to 2019 . In 2019 , the average ticket price was at 20.86 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[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] templateTitleSubject[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: This graph depicts the Average ticket price for Arizona Diamondbacks games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 20.86 U.S. dollars .

Example 119:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Revenue of top growing private specialty pharmacies in U.S. 2018
X_Axis['Company', 'name', '(year', 'founded)']: ['Pantherx_Rare_(2011)', 'Benzer_Pharmacy_(2009)', 'SMP_Pharmacy_Solutions_(2003)', 'ExactCare_Pharmacy_(2009)', 'SandsRX_(2013)', 'AmeriPharma_(2006)', 'Baybridge_Pharmacy_Corp._(2015)', 'Tamimi_Pharmacy_(2013)']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['726.1', '352.3', '213.9', '203.4', '42.9', '31.7', '17.5', '15.5']

gold: This statistic displays the revenue of the fastest-growing private specialty pharmacies in the United States , sorted by revenue in 2018 . During this year , Benzer Pharmacy , founded in 2009 , generated over 352 million U.S. dollars in revenue .
gold_template: This statistic displays the templateYLabel[0] of the fastest-growing templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] , sorted by templateYLabel[0] in templateTitleDate[0] . During this year , templateXValue[1] , founded in 2009 , generated over templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] .

generated_template: This statistic provides information on the templateTitle[4] templateYLabel[0] of the templateTitle[0] templateTitleSubject[0] . As of the second quarter of that templateXLabel[0] , the templateXValue[0] accounted for templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the previous year .
generated: This statistic provides information on the specialty Revenue of the Revenue U.S. As of the second quarter of that Company , the Pantherx_Rare_(2011) accounted for 726.1 million U.S. dollars , up from 352.3 million U.S. dollars in the previous year .

Example 120:
titleEntities: {'Subject': ['Haiti'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Haiti 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']: ['10.21', '9.82', '9.45', '9.07', '8.71', '8.82', '9.66', '8.41', '7.98', '8.72', '8.78', '8.45', '7.89', '7.52', '6.62', '6.58', '6.55', '5.89', '4.76', '4.31', '3.54', '2.96', '3.47', '3.6', '3.95', '4.15', '3.72', '3.34', '2.91', '2.81', '1.78', '0.61', '0.53', '0.89', '0.99', '0.78', '0.84', '1.28', '2.6', '2.34', '2.11']

gold: The statistic shows gross domestic product ( GDP ) in Haiti 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[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: The statistic shows Gross domestic product ( GDP ) in Haiti 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 .

Example 121:
titleEntities: {'Subject': ['U.S. Black Friday'], 'Date': ['2019']}
title: Share of U.S. consumers shopping on Black Friday 2015 to 2019
X_Axis['Year']: ['2015', '2016', '2017', '2018', '2019']
Y_Axis['Share', 'of', 'respondents']: ['59', '51', '35', '37', '36']

gold: Some 36 percent of survey respondents stated that they planned to do most of their holiday shopping on Black Friday in the United States in 2019 . This is a decrease of 23 percent since 2015 , when some 59 percent of respondents were planning to do the majority of their shopping on Black Friday .
gold_template: Some templateYValue[last] templateScale of survey templateYLabel[1] stated that they planned to do most of their holiday templateTitle[3] on templateTitleSubject[0] in the templateTitle[1] in templateXValue[max] . This is a templateNegativeTrend of 23 templateScale since templateXValue[min] , when some templateYValue[idxmin(X)] templateScale of templateYLabel[1] were planning to do the majority of their templateTitle[3] on templateTitleSubject[0] .

generated_template: This statistic presents the development of templateYLabel[0] of current account holders , who use templateTitle[1] templateTitle[2] to communicate with their bank at least once a month in the templateTitleSubject[1] ( templateTitleSubject[2] ) from templateXValue[min] to templateXValue[max] . By templateXValue[max] , it can be seen that templateYValue[idxmax(X)] templateScale of templateYLabel[1] stated they used templateTitle[1] templateTitle[2] at least once a month . The increases observed in templateTitle[1] templateTitle[2] usage were steep over time : in templateXValue[min] , only templateYValue[min] templateScale of current account holders on the British market turned monthly to templateTitle[1] templateTitle[2] as a secure way of communicating with their bank .
generated: This statistic presents the development of Share current account holders , who use U.S. consumers to communicate with their bank at least once a month in the U.S. Black Friday ( ) from 2015 to 2019 .  By 2019 , it can be seen that 36 % of respondents stated they used U.S. consumers at least once a month .  The increases observed in U.S. consumers usage were steep over time : in 2015 , only 35 % of current account holders on the British market turned monthly to U.S. consumers as a secure way of communicating with their bank .

Example 122:
titleEntities: {'Subject': ['Chicago'], 'Date': ['2019']}
title: Player expenses ( payroll ) of Chicago Fire 2019
X_Axis['Month']: ['Bastian_Schweinsteiger', 'Nicolas_Gaitan', 'Nemanja_Nikolic', 'Aleksandar_Katai', 'Marcelo_Ferreira', 'Dax_McCarty', 'Francisco_Calvo', 'Przemyslaw_Frankoswki', 'Johan_Kappelhof', 'CJ_Sapong', 'David_Ousted', 'Richard_Sanchez', 'Kenneth_Kronholm', 'Mohammed_Adams', 'Fabian_Herbers', 'Djordje_Mihailovic', 'Grant_Lillard', 'Brandt_Bronico', 'Cristian_Martinez', 'Gabriel_Slonina', 'Diego_Campos', 'Amando_Moreno', 'Jorge_Corrales', 'Raheem_Edwards', 'Stefan_Cleveland', 'Andre_Reynolds', 'Elliot_Collier', 'Jeremiah_Gutjahr']
Y_Axis['Payroll', 'in', 'thousand', 'U.S.', 'dollars']: ['5600.0', '2197.5', '1913.33', '1380.33', '797.5', '700.0', '672.6', '661.69', '577.83', '475.0', '361.14', '152.5', '151.0', '135.0', '135.0', '111.0', '97.5', '90.63', '88.41', '74.8', '72.83', '72.75', '70.88', '70.25', '70.25', '64.25', '57.23', '56.25']

gold: The statistic shows the player expenses ( payroll ) of the Chicago Fire club of Major League Soccer by player in 2019 . Bastian Schweinsteiger received a salary of 5.6 million U.S. dollars in that year .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] templateTitle[4] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] received a salary of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in that year .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] Earthquakes club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] `` Vako '' templateXValue[0] received a salary of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Player expenses ( Payroll ) of the Chicago Earthquakes club of Major League Soccer by Player in 2019 .  Bastian_Schweinsteiger `` Vako '' Bastian_Schweinsteiger received a salary of 5600.0 thousand U.S. dollars .

Example 123:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2009', '2018']}
title: Retail sales value of drinking milk in the United Kingdom ( UK ) 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Retail', 'value', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['6391.8', '6308.0', '6235.0', '6174.2', '6077.0', '6094.3', '5997.5', '5984.0', '5963.5', '5881.8']

gold: This statistic presents data on the retail sales value of drinking milk in the UK from 2009 to 2018 . In 2013 , the retail sales value of milk amounted to approximately six billion U.S. dollars , according to historic retail figures . This is expected to increase further by 2018 .
gold_template: This statistic presents data on the templateYLabel[0] templateYLabel[2] templateYLabel[1] of templateTitle[3] templateTitle[4] in the templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateYLabel[0] templateYLabel[2] templateYLabel[1] of templateTitle[4] amounted to approximately templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] , according to historic templateYLabel[0] figures . This is expected to templatePositiveTrend further by templateXValue[idxmax(Y)] .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateTitle[1] templateYLabel[2] 100,000 templateYLabel[4] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . During the time under consideration , the templateYLabel[0] templateYLabel[1] templateTitle[1] templateYLabel[2] 100,000 amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[4] in templateXValue[max] .
generated: This statistic presents the Retail value sales 100,000 U.S. in United Kingdom between 2009 and 2018 .  During the time under consideration , the Retail value sales 100,000 amounted to approximately 6391.8 million U.S. in 2018 .

Example 124:
titleEntities: {'Subject': ['Coast Guard'], 'Date': ['1995', '2010']}
title: Active duty U.S Coast Guard personnel numbers from 1995 to 2010
X_Axis['Year']: ['2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995']
Y_Axis['Number', 'of', 'Coast', 'Guard', 'personnel']: ['41327', '42426', '41362', '40650', '39980', '39361', '34804', '35582']

gold: This graph shows the total number of active duty U.S. Coast Guard personnel from 1995 to 2010 . In 2010 there were 41,327 active duty U.S. Coast Guard members .
gold_template: This graph shows the total templateYLabel[0] of templateTitle[0] templateTitle[1] U.S. templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[7] templateXValue[min] to templateXValue[max] . In templateXValue[max] there were templateYValue[idxmax(X)] templateTitle[0] templateTitle[1] U.S. templateYLabel[1] templateYLabel[2] members .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateTitle[1] templateTitle[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the total Number of Coast duty U.S in the Coast from 1995 to 2010 .  In 2010 , the Number of Coast ( aged six years and older ) in duty U.S amounted to approximately 41327 million .

Example 125:
titleEntities: {'Subject': ['Leading'], 'Date': ['2018']}
title: Leading export countries worldwide 2018
X_Axis['Country']: ['China', 'United_States_of_America', 'Germany', 'Japan', 'Netherlands', 'Korea_Republic_of', 'France', 'Hong_Kong_China', 'Italy', 'United_Kingdom', 'Belgium', 'Mexico', 'Canada', 'Russian_Federation', 'Singapore', 'Spain', 'Chinese_Taipei', 'India', 'United_Arab_Emirates', 'Switzerland']
Y_Axis['Exports', 'in', 'billion', 'U.S.', 'dollars']: ['2486.7', '1665.99', '1560.65', '738.14', '723.75', '604.86', '581.87', '568.45', '546.64', '486.85', '466.72', '450.69', '450.59', '443.13', '412.95', '345.17', '335.91', '324.78', '316.9', '310.75']

gold: This is a ranking of the top 20 export countries worldwide in 2018 . China ranked first in exports with an export value of about 2.5 trillion US dollars . The leading import country in 2017 was the United States with an import value of about 2.4 trillion U.S. dollars Leading export countries worldwide China leads the world in exports in 2018 .
gold_template: This is a ranking of the top 20 templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] . templateXValue[0] ranked first in templateYLabel[0] with an templateTitle[1] value of about templateYValue[max] templateScale US templateYLabel[3] . The templateTitleSubject[0] import templateXLabel[0] in 2017 was the templateXValue[1] with an import value of about 2.4 templateScale templateYLabel[2] templateYLabel[3] templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateXValue[0] leads the world in templateYLabel[0] in templateTitleDate[0] .

generated_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . In the templateXValue[0] , the templateYLabel[0] templateNegativeTrend by about templateYValue[max] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] , making it the templateXLabel[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . The templateYLabel[0] Today , the global templateYLabel[0] amounts to around 7 templateScale templateYLabel[2] templateYLabel[3] , i.e .
generated: This statistic shows the 20 Leading export the countries Exports billion 2018 in .  In the China , the Exports decreased by about 2486.7 billion U.S. to the dollars , making it the Country export the countries Exports billion 2018 in .  The Exports Today , the global Exports amounts to around 7 billion U.S. dollars , i.e .

Example 126:
titleEntities: {'Subject': ['Poland'], 'Date': ['2020']}
title: Opinion on preparations for coronavirus epidemic in Poland 2020
X_Axis['Response']: ['Yes', 'No', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['61.4', '24.7', '13.9']

gold: No case of coronavirus ( COVID-19 ) was confirmed in Poland as of February 27 , 2020 . Nevertheless , more than 60 percent of the society believes that Poland is not prepared for a possible virus epidemic .
gold_template: templateXValue[1] case of templateTitle[3] ( COVID-19 ) was confirmed in templateTitleSubject[0] as of 27 , templateTitleDate[0] . Nevertheless , more than 60 templateScale of the society believes that templateTitleSubject[0] is not prepared templateTitle[2] a possible virus templateTitle[4] .

generated_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: This statistic shows the results of a 2020 survey among adult Cubans living in Poland on their opinion of preparations for , their former President .  61.4 % of respondents stated they have a very or somewhat No opinion of preparations for .

Example 127:
titleEntities: {'Subject': ['Sales'], 'Date': ['2013']}
title: Sales of the leading toy companies worldwide 2013
X_Axis['Company']: ['Mattel', 'Lego', 'Hasbro', 'MGA_Entertainment', 'Playmobil', 'Jakks_Pacific', 'LeapFrog', 'MEGA_Bloks', 'Melissa_&_Doug']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['6300', '4500', '4000', '2000', '790', '700', '580', '400', '325']

gold: This statistic shows the sales of the leading toy companies worldwide in 2013 . In that year , Mattel was the largest global toy company with estimated sales that amounted to 6.3 billion U.S. dollars . Lego and Hasbro rounded off the leading three toy companies .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , templateXValue[0] was the largest global templateTitle[2] templateXLabel[0] with estimated templateYLabel[0] that amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateXValue[1] and templateXValue[2] rounded off the templateTitle[1] three templateTitle[2] templateTitle[3] .

generated_template: This statistic depicts 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: This statistic depicts the Sales of the Sales leading conditioner/creme rinse companies in the worldwide in 2013 .  In that year , the Sales leading toy Company of the worldwide was OGX with Sales that amounted to approximately 6300 million U.S. dollars .

Example 128:
titleEntities: {'Subject': ['Scania'], 'Date': ['2018']}
title: Scania - key figures 2018
X_Axis['Key', 'area']: ['New_vehicle_deliveries_(units)', 'Net_sales_Scania_Group_(million_Swedish_kronor)', 'Net_income_for_the_period_(million_euros)', 'Operating_income_(million_Swedish_kronor)', 'Operating_margin_()']
Y_Axis['Unnamed:', '1']: ['96477.0', '137126.0', '9734.0', '13832.0', '10.1']

gold: This statistic shows Scania 's key figures in 2018 . In that year , the company reported net sales of around 137 billion Swedish kronas . Scania – key figures Scania Aktiebolag is an automotive industry manufacturer of trucks and buses for heavy transport applications , as well as industrial and marine engines .
gold_template: This statistic shows templateXValue[1] 's templateXLabel[0] templateTitle[2] in templateTitleDate[0] . In that year , the company reported templateXValue[1] of around 137 templateScale templateXValue[1] kronas . templateXValue[1] – templateXLabel[0] templateTitle[2] templateXValue[1] Aktiebolag is an automotive industry manufacturer of trucks and buses templateXValue[2] heavy transport applications , as well as industrial and marine engines .

generated_template: The statistic depicts the templateTitle[1] of templateTitle[2] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitle[3] on templateTitle[4] in templateTitleDate[0] . templateYValue[last] templateScale of the respondents stated that they templateTitle[3] between $ 1,000 and $ 1,999 on templateTitle[4] in templateTitleDate[0] .
generated: The statistic depicts the key of figures 1 in the Scania 2018 on in 2018 .  10.1 % of the respondents stated that they 2018 between $ 1,000 and $ 1,999 on 2018 in .

Example 129:
titleEntities: {'Subject': ['Serie A Italy'], 'Date': ['2018']}
title: Brand value of Serie A ( Italy ) football teams 2018
Data:
                   0                         1         2        3           4          5         6               7               8
unknown  Juventus_FC  FC_Internazionale_Milano  AC_Milan  AS_Roma  SSC_Napoli  SSC_Lazio  Atalanta  ACF_Fiorentina  Udinese_Calcio
2011             184                       263       273      101          70         55         0              56               0
2012             160                       215       292       85          85         46         0              46              20
2013             180                       151       263       82         101         52         0              52               0
2014             247                       152       236      108         121         62         0               0               0
2015             350                       160       244      117         147         78         0              76               0
2016             287                       151       207      131         118          0         0               0               0
2017             492                       217       286      180         177          0         0               0               0
2018             605                       475       315      264         183        155       136               0               0 

gold: The statistic depicts the brand value of the most valuable Italian football teams from 2011 to 2018 . AC Milan had a brand value of 315 million U.S. dollars in 2018 . 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 Italian templateTitle[4] templateTitle[5] from templateLabel[1][0] to templateTitleDate[0] . templateValue[0][2] had a templateTitle[0] templateTitle[1] of templateValue[8][2] templateScale U.S. dollars in templateTitleDate[0] . A templateTitle[0] is defined here as the trademark and associated intellectual property .

generated_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: This statistic provides information on the Serie A Italy value of Serie Italy in football European teams from 2018 to .  In 2018 , approximately 247 % of Serie sales in the Juventus_FC were generated Serie A Italy .  The Juventus_FC led the unknown comparison in each year .

Example 130:
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: The statistic shows the templateScale of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateTitle[4] templateXLabel[0] in 2010 . templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] were located in templateXValue[2] .
generated: The statistic shows the percentage of Cameroon 1 2018 Share in 2010 .  56.37 % of 1 were located in 2016 .

Example 131:
titleEntities: {'Subject': ['England'], 'Date': ['2006', '2019']}
title: England : women smoking during pregnancy 2006 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']
Y_Axis['Percentage', 'of', 'pregnant', 'women']: ['10.6', '10.8', '10.7', '11', '11.7', '12.2', '12.9', '13.3', '13.7', '14.2', '14.6', '14.7', '15.8']

gold: In the period 2018/19 , 10.6 percent of pregnant women in England smoked at the time of delivery . The share of pregnant women smoking has decreased since 2006 when almost sixteen percent of pregnant women did so . Smoking during pregnancy can lead to many birth complications , so it is advised that the expecting mother quits smoking for the health of the baby .
gold_template: In the period templateXValue[0] , templateYValue[min] templateScale of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] smoked at the time of delivery . The share of templateYLabel[1] templateYLabel[2] templateTitle[2] has templateNegativeTrend since templateTitleDate[min] when almost templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] did so . templateTitle[2] templateTitle[3] templateTitle[4] can lead to many birth complications , so it is advised that the expecting mother quits templateTitle[2] for the health of the baby .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] per templateXLabel[0] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateTitle[5] templateTitle[6] . In templateTitleDate[0] , templateYValue[max] templateYLabel[1] templateYLabel[2] were templateTitle[5] in templateTitleDate[0] .
generated: This statistic shows the Percentage of women smoking pregnant per Year in England 2006 , pregnancy 2006 2019 .  In 2006 , 15.8 pregnant women were 2006 in .

Example 132:
titleEntities: {'Subject': ['Tiffany'], 'Date': ['2015', '2018']}
title: Advertising spend of Tiffany & Co. worldwide from 2015 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015']
Y_Axis['Million', 'U.S.', 'dollars']: ['394.1', '314.9', '299.0', '302.0']

gold: This timeline shows the advertising spend of Tiffany and Co. worldwide from 2015 to 2018 . In 2018 , global advertising spend of Tiffany & Co. amounted to approximately 394.1 million U.S. dollars .
gold_template: This timeline shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] and templateTitle[4] templateTitle[5] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , global templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the templateYLabel[1] of the Disaster Recovery as a Service ( templateTitleSubject[0] ) templateYLabel[0] templateTitle[2] , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateTitleSubject[0] templateYLabel[0] was predicted to reach templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] in templateYLabel[1] . Additional information - Disaster Recovery as a Service ( templateTitleSubject[0] ) Within the field of information technology , disaster recovery is the process of replicating data on servers , either physical or virtual , as a precaution against man-made or natural disasters .
generated: The statistic shows the U.S. of the Disaster Recovery as a Service ( Tiffany ) Million Tiffany , from 2015 to 2018 .  In 2018 , the global Tiffany Million was predicted to reach 394.1 million dollars in U.S. Additional information - Disaster Recovery as a Service ( Tiffany ) Within the field of information technology , disaster recovery is the process of replicating data on servers , either physical or virtual , as a precaution against man-made or natural disasters .

Example 133:
titleEntities: {'Subject': ['India'], 'Date': ['2010', '2019']}
title: Export volume of automobiles India 2010 - 2019 by type
Data:
                           0        1        2        3        4        5        6        7        8
unknown              FY_2019  FY_2018  FY_2017  FY_2016  FY_2015  FY_2014  FY_2013  FY_2012  FY_2011
Two wheelers          3280.8   2815.0  2339.27  2481.19  2457.47   2084.0  1956.38  1975.11  1531.62
Passenger vehicles     676.2    748.4    758.7   653.89   621.34   596.14   559.41   508.78   444.33
Three wheelers         567.7    381.0   271.89   404.44    407.6   353.39   303.09   361.75   269.97
Commercial vehicles     99.9     96.9   108.27   101.69    86.94    77.05    80.03    92.26    74.04 

gold: At the end of fiscal year 2019 , the export volume of two-wheelers from India stood at around 3.2 million units . The export sector had witnessed a double digit growth in the recent years . Bajaj Auto was the leading exporter of two-wheelers from the south Asian country , with almost half of the export share , followed by TVS Motors with a share of about 20 percent in the exports .
gold_template: At the end of fiscal year templateValue[0][0] , the templateTitle[0] templateTitle[1] of two-wheelers from templateTitleSubject[0] stood at around 3.2 templateScale units . The templateTitle[0] sector had witnessed a double digit growth in the recent years . Bajaj Auto was the leading exporter of two-wheelers from the south Asian country , with almost half of the templateTitle[0] share , followed templateTitle[6] TVS Motors with a share of about 20 templateScale in the exports .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among American templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] in templateTitleDate[0] , by templateValue[0][1] and templateValue[0][3] . In templateTitleDate[0] , templateValue[1][0] templateScale of the respondents reported that they would be templateValue[0][0] templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] .
generated: This statistic shows the results of a 2010 survey among American automobiles India 2010 in the India in 2010 , by FY_2018 and FY_2016 .  In 2010 , 3280.8 % of the respondents reported that they would be FY_2019 India automobiles 2010 .

Example 134:
titleEntities: {'Subject': ['Germany'], 'Date': ['2000', '2018']}
title: Average annual wages in Germany 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']: ['40522', '39991', '39041', '38471', '37652', '37084', '36642', '36228', '35621', '35462', '35322', '35100', '35160', '35132', '35193', '35195', '35241', '35058', '34711']

gold: As of 2018 , the average annual wage of Germany was 40.5 thousand Euros per year , a growth of just over 5.8 thousand Euros when compared with 2000 . From 2000 until 2007 , wages rose by less than a thousand euros , with wage growth accelerating mainly in the period after 2010 . The 607 Euro increase recorded between 2010 and 2011 was the largest wage rise seen during this period .
gold_template: As of templateXValue[max] , the templateYLabel[0] templateYLabel[1] wage of templateTitleSubject[0] was templateYValue[max] thousand templateYLabel[3] per templateXLabel[0] , a growth of just over 5.8 thousand templateYLabel[3] when compared with templateXValue[min] . From templateXValue[min] until templateXValue[11] , templateYLabel[2] templatePositiveTrend by less than a thousand templateYLabel[3] , with wage growth accelerating mainly in the period after templateXValue[8] . The 607 Euro templatePositiveTrend recorded between templateXValue[8] and templateXValue[7] was the largest wage rise seen during this period .

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 Germany 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 135:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Births delivered by forceps or vacuum extraction in the U.S. 1990 to 2018
Data:
                      0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15
Year               2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2005  2000  1995  1990
Forceps             0.5   0.5   0.5  0.56  0.57  0.59  0.61  0.65  0.66  0.67  0.71  0.76  0.93  2.07  3.48  5.11
Vacuum extraction   2.5   2.6   2.6  2.58  2.64  2.72  2.79  2.85  2.96  3.04  3.22  3.47  3.87  4.85   5.9   3.9 

gold: This statistic depicts the percentage of births that were delivered using forceps or vacuum extraction in the United States from 1990 to 2018 . In 1990 , over five percent of all births in the United States were delivered by forceps . As of 2018 the percentage of births delivered by forceps decreased to 0.5 percent .
gold_template: This statistic depicts the templateScale of templateTitle[0] that were templateTitle[1] using templateTitle[3] or templateTitle[4] templateTitle[5] in the templateTitle[6] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][last] , over templateValue[1][last] templateScale of all templateTitle[0] in the templateTitle[6] were templateTitle[1] templateTitle[2] templateTitle[3] . As of templateValue[0][0] the templateScale of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateNegativeTrend to templateValue[1][0] templateScale .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] 6 to 17 templateLabel[0][0] olds in the templateTitle[6] templateTitle[7] templateValue[0][last] to templateValue[0][0] , by participation rate . In templateValue[0][0] , templateValue[1][0] templateScale of templateTitle[5] templateTitle[6] participated in templateLabel[1][3] , templateLabel[1][1] and templateLabel[1][2] templateLabel[1][3] in the templateTitle[7] .
generated: This statistic shows the Births delivered by forceps vacuum 6 to 17 Year olds in the U.S. 1990 to 2018 , by participation rate .  In 2018 , 0.5 % of extraction U.S. participated in Forceps , and Forceps in the 1990 .

Example 136:
titleEntities: {'Subject': ['San Francisco'], 'Date': ['2016', '2017']}
title: Quarterly average daily rate of hotels in San Francisco 2016 to 2017
Data:
           0    1    2    3
Quarter   Q4   Q3   Q2   Q1
2016     320  324  322  324
2017       0    0    0  338 

gold: This statistic shows the quarterly average daily rate of hotels in San Francisco in 2016 and 2017 . In the first quarter of 2017 , the average daily rate of hotels in San Francisco in the United States was 338 U.S. dollars . Accommodation in San Francisco - additional information San Francisco is located in northern California on the west coast of the United States and is the second most densely populated city in the country after New York City .
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 templateTitleSubject[0] is located in northern California on the west coast of the country and is the second most densely populated city in the country after New York City .

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 San Francisco 2016 and 2017 .  In the first Quarter of 2017 , the average daily rate of hotels in San Francisco the country was 338 U.S. dollars .

Example 137:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010']}
title: Active duty U.S. military personnel numbers , by service branch 2010
X_Axis['Month']: ['Army', 'Air_Force', 'Navy', 'Marine_Corps', 'Coast_Guard']
Y_Axis['Share', 'of', 'military', 'personnel']: ['561979', '329640', '323139', '202612', '41327']

gold: This graph shows the total number of active duty U.S. military personnel by service branch as of 2010 . At this time there were 323,139 military personnel in the Navy and 329,640 personnel in the Air Force .
gold_template: This graph shows the total number of templateTitle[0] templateTitle[1] templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateTitle[6] templateTitle[7] templateTitle[8] as of templateTitleDate[0] . At this time there were templateYValue[2] templateYLabel[1] templateYLabel[2] in the templateXValue[2] and templateYValue[1] templateYLabel[2] in the templateXValue[1] .

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] . According to the source , templateYValue[max] templateScale of templateXValue[last] who subscribe to online video templateTitle[4] at least once .
generated: This statistic provides information on the Share of Active duty an active U.S. or subscription in the by as of 2017 , sorted branch .  According to the source , 561979 % of Coast_Guard who subscribe to online video personnel at least once .

Example 138:
titleEntities: {'Subject': ['TV'], 'Date': ['2014']}
title: Major sporting events worldwide - projected TV viewership 2014
X_Axis['Event']: ['FIFA_World_Cup_Final', 'UEFA_Champions_League_Final', 'Winter_Olympics_opening_ceremony', 'Super_Bowl', 'Winter_Olympics_closing_ceremony']
Y_Axis['Viewership', 'in', 'millions']: ['350', '170', '140', '125', '80']

gold: The statistic depicts projected figures for total worldwide TV viewership of major sporting events in 2014 . The FIFA World Cup Final in 2014 is projected to draw around 350 million TV viewers worldwide . Viewership for major sporting events – additional information The World Cup , an international soccer competition organized by FIFA , is one of the biggest sports events in the world .
gold_template: The statistic depicts templateTitle[4] figures for total templateTitle[3] templateTitleSubject[0] templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleDate[0] . The templateXValue[0] Cup templateXValue[0] in templateTitleDate[0] is templateTitle[4] to draw around templateYValue[max] templateScale templateTitleSubject[0] viewers templateTitle[3] . templateYLabel[0] for templateTitle[0] templateTitle[1] templateTitle[2] – additional information The templateXValue[0] , an international soccer competition organized by templateXValue[0] , is one of the biggest sports templateTitle[2] in the templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] paid hourly rates at or below the prevailing federal templateTitle[2] templateTitle[3] in the templateTitle[1] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , around templateYValue[0] people without a templateXValue[0] diploma earned the templateTitle[2] templateTitle[3] or below .
generated: This statistic shows the Viewership of millions paid hourly rates at or below the prevailing federal events worldwide in the sporting in 2014 , TV Event .  In 2014 , around 350 people without a FIFA_World_Cup_Final diploma earned the events worldwide or below .

Example 139:
titleEntities: {'Subject': ['Moscow'], 'Date': ['2019']}
title: Prime office rental prices in Moscow Q1 2015-Q2 2019
X_Axis['Quarter']: ["Q1_'15", "Q2_'15", "Q3_'15", "Q4_'15", "Q1_'16", "Q2_'16", "Q3_'16", "Q4_'16", "Q1_'17", "Q2_'17", "Q3_'17", "Q4_'17", "Q2_'18", "Q3_'18", "Q1_'19", "Q2_'19"]
Y_Axis['Cost', 'per', 'square', 'meter', 'in', 'euros']: ['760', '697', '692', '670', '670', '720', '613', '760', '726', '684', '669', '654', '693', '693', '703', '704']

gold: The statistic displays the rental costs per square meter of prime office spaces in Moscow , Russia , from the first quarter 2015 to the second quarter 2019 . It can be seen that the price of prime office properties in Moscow fluctuated , reaching the lowest price in the third quarter of 2016 at 613 euros per square meter per year . As of the second quarter of 2019 , rental costs per square meter of prime office spaces in Moscow amounted to 703 .
gold_template: The statistic displays the templateTitle[2] costs templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] spaces in templateTitleSubject[0] , Russia , from the first templateXLabel[0] 2015 to the second templateXLabel[0] templateTitleDate[0] . It can be seen that the price of templateTitle[0] templateTitle[1] properties in templateTitleSubject[0] fluctuated , reaching the lowest price in the third templateXLabel[0] of 2016 at templateYValue[min] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year . As of the second templateXLabel[0] of templateTitleDate[0] , templateTitle[2] costs templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] spaces in templateTitleSubject[0] amounted to templateYValue[14] .

generated_template: The statistic displays the templateTitle[2] costs templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] spaces in templateTitleSubject[0] ( CBD ) , France , from the first templateXLabel[0] templateXValue[0] to the first templateXLabel[0] templateXValue[last] . It can be seen that the price of Parisian templateTitle[0] templateTitle[1] properties templatePositiveTrend over time , reaching templateYValue[max] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year as of the first templateXLabel[0] of templateXValue[last] .
generated: The statistic displays the rental costs per square meter of Prime office spaces in Moscow ( CBD ) , France from the first Quarter Q1_'15 to the first Quarter Q2_'19 .  It can be seen that the price of Parisian Prime office properties increased over time , reaching 760 euros per square meter per year as of the first Quarter of Q2_'19 .

Example 140:
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
unknown  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: templateValue[0][0] cars were the most expensive automobiles sold in the templateTitleSubject[0] in templateTitleDate[max] . With an templateTitle[3] price tag of templateValue[2][max] euros , the templateTitle[1] maker ranked ahead of fellow German manufacturer templateValue[0][1] . The only templateTitle[1] templateTitle[8] which had seen its templateTitle[4] templateNegativeTrend since templateTitleDate[min] was Citroen .
generated: Tesla_Model_3 cars were the most expensive automobiles sold in the United States in 2019 .  With an sales price tag of 140317 euros , the luxury maker ranked ahead of fellow German manufacturer Lexus_ES .  The only luxury 2019 which had seen its by falling since 2019 was Citroen .

Example 141:
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 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)] templateTitleSubject[0] average templateTitle[3] templateYLabel[1] - additional information The templateTitleSubject[0] ' templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] has remained relatively constant in recent years , with the templateYLabel[0] in the templateXValue[max] templateTitle[2] standing at templateYValue[idxmax(X)] .
generated: This graph depicts 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 National Football League average Football attendance - additional information The National Football League ' Average attendance National Football attendance has remained relatively constant in recent years , with the Average in the 2019 National standing at 66151 .

Example 142:
titleEntities: {'Subject': ['American'], 'Date': ['2013']}
title: Frequency of American families having dinner together at home 2013
X_Axis['Response']: ['0_to_3_nights', '4_to_5_nights', '6_to_7_nights']
Y_Axis['Share', 'of', 'respondents']: ['21', '28', '53']

gold: This statistic shows the results of a survey , conducted in 2013 , among adult Americans on the frequency of having dinner at home as a family . In December 2013 , 53 percent of the respondents answered that their family eat dinner together at home on 6 to 7 nights a week .
gold_template: This statistic shows the results of a survey , conducted in templateTitleDate[0] , among adult Americans on the templateTitle[0] of templateTitle[3] templateTitle[4] at templateTitle[6] as a family . In 2013 , templateYValue[max] templateScale of the templateYLabel[1] answered that their family eat templateTitle[4] templateTitle[5] at templateTitle[6] on templateXValue[last] to templateXValue[last] templateXValue[0] a week .

generated_template: The statistic shows the answers to the following survey question : `` The templateTitle[2] templateXValue[0] templateTitle[5] will probably cost a thousand euros . templateXValue[last] you willing to pay that ? '' As of templateTitleDate[0] , roughly 20 templateScale of the templateYLabel[1] said to templateXValue[0] the templateXValue[0] from templateTitleSubject[0] when it is released , even if it templateTitle[6] them a thousand euros . However , more than half of the templateYLabel[1] said the price is templateXValue[1] absurd for an templateTitleSubject[0] templateXValue[0] .
generated: The statistic shows the answers to the following survey question : `` The families 0_to_3_nights together will probably cost a thousand euros .  6_to_7_nights you willing to pay that ? '' As of 2013 , roughly 20 % of the respondents said to 0_to_3_nights the from American when it is released , even if it home them a thousand euros .  However , more than half of the respondents said the price is 4_to_5_nights absurd for an American 0_to_3_nights .

Example 143:
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: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] peaked in templateXValue[max] , with over 110,000 templateYLabel[1] .
generated: This statistic shows the Number of hospitals in Finland from 2007 to 2017 .  The Number of hospitals peaked in 2017 , with over 110,000 hospitals .

Example 144:
titleEntities: {'Subject': ['Wimbledon'], 'Date': ['2018']}
title: Wimbledon : broadcast viewing figures 2018 , by channel and event
X_Axis['Broadcast', 'Category']: ['ESPN_highest_peak_(singles_finals_ladies)', 'ESPN_cumulative', 'BBC_singles_finals_gentlemen', 'BBC_singles_finals_ladies', 'BBC_highest_peak_(3rd_round_Djokovic_vs_Edmund)', 'BBC_cumulative_reach']
Y_Axis['Number', 'of', 'viewers', 'in', 'million']: ['2.91', '29.42', '4.5', '4.6', '6.54', '26.0']

gold: The Wimbleon Championships is not just one of the biggest , but also the oldest tennis tournament worldwide . In 2018 , it acquired a total cumulative television reach of approximately 26 million on BBC and 29.42 million on ESPN . As for single events , the peaks were reached during the third round of Djokovic versus Edmund for BBC with 6.54 million viewers and during the singles finals ladies for ESPN with 2.91 million .
gold_template: The Wimbleon Championships is not just one of the biggest , but also the oldest tennis tournament worldwide . In templateTitleDate[0] , it acquired a total templateXValue[1] television templateXValue[last] of approximately templateYValue[last] templateScale on templateXValue[2] and templateYValue[max] templateScale on templateXValue[0] . As for single events , the peaks were reached during the third templateXValue[4] of templateXValue[4] versus Edmund for templateXValue[2] with templateYValue[4] templateScale templateYLabel[1] and during the templateXValue[2] templateXValue[0] templateXValue[3] for templateXValue[0] with templateYValue[min] templateScale .

generated_template: This statistic shows the templateScale of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitleDate[0] , by the templateXLabel[0] of templateXLabel[1] . templateYValue[max] templateScale of templateYLabel[1] with templateXValue[last] and templateYValue[2] templateScale templateXLabel[1] played templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] .
generated: This statistic shows the million of Wimbledon viewers figures 2018 by channel event Category in 2018 , by the Broadcast of Category .  29.42 million of viewers with BBC_cumulative_reach and 4.5 million Category played viewing figures 2018 in .

Example 145:
titleEntities: {'Subject': ['GDP'], 'Date': ['2050']}
title: U.S. health expenditure as percentage of GDP 2050 forecast
X_Axis['Year']: ['2007', '2009', '2050']
Y_Axis['Expenditure', 'as', 'percentage', 'of', 'GDP']: ['16', '18', '37']

gold: The statistic depicts U.S. health expenditure as a percentage of the GDP from 2007 to 2009 , and a forecast for 2050 . In 2009 , U.S. health expenditure accounted for 18 percent of the GDP .
gold_template: The statistic depicts templateTitle[0] templateTitle[1] templateYLabel[0] as a templateScale of the templateYLabel[2] from templateXValue[min] to templateXValue[1] , and a templateTitle[6] for templateXValue[max] . In templateXValue[1] , templateTitle[0] templateTitle[1] templateYLabel[0] accounted for templateYValue[1] templateScale of the templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] in the templateTitleSubject[0] who were using templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the source , approximately 240 thousand templateYLabel[1] are templateTitle[0] to have templateTitle[4] by templateXValue[max] in the templateTitleSubject[0] .
generated: This statistic shows the Expenditure of GDP in the GDP who were using U.S. from 2007 to 2050 .  According to the source , approximately 240 thousand percentage are U.S. to have GDP by 2050 in the GDP .

Example 146:
titleEntities: {'Subject': ['Oakland Raiders'], 'Date': ['2019']}
title: Average ticket price of the Oakland Raiders 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']: ['87.78', '81.75', '71.03', '64.8', '64.8', '64.8', '62.23', '62.23', '62.23', '62.23', '62.23', '62.23', '62.23']

gold: This graph depicts the average ticket price for Oakland Raiders games in the National Football League from 2006 to 2019 . In 2019 , the average ticket price was at 87.78 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] 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 Oakland Raiders games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 87.78 U.S. dollars .

Example 147:
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 templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[1] templateYLabel[2] 100,000 templateTitle[6] , as of templateTitleSubject[0] 4 , templateTitleDate[0] . The templateXValue[0] had the highest prisoner rate , templateTitle[1] templateYValue[max] templateYLabel[1] templateYLabel[2] 100,000 of the national templateYLabel[4] . templateYLabel[1] in the templateXValue[0] As the statistic above illustrates , the templateXValue[0] has one of the highest rates of incarceration in the world .
generated: This statistic shows the Countries lowest the infant deaths first 100,000 2017 , as of Countries 4 , 2017 .  The Monaco had the highest prisoner rate , lowest 3.4 deaths first 100,000 of the national life .  deaths in the Monaco As the statistic above illustrates , the Monaco has one of the highest rates of incarceration in the world .

Example 148:
titleEntities: {'Subject': ['Instagram Stories'], 'Date': ['16', '19']}
title: Daily active users of Instagram Stories 2019
X_Axis['Month']: ["Jan_'19", "Jun_'18", "Oct_'17", "Jun_'17", "Apr_'17", "Jan_'17", "Oct_'16"]
Y_Axis['Number', 'of', 'DAU', 'in', 'millions']: ['500', '400', '300', '250', '200', '150', '100']

gold: In January 2019 , photo sharing platform Instagram reported 500 million daily active Stories users worldwide , up from 400 million global DAU in June 2018 . Stories is a feature of the app allowing users post photo and video sequences that disappear 24 hours after being posted . Instagram usageInstagram has over one billion monthly active users and is one of the most popular social networks worldwide .
gold_template: In 2019 , photo sharing platform templateTitleSubject[0] reported templateYValue[max] templateScale templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[2] worldwide , up from templateYValue[1] templateScale global templateYLabel[1] in 2018 . templateTitleSubject[0] is a feature of the app allowing templateTitle[2] post photo and video sequences that disappear 24 hours after being posted . templateTitleSubject[0] usageInstagram has over templateYValue[max] templateScale monthly templateTitle[1] templateTitle[2] and is one of the most popular social networks worldwide .

generated_template: As of 2020 , templateYValue[max] templateScale of templateTitle[2] templateTitleSubject[0] templateTitle[3] were aged between 25 and 34 years . In total , over templateYValue[min] thirds of total templateTitleSubject[0] templateTitle[3] were templateYLabel[1] in templateXValue[0] and younger and templateYValue[2] templateScale of the most popular platform . templateTitleSubject[0] belongs to over one of the most popular social networks worldwide .
generated: As of 2020 , 500 millions of users Instagram Stories Instagram were aged between 25 and 34 years .  In total , over 100 thirds of total Instagram Stories Instagram were DAU in Jan_'19 and younger 300 millions of the most popular platform .  Instagram Stories belongs to over one of the most popular social networks worldwide .

Example 149:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global construction machinery market size by region : outlook 2019
Data:
                    0       1       2       3       4       5
Year             2014    2015    2016    2017    2018    2019
Europe         124.78  125.71   130.5  132.67  131.25  133.35
North America  171.31  177.94  186.03  175.69  160.61   167.9
Japan           84.23    80.0   73.83   71.62   71.65   74.31
China          209.76  131.35  137.82  147.43  156.99  169.38
India           36.81   38.55   46.41   54.18   59.28   63.06 

gold: This statistic shows the regional structure of the global construction equipment market in 2014 and gives an outlook for 2019 , sorted by region . About 133,000 units are expected to be sold in European countries in 2019 . That year , the European construction equipment market is expected to account for around 21 percent of the market .
gold_template: This statistic shows the regional structure of the templateTitleSubject[0] templateTitle[1] equipment templateTitle[3] in templateValue[0][0] and gives an templateTitle[7] for templateValue[0][last] , sorted templateTitle[5] templateTitle[6] . About 133,000 units are expected to be sold in European countries in templateValue[0][last] . That templateLabel[0][0] , the European templateTitle[1] equipment templateTitle[3] is expected to account for around 21 templateScale of the templateTitle[3] .

generated_template: The statistic depicts the templateTitle[0] of templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateValue[0][0] to templateValue[0][last] , templateTitle[7] the templateTitle[8] of the templateTitle[9] . In templateValue[0][0] , templateValue[5][0] marathons took place in the templateTitleSubject[0] .
generated: The statistic depicts the Global of construction machinery in the Global from 2014 to 2019 , outlook the 2019 of the 2019 .  In 2014 , 36.81 marathons took place in the Global .

Example 150:
titleEntities: {'Subject': ['Michael Kors'], 'Date': ['2020']}
title: Number of followers of Michael Kors on social media 2020
X_Axis['Platform']: ['Facebook', 'Instagram', 'Twitter']
Y_Axis['Number', 'of', 'followers', 'in', 'millions']: ['17.91', '16.0', '3.5']

gold: This statistic depicts the number of followers of Michael Kors on social media as of January 2020 . During the measured period , the largest social media presence of the brand was on Facebook with 17.91 million followers , as opposed to its 3.5 million follower base on Twitter .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] on templateTitle[4] templateTitle[5] as of 2020 . During the measured period , the largest templateTitle[4] templateTitle[5] presence of the brand was on templateXValue[0] with templateYValue[max] templateScale templateYLabel[1] , as opposed to its templateYValue[min] templateScale follower base on templateXValue[last] .

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 followers of Michael Kors followers worldwide as of 2020 , sorted media 2020 .  During the survey period , 3.5 millions of Michael Kors Kors were Facebook and 17.91 millions were Twitter .

Example 151:
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 152:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2010', '2019']}
title: Electricity prices for households in Luxembourg 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']: ['17.98', '16.91', '16.71', '16.18', '16.15', '16.98', '16.98', '17.67', '17.67', '17.38', '17.38', '16.46', '16.65', '17.06', '16.96', '16.62', '16.78', '17.47', '17.26']

gold: This statistic shows the electricity prices for household end users in Luxembourg semi-annually from 2010 to 2019 . In the second half of 2018 , the average electricity price for households was 16.91 euro cents per kWh .
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[1] 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[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh . This was an templatePositiveTrend from the previous period .
generated: This statistic shows the Electricity prices for household end users in Luxembourg semi-annually from 2010_S2 to 2019_S1 .  In the second half of 2018_S2 , the average Electricity price for households was 17.98 Euro cents per kWh .  This was an increase from the previous period .

Example 153:
titleEntities: {'Subject': ['EU', 'Euro'], 'Date': ['2018']}
title: Unemployment rate in EU and Euro area 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
EU            7   7.6   8.6   9.4  10.2  10.8  10.5   9.7   9.6     9     7
Euro area   8.2   9.1    10  10.9  11.6    12  11.4  10.2  10.2   9.6   7.6 

gold: The statistic shows the unemployment rate in the European Union and the Euro area from 2008 to 2018 . In 2018 , the average unemployment rate in the EU was 7 percent .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in the European Union and the templateTitleSubject[1] templateTitle[4] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the average templateTitle[0] templateTitle[1] in the templateTitleSubject[0] was templateValue[1][last] templateScale .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] and the templateTitleSubject[0] templateTitle[6] from templateValue[0][last] to templateValue[0][0] . The figures refer to those younger than 25 years . In templateValue[0][0] , the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] amounted to templateValue[2][0] templateScale .
generated: This statistic shows the Unemployment rate EU in the EU and the EU 2018 from 2008 to 2018 .  The figures refer to those younger than 25 years .  In 2018 , the Unemployment rate EU in the EU amounted to 8.2 % .

Example 154:
titleEntities: {'Subject': ['Canada'], 'Date': ['2015', '2019']}
title: Average retail price for pork chops 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   13.09  12.89  13.26  13.16  12.99  12.77  12.72  12.84  12.93  12.97  12.93  12.99
2016   12.47  12.37  12.29  12.55   12.6   12.9  12.24  12.38  12.46   13.0  12.51  12.99
2017   12.54  12.31  12.47  12.34  12.77  12.61  12.44  12.31  12.43  11.86  11.96  12.15
2018   12.04  12.07  12.31  12.21  12.21  12.43  12.12  11.87   12.3  11.91  12.13  12.05
2019   12.02  11.58   11.8  12.07  11.72  12.02  12.26   12.2  11.92  11.72  12.05  12.33 

gold: The average retail price for one kilogram of pork chops was 12.02 Canadian dollars in December 2019 in Canada . Monthly prices have gradually decreased within the given time period . A high of 13.26 Canadian dollars per kilogram was recorded in October 2015 , whilst prices did not rise above 12.33 Canadian dollars throughout the whole of 2019 .
gold_template: The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] one kilogram of templateTitle[4] templateTitle[5] was templateValue[5][0] Canadian dollars in 2019 in templateTitleSubject[0] . Monthly prices have gradually templateNegativeTrend within the given time period . A high of templateValue[1][max] Canadian dollars per kilogram was recorded in October templateTitleDate[min] , whilst prices did not rise above templateValue[5][max] Canadian dollars throughout the whole of templateTitleDate[max] .

generated_template: The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] one-kilogram templateTitle[4] templateTitle[5] templateTitle[6] was templateValue[5][max] Canadian dollars in 2019 in templateTitleSubject[0] . This templateTitle[2] is an all-time high templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . templateTitle[4] templateTitle[5] templateTitle[6] is a relatively expensive option when compared to other cuts of beef , such as ground beef , which retailed at a templateTitle[2] of 11.3 Canadian dollars per kilogram in templateValue[0][7] templateTitleDate[max] .
generated: The Average retail price for one-kilogram pork chops Canada was 12.33 Canadian dollars in 2019 Canada .  This price is an all-time high for pork chops Canada .  pork chops Canada is a relatively expensive option when compared to other cuts of beef , such as ground beef , which retailed at a price of 11.3 Canadian dollars per kilogram in May 2019 .

Example 155:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2011', '2019']}
title: Facebook : worldwide 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"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['21082', '17652', '16886', '15077', '16914', '13727', '13231', '11966', '12972', '10328', '9321', '8032', '8809', '7011', '6436', '5382', '5841', '4501', '4042', '3543', '3851', '3203', '2910', '2502', '2585', '2016', '1813', '1458', '1585', '1262', '1184', '1058', '1131']

gold: In the fourth quarter of 2019 , social network Facebook 's total revenues amounted to 21.08 billion U.S. dollars , the majority of which were generated through advertising . The company announced over seven million active advertisers on Facebook during the third quarter of 2019 . During that fiscal period , the company 's net income was 7.35 billion U.S. dollars .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[max] , social network templateTitleSubject[0] 's total revenues amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , the majority of which were generated through advertising . The company announced over seven templateScale active advertisers on templateTitleSubject[0] during the third templateXLabel[0] of templateTitleDate[max] . During that fiscal period , the company 's net income was 7.35 templateScale templateYLabel[2] templateYLabel[3] .

generated_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 templateTitleDate[max] , 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: The Facebook Times 's 2019 Revenue amounted to 21082 million U.S. dollars in the final Quarter of 2019 , up 2019 263.5 million in the corresponding Quarter of Q4_'18 .  The Facebook ' 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 .

Example 156:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2000', '2018']}
title: Number of live births in the United Kingdom 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', 'live', 'births', '(in', '1,000s)']: ['731.21', '755.04', '774.84', '777.17', '776.35', '778.8', '812.97', '807.78', '807.27', '790.2', '794.38', '772.25', '748.56', '722.55', '716.0', '695.55', '668.78', '669.12', '679.03']

gold: There were 731 thousand live births recorded in the United Kingdom in 2018 , a decline of almost 24 thousand births when compared with the previous year . Between 2000 and 2018 the year with the highest number of live births was 2012 when there were almost 813 thousand births . Birth rate at a 15-year low At 11.4 births per 1,000 people , the birth rate of the United Kingdom in 2017 marked a fifteen-year low .
gold_template: There were templateYValue[0] thousand templateYLabel[1] templateYLabel[2] recorded in the templateTitleSubject[0] in templateXValue[max] , a decline of almost 24 thousand templateYLabel[2] when compared with the previous templateXLabel[0] . Between templateXValue[min] and templateXValue[max] the templateXLabel[0] with the highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] was templateXValue[6] when there were almost templateYValue[max] thousand templateYLabel[2] . Birth rate at a 15-year low At 11.4 templateYLabel[2] per 1,000 people , the birth rate of the templateTitleSubject[0] in templateXValue[1] marked a fifteen-year low .

generated_template: This statistic 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: This statistic shows the Number of births in the live (in permanent 1,000s) via from 2000 to 2018 .  In the most recently reported period , close to 731.21 million live births had fixed 1,000s) , up from close to 790.2 million in 2009 .  The live are one of the biggest online markets worldwide .

Example 157:
titleEntities: {'Subject': ['Russia'], 'Date': ['2017']}
title: Main import partners for Russia 2017
X_Axis['Country']: ['China', 'Germany', 'United_States', 'Belarus', 'Italy', 'France']
Y_Axis['Share', 'of', 'total', 'imports']: ['21.2', '10.7', '5.6', '5', '4.5', '4.2']

gold: The statistic shows the estimated main import partners for Russia in 2017 . In 2017 , Russia 's main import partner was China , accounting for 21.2 percent of all imports .
gold_template: The statistic shows the estimated templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] partner was templateXValue[0] , accounting templateTitle[3] templateYValue[max] templateScale of all templateYLabel[2] .

generated_template: This statistic shows the distribution of non-white templateXValue[2] backgrounds in templateTitleSubject[0] in templateTitleDate[0] . templateYValue[max] templateScale of the population identified as templateXValue[0] . The next highest templateXValue[2] was Asians with templateYValue[1] templateScale .
generated: This statistic shows the distribution of non-white United_States backgrounds in Russia 2017 .  21.2 % of the population identified as China .  The next highest United_States was Asians with 10.7 % .

Example 158:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005', '2018']}
title: U.S. electricity generation by independent power producers 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Net', 'generation', 'in', 'million', 'megawatt', 'hours']: ['1680.72', '1603.17', '1613.16', '1603.97', '1554.53', '1515.66', '1551.19', '1487.34', '1500.75', '1437.06', '1498.98', '1501.21', '1424.42', '1424.35']

gold: The statistic shows the net amount of electricity generated by independent power producers in the U.S. between 2005 and 2018 . In 2018 , independent power producers generated some 1.68 billion megawatt hours of electricity .
gold_template: The statistic shows the templateYLabel[0] amount of templateTitle[1] generated templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[4] templateTitle[5] templateTitle[6] generated some templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] of templateTitle[1] .

generated_template: In templateXValue[max] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] was approximately templateYValue[min] templateScale , a slight templateNegativeTrend from the previous templateXLabel[0] . This was the lowest templateYLabel[0] for the entire period shown in this graph . Despite a small rebound in templateXValue[4] and templateXValue[3] this constitutes a slow long-term decline of herd sizes .
generated: In 2018 , the by of U.S. and calves in the U.S. was approximately 1424.35 million , a slight decrease from the previous Year .  This was the lowest Net for the entire period shown in this graph .  Despite a small rebound in 2014 and 2015 this constitutes a slow long-term decline of herd sizes .

Example 159:
titleEntities: {'Subject': ['Chinese'], 'Date': ['2018']}
title: Chinese domestic prices for rare earth oxides 2018
X_Axis['Molecule']: ['Praesodymium_oxide', 'Neodymium_oxide', 'Didymium_oxide', 'Erbium_oxide', 'Gadoliniumm_oxide', 'Yttrium_oxide', 'Cerium_oxide', 'Lanthanum_oxide', 'Samarium_oxide', 'Terbium_oxide', 'Dysprosium_oxide', 'Europium_oxide']
Y_Axis['Price', 'in', 'renminbi', 'per', 'metric', 'ton']: ['440000', '360000', '350000', '170000', '100000', '21500', '14000', '14000', '14000', '3075', '1180', '420']

gold: This statistic displays the average Chinese domestic prices for selected rare earth oxides as of January 8 , 2018 . At that time , Cerium oxide had an average domestic price of 14,000 renminbi per metric ton .
gold_template: This statistic displays the average templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] selected templateTitle[4] templateTitle[5] templateTitle[6] as of 8 , templateTitleDate[0] . At that time , templateXValue[6] templateXValue[0] had an average templateTitle[1] templateYLabel[0] of templateYValue[6] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the estimated templateYLabel[0] of templateTitle[4] of in the templateTitle[1] and templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[7] templateTitle[8] . On average , templateXValue[4] and templateXValue[3] templateXValue[4] templateXValue[0] were templateYValue[4] templateYLabel[1] old .
generated: This statistic shows the estimated Price of rare in the domestic and prices for in the United Kingdom ( Chinese ) in 2018 , .  On average , Gadoliniumm_oxide and Erbium_oxide Gadoliniumm_oxide Praesodymium_oxide were 100000 renminbi old .

Example 160:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Preferred place of movie consumption in the U.S. 2018
X_Axis['Response']: ['In_a_movie_theater', 'At_home', 'Neither', 'Both_equally', 'Not_sure']
Y_Axis['Share', 'of', 'respondents']: ['13', '54', '7', '22', '4']

gold: As of 2018 , 54 percent of U.S.-based respondents stated that their favorite moving watching location was at home . Only around 13 percent of respondents stated that they preferred to watch movies in a theatre , while 22 percent stated that they like the theatre and home options equally . Movie viewership Movies have long been a staple of the entertainment industry in both the United States and across the globe .
gold_template: As of templateTitleDate[0] , templateYValue[max] templateScale of U.S.-based templateYLabel[1] stated that their favorite moving watching location was at templateXValue[1] . Only around templateYValue[0] templateScale of templateYLabel[1] stated that they templateTitle[0] to watch movies in a theatre , while templateYValue[3] templateScale stated that they like the theatre and templateXValue[1] options templateXValue[3] . templateXValue[0] viewership Movies have long been a staple of the entertainment industry in templateXValue[3] the templateTitle[4] and across the globe .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among Americans aged 16 and older regarding the templateTitle[0] they are looking for in a close templateTitle[2] . This statistic only shows the top five answers to that question . templateYValue[max] templateScale of the templateYLabel[1] stated a close templateTitle[2] has to be loyal .
generated: This statistic shows the results of a 2018 survey among Americans aged 16 and older regarding the Preferred they are looking for in a close movie .  This statistic only shows the top five answers to that question .  54 % of the respondents stated a close movie has to be loyal .

Example 161:
titleEntities: {'Subject': ['Nike', 'North American'], 'Date': ['2009', '2019']}
title: Nike 's North American revenue 2009 - 2019 , by segment
Data:
               0     1     2     3     4     5     6     7     8     9    10
Year        2019  2018  2017  2016  2015  2014  2013  2012  2011  2010  2009
Footwear   10045  9322  9684  9299  8506  7495  6751  5941  5111  4610  4694
Apparel     5260  4938  4886  4746  4410  3937  3591  2993  2103  1740  1740
Equipment    597   595   646   719   824   867   816   604   365   346   344 

gold: This timeline presents Nike 's North American revenue from 2009 to 2019 , by segment . Nike 's North American revenue in the footwear sector amounted to about 10.05 billion U.S. dollars in 2019 . Nike and Athletic Apparel Sports and fitness clothing is highly fragmented , with many brands competing , from basic discount brands to high-end fashion names .
gold_template: This timeline presents templateTitleSubject[0] templateTitle[1] templateTitleSubject[1] revenue from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . templateTitleSubject[0] templateTitle[1] templateTitleSubject[1] revenue in the templateLabel[1][0] sector amounted to about 10.05 templateScale U.S. dollars in templateValue[0][0] . templateTitleSubject[0] and Athletic templateLabel[2][0] Sports and fitness clothing is highly fragmented , with many brands competing , from basic discount brands to high-end fashion names .

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 2019 , the Nike of numbered around 11.4 % .  Most of these inhabitants lived in the Dutch-speaking Footwear 2009 , which was home to nearly half of Nike 's .  The Equipment 2009 , broadly in line with the French-speaking part of Nike , numbered roughly 597 thousand inhabitants , and another 5260 thousand lived in Brussels and the surrounding areas ( which are bilingual ) .

Example 162:
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 displays the value of business-to-business ( templateTitle[10] , including-business-to-government or B2G ) and business-to-consumer ( templateTitle[11] ) templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] from templateValue[0][0] to templateValue[0][last] . In templateValue[0][last] , templateTitle[1] to templateLabel[1][1] templateLabel[1][2] ( templateTitle[11] ) amounted to templateValue[1][last] templateScale British pounds . This is an templatePositiveTrend of over 70 templateScale British pounds since templateValue[0][3] .
generated: This statistic displays the value of business-to-business ( 2018 , including-business-to-government or B2G ) and business-to-consumer ( 2018 ) Coal global proved reserves in the Coal from 2010 to 2018 .  In 2018 , global to and bituminous ( 2018 ) amounted to 734903 million British pounds .  This is an increase of over 70 million British pounds since 2016 .

Example 163:
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: 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: The poverty rate in Brazil has been oscillating throughout recent years .  In 2017 , 6.9 percentage of the Argentine population were living on less than 3.20 U.S. dollars day 2005 , down from 19.7 percentage of the population in 2005.In nominal terms , household income day capita in Brazil has shown a significant improvement in 2014 .

Example 164:
titleEntities: {'Subject': ['Johnson Johnson'], 'Date': ['2013']}
title: Johnson & Johnson 's share of the baby care market worldwide 2013 to 2021
X_Axis['Year']: ['2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Market', 'share']: ['17.9', '18.8', '19.6', '20.5', '21.3', '22.2', '23.2', '24.2', '25.7']

gold: Johnson & Johnson 's share of the baby care market worldwide is expected to gradually decline in the coming years to hit a low of 17.9 percent in 2021 , a decrease of almost eight percent since 2013 . Despite such forecast downturns within their baby care segment , Johnson & Johnson 's total revenue has continued to increase in recent years . Johnson & Johnson origins Johnson & Johnson , headquartered in New Jersey , operates over 200 subsidiary companies in 60 countries within the pharmaceutical , medical devices and consumer healthcare segments .
gold_template: templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateYLabel[1] of the templateTitle[5] templateTitle[6] templateYLabel[0] templateTitle[8] is expected to gradually decline in the coming years to hit a low of templateYValue[min] templateScale in templateXValue[idxmin(Y)] , a templateNegativeTrend of almost eight templateScale since templateXValue[min] . Despite such forecast downturns within their templateTitle[5] templateTitle[6] segment , templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] total revenue has continued to templatePositiveTrend in recent years . templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] origins templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] , headquartered in New Jersey , operates over 200 subsidiary companies in 60 countries within the pharmaceutical , medical devices and consumer healthcare segments .

generated_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateYLabel[0] templateYLabel[1] of the over-the-counter and templateTitle[8] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateYLabel[0] templateYLabel[1] of the templateTitle[6] over-the-counter and templateTitle[8] templateYLabel[0] is estimated to be templateYValue[2] templateScale .
generated: This statistic shows Johnson Johnson & 's Market share of the over-the-counter and worldwide Market from 2013 to 2021 .  In 2019 , Johnson Johnson & 's Market share of the care over-the-counter and worldwide Market is estimated to be 19.6 % .

Example 165:
titleEntities: {'Subject': ['Bosnia Herzegovina'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Bosnia & Herzegovina 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', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['7468.04', '7037.41', '6662.6', '6322.16', '6009.57', '5741.76', '5754.74', '5155.55', '4808.3', '4584.26', '5194.01', '5036.03', '4716.64', '5050.03', '4611.35', '4697.84', '4971.82', '4060.06', '3296.75', '2891.68', '2686.0']

gold: The statistic shows gross domestic product ( GDP ) per capita in Bosnia & Herzegovina from 2004 to 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] templateTitle[7] templateTitleSubject[0] from templateXValue[min] to 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 templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] , also known as Burma , from templateXValue[min] to templateXValue[9] with projections up to 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: The statistic shows Gross domestic product ( GDP ) per capita in Bosnia Herzegovina , also known as Burma , from 2004 to 2015 with projections up to 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 .

Example 166:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2007', '2017']}
title: Gross domestic product ( GDP ) growth rate in Sweden 2007 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['GDP', 'growth', 'rate']: ['2.3', '3.2', '4.5', '2.6', '1.2', '-0.3', '2.7', '6', '-5.2', '-0.6', '3.4']

gold: This statistic shows the growth of the gross domestic product ( GDP ) in Sweden from 2007 to 2017 . The Swedish gross domestic product declined the most in the year 2009 , when it was at a negative rate of 5.2 percent .
gold_template: This statistic shows the templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The Swedish templateTitle[0] templateTitle[1] templateTitle[2] declined the most in the templateXLabel[0] templateXValue[8] , when it was at a negative templateYLabel[2] of 5.2 templateScale .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] to templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[1] worth approximately templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] were imported to templateTitleSubject[0] . templateYLabel[0] to templateTitleSubject[0] – additional information In templateXValue[4] , templateTitleSubject[0] had surpassed the country as the world 's largest templateTitle[1] trader .
generated: The statistic shows the Gross of domestic to Sweden from 2007 to 2017 .  In 2017 , domestic worth approximately 2.3 % rate were imported to Sweden .  GDP to Sweden – additional information In 2013 , Sweden had surpassed the country as the world 's largest domestic trader .

Example 167:
titleEntities: {'Subject': ['Jamaica'], 'Date': ['2018']}
title: Infant mortality rate in Jamaica 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['12.4', '12.8', '13.2', '13.7', '14.1', '14.5', '14.9', '15.3', '15.7', '16.1', '16.4']

gold: The statistic shows the infant mortality rate in Jamaica from 2008 to 2018 . In 2018 , the infant mortality rate in Jamaica was at about 12.4 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 Jamaica from 2008 to 2018 .  In 2018 , the Infant mortality rate in Jamaica was at about 12.4 Deaths per 1,000 live births .

Example 168:
titleEntities: {'Subject': ['Leading'], 'Date': ['2018']}
title: Leading car manufacturing countries worldwide 2018
Data:
                            0              1        2        3        4        5            6        7       8       9        10       11       12       13
Country                 China  United_States    Japan    India  Germany   Mexico  South_Korea   Brazil   Spain  France  Thailand   Canada   Russia       UK
Cars                 23529.42        2795.97  8358.22  4064.77  5120.41  1575.81      3661.73  2386.76  2267.4  1763.0    877.02    655.9  1563.57  1519.44
Commercial vehicles   4279.77        8518.73  1370.31  1109.87      0.0  2524.72        367.1   493.05  552.17   507.0   1290.68  1364.94    204.1    84.89 

gold: China was the leading market for motor vehicle production with about 27.8 million cars and commercial vehicles produced . The source defines commercial vehicles as motor vehicles with at least four wheels that are used to transport goods or passengers and weigh more than 3.5 metric tons . The second-ranked United States produced more commercial vehicles than passenger vehicles , according to this definition .
gold_template: templateValue[0][0] was the templateTitleSubject[0] market for motor vehicle production with about 27.8 templateScale templateLabel[1][0] and templateLabel[2][0] templateLabel[2][1] produced . The source defines templateLabel[2][0] templateLabel[2][1] as motor templateLabel[2][1] with at least four wheels that are used to transport goods or passengers and weigh more than 3.5 metric tons . The second-ranked templateValue[0][1] produced more templateLabel[2][0] templateLabel[2][1] than passenger templateLabel[2][1] , according to this definition .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[9] templateTitle[10] . The templateTitle[3] templateTitle[4] of the U.S. templateTitleSubject[0] templateTitle[5] in templateValue[0][0] amounted to approximately templateValue[2][0] templateScale U.S. dollars .
generated: This statistic shows the Leading car of the manufacturing countries worldwide 2018 from to 2018 , .  The countries worldwide of the U.S. Leading 2018 in China amounted to approximately 4279.77 million U.S. dollars .

Example 169:
titleEntities: {'Subject': ['Major'], 'Date': []}
title: Major rice importing countries worldwide 2018/2019
X_Axis['Country']: ['China', 'Philippines', 'Nigeria', 'European_Union', 'Bangladesh', "Cote_d'Ivoire", 'Iran', 'Iraq', 'Saudi_Arabia', 'Senegal']
Y_Axis['Import', 'in', 'thousand', 'metric', 'tons']: ['4500', '2300', '2200', '2000', '1600', '1600', '1400', '1300', '1300', '1250']

gold: This statistic shows the principal rice importing countries worldwide in 2018/2019 , measured in 1,000 metric tons . As of February 2019 , rice import to China is projected to amount to about 4.5 million metric tons in 2018/2019 . The total production of milled rice worldwide amounted to about 481.1 million metric tons that year .
gold_template: This statistic shows the principal templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] , measured in 1,000 templateYLabel[2] templateYLabel[3] . As of 2019 , templateTitle[1] templateYLabel[0] to templateXValue[0] is projected to amount to about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateTitle[5] . The total production of milled templateTitle[1] templateTitle[4] amounted to about 481.1 templateScale templateYLabel[2] templateYLabel[3] that year .

generated_template: This statistic represents the global templateTitleSubject[0] 10 templateTitle[1] templateTitle[3] worldwide in templateTitleDate[0] . In that year , the templateXValue[0] templateXValue[2] was the biggest producer with a templateYLabel[0] volume of templateYValue[1] templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic represents the global Major 10 rice countries worldwide in .  In that year , the China Nigeria was the biggest producer with a Import volume of 2300 thousand metric .

Example 170:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2017']}
title: Malaysia : 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']: ['26.1', '25.7', '25.2', '24.7', '24.1', '23.4', '22.7']

gold: This statistic shows the number of social network users in Malaysia from 2017 to 2023 . In 2023 , it is estimated that there will be around 26.1 million social network users in the country , up from 22.7 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[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_template: In templateXValue[4] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[5] templateScale and is projected to grow to templateYValue[max] templateScale in templateXValue[2] . The current templateTitleSubject[1] usage penetration in templateTitleSubject[0] is about 64 templateScale of the population . Social media in templateTitleSubject[0] Social networking is a popular online activity in templateTitleSubject[0] .
generated: In 2019 , the Number of Malaysia users in Malaysia amounted to 23.4 millions and is projected to grow 26.1 millions in 2021 .  The current Malaysia usage penetration in Malaysia is about 64 millions of the population .  Social media in Malaysia Social networking is a popular online activity in Malaysia .

Example 171:
titleEntities: {'Subject': ['UK'], 'Date': ['1996', '2018']}
title: UK households : ownership of mobile telephones 1996 to 2018
X_Axis['Year']: ['2017-18', '2016-17', '2015-16', '2014-15', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005-06', '2004-05', '2003-04', '2002-03', '2001-02', '2000-01', '1999-2000', '1998-99', '1997-98', '1996-97']
Y_Axis['Percentage', 'of', 'respondents']: ['95', '95', '95', '94', '94', '92', '87', '87', '80', '81', '79', '78', '80', '79', '78', '76', '70', '64', '47', '44', '26', '20', '16']

gold: Mobile telephones have become increasingly relevant to many aspects of modern life , a phenomenon that is evidenced by the development in the rate of households owning a mobile telephone . Currently , approximately 95 percent of households in the United Kingdom ( UK ) own a mobile phone , a figure that has remained constant since 2015 . That sits in stark contrast with 2000 - 2001 , when less than half of all households in the UK reported owning a mobile telephone .
gold_template: templateTitle[3] templateTitle[4] have become increasingly relevant to many aspects of modern life , a phenomenon that is evidenced by the development in the rate of templateTitle[1] owning a templateTitle[3] telephone . Currently , approximately templateYValue[max] templateScale of templateTitle[1] in the United Kingdom ( templateTitleSubject[0] ) own a templateTitle[3] phone , a figure that has remained constant since 2015 . That sits in stark contrast with 2000 - 2001 , when less than half of all templateTitle[1] in the templateTitleSubject[0] reported owning a templateTitle[3] telephone .

generated_template: The statistic presents the templateYLabel[0] of templateTitle[1] in the United Kingdom ( templateTitleSubject[0] templateTitle[3] ) from templateTitleDate[min] to templateTitleDate[max] . In the survey , templateYValue[min] templateScale of templateTitle[1] users in the templateTitleSubject[0] were templateTitle[1] were templateTitle[3] .
generated: The statistic presents the Percentage of households in the United Kingdom ( UK mobile ) from 1996 to 2018 .  In the survey , 16 percentage of households users in the UK were households mobile .

Example 172:
titleEntities: {'Subject': ['Apple App Store', 'iOS'], 'Date': ['2014']}
title: Total Apple App Store iOS developer payout 2014 to 2020
X_Axis['Month']: ["Jan_'20", "Jan_'19", "Jun_'18", "Jan_'18", "Jun_'17", "Jan_'17", "Jun_'16", "Jan_'16", "Aug_'15", "Jan_'15", "Jul_'14", "Jan_'14"]
Y_Axis['Cumulative', 'developer', 'payout', 'in', 'billion', 'U.S.', 'dollars']: ['155', '120', '100', '86', '70', '60', '50', '40', '33', '25', '20', '15']

gold: This statistic presents the cumulative Apple App Store earnings of mobile app developers as of January 2020 . As of the last reported period , Apple had paid a total of 155 billion U.S. dollars to iOS app developers . A year ago , this figure amounted to 120 billion U.S. dollars .
gold_template: This statistic presents the templateYLabel[0] templateTitleSubject[0] Store earnings of mobile templateTitleSubject[0] developers as of 2020 . As of the last reported period , templateTitleSubject[0] had paid a templateTitle[0] of templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] to templateTitleSubject[1] templateTitleSubject[0] developers . A year ago , this figure amounted to templateYValue[1] templateScale templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the estimated templateYLabel[0] of templateTitle[4] of in the templateTitle[1] and templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[7] templateTitle[8] . On average , templateXValue[4] and templateXValue[3] templateXValue[4] templateXValue[0] were the most expensive templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] , in which was templateYValue[max] templateScale .
generated: This statistic shows the estimated Cumulative of iOS in the Apple and App Store in the United Kingdom ( Apple App Store ) in 2014 , 2020 .  On average , Jun_'17 and Jan_'18 Jun_'17 Jan_'20 were the most expensive Apple App Store in the Apple App Store , in which was 155 billion .

Example 173:
titleEntities: {'Subject': ['QVC'], 'Date': ['2012', '2018']}
title: QVC number of employees 2012 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'employees', 'in', 'thousands']: ['21.4', '17.1', '17.7', '17.6', '17.3', '17.5', '17.0']

gold: The timeline shows the number of employees at QVC Incorporated from 2012 to 2018 . The TV shopping network employed 21.4 thousand people in 2018 .
gold_template: The timeline shows the templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] Incorporated from templateXValue[min] to templateXValue[max] . The TV shopping network employed templateYValue[idxmax(X)] thousand people in templateXValue[idxmax(Y)] .

generated_template: In templateXValue[max] , the Italian fashion company templateTitle[0] templateTitleSubject[0] S.p.A. , well known worldwide for designing and producing high-end clothing , footwear and accessories for women , men and kids , reported a total workforce of templateYValue[idxmax(X)] templateYLabel[1] . This figure represented an templatePositiveTrend of approximately 200 units compared to the first templateXLabel[0] considered in the graph , when the templateYLabel[0] of templateYLabel[1] amounted to templateYValue[min] . However , the most remarkable templatePositiveTrend in the workforce of the company was seen in templateXValue[idxmax(Y)] , when templateTitleSubject[0] hired 133 additional templateYLabel[1] .
generated: In 2018 , the Italian fashion company QVC S.p.A. , well known worldwide for designing and producing high-end clothing , footwear and accessories for women , men and kids , reported a total workforce of 21.4 employees .  This figure represented an increase of approximately 200 units compared to the first Year considered in the graph , when the Number of employees amounted to 17.0 However , the most remarkable increase in the workforce of the company was seen in 2018 , when QVC hired 133 additional employees .

Example 174:
titleEntities: {'Subject': ['Scottish'], 'Date': ['2014', '2014']}
title: Scottish independence voting intention September 2014 , by age group
Data:
                0      1      2      3      4    5
Age group   16-24  25-34  35-44  45-54  55-64  65+
"Yes"          44     38     47     39     39   25
"No"           34     31     31     35     39   57
Don't know     22     31     22     26     22   18 

gold: This statistic shows the voting intention of Scottish people in the upcoming referendum on independence from the United Kingdom , by age group . The referendum will take place on September 18 , 2014 . If the majority of voters say `` yes '' , Scotland will become an independent nation .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] people in the upcoming referendum on templateTitle[1] from the United Kingdom , templateTitle[6] templateLabel[0][0] templateLabel[0][1] . The referendum will take place on templateTitle[4] templateValue[3][last] , templateTitleDate[0] . If the majority of voters say `` yes '' , Scotland will become an independent nation .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[5] and templateTitle[4] users in the templateTitle[6] as of 2017 , templateTitle[10] templateTitle[11] . During the survey , templateValue[1][last] templateScale of the respondents aged templateLabel[1][0] to 29 years old stated that they used templateTitle[2] .
generated: This statistic shows the Scottish independence voting of 2014 and September users in the by as of 2017 , group .  During the survey , 25 % of the respondents aged "Yes" to 29 years old stated that they used voting .

Example 175:
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: The statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] of the average templateTitleSubject[0] templateTitle[5] templateTitle[6] user as of templateTitleSubject[0] templateTitleDate[0] . During the survey , templateYValue[1] templateScale of templateYLabel[1] stated they would choose templateXValue[1] templateTitle[1] if they could only listen to templateYValue[11] genre of templateTitle[1] for the rest of their lives . The most popular answer was templateXValue[0] , which templateYValue[max] templateScale of templateYLabel[1] selected as the templateYValue[11] genre of templateTitle[1] they would listen to for the rest of their lives .
generated: The statistic presents the Music genres preferred of the average U.S. 2018 user as of U.S. 2018 .  During the survey , 56.1 % of respondents stated they would choose Pop genres if they could only listen to 16.1 genre of genres for the rest of their lives .  The most popular answer was Rock , which 56.8 % of respondents selected as the 16.1 genre of genres they would listen to for the rest of their lives .

Example 176:
titleEntities: {'Subject': ['Automobile'], 'Date': ['2010']}
title: Automobile production - automatic transmission forecast 2010 to 2015
X_Axis['Year']: ['2010', '2011', '2012', '2013', '2014', '2015']
Y_Axis['Million', 'transmissions']: ['25.03', '25.36', '26.72', '27.82', '28.46', '28.65']

gold: The timeline shows an automatic transmission forecast for automobile production worldwide from 2010 to 2015 . In 2010 , about 25.03 million cars and light vehicles produced worldwide were equipped with an automatic transmission . It is forecast that this number will increase to about 28.65 million automobiles produced worldwide in 2015 .
gold_template: The timeline shows an templateTitle[2] templateTitle[3] templateTitle[4] for templateTitleSubject[0] templateTitle[1] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[min] , about templateYValue[idxmin(X)] templateScale cars and light vehicles produced worldwide were equipped with an templateTitle[2] templateTitle[3] . It is templateTitle[4] that this number will templatePositiveTrend to about templateYValue[idxmax(X)] templateScale automobiles produced worldwide in templateXValue[idxmax(Y)] .

generated_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[3] 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: This statistic provides information on the level of Automobile production automatic Million from 2010 to 2015 .  In 2014 , Automobile production automatic Million amounted to 27.82 petabytes transmissions and is expected to multiply 28.65 transmissions in 2015 .

Example 177:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Federal spending per capita on arts and culture in the U.S. 2013
X_Axis['Year']: ['2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Per', 'capita', 'spending', 'in', 'U.S.', 'dollars']: ['6.13', '5.89', '5.9', '6.04', '6.39', '6.2', '5.89', '5.86', '5.96', '5.82', '5.5', '5.3']

gold: This statistic shows the federal arts and culture spending in the United States per capita from 2002 to 2013 . In 2013 , 6.13 U.S. dollars were spent per capita on arts and culture in the U.S. by federal institutions .
gold_template: This statistic shows the templateTitle[0] templateTitle[4] and templateTitle[5] templateYLabel[2] in the templateTitle[6] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] were spent templateYLabel[0] templateYLabel[1] on templateTitle[4] and templateTitle[5] in the templateYLabel[3] by templateTitle[0] institutions .

generated_template: This statistic represents a forecast of the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , more than templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] were templateYLabel[2] in templateTitleSubject[0] .
generated: This statistic represents a forecast of the Per of capita spending in U.S. between 2002 and 2013 .  In 2013 , more than 6.13 % capita spending were in U.S. .

Example 178:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: U.S. unemployment rate by age 1990 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    28    29
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
16-24        8.4  8.6  9.2  10.4  11.6  13.4  15.5  16.2  17.3  18.4  17.6  12.8  10.5  10.5  11.3  11.8  12.4   12  10.6  9.3  9.9  10.4  11.3   12  12.1  12.5  13.4  14.2  13.4  11.2
25-29        4.1  4.3  4.9   5.6     6   7.2   8.1   8.9  10.3  10.9  10.6   6.5   5.2   5.1   5.8   6.1   6.6  6.4   4.9  4.1  4.4   4.7   5.3  5.8   5.8   6.6   7.5   8.2   7.4     6
30-34        3.3  3.5  4.2   4.5   4.9   5.9   6.7   7.6   8.6   9.2     9   5.2   4.1   4.2   4.5     5   5.5  5.4   4.3  3.3  3.6     4   4.2  4.7   5.1   5.5   6.4   7.2   6.4   5.1
35-44        2.7    3  3.5   3.8   4.1   4.7   5.9   6.6   7.3   8.1   7.9   4.6   3.4   3.6   3.9   4.4   4.9  4.6   3.6    3    3   3.4   3.8  4.1   4.3   4.6   5.5   5.8   5.2   4.1
45 and over  2.7  2.9  3.2   3.6   3.7   4.4   5.5   6.1   6.8   7.4   6.9     4   3.2     3   3.4   3.8   4.1    4   3.1  2.5  2.6   2.7     3  3.4   3.5     4   4.6     5   4.3   3.4 

gold: The unemployment rate for people ages 16 to 24 in the United States in 2019 was 8.4 percent . However , this rate was much lower for people aged 45 and over , at 2.7 percent . U.S. unemployment The unemployment rate in the United States varies based on several factors , such as race , gender , and level of education .
gold_template: The templateTitle[1] templateTitle[2] for people ages templateValue[1][6] to 24 in the templateTitle[0] in templateTitleDate[max] was templateValue[1][min] templateScale . However , this templateTitle[2] was much lower for people aged templateLabel[5][0] and templateLabel[5][1] , at templateValue[4][idxmin(4)] templateScale . templateTitleSubject[0] templateTitle[1] The templateTitle[1] templateTitle[2] in the templateTitle[0] varies based on several factors , such as race , gender , and level of education .

generated_template: This statistic shows the share of different templateTitle[6] groups across the templateTitleSubject[0] adult population who were enrolled in college or other templateTitle[1] templateTitle[2] from templateTitleDate[min] to templateTitleDate[max] . Of those aged templateLabel[2][1] to templateLabel[2][2] years of templateTitle[6] , templateValue[2][0] templateScale were enrolled in templateTitle[1] templateTitle[2] as of templateTitleDate[max] , a considerable templatePositiveTrend compared to templateValue[2][last] templateScale in templateTitleDate[min] .
generated: This statistic shows the share of different 2019 groups across the U.S. adult population who were enrolled in college or other unemployment rate from 2019 to .  Of those aged 25-29 to years of 2019 , 4.1 % were enrolled in unemployment rate as of 2019 , a considerable increase compared to 6 % in 2019 .

Example 179:
titleEntities: {'Subject': ['Italy'], 'Date': ['2010', '2018']}
title: Natural gas prices for households in Italy 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']: ['7.14', '8.74', '7.04', '8.38', '7.31', '9.05', '7.66', '9.51', '7.97', '9.46', '8.34', '9.68', '7.69', '8.75', '6.94', '7.87', '6.17']

gold: This statistic shows the natural gas prices for household end users in Italy semi-annually from 2010 to 2018 . In the first half of 2018 , the average natural gas price for households was 7.14 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] 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: This statistic shows the Natural gas prices for household end users in Italy semi-annually from 2010_S2 to 2018_S1 .  In the first half of 2018_S1 , the average Natural gas price for households was 7.14 Euro cents per kWh .

Example 180:
titleEntities: {'Subject': ['AstraZeneca'], 'Date': ['2013', '2018']}
title: AstraZeneca 's revenue 2013 - 2018 by region
Data:
                              0     1     2     3      4     5
Year                       2018  2017  2016  2015   2014  2013
United States              6876  6169  7365  9474  10120  9691
Europe                     4459  4753  5064  5323   6638  6658
Established rest of world  2823  3081  3096  3022   3510  3973
Emerging markets           6891  6149  5794  5822   5827  5389 

gold: This statistic shows the revenue of pharmaceutical company AstraZeneca worldwide from 2013 to 2018 , by region . AstraZeneca plc is a leading pharmaceutical company worldwide . The multinational company is headquartered in London , UK .
gold_template: This statistic shows the templateTitle[2] of pharmaceutical company templateTitleSubject[0] worldwide from templateValue[0][last] to templateValue[0][0] , templateTitle[5] templateTitle[6] . templateTitleSubject[0] plc is a leading pharmaceutical company worldwide . The multinational company is headquartered in London , UK .

generated_template: The templateTitle[0] of templateTitle[3] at templateTitleSubject[0] has been steadily templateNegativeTrend in recent years , with templateValue[1][0] templateLabel[1][0] templateTitle[3] and templateValue[2][0] templateLabel[2][0] templateTitle[3] in templateValue[0][0] . This compares to templateValue[1][2] templateLabel[1][0] templateTitle[3] and templateValue[2][2] templateLabel[2][0] templateTitle[3] in templateValue[0][2] . templateTitleSubject[0] Sonic templateTitleSubject[0] is the operating company of the templateTitle[4] drive-through quick service chain templateTitleSubject[0] .
generated: The AstraZeneca of 2013 at AstraZeneca has been steadily decreasing in recent years , with 6876 United 2013 and 4459 Europe 2013 in 2018 .  This compares to 7365 United 2013 and 5064 Europe 2013 in 2016 .  AstraZeneca Sonic AstraZeneca is the operating company of the 2018 drive-through quick service chain AstraZeneca .

Example 181:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2017']}
title: Travel & tourism industry : economic impact in the United Kingdom ( UK ) 2017 , by sector
X_Axis['Category']: ['Direct_contribution_to_GDP', 'Total_contribution_to_GDP', 'Visitor_exports', 'Domestic_spending', 'Leisure_spending', 'Business_spending', 'Capital_investment']
Y_Axis['Value', 'in', 'billion', 'U.S.', 'dollars']: ['93.5', '266.1', '35.6', '169.6', '133.7', '71.5', '21.6']

gold: This statistic shows the economic contribution of the travel and tourism industry in the United Kingdom ( UK ) across various areas in 2017 . Direct contribution to GDP amounted to 93.5 billion U.S. dollars .
gold_template: This statistic shows the templateTitle[4] templateXValue[0] of the templateTitle[0] and templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) across various areas in templateTitleDate[0] . templateXValue[0] to templateXValue[0] amounted to templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic provides information on the average templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] in the templateTitle[3] , accounting for a total of templateYValue[5] templateScale .
generated: This statistic provides information on the average Value of Travel & billion in the United Kingdom in 2017 .  In that year , Direct_contribution_to_GDP was the Travel & tourism Category in the industry , accounting for a total of 71.5 billion .

Example 182:
titleEntities: {'Subject': ['South Africa', 'Twitter'], 'Date': ['2014', '2016']}
title: South Africa : number of Twitter users 2014 to 2016
X_Axis['Year']: ['2016', '2015', '2014']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['5.29', '4.81', '4.3']

gold: This statistic shows the number of Twitter users in South Africa from 2014 to 2016 . In 2015 , the number of Twitter users in South Africa reached 4.81 million .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] reached templateYValue[1] templateScale .

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] reached templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Number of Twitter users in South Africa from 2014 to 2016 .  In 2016 , the Number of Twitter users in South Africa reached 5.29 millions .

Example 183:
titleEntities: {'Subject': ['Atlanta Braves'], 'Date': ['2019']}
title: Franchise value of the Atlanta Braves 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']: ['1700', '1625', '1500', '1175', '1150', '730', '629', '508', '482', '450', '446', '497', '458', '405', '382', '374', '423', '424']

gold: This graph depicts the value of the Atlanta Braves franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 1.7 billion U.S. dollars . The Atlanta Braves are owned by Liberty Media , who bought the franchise for 400 million U.S. dollars in 2007 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] franchise 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] are owned by Liberty Media , who bought the templateYLabel[0] for 400 templateScale templateYLabel[3] templateYLabel[4] in templateXValue[12] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitleSubject[0] franchise 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 templateTitle[2] templateTitleSubject[0] are owned by William DeWitt Jr. , who bought the templateYLabel[0] for 150 templateScale templateYLabel[3] templateYLabel[4] in 1996 .
generated: This graph depicts the value of the Atlanta Atlanta Braves franchise of Major League Baseball from 2002 to 2019 .  In 2019 , the Franchise had an estimated value of 1700 million U.S. dollars .  The Atlanta Atlanta Braves are owned by William DeWitt Jr. , who bought the Franchise for 150 million U.S. dollars in 1996 .

Example 184:
titleEntities: {'Subject': ['AbbVie'], 'Date': ['2012', '2019']}
title: AbbVie 's R & D expenditures 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['R&D', 'spending', 'in', 'million', 'U.S.', 'dollars']: ['6407', '10329', '5007', '4385', '4285', '3297', '2855', '2778']

gold: This statistic illustrates the expenditure of AbbVie on research and development from 2012 to 2019 . In 2018 , AbbVie spent some 10.3 billion U.S. dollars on research and development . Abbot was a U.S.-based global pharmaceutical and healthcare products company , headquartered in Chicago , Illinois .
gold_template: This statistic illustrates the expenditure of templateTitleSubject[0] on research and development from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateTitleSubject[0] spent some templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] on research and development . Abbot was a U.S.-based global pharmaceutical and healthcare products company , headquartered in Chicago , Illinois .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , around templateYValue[max] thousand babies were born in templateTitleSubject[0] in templateXValue[idxmax(Y)] , an templatePositiveTrend from the previous templateXLabel[0] were templateYValue[1] thousand babies were born .
generated: This statistic displays the R&D of spending in AbbVie from 2012 to 2019 .  According to the report , around 10329 thousand babies were born in AbbVie 2018 , an increase from the previous Year were 10329 thousand babies were born .

Example 185:
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 templateScale of the templateYLabel[1] templateYLabel[2] living in urban areas in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] was living in cities and urban areas .
generated: This statistic shows the percentage of the rate living in urban areas in Belgium from 2008 to 2018 .  In 2018 , 16.4 % of the rate of Belgium was living in cities and urban areas .

Example 186:
titleEntities: {'Subject': ['lululemon athletica'], 'Date': ['2011', '2018']}
title: Number of lululemon athletica stores worldwide 2011 - 2018 , by country
Data:
                     0       1          2      3               4            5      6        7            8          9       10           11      12      13           14         15
Country  United_States  Canada  Australia  China  United_Kingdom  New_Zealand  Japan  Germany  South_Korea  Singapore  Ireland  Switzerland  France  Sweden  Puerto_Rico  Hong_Kong
2011               108      47         18      0               0            1      0        0            0          0        0            0       0       0            0          0
2012               135      51         23      0               0            2      0        0            0          0        0            0       0       0            0          0
2013               168      45         25      0               0            4      0        0            0          0        0            0       0       0            0          0
2014               200      46         26      0               2            5      0        0            0          1        0            0       0       0            0          0
2015               229      48         26      0               6            5      0        1            0          2        0            0       0       0            1          2
2016               245      51         27      3               9            5      0        1            2          3        0            1       0       0            1          3
2017               269      57         28     12               9            6      2        2            3          3        1            1       0       0            1          3
2018               285      64         29     22              12            7      5        5            4          3        1            1       1       1            0          0 

gold: lululemon stores started out in Canada , but soon spread to the United States . The company currently has stores in North America , Europe , and the Asian Pacific region . lululemon has grown considerably from its beginnings out of a single design/yoga studio in Canada to operating a total of 440 stores around the world as of February 3 , 2019 .
gold_template: templateTitleSubject[0] templateTitle[3] started out in templateValue[0][1] , but soon spread to the templateValue[0][0] . The company currently has templateTitle[3] in North America , Europe , and the Asian Pacific region . templateTitleSubject[0] has grown considerably from its beginnings out of a single design/yoga studio in templateValue[0][1] to operating a total of 440 templateTitle[3] around the world as of 3 , 2019 .

generated_template: templateValue[0][0] was the European templateLabel[0][0] with the largest stock of templateTitle[1] vehicles in all three years here recorded . The total number of templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] stood at 286.8 templateScale units in templateTitleDate[max] , of which templateValue[0][0] accounted for 46.5 templateScale . With the greatest population among all European countries and home to a prominent number of automobile manufacturers , this was unsurprising .
generated: United_States was the European Country with the largest stock of lululemon vehicles in all three years here recorded .  The total number of lululemon athletica in lululemon athletica stood at 286.8 million units in 2018 , of which United_States accounted for 46.5 % .  With the greatest population among all European countries and home to a prominent number of automobile manufacturers , this was unsurprising .

Example 187:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2019']}
title: Unemployment rate in Indonesia 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']: ['4.38', '4.3', '4.19', '4.3', '4.51', '4.05', '4.34', '4.47', '5.15', '5.61', '6.11', '7.21', '8.06', '7.55', '7.95', '7.3', '6.66', '6.6', '6.08', '6.08', '6.3']

gold: This statistic shows the unemployment rate in Indonesia from 1999 to 2019 . In 2019 , the unemployment rate in Indonesia was at approximately 4.38 percent . Indonesia 's population and economy The Republic of Indonesia is a sovereign state archipelago in Southeast Asia , composed of more than 17,000 islands .
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 . templateTitleSubject[0] 's population and economy The Republic of templateTitleSubject[0] is a sovereign state archipelago in Southeast Asia , composed of more than 17,000 islands .

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 Indonesia from 1999 to 2019 .  In 2019 , the Unemployment rate in Indonesia was at approximately 4.38 % .

Example 188:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017', '2018']}
title: U.S. energy consumption by source 2017 to 2018
Data:
                 0            1      2                       3        4                           5     6      7           8
unknown  Petroleum  Natural_gas   Coal  Nuclear_electric_power  Biomass  Hydroelectric_conventional  Wind  Solar  Geothermal
2017         36.17        28.03  13.84                    8.42     5.08                        2.77  2.34   0.78        0.21
2018         36.95        30.98  13.24                    8.44     5.13                        2.69  2.53   0.95        0.22 

gold: The statistic represents U.S. energy consumption in 2017 and 2018 , with a breakdown by energy source . In the United States consumed some 30.98 quadrillion British thermal units of energy derived from natural gas in 2018 . Energy consumption by source Petroleum is the main source of energy in the United States .
gold_template: The statistic represents templateTitleSubject[0] templateTitle[1] templateTitle[2] in templateTitleDate[min] and templateTitleDate[max] , with a breakdown templateTitle[3] templateTitle[1] templateTitle[4] . In the templateTitle[0] consumed some templateValue[2][1] quadrillion British thermal units of templateTitle[1] derived from templateValue[0][1] in templateTitleDate[max] . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateValue[0][0] is the main templateTitle[4] of templateTitle[1] in the templateTitle[0] .

generated_template: This statistic shows the leading templateTitleSubject[0] templateTitle[2] based on templateTitle[3] templateTitle[4] from 2014 to templateTitleDate[max] . In templateTitleDate[max] , some 234.7 templateScale pounds of templateTitle[3] were produced in templateTitleDate[max] . China was the biggest templateTitle[3] producer worldwide in that year .
generated: This statistic shows the leading U.S. consumption based on by source from 2014 to 2018 .  In 2018 , some 234.7 million pounds of by were produced in 2018 .  China was the biggest by producer worldwide in that year .

Example 189:
titleEntities: {'Subject': ['European'], 'Date': ['2005', '2018']}
title: European ATM numbers 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Total', 'number', 'of', 'ATMs']: ['406532', '413414', '420200', '411243', '409136', '407001', '412799', '403996', '398040', '391175', '383951', '362244', '335083', '324797']

gold: This statistic presents the development of ATM numbers ( automated teller machines ) for self-operated cash withdrawals in European countries from 2005 to 2018 . In 2005 , there were approximately 325 thousand ATMs in Europe and the number grew up to more than 420 thousand as of 2016 . By 2018 , the number of ATMs in Europe had decreased to approximately 406.5 thousand .
gold_template: This statistic presents the development of templateTitle[1] templateTitle[2] ( automated teller machines ) for self-operated cash withdrawals in templateTitleSubject[0] countries from templateXValue[min] to templateXValue[max] . In templateXValue[min] , there were approximately templateYValue[min] thousand templateYLabel[2] in Europe and the templateYLabel[1] templatePositiveTrend up to more than templateYValue[max] thousand as of templateXValue[idxmax(Y)] . By templateXValue[max] , the templateYLabel[1] of templateYLabel[2] in Europe had templateNegativeTrend to approximately templateYValue[0] thousand .

generated_template: In templateXValue[max] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] was approximately templateYValue[min] templateScale , a slight templateNegativeTrend from the previous templateXLabel[0] . This was the lowest templateYLabel[0] for the entire period shown in this graph . Despite a small rebound in templateXValue[4] and templateXValue[3] this constitutes a slow long-term decline of herd sizes .
generated: In 2018 , the 2005 of European and calves in the European was approximately 324797 million , a slight decrease from the previous Year .  This was the lowest Total for the entire period shown in this graph .  Despite a small rebound in 2014 and 2015 this constitutes a slow long-term decline of herd sizes .

Example 190:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2012']}
title: U.S. public opinion on the separation of church and state in 2012
Data:
                        0          1            2
unknown   All_respondents  Democrats  Republicans
Yes                    41         65           24
No                     34         17           54
Not sure               25         19           22 

gold: This statistic shows the results of a survey on the separation of church and state among the U.S. population in 2012 . 41 percent of the respondents stated that the separation of church and state should be absolute . However , 65 percent of the Democrats endorse secularization , while 54 percent of the Republicans are against it .
gold_template: This statistic shows the results of a survey on the templateTitle[3] of templateTitle[4] and templateTitle[5] among the templateTitleSubject[0] population in templateTitleDate[0] . templateValue[1][0] templateScale of the templateValue[0][0] stated that the templateTitle[3] of templateTitle[4] and templateTitle[5] should be absolute . However , templateValue[1][1] templateScale of the templateValue[0][1] endorse secularization , while templateValue[2][last] templateScale of the templateValue[0][2] are against it .

generated_template: This statistic shows the results 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: This statistic shows the results of a survey asking respondents how they voted in the U.S. referendum of 2012 , church state 2012 .  Of respondents , 24 % of those in the AB state 2012 advised they had voted to Yes , while just 41 % of C2 and DE respondents said they had voted to Yes .

Example 191:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2009', '2019']}
title: Facebook : annual revenue 2009 - 2019 , by segment
Data:
                                     0      1      2      3      4      5     6     7     8     9    10
Year                              2019   2018   2017   2016   2015   2014  2013  2012  2011  2010  2009
Advertising revenue              69655  55013  39942  26885  17079  11492  6986  4279  3154  1868   764
Payments and other fees revenue   1042    825    711    753    849    974   886   810   557   106    13 

gold: The statistic shows the revenue of Facebook from 2009 to 2019 , by segment . The social network 's advertising revenue in 2019 amounted to 69.6 billion U.S. dollars , a 27 percent increase from 55 billion U.S. dollars in 2018 .
gold_template: The statistic shows the templateTitle[2] of templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , templateTitle[5] templateTitle[6] . The social network 's templateLabel[1][0] templateTitle[2] in templateValue[0][0] amounted to 69.6 templateScale U.S. dollars , a 27 templateScale templatePositiveTrend from 55 templateScale U.S. dollars in templateValue[0][1] .

generated_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: This statistic shows the Facebook of Payments and Advertising annual in revenue 2009 from to 2019 .  In 2019 , around 947 million Payments and 335 million Advertising annual were in operation 2009 .

Example 192:
titleEntities: {'Subject': ['Russia'], 'Date': ['2018', '2018']}
title: Average player height of participating national teams at the 2018 World Cup in Russia
X_Axis['Country']: ['Serbia', 'Sweden', 'Iceland', 'Denmark', 'Croatia', 'Russia', 'Tunisia', 'Germany', 'Belgium', 'Senegal', 'Switzerland', 'Iran', 'Poland', 'Morocco', 'South_Korea', 'England', 'Costa_Rica', 'Australia', 'Nigeria', 'Panama', 'Portugal', 'France', 'Brazil', 'Colombia', 'Egypt', 'Uruguay', 'Mexico', 'Spain', 'Argentina', 'Peru', 'Japan', 'Saudi_Arabia']
Y_Axis['Average', 'player', 'height', 'in', 'centimeters']: ['185.6', '185.2', '185.0', '185.0', '184.9', '184.3', '184.0', '183.8', '183.8', '183.7', '183.5', '183.4', '183.1', '182.4', '182.2', '182.1', '181.6', '181.3', '181.2', '181.1', '180.5', '180.5', '180.4', '180.2', '180.1', '179.8', '179.5', '179.5', '179.4', '178.3', '178.1', '176.2']

gold: While they may not have made it out of the group stages of the 2018 World Cup , the Serbian national team were top of the table when it came to the average height of their players – their squad boasted an average height of 185.6 centimeters per player . The first-choice goalkeeper for Serbia , Vladimir Stojković , was one of the tallest members of the team at 195 centimeters . At the other end of the scale , the squad of Saudi Arabia came in at an average of just 176.2 centimeters , making them the shortest squad at the 2018 World Cup .
gold_template: While they may not have made it out of the group stages of the templateTitleDate[0] templateTitle[7] templateTitle[8] , the Serbian templateTitle[4] team were top of the table when it came to the templateYLabel[0] templateYLabel[2] of their players – their squad boasted an templateYLabel[0] templateYLabel[2] of templateYValue[max] templateYLabel[3] per templateYLabel[1] . The first-choice goalkeeper for templateXValue[0] , Vladimir Stojković , was one of the tallest members of the team at 195 templateYLabel[3] . At the other end of the scale , the squad of templateXValue[last] came in at an templateYLabel[0] of just templateYValue[min] templateYLabel[3] , making them the shortest squad at the templateTitleDate[0] templateTitle[7] templateTitle[8] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateTitle[4] templateYLabel[5] in templateTitleSubject[0] templateTitle[7] as of 2018 . During that month , templateYValue[2] templateScale templateYLabel[5] in templateXValue[2] accessed online services via templateYLabel[3] device . First-ranked templateXValue[0] accounted for templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] network templateYLabel[5] .
generated: This statistic shows the Average of player height centimeters national in Russia World as of 2018 .  During that month , 185.0 million centimeters in Iceland accessed online services via centimeters device .  First-ranked Serbia accounted for 185.6 million centimeters network .

Example 193:
titleEntities: {'Subject': ['Kenya'], 'Date': ['2019']}
title: Unemployment rate in Kenya 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']: ['9.31', '9.31', '9.29', '9.63', '9.68', '9.59', '9.79', '9.66', '9.68', '9.73', '9.6', '8.93', '9.07', '9.44', '9.78', '9.97', '10.1', '10.09', '9.9', '9.9', '10']

gold: Kenya 's unemployment rate was 9.31 percent in 2019 . This represents a steady decline from the increase after the financial crisis , but pre-recession levels were still above 10 percent . What is unemployment ? The unemployment rate of a country refers to the share of people who want to work but can not find jobs .
gold_template: templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] was templateYValue[0] templateScale in templateXValue[max] . This represents a steady decline from the templatePositiveTrend after the financial crisis , but pre-recession levels were still above templateYValue[3] templateScale . What is templateYLabel[0] ? The templateYLabel[0] templateYLabel[1] of a country refers to the share of people who want to work but can not find jobs .

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 Kenya from 1999 to 2019 .  In 2019 , the Unemployment rate in Kenya was at approximately 9.31 % .

Example 194:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2009', '2018']}
title: Population density of Indonesia 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Population', 'density', 'in', 'people', 'per', 'square', 'kilometer']: ['147.75', '146.09', '144.38', '142.63', '140.83', '139.0', '137.15', '135.31', '133.49', '131.72']

gold: In 2018 , the population density in Indonesia was at about 147.75 people per square kilometer . Despite being the fourth largest country in the world in terms of population , Indonesia 's population density is mitigated by its abundance of land – Indonesia is an archipelago of more than 17 thousand islands sprawled across more than five thousand kilometers from east to west . Java as the heart of Indonesia Despite the many thousands of islands , Indonesia 's population , politics , and economy are mostly centered on the island of Java .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . Despite being the fourth largest country in the world in terms of templateYLabel[0] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is mitigated by its abundance of land – templateTitleSubject[0] is an archipelago of more than 17 thousand islands sprawled across more than five thousand kilometers from east to west . Java as the heart of templateTitleSubject[0] Despite the many thousands of islands , templateTitleSubject[0] 's templateYLabel[0] , politics , and economy are mostly centered on the island of Java .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Population density Indonesia 2009 2018 in Indonesia from 2009 to 2018 .  In 2018 , the Population density in Indonesia was approximately 147.75 million per square .

Example 195:
titleEntities: {'Subject': ['General Motors'], 'Date': ['2010', '2019']}
title: General Motors - number of vehicles sold worldwide 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Number', 'of', 'cars', 'sold', 'in', 'millions']: ['7.71', '8.38', '9.6', '10.01', '9.96', '9.93', '9.7', '9.3', '9.0', '8.4']

gold: This statistic shows the number of General Motors vehicles sold worldwide between 2010 and 2019 . In 2019 , General Motors sold about 7.7 million vehicles . This compares to around 8.4 million units in 2018 , representing a decline of around eight percent .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] vehicles templateYLabel[2] templateTitle[5] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] sold about templateYValue[min] templateScale templateTitle[3] . This compares to around templateYValue[1] templateYValue[idxmin(X)] units in templateXValue[1] , representing a decline of around templateYValue[min] templateScale .

generated_template: This statistic shows the average templateYLabel[0] of templateYLabel[1] of the templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the total templateYLabel[0] of the templateTitle[3] templateTitle[4] of the templateTitleSubject[0] templateTitle[4] was approximately templateYValue[idxmax(X)] templateScale British pounds . This figure is an templatePositiveTrend of templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[max] .
generated: This statistic shows the average Number of cars the vehicles sold in the United Kingdom ( General Motors ) from 2010 to 2019 .  In 2019 , the total Number of the vehicles sold of the General Motors sold was approximately 7.71 millions British pounds .  This figure is an increase of 7.71 millions cars sold in 2019 .

Example 196:
titleEntities: {'Subject': ['Alphabet'], 'Date': ['2011', '2019']}
title: Alphabet : global annual revenue 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['161857', '136819', '110855', '90272', '74989', '66001', '55519', '46039', '37905']

gold: In the most recently reported fiscal year , Alphabet 's revenue amounted to 161.9 billion U.S. dollars , up from close to 137 billion U.S. dollars in the previous year . Alphabet Inc. is an American multinational conglomerate created in 2015 as the parent company of Google , along with several other companies previously owned by or tied to Google . Alphabet business segmentsAlphabet has several operating segments , including Google and Other Bets .
gold_template: In the most recently reported fiscal templateXLabel[0] , templateTitleSubject[0] 's templateYLabel[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , up from close to templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . templateTitleSubject[0] Inc. is an American multinational conglomerate created in templateXValue[4] as the parent company of Google , along with several other companies previously owned by or tied to Google . templateTitleSubject[0] business segmentsAlphabet has several operating segments , including Google and Other Bets .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the last reported templateXLabel[0] , the templateTitleSubject[0] 's dating templateYLabel[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . Up until early 2020 , the templateTitleSubject[0] belongs to IAC and includes online dating platforms such as the eponymous Match.com , OkCupid , Tinder , PlentyofFish and others .
generated: The statistic shows the annual Revenue of the Alphabet from 2011 to 2019 .  In the last reported Year , the Alphabet 's dating Revenue amounted to 161857 million U.S. dollars .  Up until early 2020 , the Alphabet belongs to IAC and includes online dating platforms such as the eponymous Match.com , OkCupid Tinder , PlentyofFish and others .

Example 197:
titleEntities: {'Subject': ['Portland'], 'Date': ['2016']}
title: Quarterly average daily rate of hotels in Portland 2016 to 2017
Data:
           0    1    2    3
Quarter   Q1   Q2   Q3   Q4
2016     162  179  191  171
2017     165    0    0    0 

gold: This statistic shows the quarterly average daily rate of hotels in Portland in 2016 and 2017 . In the first quarter of 2017 , the average daily rate of hotels in Portland in the United States was 165 U.S. dollars .
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 .

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 Portland 2016 and .  In the first Quarter of 2016 , the average daily rate of hotels in Portland the country was 165 U.S. dollars .

Example 198:
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: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the most important templateTitle[1] partner templateTitle[3] templateTitleSubject[0] was templateXValue[0] , accounting templateTitle[3] templateYValue[max] templateScale of all templateYLabel[2] .
generated: This statistic shows the Countries highest proportion soccer Countries in 2010 .  In 2010 , the most important highest partner soccer Countries was Germany , accounting soccer 19.8 % of all players .

Example 199:
titleEntities: {'Subject': ['ExxonMobil'], 'Date': ['2001', '2018']}
title: ExxonMobil 's exploration expenses 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['Exploration', 'expenses', 'in', 'million', 'U.S.', 'dollars']: ['1466', '1790', '1467', '1523', '1669', '1976', '1840', '2081', '2144', '2021', '1451', '1469', '1181', '964', '1098', '1010', '920', '1175']

gold: This statistic outlines the exploration expenses of ExxonMobil from 2001 to 2018 . In 2018 , ExxonMobil 's exploration expenses amounted to approximately 1.47 billion U.S. dollars . ExxonMobil is a United States-based multinational oil and gas corporation headquartered in Irving , Texas .
gold_template: This statistic outlines the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] is a United States-based multinational oil and gas corporation headquartered in Irving , Texas .

generated_template: In templateXValue[max] , the templateYLabel[0] of German templateYLabel[1] templateYLabel[2] amounted to templateYValue[idxmax(X)] templateScale , an templatePositiveTrend compared to the previous templateXLabel[0] at templateYValue[1] templateScale . This templateYLabel[0] has only been templatePositiveTrend in recent years . Considering current German population numbers stand at almost 83 templateScale , such a high templateYLabel[0] of templateYLabel[1] templateYLabel[2] is significant in itself and also for predicting future trends on digitalization and online connectivity in the country .
generated: In 2018 , the Exploration of German expenses million amounted to 1466 million , an increase compared to the previous Year at 1790 million .  This Exploration has only been increased in recent years .  Considering current German population numbers stand at almost 83 million , such a high Exploration of expenses million is significant in itself and also for predicting future trends on digitalization and online connectivity in the country .

Example 200:
titleEntities: {'Subject': ['Marriott International'], 'Date': ['2010', '2019']}
title: Average daily rate of Marriott International hotels 2010 - 2019 , by region
Data:
                                0       1       2       3       4       5       6       7       8       9
Year                         2010    2011    2012    2013    2014    2015    2016    2017    2018    2019
North America              142.85   150.0  157.05  163.24  173.11  179.53  194.64  195.15  198.66  202.75
Caribbean & Latin America  178.59  183.64  190.75  209.79  239.95  248.05  213.99  196.31  202.84  203.23
Europe                     161.63   175.2  170.72  172.01   193.2  173.07  173.84  188.69  205.15  200.21
United Kingdom             159.27       0       0       0       0       0       0       0       0       0
Middle East & Africa       133.18  141.22  133.14  147.63   190.6  181.16   164.9  161.95  154.17  156.43
Asia Pacific               125.88   137.8  133.01  142.76  176.48  153.83   149.8  137.85  146.14  141.38
Overall worldwide          150.46  158.15  162.39  185.74  178.96  178.46  177.11       0  182.67   182.6 

gold: This statistic shows the average daily rate of Marriott International hotels worldwide from 2010 to 2019 , by region . The average daily rate of Marriott hotels in North America was 202.75 U.S. dollars in 2019 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] hotels templateLabel[7][1] from templateValue[0][0] to templateValue[0][last] , templateTitle[8] templateTitle[9] . The templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] templateTitle[5] in templateLabel[1][0] templateLabel[1][1] was templateValue[1][last] U.S. dollars in templateValue[0][last] .

generated_template: The statistic represents the templateLabel[2][0] templateTitle[3] and templateTitle[5] templateTitle[6] templateLabel[1][2] templateTitle[7] by the templateTitle[1] templateTitle[2] in the templateTitle[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] consumed more than templateValue[0][15] templateScale barrels of templateTitle[5] templateTitle[6] templateLabel[1][2] daily . templateTitle[3] and templateTitle[6] templateLabel[1][2] and diesel templateTitle[7] in the templateTitle[0] .
generated: The statistic represents the Caribbean Marriott and hotels 2010 America 2019 by the daily rate in the Average from 2019 to 2010 .  In 2010 , the Marriott International daily rate consumed more than 2019 million barrels of hotels 2010 America daily .  Marriott and 2010 America and diesel 2019 in the Average .

Example 201:
titleEntities: {'Subject': ['Global'], 'Date': ['2012', '2019']}
title: Global broiler meat production 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Production', 'in', 'thousand', 'metric', 'tons']: ['97802', '95500', '93622', '92252', '91352', '86761', '84369', '83267']

gold: This statistic depicts broiler meat production worldwide from 2012 to 2019 . In 2018 , the world 's broiler meat production amounted to about 95.5 million metric tons , and is forecasted to increase to about 97.8 million metric tones by the end of following year .
gold_template: This statistic depicts templateTitle[1] templateTitle[2] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the world 's templateTitle[1] templateTitle[2] templateYLabel[0] amounted to about templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] , and is forecasted to templatePositiveTrend to about templateYValue[max] templateScale templateYLabel[2] tones by the end of following templateXLabel[0] .

generated_template: This statistic shows the total templateTitle[1] templateYLabel[0] of templateTitleSubject[0] in the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . For the fiscal templateXLabel[0] of templateXValue[max] , the Cincinnati-based specialized facility services company reported an templateTitle[1] templateYLabel[0] of just under templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .
generated: This statistic shows the total broiler Production of Global in the fiscal Year of 2012 and the fiscal Year of 2019 .  For the fiscal Year of 2019 , the Cincinnati-based specialized facility services company reported an broiler Production of just under 97802 thousand metric tons in 2019 .

Example 202:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2020']}
title: Most popular politicians on Facebook 2020
X_Axis['Government', 'Figures']: ['Barack_Obama', 'Narendra_Modi', 'Imran_Khan_(official)', 'Hillary_Clinton', 'Mitt_Romney', 'Recep_Tayyip_Erdoğan', 'Arvind_Kejriwal', 'U.S._Senator_Bernie_Sanders', 'Justin_Trudeau', 'Bernie_Sanders']
Y_Axis['Number', 'of', 'Facebook', 'followers', 'in', 'millions']: ['55.04', '44.65', '9.72', '9.71', '9.6', '9296.0', '7.96', '7.45', '6.81', '5.35']

gold: This statistic gives information on the most popular politicians on Facebook , ranked by number of fans on the social network . As of February 2020 , former U.S. president Barack Obama was ranked first with over 55 million Facebook fans .
gold_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] on templateYLabel[1] , ranked by templateYLabel[0] of fans on the social network . As of 2020 , former templateXValue[7] president templateXValue[0] was ranked first with over templateYValue[0] templateScale templateYLabel[1] fans .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] titles templateTitle[5] as of 2019 . With templateYValue[max] templateScale templateYLabel[2] of templateXValue[0] , templateXValue[0] 7 was the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] game as of 2019 .
generated: This statistic shows the Most popular Facebook titles 2020 as of 2019 .  With 9296.0 millions followers of Barack_Obama , 7 was the Most popular Facebook game as of 2019 .

Example 203:
titleEntities: {'Subject': ['Spain'], 'Date': ['2008/2009', '2018/2019']}
title: Enrolment numbers in universities in Spain 2008/2009 to 2018/2019
X_Axis['Year']: ['2018/19', '2017/18', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09']
Y_Axis['Number', 'of', 'students']: ['1595039', '1492741', '1496017', '1506179', '1539709', '1548534', '1572617', '1529862', '1471719', '1430147']

gold: The number of students enrolled at Spanish universities maintained steadily above 1.4 million over the past decade . The number of university students fluctuated over the period under review , with figures registering the lowest point during the 2008/2009 academic year at over 1.4 million students and peaking during the 2018/2019 academic year at almost 1.6 million . Most of the university students in Spain opted for public institutions to complete their studies , with approximately 1.3 million enrolled during the 2018/2019 academic year .
gold_template: The templateYLabel[0] of templateYLabel[1] enrolled at Spanish templateTitle[2] maintained steadily above templateYValue[min] templateScale over the past decade . The templateYLabel[0] of university templateYLabel[1] fluctuated over the period under review , with figures registering the lowest point during the templateTitle[4] academic templateXLabel[0] at over templateYValue[min] templateScale templateYLabel[1] and peaking during the templateTitle[5] academic templateXLabel[0] at almost templateYValue[max] templateScale . Most of the university templateYLabel[1] in templateTitleSubject[0] opted for public institutions to complete their studies , with approximately 1.3 templateScale enrolled during the templateTitle[5] academic templateXLabel[0] .

generated_template: 40 - 59-year-olds make up the largest templateTitle[4] templateTitle[5] in templateTitleSubject[0] , at templateYValue[max] templateScale people . The most recent figures from templateTitleDate[0] confirm that the next-largest templateTitle[4] templateTitle[5] was templateXValue[last] templateXValue[1] and templateXValue[last] , at templateYValue[last] templateScale . Aging templateYLabel[0] With the number of people belonging to templateXValue[last] templateTitle[4] groups visibly outstripping younger ones , in recent templateXValue[1] it has become clear that templateTitleSubject[0] 's templateYLabel[0] is aging more rapidly than developing .
generated: 40 - 59-year-olds make up the largest 2008/2009 2018/2019 in Spain , at 1595039 million people .  The most recent figures from 2008/2009 confirm that the next-largest 2008/2009 2018/2019 was 2008/09 2017/18 and 2008/09 , at 1430147 % .  Aging Number With the number of people belonging to 2008/09 2008/2009 groups visibly outstripping younger ones , in recent 2017/18 it has become clear that Spain 's Number is aging more rapidly than developing .

Example 204:
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 templateTitle[0] of templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateValue[0][0] to templateValue[0][last] , templateTitle[7] the templateTitle[8] of the templateTitle[9] . In templateValue[0][0] , templateValue[5][0] marathons took place in the templateTitleSubject[0] .
generated: The statistic shows the Market of share UK in the UK from 2013/14 to 2009/10 , GGY the 2009 of the 2014 .  In 2013/14 , 5 marathons took place in the UK .

Example 205:
titleEntities: {'Subject': ['Most'], 'Date': ['2014']}
title: Most trustworthy countries worldwide 2014
X_Axis['Country']: ['Canada', 'UK', 'US', 'Australia', 'Germany', 'Japan', 'France', 'Italy', 'Spain', 'China', 'South_Korea', 'Brazil', 'Russia', 'Mexico', 'India']
Y_Axis['Share', 'of', 'respondents']: ['42', '39', '36', '36', '30', '28', '25', '20', '15', '11', '10', '9', '9', '7', '7']

gold: This statistic shows the countries considered to be the most trustworthy worldwide in 2014 , based on a survey conducted in six countries . Of respondents , 42 percent viewed Canada as a country with trustworthy people , with the United Kingdom ( UK ) following at 39 percent . In 2016 a similar report on most reputable countries also found Canada to have one of the best reputations worldwide , next to Sweden .
gold_template: This statistic shows the templateTitle[2] considered to be the templateTitleSubject[0] templateTitle[1] templateTitle[3] in templateTitleDate[0] , based on a survey conducted in six templateTitle[2] . Of templateYLabel[1] , templateYValue[max] templateScale viewed templateXValue[0] as a templateXLabel[0] with templateTitle[1] people , with the United Kingdom ( templateXValue[1] ) following at templateYValue[1] templateScale . In 2016 a similar report on templateTitleSubject[0] reputable templateTitle[2] also found templateXValue[0] to have one of the best reputations templateTitle[3] , next to Sweden .

generated_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: This statistic shows the Share of Most users who have accessed Most to consume 2014 as of 2014 , sorted 2014 Country .  During the survey period , 20 % of respondents from the Spain said that they had used Most worldwide 2014 .

Example 206:
titleEntities: {'Subject': ['Share'], 'Date': ['2014', '2019']}
title: Share of art buyers who bought art and/or collectibles via the internet 2014 to 2019
Data:
         0     1     2     3     4     5
Year  2019  2018  2017  2016  2015  2014
Yes     44    43    49    49    49    38
No      56    57    51    51    51    62 

gold: This statistic depicts the share of art buyers who bought art and/or collectibles directly online through a click-and-buy or click-and-bid ( with or without having seen the physical object ) in the 12 months previous from 2014 to 2019 ( as of April each year ) . According to the source , 44 percent of respondents said they had bought art and/or collectibles online in 2019 , up from 38 percent in 2014 .
gold_template: This statistic depicts the templateTitleSubject[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[1] templateTitle[6] templateTitle[7] directly online through a click-and-buy or click-and-bid ( with or without having seen the physical object ) in the 12 months previous from templateValue[0][last] to templateValue[0][0] ( as of each templateLabel[0][0] ) . According to the source , templateValue[1][0] templateScale of respondents said they had templateTitle[4] templateTitle[1] templateTitle[6] templateTitle[7] online in templateValue[0][0] , up from templateValue[1][last] templateScale in templateValue[0][last] .

generated_template: This statistic illustrates the distribution of templateTitleSubject[0] employees worldwide from templateValue[0][last] to templateValue[0][0] , sorted templateTitle[6] templateTitle[7] . As of templateValue[0][0] , templateValue[2][last] templateScale of templateTitle[1] templateTitleSubject[0] employees were templateLabel[2][0] . The majority of employees were templateLabel[1][0] .
generated: This statistic illustrates the distribution of Share employees worldwide from 2014 to 2019 , sorted and/or collectibles .  As of 2019 , 62 % of art Share employees were No .  The majority of employees were Yes .

Example 207:
titleEntities: {'Subject': ['Pandora'], 'Date': ['2006']}
title: Pandora 's annual revenue 2006 to 2017
X_Axis['Fiscal', 'year']: ['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']: ['1466.81', '1384.83', '1164.04', '920.8', '600.23', '427.15', '274.34', '137.76', '55.19', '19.33', '14.3', '4.18']

gold: The statistic shows data on the revenue generated by Pandora Media , Inc. in the fiscal periods between February 2006 to December 2017 . In the fiscal year which ended December 31 , 2014 , Pandora generated a total revenue of 920.8 million U.S. dollars . Here you can find out how much of the revenue is generated by subscription services and how much by advertising .
gold_template: The statistic shows data on the templateYLabel[0] generated by templateTitleSubject[0] Media , Inc. in the templateXLabel[0] periods between 2006 to 2017 . In the templateXLabel[0] templateXLabel[1] which ended 31 , templateXValue[3] , templateTitleSubject[0] generated a total templateYLabel[0] of templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] . Here you can find out how much of the templateYLabel[0] is generated by subscription services and how much by advertising .

generated_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: The statistic shows data on the annual Revenue generated by Pandora Media , Inc. in the fiscal periods between 2006 and 2017 .  In the fiscal year which ended 31 , Jan_to_Dec_2016 Pandora generated 1466.81 million U.S. dollars in annual Revenue , the first time that the Revenue has exceeded 1466.81 million U.S. dollars .

Example 208:
titleEntities: {'Subject': ['Chad'], 'Date': ['2019']}
title: Unemployment rate in Chad 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']: ['2.32', '2.23', '2.2', '2.42', '2.17', '2.05', '2.11', '1.78', '1.76', '1.62', '1.61', '1.29', '1.27', '1.36', '1.49', '1.47', '1.16', '1.05', '1.17', '1.28', '1.23']

gold: The statistic shows the unemployment rate in Chad from 1999 to 2019 . In 2019 , the unemployment rate in Chad was at 2.32 percent .
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 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 Chad from 1999 to 2019 .  In 2019 , the Unemployment rate in Chad was at approximately 2.32 % .

Example 209:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2005', '2018']}
title: Consumer spending in the United Kingdom ( UK ) 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']: ['1341849', '1288984', '1235247', '1178915', '1142350', '1097963', '1052306', '1012673', '983196', '959219', '984020', '952873', '911678', '873079']

gold: This statistic shows total domestic consumption expenditure in the United Kingdom ( UK ) from 2005 to 2018 . Over this period , consumer spending in the UK increased , reaching approximately over 1.34 trillion British pounds in 2018 . Household consumption expenditure looks at the overall spending on consumer goods and services of a wide variety .
gold_template: This statistic shows total domestic consumption templateYLabel[0] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . Over this period , templateTitle[0] templateTitle[1] in the templateTitleSubject[1] templatePositiveTrend , reaching approximately over templateYValue[max] templateScale British pounds in templateXValue[idxmax(Y)] . Household consumption templateYLabel[0] looks at the overall templateTitle[1] on templateTitle[0] goods and services of a wide variety .

generated_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: In 2018 , consumers spent 1341849 million British pounds on spending in the United Kingdom ( UK ) .  This is the highest consumer spending recorded in the past thirteen years and the second time spending surpassed 1288984 million pounds .  Spending has generally grown since 2010 .

Example 210:
titleEntities: {'Subject': ['Alphabet'], 'Date': ['2015', '2019']}
title: Alphabet : revenue distribution 2015 - 2019 , by region
Data:
                   0     1     2     3     4
Year            2019  2018  2017  2016  2015
United States     46    46    47    47    46
EMEA              31    33    33    34    35
APAC              17    15    15    14    14
Other Americas     6     6     5     5     5 

gold: This statistic gives information on the distribution of Alphabet 's revenue from 2015 to 2019 , by region . In 2019 , 46 percent of the internet company 's revenue was generated in the United States and 31 percent in the EMEA region .
gold_template: This statistic gives information on the templateTitle[2] of templateTitleSubject[0] 's templateTitle[1] from templateValue[0][last] to templateValue[0][0] , templateTitle[5] templateTitle[6] . In templateValue[0][0] , templateValue[1][last] templateScale of the internet company 's templateTitle[1] was generated in the country and templateValue[2][0] templateScale in the templateLabel[2][0] templateTitle[6] .

generated_template: Data on the number of templateTitle[0] at templateTitleSubject[0] templateTitle[2] from templateValue[0][last] to templateValue[0][0] , shows that as of 30 , templateValue[0][0] , the media giant had approximately 28,000 templateTitle[0] , templateValue[1][0] thousand of which were located in the country . An additional templateValue[1][0] thousand were located in templateLabel[2][0] , and templateTitleSubject[0] templateTitle[2] also employed templateValue[2][last] thousand people in the templateLabel[1][0] templateLabel[3][1] .
generated: Data on the number of Alphabet at distribution from 2015 to 2019 , shows that as of 30 , 2019 the media giant had approximately 28,000 Alphabet , 46 thousand of which were located in the country .  An additional 46 thousand were located in EMEA , and Alphabet distribution also employed 35 thousand people in the United APAC .

Example 211:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of cheese 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']: ['40.0', '39.0', '38.5', '37.2', '36.2', '35.6', '35.5', '35.3', '35.0', '34.6', '34.5', '35.2', '34.8', '34.0', '33.6', '32.9', '32.8', '32.4', '32.1']

gold: The timeline pictures the per capita consumption of cheese in the United States from 2000 to 2018 . The per capita consumption of the entire cheese category in the United States amounted to about 39 pounds in 2017 .
gold_template: The timeline pictures the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] of the entire templateTitle[4] category in the templateTitle[0] amounted to about templateYValue[1] 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 approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of cheese 2000 in the U.S. from 2000 to 2018 .  According to the report , the U.S. Per capita consumption of cheese 2000 amounted to approximately 40.0 pounds in 2018 .

Example 212:
titleEntities: {'Subject': ['Chelsea FC'], 'Date': []}
title: Chelsea FC revenue by stream 2018/19
Data:
                    0        1        2        3        4        5        6        7        8        9       10
Season        2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09
Matchday         75.6     83.4     76.2     93.2     93.1     84.9     82.5     96.1     74.7     82.1     87.4
Broadcasting    227.1    230.5    189.1    191.1    178.2    167.3    123.0    139.4    112.3    105.0     92.9
Commercial      210.4    191.8    162.7    163.1    148.7    135.7     97.9     87.1     62.8     68.8     62.0 

gold: Broadcasting is the largest source of revenue for Chelsea FC . In 2018/2019 , the club earned approximately 227.1 million euros from broadcasting , more than double than in 2010/2011 . The second biggest revenue stream is the commercial stream .
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 double than in 2010/2011 . The second biggest templateTitle[2] templateTitle[4] is the templateLabel[3][0] templateTitle[4] .

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 triple than in 2010/2011 . The second biggest templateTitle[2] templateTitle[4] is the templateLabel[3][0] one .
generated: Broadcasting is the largest source of revenue for Chelsea FC .  In 2018/2019 , the club earned approximately 227.1 million euros from Broadcasting , more than triple in 2010/2011 .  The second biggest revenue stream is the Commercial one .

Example 213:
titleEntities: {'Subject': ['Cloud'], 'Date': ['2017', '2019']}
title: Cloud infrastructure services market share worldwide 2017 - 2019 , by vendor
Data:
                           0        1        2        3        4
Quarter              Q2_2017  Q4_2017  Q3_2018  Q4_2018  Q2_2019
Amazon Web Services       34       34       34       34       33
Microsoft                 11       13       15       15       16
IBM                        8        8        7        7        8
Google                     6        6        7        7        0
Alibaba                    0        4        4        4        0
Next 10                   20       16       16       15        0
Other                     23       20       18       17       43
Rackspace                  0        0        0        0        0 

gold: This statistic shows the market share of the leading cloud infrastructure service providers from 2017 to 2019 . In the second quarter of 2019 , Amazon , via Amazon Web Services , held around 33 percent of the cloud infrastructure services market . Amazon 's share of the cloud infrastructure services market has remained steady at around a third of the total market since at least 2014 .
gold_template: This statistic shows the templateTitle[3] templateTitle[4] of the leading templateTitleSubject[0] templateTitle[1] service providers from templateValue[0][0] to templateValue[0][4] . In the second templateLabel[0][0] of templateValue[0][4] , templateLabel[1][0] , via templateLabel[1][0] templateLabel[1][1] templateTitle[2] , held around templateValue[1][last] templateScale of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] . templateLabel[1][0] 's templateTitle[4] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] has remained steady at around a third of the total templateTitle[3] since at least 2014 .

generated_template: This statistic shows the templateTitle[4] of templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from the templateTitle[8] templateLabel[0][0] of templateTitleDate[min] to the first templateLabel[0][0] of templateTitleDate[max] , templateTitle[7] templateTitle[8] templateTitle[10] group . For the survey period , it was found that templateValue[2][last] templateScale of the market 's templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[7] .
generated: This statistic shows the share of infrastructure services market in the Cloud ( ) from the by Quarter of 2017 to the first Quarter of 2019 , by vendor group .  For the survey period , it was found that 16 % of the market 's services market share in the 2019 .

Example 214:
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 displays templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the internet company 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateScale templateYLabel[4] dollars . templateTitleSubject[0] is the main revenue generator of online business conglomerate Alphabet .
generated: This statistic displays Deere 's Total assets from 2013 to 2019 .  In 2019 , the internet company 's Total assets amounted to 73011.0 million dollars .  Deere is the main revenue generator of online business conglomerate Alphabet .

Example 215:
titleEntities: {'Subject': ['Arizona Diamondbacks'], 'Date': ['2001', '2018']}
title: Arizona Diamondbacks 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']: ['275', '258', '253', '223', '211', '192', '195', '186', '180', '172', '177', '165', '154', '145', '136', '126', '122', '127']

gold: The statistic depicts the revenue of the Arizona Diamondbacks from 2001 to 2018 . In 2018 , the revenue of the Major League Baseball franchise amounted to 275 million U.S. dollars.The Arizona Diamondbacks are owned by Ken Kendrick , who bought the franchise for 238 million U.S. dollars in 2004 .
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 Ken Kendrick , who bought the franchise for 238 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[14] .

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 Arizona Diamondbacks , a franchise of the National Football League , from 2001 to 2018 .  In 2018 , the Revenue of the Arizona Diamondbacks was 275 U.S. dollars .

Example 216:
titleEntities: {'Subject': ['New Orleans Saints'], 'Date': ['2019']}
title: Franchise value of the New Orleans Saints 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', '2075', '2000', '1750', '1515', '1110', '1004', '971', '965', '955', '943', '937', '854', '738', '718', '627', '585', '481']

gold: This graph depicts the franchise value of the New Orleans Saints from the National Football League from 2002 to 2019 . In 2019 , the franchise value amounted to about 2.3 billion U.S. dollars . Since 1985 , the New Orleans Saints have been owned by Gayle Benson , wife of the late Thomas Benson , who bought the franchise for 70.2 million U.S. dollars in 1985 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Saints from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] amounted to about templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . Since 1985 , the templateTitleSubject[0] Saints have been owned by Gayle Benson , wife of the late Thomas Benson , who bought the templateYLabel[0] for 70.2 templateScale templateYLabel[3] templateYLabel[4] in 1985 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitleSubject[0] franchise 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 templateTitle[2] templateTitleSubject[0] are owned by William DeWitt Jr. , who bought the templateYLabel[0] for 150 templateScale templateYLabel[3] templateYLabel[4] in 1996 .
generated: This graph depicts the value of the New New Orleans Saints franchise of Major League Baseball from 2002 to 2019 .  In 2019 , the Franchise had an estimated value of 2300 million U.S. dollars .  The New New Orleans Saints are owned by William DeWitt Jr. , who bought the Franchise for 150 million U.S. dollars in 1996 .

Example 217:
titleEntities: {'Subject': ['France'], 'Date': ['2004', '2018']}
title: France : total number of marriages 2004 to 2018
X_Axis['Year']: ['2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Number', 'of', 'marriages']: ['278439', '283036', '273914', '273669', '265404', '251478', '251654', '236826', '245930', '238592', '241300', '236300', '232700', '233900', '235000']

gold: This statistic depicts the total number of marriages in France from 2004 to 2018 . In 2005 the number of marriages amounted to over 283 thousand , this was the highest number of marriages recorded during the 14 year period . Same-sex marriage was legalized in France in 2013 , Since then the number of same-sex marriages in France reached 7,751 marriages in 2015 , with the highest number recorded the previous year with 10,522 nuptials .
gold_template: This statistic depicts the templateTitle[1] templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] the templateYLabel[0] of templateYLabel[1] amounted to over templateYValue[max] thousand , this was the highest templateYLabel[0] of templateYLabel[1] recorded during the 14 templateXLabel[0] period . Same-sex marriage was legalized in templateTitleSubject[0] in templateXValue[9] , Since then the templateYLabel[0] of same-sex templateYLabel[1] in templateTitleSubject[0] reached 7,751 templateYLabel[1] in templateXValue[11] , with the highest templateYLabel[0] recorded the previous templateXLabel[0] with 10,522 nuptials .

generated_template: In templateXValue[13] , there were exactly templateYValue[13] templateYLabel[1] in templateTitleSubject[0] . That same templateXLabel[0] , templateTitleSubject[0] was the European country with the second highest templateYLabel[0] of live templateYLabel[1] behind Germany . Thus , templateTitleSubject[0] had a birth rate of 11.7 templateYLabel[1] per 1,000 population in templateXValue[12] , which was one of the highest birth rate in Europe .
generated: In 2017 , there were exactly 233900 marriages in France .  That same Year , France was the European country with the second highest Number of live marriages behind Germany .  Thus , France had a birth rate of 11.7 marriages per 1,000 population in 2016 , which was one of the highest birth rate in Europe .

Example 218:
titleEntities: {'Subject': ['Countries'], 'Date': []}
title: Countries with the highest unemployment rate
X_Axis['Country']: ['Burkina_Faso', 'Syria', 'Senegal', 'Haiti', 'Kenya', 'Djibouti', 'Marshall_Islands', 'Republic_of_the_Congo', 'Namibia', 'Kiribati', 'Kosovo', 'Libya', 'American_Samoa', 'Lesotho', 'Eswatini', 'Gabon', 'South_Africa', 'Yemen', 'Gaza_Strip', 'West_Bank']
Y_Axis['Unemployment', 'rate']: ['77', '50', '48', '40.6', '40', '40', '36', '36', '34', '30.6', '30.5', '30', '29.8', '28.1', '28', '28', '27.6', '27', '26.7', '26.7']

gold: In 2017 , Burkina Faso had the highest unemployment rate in the world , at 77 percent . This means that for every 100 members of the workforce , 77 did not have jobs at the time of the survey . Of the 20 countries in this statistic , each had a ratio of more than 1 in 4 workers without a job .
gold_template: In 2017 , templateXValue[0] had the templateTitle[2] templateYLabel[0] templateYLabel[1] in the world , at templateYValue[max] templateScale . This means that for every 100 members of the workforce , templateYValue[max] did not have jobs at the time of the survey . Of the 20 templateTitleSubject[0] in this statistic , each had a ratio of more than 1 in 4 workers without a job .

generated_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] an estimated templateYLabel[0] templateYLabel[1] of about 27.6 templateScale .
generated: The statistic lists the 20 Countries highest the unemployment rate in .  In , Burkina_Faso ranked 1st highest an estimated Unemployment rate of about 27.6 % .

Example 219:
titleEntities: {'Subject': ['UK'], 'Date': ['2005', '2018']}
title: Annual spending on veterinary and pet services in the UK by volume 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']: ['5059', '4235', '3527', '3180', '3220', '3175', '3083', '2810', '3136', '2923', '2962', '3273', '2839', '2990']

gold: This statistic shows the total annual expenditure on veterinary and other services for pets in the United Kingdom from 2005 to 2018 , based on volume . In 2018 , households purchased over five billion British pounds worth of veterinary and other services for their pets .
gold_template: This statistic shows the total templateTitle[0] templateYLabel[0] on templateTitle[2] and other templateTitle[4] for pets in the United Kingdom from templateXValue[min] to templateXValue[max] , based on templateTitle[7] . In templateXValue[max] , households purchased over templateYValue[max] templateScale British pounds worth of templateTitle[2] and other templateTitle[4] for their pets .

generated_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: In 2018 , consumers spent 5059 million British pounds on spending in the UK ( ) .  This is the highest consumer spending recorded in the past thirteen years and the second time spending surpassed 4235 million pounds .  Spending has generally grown since 2010 .

Example 220:
titleEntities: {'Subject': ['Groupon'], 'Date': ['2009', '2019']}
title: Groupon : quarterly revenue 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"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['612.32', '495.61', '532.58', '578.41', '799.93', '592.88', '617.4', '626.54', '873.17', '634.47', '662.62', '673.63', '904.87', '686.56', '723.76', '698.44', '917.17', '713.6', '738.4', '750.36', '883.23', '714.27', '716.21', '728.42', '768.45', '595.06', '608.75', '601.4', '638.3', '568.55', '568.34', '559.28', '492.16', '430.16', '392.58', '295.52', '172.22', '81.78', '38.67', '20.27', '9.25', '4.0', '1.21']

gold: During the fourth quarter of 2019 , daily deal and local offer platform Groupon generated global revenues of 612.3 million U.S. dollars . A considerable amount of Groupon 's revenue is generated through its mobile platform , where the number of mobile Groupon app downloads worldwide reached 200 million downloads in June 2019 .
gold_template: During the fourth templateXLabel[0] of templateTitleDate[max] , daily deal and local offer platform templateTitleSubject[0] generated global revenues of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] . A considerable amount of templateTitleSubject[0] 's templateYLabel[0] is generated through its mobile platform , where the number of mobile templateTitleSubject[0] app downloads worldwide reached 200 templateScale downloads in 2019 .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] 's templateYLabel[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the preceding templateXLabel[0] . templateTitleSubject[0] 's main templateYLabel[0] source is advertising through templateTitleSubject[0] sites and its network . In templateTitleDate[max] , templateTitleSubject[0] accounted for the majority of parent company Alphabet 's revenues with 113.26 templateScale templateYLabel[2] templateYLabel[3] in templateTitleSubject[0] website ad revenues alone .
generated: In the fourth Quarter of 2019 , Groupon 's Revenue amounted to 917.17 million U.S. dollars , up from 495.61 million U.S. dollars in the preceding Quarter .  Groupon 's main Revenue source is advertising through Groupon sites and its network .  In 2019 , Groupon accounted for the majority of parent company Alphabet 's revenues with 113.26 million U.S. dollars in Groupon website ad revenues alone .

Example 221:
titleEntities: {'Subject': ['Philippines'], 'Date': ['2017', '2023']}
title: Philippines : 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']: ['50.8', '48.9', '46.8', '44.3', '41.3', '38.3', '34.5']

gold: The statistic shows the number of mobile internet users in the Philippines from 2017 to 2023 . In 2017 , 34.5 million people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 50.8 million mobile phone internet users .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in the 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_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] , approximately templateYValue[5] templateScale people accessed the templateYLabel[3] through templateYLabel[1] devices . In templateXValue[max] , this figure is projected to reach about templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number of mobile internet users in Philippines from 2017 to 2023 .  In 2018 , approximately 38.3 millions people accessed the internet through mobile devices .  In 2023 , this figure is projected to reach about 50.8 millions mobile internet users .

Example 222:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: Employment rate of men 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['Employment', 'rate']: ['66.6', '66.3', '66', '65.8', '65.3', '64.9', '64.4', '64.4', '63.9', '63.7', '64.5', '68.5', '69.8', '70.1', '69.6', '69.2', '68.9', '69.7', '70.9', '71.9', '71.6', '71.6', '71.3', '70.9', '70.8', '70.4', '70', '69.8', '70.4', '72']

gold: This statistic shows the employment rate of men in the United States from 1990 to 2019 . In 2019 , this rate was 66.6 percent . The monthly figure of full-time employees in the U.S. can be accessed here .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] in the templateTitle[3] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , this templateYLabel[1] was templateYValue[0] templateScale . The monthly figure of full-time employees in the templateTitleSubject[0] can be accessed here .

generated_template: In templateTitleDate[max] , there were templateYValue[0] templateScale templateYLabel[1] in the templateTitle[3] . This is a slight templateNegativeTrend from the previous templateXLabel[0] , it is about templateYValue[min] templateScale templateYLabel[1] in the world . templateTitle[0] templateYLabel[1] in the templateTitleSubject[0] As can be defined here .
generated: In 2019 , there were 66.6 % rate in the U.S. This is a slight decrease from the previous Year , it is about 63.7 % rate in the world .  Employment rate in the U.S. As can be defined here .

Example 223:
titleEntities: {'Subject': ['Florida'], 'Date': ['2000', '2018']}
title: Florida - 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', '14.7', '15.7', '16.5', '17', '17.1', '17', '16.5', '14.9', '13.2', '12.1', '12.6', '12.8', '12.2', '13.1', '12.8', '12.5', '12.8']

gold: This statistic shows the poverty rate in Florida from 2000 to 2018 . In 2018 , about 13.6 percent of Florida '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 Florida from 2000 to 2018 .  In 2018 , about 13.6 percentage of Florida 's population lived below the Poverty line .

Example 224:
titleEntities: {'Subject': ['Alibaba'], 'Date': ['2016', '2019']}
title: Alibaba : annual segment revenue distribution 2016 to 2019
Data:
                                      0     1     2     3
Year                               2019  2018  2017  2016
China commerce retail                66    71    72    79
China commerce wholesale              3     3     4     4
International commerce retail         5     6     5     2
International commerce wholesale      2     2     4     6
Cloud computing                       7     5     4     3
Digital media and entertainment       6     8     9     4
Innovation initiatives and others     1     1     2     2 

gold: In the fiscal year ending March 31 , 2019 , the Chinese e-commerce corporation Alibaba Group reported a consolidated annual revenue of around 376.8 billion yuan . The largest share of the revenue , 66 percent , came from the domestic e-commerce retail segment . Alibaba Group Founded in 1999 , today Alibaba Group is the leading e-commerce provider in China , offering a wide range of B2B , B2C , and C2C services .
gold_template: In the fiscal templateLabel[0][0] ending 31 , templateValue[0][0] , the Chinese e-commerce corporation templateTitleSubject[0] Group reported a consolidated templateTitle[1] templateTitle[3] of around 376.8 templateScale yuan . The largest share of the templateTitle[3] , templateValue[1][0] templateScale , came from the domestic e-commerce templateLabel[1][2] templateTitle[2] . templateTitleSubject[0] Group Founded in 1999 , today templateTitleSubject[0] Group is the leading e-commerce provider in templateLabel[1][0] , offering a wide range of B2B , B2C , and C2C services .

generated_template: This statistic provides a forecast of the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] compared to the templateLabel[3][0] templateTitle[1] templateTitle[2] from templateValue[0][last] to templateValue[0][0] , excluding 2013 and 2014 . As of the last measured period , templateTitle[0] templateTitle[1] in templateTitleSubject[0] was at templateValue[1][0] templateScale . The templateLabel[3][0] templateLabel[3][1] was templateValue[3][0] templateScale .
generated: This statistic provides a forecast of the Alibaba annual segment in Alibaba compared to the International annual segment from 2016 to 2019 , excluding 2013 and 2014 .  As of the last measured period , Alibaba annual in Alibaba was at 66 % .  The International commerce was 5 % .

Example 225:
titleEntities: {'Subject': ['vegetarian', 'United Kingdom', 'UK'], 'Date': ['2008', '2012']}
title: Individuals following a vegetarian diet in the United Kingdom ( UK ) 2008 - 2012 , by age
Data:
                                       0           1            2            3                  4
Year                         1.5-3_years  4-10_years  11-18_years  19-64_years  65_years_and_over
Neither vegetarian or vegan           99          98           98           97                 98
Vegetarian                             1           2            2            3                  1 

gold: This statistic displays the proportion of individuals following a vegetarian or vegan diet in the United Kingdom from 2008 to 2012 . During the period in question , three percent of individuals aged between 19 and 64 years old were following a vegetarian diet , however 97 percent were following neither a vegetarian or a vegan diet.For more recent numbers focusing on adults click here .
gold_template: This statistic displays the proportion of templateTitle[0] templateTitle[1] a templateTitleSubject[0] or templateLabel[1][2] templateTitle[3] in the templateTitleSubject[1] from templateTitleDate[min] to templateTitleDate[max] . During the period in question , templateValue[2][3] templateScale of templateTitle[0] aged between 19 and 64 templateValue[0][0] old were templateTitle[1] a templateTitleSubject[0] templateTitle[3] , however templateValue[1][3] templateScale were templateTitle[1] templateLabel[1][0] a templateTitleSubject[0] or a templateLabel[1][2] diet.For more recent numbers focusing on adults click here .

generated_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: This graph displays the percentage of Americans diet were Kingdom in 2008 , distinguished 2008 2012 and by .  In 2008 , 47.51 % of the Neither Americans , aged 65_years_and_over 1.5-3_years and 65_years_and_over , were Kingdom .

Example 226:
titleEntities: {'Subject': ['Denver'], 'Date': ['2016', '2017']}
title: Quarterly average daily rate of hotels in Denver 2016 to 2017
Data:
           0    1    2    3
Quarter   Q1   Q2   Q3   Q4
2016     164  182  191  174
2017     170    0    0    0 

gold: This statistic shows the quarterly average daily rate of hotels in Denver in 2016 and 2017 . In the first quarter of 2017 , the average daily rate of hotels in Denver in the United States was 170 U.S. dollars .
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 .

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 Denver 2016 and 2017 .  In the first Quarter of 2017 , the average daily rate of hotels in Denver the country was 170 U.S. dollars .

Example 227:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2018', '2018']}
title: Leading video gaming brands on Twitter 2018 , by followers
X_Axis['Brand']: ['PlayStation', 'Xbox', 'Nintendo_of_America', 'Rockstar_Games', 'Ubisoft', 'EA_SPORTS_FIFA', 'Sonic_the_Hedgehog', 'Fortnite', 'Electronic_Arts', 'League_of_Legends']
Y_Axis['Brand', 'fans', 'in', 'millions']: ['15.63', '12.87', '9.44', '9.31', '7.11', '6.4', '5.76', '5.64', '5.19', '4.36']

gold: This statistic gives information on the most popular gaming brands on Twitter , ranked by number of followers on the social network . As of September 2018 , Sony 's PlayStation was ranked first with 15.63 million Twitter followers . Ubisoft was ranked fifth with 7.1 million followers .
gold_template: This statistic gives information on the most popular templateTitle[2] templateTitle[3] on templateTitleSubject[0] , ranked templateTitle[6] number of templateTitle[7] on the social network . As of 2018 , Sony 's templateXValue[0] was ranked first with templateYValue[max] templateScale templateTitleSubject[0] templateTitle[7] . templateXValue[4] was ranked fifth with templateYValue[4] templateScale templateTitle[7] .

generated_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: This statistic gives information on the most brands Twitter 2018 on Twitter , ranked by video of gaming on the social network .  As of 2020 , personal care Brand PlayStation Body was ranked first with close to 15.63 millions Twitter gaming .

Example 228:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Airport market share of major car rental companies in the U.S. in 2013
X_Axis['Company']: ['Hertz_Global_Holdings', 'Enterprise_Holdings', 'Avis_Budget_Group', 'Other']
Y_Axis['Airport', 'market', 'share']: ['36.1', '33.2', '26', '4.7']

gold: This statistic shows the airport market share of major car rental companies in the United States in 2013 . Hertz Global Holdings had the largest share of the airport car rental market in 2013 at 36.1 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[7] in templateTitleDate[0] . templateXValue[0] Holdings had the largest templateYLabel[2] of the templateYLabel[0] templateTitle[4] templateTitle[5] templateYLabel[1] in templateTitleDate[0] at templateYValue[max] templateScale .

generated_template: This statistic shows the templateScale of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitleDate[0] , by the templateXLabel[0] of templateXLabel[1] . templateYValue[max] templateScale of templateYLabel[1] with templateXValue[last] and templateXValue[last] templateXLabel[1] used templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] .
generated: This statistic shows the percentage of U.S. market major car rental companies U.S. Company in 2013 , by the Company of .  36.1 % of market with Other and Company used car rental companies in 2013 .

Example 229:
titleEntities: {'Subject': ['U.S.'], 'Date': []}
title: U.S. winter heating oil prices 2005/06 - 2019/20
X_Axis['Winter', 'of']: ['2019/20', '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['Price', 'in', 'U.S.', 'dollars', 'per', 'gallon']: ['3.02', '3.07', '2.78', '2.41', '2.06', '3.04', '3.88', '3.87', '3.73', '3.38', '2.85', '2.65', '3.33', '2.42', '2.44']

gold: The average price of heating oil in the United States in the winter between 2019 and 2020 is expected to reach 3.02 U.S. dollars per gallon . The number of heating degree days , days the average temperature is below 18° Celsius ( 65° Fahrenheit ) , helps quantify the energy demand required to heat a building . Heating oil basics Heating oil is a liquid petroleum product that is , among other things , used to heat residential buildings as a fuel oil in furnaces or boilers .
gold_template: The average templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitle[0] in the templateXLabel[0] between 2019 and 2020 is expected to reach templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The number of templateTitle[2] degree days , days the average temperature is below 18° Celsius ( 65° Fahrenheit ) , helps quantify the energy demand required to heat a building . templateTitle[2] templateTitle[3] basics templateTitle[2] templateTitle[3] is a liquid petroleum product that is , among other things , used to heat residential buildings as a fuel templateTitle[3] in furnaces or boilers .

generated_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 total of templateYValue[5] templateScale .
generated: This statistic shows the U.S. of the winter heating oil prices in , 2019/20 Winter .  In that year , the 2017/18 was the third largest heating Winter in the world , with a total of 3.04 million .

Example 230:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2005', '2013']}
title: Number of computers in households in Great Britain 2005 to 2013
Data:
                     0     1     2     3     4
Year              2013  2011  2009  2007  2005
No computer         24    24    26    28    30
One computer        37    34    41    49    53
Two computers       21    25    20    17    11
Three+ computers    18    18    13     7     5 

gold: This survey illustrates the number of computers  available in households in Great Britain from 2005 to 2013 . In 2005 , five percent of respondents reported to have three or more computers in their possession , whereas in 2013 , 18 percent of the respondents had three or more computers in their household .
gold_template: This survey illustrates the templateTitle[0] of templateTitle[1] available in templateTitle[2] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][last] , templateValue[4][last] templateScale of respondents reported to have three or more templateTitle[1] in their possession , whereas in templateValue[0][0] , templateValue[4][0] templateScale of the respondents had three or more templateTitle[1] in their household .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleDate[min] - templateTitleDate[max] , templateTitle[7] templateTitle[8] . templateValue[1][last] templateScale of the Dutch respondents aged 16 or 17 templateValue[0][0] reported that they believe templateTitle[2] templateTitle[3] are a templateLabel[1][0] .
generated: This statistic shows the Number computers households Great in the Great Britain 2005 - 2013 , .  30 % of the Dutch respondents aged 16 or 17 2013 reported that they believe households Great are a No .

Example 231:
titleEntities: {'Subject': ['Ford'], 'Date': ['2003', '2019']}
title: Ford 's vehicle sales market share in the U.S. 2003 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2012', '2009', '2006', '2005', '2003']
Y_Axis['Market', 'share']: ['14.08', '14.29', '14.8', '15.22', '15.29', '16.04', '17.01', '19.19']

gold: In 2019 , Ford 's U.S. market share was around 14.1 percent , trailing only General Motors ( GM ) . The two largest U.S. brands are relentless competitors in the global automobile industry . GM and Ford The competition between Ford and General Motors has played out strongly in the case of truck sales , with GM taking the lead in the first quarter of 2019 with a market share of 44 percent .
gold_template: In templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateTitle[6] templateYLabel[0] templateYLabel[1] was around templateYValue[min] templateScale , trailing only General Motors ( GM ) . The two largest templateTitle[6] brands are relentless competitors in the global automobile industry . GM and templateTitleSubject[0] The competition between templateTitleSubject[0] and General Motors has played out strongly in the case of truck templateTitle[3] , with GM taking the lead in the first quarter of templateXValue[idxmin(Y)] with a templateYLabel[0] templateYLabel[1] of 44 templateScale .

generated_template: This statistic illustrates the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] generated about templateYValue[0] templateScale British pounds in templateYLabel[0] templateYLabel[1] .
generated: This statistic illustrates the Market share of the vehicle sales market share in the U.S. from 2003 to 2019 .  In 2019 , the share vehicle sales market share generated about 14.08 billion British pounds in Market share .

Example 232:
titleEntities: {'Subject': ['Iceland'], 'Date': ['2009', '2019']}
title: Population of Iceland 2009 - 2019 , by gender
Data:
              0       1       2       3       4       5       6       7       8       9      10
Year       2019    2018    2017    2016    2015    2014    2013    2012    2011    2010    2009
Males    182837  177600  171033  167270  165186  163318  161438  160364  160006  159936  162068
Females  174154  170850  167316  165259  163914  162353  160419  159211  158446  157694  157300 

gold: How many men and women lived in Iceland from 2019 to 2019 ? In short , the number of male and female inhabitants was quite steady during the period . In 2019 , the population of Iceland consisted of approximately 182 thousand male and 174 thousand female inhabitants . What is more , the largest group of Icelanders is between 20 and 39 years old .
gold_template: How many men and women lived in templateTitleSubject[0] from templateValue[0][0] to templateValue[0][0] ? In short , the number of male and female inhabitants was quite steady during the period . In templateValue[0][0] , the templateTitle[0] of templateTitleSubject[0] consisted of approximately 182 thousand male and templateValue[2][0] thousand female inhabitants . What is more , the largest group of Icelanders is between 20 and 39 years old .

generated_template: In templateValue[0][9] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] was down at the lowest point of templateValue[2][4] templateScale . Since then , it templatePositiveTrend annually , reaching the highest point of the period in templateValue[0][1] and templateValue[0][0] at 68.3 templateScale both years . The templateTitle[0] differed among the genders , for templateLabel[1][0] it was templateValue[1][0] templateScale , and for templateLabel[2][0] it was lower , templateValue[2][0] templateScale .
generated: In 2010 , the Population Iceland in was down at the lowest point of 163914 % .  Since then , it increased annually , reaching the highest point of the period in 2018 and 2019 at 68.3 % both years .  The Population differed among the genders , for Males it was 182837 % , and for Females it was lower , 174154 % .

Example 233:
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 templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[2] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[2] in the templateTitle[6] .
generated: The statistic shows the Population of millions U.S. 2000 2018 millions in the 2018 from 2000 to 2018 .  In 2018 , there were 59.87 millions U.S. 2000 2018 millions in the 2018 .

Example 234:
titleEntities: {'Subject': ['NHL'], 'Date': ['2014/15', '2014/15']}
title: Average ticket price Pittsburgh Penguins ( NHL ) 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']: ['73.59', '70.52', '67.52', '63.06', '60.04', '60.04', '55.55', '51.45', '46.81', '36.61']

gold: This graph depicts the average ticket price of Pittsburgh Penguins games within the National Hockey League from 2005/06 to 2014/15 In the 2005/06 season , the average ticket price was 36.61 U.S. dollars . The Penguins play their home games at the Consol Energy Center in Pittsburgh , Pennsylvania .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] games within the National Hockey League from 2005/06 to templateTitle[6] In the 2005/06 season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] . The templateTitle[4] play their home games at the Consol Energy Center in templateTitle[3] , Pennsylvania .

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 NHL games of the National Basketball Association from 05/06 to 14(15 .  In the 05/06 season , the Average ticket price was 36.61 U.S. dollars .

Example 235:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of fresh potatoes 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']: ['31.1', '34.4', '33.7', '34.2', '33.6', '34.5', '34.5', '34.0', '36.8', '36.6', '37.8', '38.7', '38.6', '41.3', '45.8', '46.8', '44.3', '46.6', '47.1']

gold: The timeline shows the per capita consumption of fresh potatoes in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh potatoes amounted to 31.1 pounds in 2018 .
gold_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[idxmin(Y)] .

generated_template: This statistic 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[idxmin(Y)] .
generated: This statistic shows the Per capita consumption of fresh potatoes in the U.S. from 2000 to 2018 .  According to the report , the U.S. Per capita consumption of fresh potatoes amounted to approximately 31.1 pounds in 2018 .

Example 236:
titleEntities: {'Subject': ['Marathon Oil'], 'Date': ['2010', '2018']}
title: Marathon Oil 's number of employees 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Number', 'of', 'employees']: ['2400', '2300', '2117', '2611', '3330', '3359', '3367', '3322', '29677']

gold: This statistic outlines Marathon Oil 's number of employees from 2010 to 2018 . Marathon Oil Corporation is an internationally leading United States-based oil and natural gas exploration and production company . In 2018 , the company had 2,400 employees .
gold_template: This statistic outlines templateTitleSubject[0] 's templateYLabel[0] of templateYLabel[1] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] Corporation is an internationally leading United States-based templateTitleSubject[0] and natural gas exploration and production company . In templateXValue[max] , the company had templateYValue[idxmax(X)] templateYValue[idxmax(X)] .

generated_template: This statistic outlines the templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] Corporation is one of the largest U.S. oil and gas production production . In templateXValue[max] , the company had approximately templateYValue[idxmax(X)] templateYValue[idxmax(X)] .
generated: This statistic outlines the Number of employees at Marathon Oil from 2010 to 2018 .  Marathon Oil Corporation is one of the largest U.S. oil and gas production .  In 2018 , the company had approximately 2400 .

Example 237:
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 between 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 between 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 238:
titleEntities: {'Subject': ['AFC'], 'Date': ['2011', '2020']}
title: AFC-NFC Pro 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    74    70    67    64    61    58    55    53    50    50
Losing team     37    35    34    32    30    29    28    26    25    25 

gold: This graph depicts the amount a NFL player will be paid for playing in the Pro Bowl from 2011 to 2020 . In 2015 , a player on the winning team in the NFL All-Star game will receive 58,000 U.S. dollars for winning the Pro Bowl game . NFL Pro Bowl play compensation – additional information The Pro Bowl , the NFL 's all-star game , was usually played one week after the Super Bowl .
gold_template: This graph depicts the amount a NFL player will be paid templateTitle[5] templateTitle[7] in the templateTitle[1] templateTitle[2] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][5] , a player on the templateLabel[1][0] templateLabel[1][1] in the NFL All-Star templateTitle[8] will receive 58,000 U.S. dollars templateTitle[5] templateLabel[1][0] the templateTitle[1] templateTitle[2] templateTitle[8] . NFL templateTitle[1] templateTitle[2] play templateTitle[4] – additional information The templateTitle[1] templateTitle[2] , the NFL 's all-star templateTitle[8] , was usually played one week after the Super templateTitle[2] .

generated_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: This statistic shows the AFC of Losing team and Winning Pro in Bowl monetary from 2011 to 2020 .  In 2020 , around 947 % Losing team and 335 % Winning Pro were in operation monetary .

Example 239:
titleEntities: {'Subject': ['North America'], 'Date': ['2019']}
title: Highest grossing 3D movies in North America 2019
X_Axis['Month']: ['Star_Wars:_The_Force_Awakens', 'Avengers:_Endgame', 'Avatar', 'Black_Panther', 'Avengers:_Infinity_War', 'Jurassic_World', "Marvel's_The_Avengers", 'Star_Wars:_The_Last_Jedi', 'Incredibles_2', 'Rogue_One:_A_Star_Wars_Story', 'Beauty_and_the_Beast_(2017)', 'Finding_Dory', 'Avengers:_Age_of_Ultron', 'Captain_Marvel', 'Jurassic_World:_Fallen_Kingdom', 'Toy_Story_3', 'Wonder_Woman', 'Iron_Man_3', 'Captain_America:_Civil_War', 'Jumanji:_Welcome_to_the_Jungle']
Y_Axis['Lifetime', 'gross', 'in', 'million', 'U.S.', 'dollars']: ['936.66', '842.76', '760.51', '700.06', '678.82', '652.27', '623.36', '620.18', '608.59', '532.18', '504.01', '486.3', '459.01', '426.82', '416.77', '415.0', '412.56', '409.01', '408.08', '404.52']

gold: As of July 2019 , the 3D movie which had the highest lifetime gross at the North American box office was ‘ Star Wars : The Force Awakens ' with 936.66 million U.S. dollars in revenue . For years , James Cameron 's 2009 hit ‘ Avatar ' ranked second with over 760 million dollars in box office revenue . However , the movie was knocked down to third place after the success of ‘ Avengers : Endgame ' which at the time of writing had generated 842.76 million U.S. dollars in box offices in Canada and the United States – more than ‘ Captain America : Civil War ' and ‘ Iron Man 3 ' combined .
gold_template: As of 2019 , the templateTitle[2] movie which had the templateTitle[0] templateYLabel[0] templateYLabel[1] at the templateTitleSubject[0] American box office was ‘ templateXValue[0] templateXValue[9] : The templateXValue[0] ' with templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in revenue . For years , James Cameron 's 2009 hit ‘ templateXValue[2] ' ranked second with over 760 templateScale templateYLabel[4] in box office revenue . However , the movie was knocked down to third place after the success of ‘ templateXValue[6] : templateXValue[1] ' which at the time of writing had generated templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in box offices in Canada and the country – more than ‘ templateXValue[13] templateTitleSubject[0] : templateXValue[18] templateXValue[4] ' and ‘ templateXValue[17] 3 ' combined .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] to templateTitle[2] templateTitle[3] templateTitle[4] worldwide in 2014 . templateXValue[0] had the most templateYLabel[2] in 2014 , with an estimated templateYLabel[0] of templateYLabel[2] of templateYValue[max] templateScale .
generated: This statistic shows the Lifetime of million to 3D movies North worldwide in 2014 .  Star_Wars:_The_Force_Awakens had the most million in 2014 , with an estimated Lifetime of million 936.66 .

Example 240:
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] templateTitleSubject[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: 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 241:
titleEntities: {'Subject': ['Japan'], 'Date': []}
title: Japan - Electricity production by energy source
X_Axis['Energy', 'source']: ['Gas_coal_petroleum', 'Nuclear_energy', 'Hydropower', 'Renewable_energies', 'Acquisition', 'Total']
Y_Axis['Electricity', 'produced', 'in', 'million', 'megawatt', 'hours']: ['43.6', '20.0', '4.1', '0.2', '13.4', '81.4']

gold: This statistic shows the distribution of energy sources used for electricity production in Japan in March 2011 .
gold_template: This statistic shows the distribution of templateXValue[1] sources used for templateYLabel[0] templateTitle[2] in templateTitleSubject[0] in 2011 .

generated_template: This statistic shows the estimated templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[2] in 2010 , sorted templateTitle[5] templateTitle[6] , in templateXLabel[3] templateXLabel[4] of templateXLabel[0] templateXLabel[1] templateXLabel[2] . In 2010 , there were templateYValue[1] templateYLabel[1] templateYLabel[2] operating within the templateTitle[2] with between templateXValue[0] and templateXValue[1] templateXLabel[3] templateXLabel[4] of templateXLabel[0] templateXLabel[1] templateXLabel[2] .
generated: This statistic shows the estimated Electricity of produced million in the production in 2010 , sorted source , in source of Energy source .  In 2010 , there were 20.0 produced million operating within the production with between Gas_coal_petroleum and Nuclear_energy source of Energy source .

Example 242:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2010', '2018']}
title: Population of the Netherlands 2010 - 2018 , by religion
Data:
                                         0     1     2     3     4     5     6     7     8
Year                                  2018  2017  2016  2015  2014  2013  2012  2011  2010
No religious denomination               53    51    50    50    49    47    48    47    45
Roman Catholic                          22    24    24    24    24    26    26    26    27
Protestant Church in the Netherlands     6     6     6     6     6     5     6     6     6
Dutch Reformed Church                    6     6     6     6     7     7     7     7     8
Reformed Churches in the Netherlands     3     3     3     3     3     4     3     4     4
Islam                                    5     5     5     5     5     5     5     5     5
Other                                    5     6     6     6     6     6     5     5     5 

gold: This statistic shows the population of the Netherlands from 2010 to 2018 , by religion . It shows that the percentage of the people that does not identify with any religion continued to grow between 2010 and 2018 , from 45 to 53 percent of the people . The largest group in 2018 was the Roman Catholic group with 22 percent of the Dutch people identifying as Roman Catholic .
gold_template: This statistic shows the templateTitle[0] of the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , templateTitle[4] templateTitle[5] . It shows that the templateScale of the people that does not identify with any templateTitle[5] continued to grow between templateValue[0][last] and templateValue[0][0] , from templateValue[1][last] to templateValue[1][0] templateScale of the people . The largest group in templateValue[0][0] was the templateLabel[2][0] templateLabel[2][1] group with templateValue[2][0] templateScale of the templateLabel[4][0] people identifying as templateLabel[2][0] templateLabel[2][1] .

generated_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: The statistic shows the number of Netherlands from 2010 to 2018 .  At the end of 2018 , 22 million people were Roman Catholic Netherlands .  Roman Catholic Catholic 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 .

Example 243:
titleEntities: {'Subject': ['Indiana'], 'Date': ['2000', '2018']}
title: Indiana - 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']: ['13.1', '13.5', '14.1', '14.5', '15.2', '15.9', '15.6', '16', '15.3', '14.1', '13.1', '12.3', '12.7', '12.2', '10.8', '10.6', '10.9', '9.8', '10.1']

gold: This statistic shows the poverty rate in Indiana from 2000 to 2018 . In 2018 , about 13.1 percent of Indiana '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 Indiana from 2000 to 2018 .  In 2018 , about 13.1 percentage of Indiana 's population lived below the Poverty line .

Example 244:
titleEntities: {'Subject': ['Production'], 'Date': ['1991']}
title: Production value of office furniture manufacturers 1991 to 2015
X_Axis['Year']: ['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']
Y_Axis['Production', 'in', 'million', 'U.S.', 'dollars']: ['7228', '7710', '8160', '8850', '9435', '10040', '11460', '12350', '12240', '13285', '10975', '8890', '8505', '8935', '10070', '10820', '11420', '11160', '7845', '8300', '9375', '9270', '9355', '9775', '10245']

gold: The statistic depicts the total production value of office furniture manufacturers located in the United States to all locations in the world from 1991 to 2015 . The total production value amounted to 10.2 billion U.S. dollars in 2015 . Office furniture industry in the United StatesThe US office furniture industry dates back to the 1800 's .
gold_template: The statistic depicts the total templateYLabel[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] located in the country to all locations in the world from templateXValue[min] to templateXValue[max] . The total templateYLabel[0] templateTitle[1] amounted to templateYValue[last] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] . templateTitle[2] templateTitle[3] industry in the United StatesThe US templateTitle[2] templateTitle[3] industry dates back to the 1800 's .

generated_template: This statistic shows that the templateYLabel[0] of templateTitle[1] templateYLabel[1] in the templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[14] , a hospital templateYLabel[1] in the templateTitle[3] had an templateTitle[0] templateYLabel[0] of templateYValue[14] templateYLabel[2] . Since then , there was no significant change in the templateYLabel[0] of templateYLabel[1] .
generated: This statistic shows that the Production of value million in the office furniture in the manufacturers from 1991 to 2015 .  In 2005 , a hospital million in the furniture had an Production of 10070 U.S. Since then , there was no significant change in the Production of million .

Example 245:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Revenue from top 20 women health products in the U.S. 2016
X_Axis['Product', 'name', '(company)']: ['Prolia_(Amgen)', 'Premarin_(Pfizer)', 'Mirena_(Bayer)', 'Forteo_(Eli_Lilly)', 'NuvaRing_(Merck)', 'Nexplanon_(Merck)', 'Lo_Loestrin_Fe_(Allergan)', 'Estrace_Cream_(Allergan)', 'Makena_(AMAG_Pharm.)', 'Minastrin_24_FE_(Allergan)', 'Vagifem_(Novo_Nordisk)', 'Lupron_(AbbVie)', 'Gonal-F_(Merck_KGaA)', 'Follistim_(Merck)', 'Yasmin_(Bayer)', 'Oral_contrception_franchise_(J&J/Pfizer)', 'Natazia_(Bayer)', 'Minivelle_(Hisamitsu)', 'Caltrate_(Pfizer)', 'Junel/Loestrin_(Teva)']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['1049', '956', '776', '771', '576', '420', '404', '379', '334', '326', '302', '285', '221', '157', '142', '129', '104', '88', '87', '79']

gold: There are many women 's health products available in the U.S . The top women 's health product based on revenue in 2016 was Prolia , marketed by Amgen . Prolia generated 1.05 billion U.S. dollars in revenue in 2016 .
gold_template: There are many templateTitle[4] 's templateTitle[5] templateTitle[6] available in the templateTitle[7] . The templateTitle[2] templateTitle[4] 's templateTitle[5] templateXLabel[0] based on templateYLabel[0] in templateTitleDate[0] was templateXValue[0] , marketed by Amgen . templateXValue[0] generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] in templateTitleDate[0] .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] of the templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] of templateTitle[7] templateTitle[8] . 'Avengers : templateXValue[0] ' was the templateTitle[4] templateTitle[5] movie of templateTitle[7] templateTitle[8] as of 2019 , having generated 2.798 templateScale templateYLabel[2] templateYLabel[3] , while 'Avatar ' ranked as a close second with a gross of around templateYValue[1] templateScale . templateTitle[4] templateTitle[5] templateTitle[6] of templateTitle[7] templateTitle[8] The movie `` templateXValue[1] '' tops the list of templateTitle[4] templateTitle[5] templateTitle[6] of templateTitle[7] templateTitle[8] , having raised 2.78 templateScale US templateYLabel[3] in templateTitle[1] templateTitle[2] templateYLabel[0] since its release in 2009 .
generated: This statistic shows the from top Revenue of the women health products U.S. of 2016 .  'Avengers : Prolia_(Amgen) ' was the women health movie of U.S. 2016 as of 2019 , having generated 2.798 million U.S. dollars , while 'Avatar ' ranked as a close second with a gross of around 956 million .  women health products of U.S. 2016 The movie `` Premarin_(Pfizer) '' tops the list of women health products of U.S. 2016 , having raised 2.78 million US dollars in from top Revenue since its release in 2009 .

Example 246:
titleEntities: {'Subject': ['New Zealand'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in New Zealand 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.49', '2.46', '2.6', '2.62', '2.7', '2.51', '2.81', '2.62', '4.18', '4', '3.13']

gold: The statistic shows the growth in real GDP in New Zealand from 2014 to 2018 , with projections up until 2024 . In 2018 , New Zealand 's real gross domestic product grew by around 2.81 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: 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: In 2018 , New Zealand 's real Gross domestic product increased by around 2.81 % compared to the previous Year .  By 2024 , the German GDP is expected to increase by 2.49 % compared to the previous Year .  Keeping it real Gross domestic product is , by definition , a measure of the value of economic output adjusted for inflation .

Example 247:
titleEntities: {'Subject': ['Kidnapping'], 'Date': ['2004', '2012']}
title: Kidnapping : percentage of cases for ransom 2004 - 2012 , by continent
Data:
                                 0     1     2
Year                          2012  2008  2004
Latin America                   25    40    55
Middle East                     19     4    15
Asia and the Pacific            31    38    15
Europe and CIS                   2     2     8
US, Canada and the Caribbean     1     2     4
Africa                          22    14     3 

gold: The statistic shows the percentage of kidnapping cases for ransom from 2004 to 2012 , by continent . In 2012 , about 31 percent of all kidnapping cases in Asia and the Pacific were for ransom .
gold_template: The statistic shows the templateScale of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] from templateValue[0][last] to templateValue[0][0] , templateTitle[7] templateTitle[8] . In templateValue[0][0] , about templateValue[3][0] templateScale of all templateTitleSubject[0] templateTitle[2] in templateLabel[3][0] and the templateLabel[3][1] were templateTitle[3] templateTitle[4] .

generated_template: This statistic outlines the amount 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: This statistic outlines the amount of the percentage cases for ransom 2004 in 2012 , 2008 and 2004 , 2012 by .  Europe cases oils have a 8 percentage Kidnapping of global cases for ransom in 2004 , a slight decrease from the 2 percentage Kidnapping of ransom Europe cases oils in 2008 .

Example 248:
titleEntities: {'Subject': ['UK'], 'Date': ['2010', '2018']}
title: Price of gas oil per metric ton : manufacturing industry in the UK 2010 to 2018
Data:
                     0      1      2      3      4      5      6      7      8
Year              2018   2017   2016   2015   2014   2013   2012   2011   2010
Small consumer   681.4  610.6  544.4  558.6  750.2  815.3  825.7  782.4  618.6
Medium consumer  676.9  584.9  493.3  553.2  748.5  803.1  806.6  766.2  620.4
Large consumer   651.6  557.5  471.6  505.0  670.7  743.2  759.6  731.7  588.0
Average          655.2  562.9  476.7  509.6  684.3  753.9  768.3  738.1  593.6 

gold: This statistic shows the price of gas oil per metric ton for the manufacturing industry in the United Kingdom ( UK ) between 2010 and 2018 in British pounds per metric ton . The price of fuel for all consumer sizes went up between 2010 and 2013 , but then decreased in the following years . However , by 2018 prices had increased to 655 British pounds .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] for the templateTitle[6] templateTitle[7] in the United Kingdom ( templateTitleSubject[0] ) between templateValue[0][last] and templateValue[0][0] in British pounds templateTitle[3] templateTitle[4] templateTitle[5] . The templateTitle[0] of fuel for all templateLabel[1][1] sizes went up between templateValue[0][last] and templateValue[0][5] , but then templateNegativeTrend in the following years . However , by templateValue[0][0] prices had templatePositiveTrend to templateValue[4][0] British pounds .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] inhabitants from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][0] templateScale of inhabitants were aged 0 to 14 years , while approximately templateValue[2][0] templateScale were aged 15 to 64 , and templateValue[3][0] templateScale of templateTitleSubject[0] inhabitants were aged templateLabel[3][1] or older .
generated: The statistic shows the Price gas of UK inhabitants from 2010 to 2018 .  In 2018 , about 681.4 % of inhabitants were aged 0 to 14 years , while approximately 676.9 % were aged 15 to 64 , and 651.6 % of UK inhabitants were aged consumer or older .

Example 249:
titleEntities: {'Subject': ['Denmark'], 'Date': ['1993', '2019']}
title: World ranking of Denmark 's 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']: ['6', '14', '9', '6', '8', '19', '11', '22', '18', '12', '13', '14', '13', '21', '31', '37', '28', '28', '11', '23', '25', '30', '42', '46', '12', '10', '10']

gold: As of 2019 , the Danish national fotball team , controlled by the Danish Football Association ( Dansk Boldspil-Union ) , made the 10th position in the FIFA World Ranking . The highest position ever reached by the team was six in the years 1993 and 1996 . So far , they already participated in one friendly match against Kosovo and three qualifiers for the UEFA Euro 2020 .
gold_template: As of templateXValue[max] , the Danish templateTitle[4] fotball templateTitle[6] , controlled by the Danish templateTitle[5] Association ( Dansk Boldspil-Union ) , made the 10th templateYLabel[3] in the templateYLabel[0] templateYLabel[1] templateYLabel[2] . The highest templateYLabel[3] ever reached by the templateTitle[6] was templateYValue[min] in the years templateXValue[idxmin(Y)] and templateXValue[3] . So far , they already participated in one friendly match against Kosovo and three qualifiers for the UEFA Euro 2020 .

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 2019 .  The highest position ever reached was 6 in 1993 .  Rank 46 was the lowest result of the team , which was reached in 2016 .

Example 250:
titleEntities: {'Subject': ['November'], 'Date': ['2019', '2019']}
title: Leading eSports tournaments worldwide as of November 2019 , by prize pool
X_Axis['Tournament', 'Name']: ['The_International_2019', 'The_International_2018', 'The_International_2017', 'The_International_2016', 'The_International_2015', 'Fortnite_World_Cup_Finals_2019_-_Solo', 'Fortnite_World_Cup_Finals_2019_-_Duo', 'The_International_2014', 'LoL_2018_World_Championship', 'LoL_2016_World_Championship']
Y_Axis['Prize', 'pool', 'in', 'million', 'U.S.', 'dollars']: ['34.33', '25.53', '24.69', '20.77', '18.43', '15.29', '15.1', '10.93', '6.45', '5.07']

gold: The graph presents the leading eSports tournaments worldwide as of November 2019 , ranked by overall prize pool . As of the measured period , The International 2019 , which took place in Shanghai in August 2019 and featured DOTA 2 , ranked first , with a total prize pool of 34.33 million U.S. dollars . eSports prize pool – additional information The entire eSports market is expected to grow over the coming years .
gold_template: The graph presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] as of templateTitleSubject[0] templateXValue[0] , ranked templateTitle[6] overall templateYLabel[0] templateYLabel[1] . As of the measured period , The templateXValue[0] , which took place in Shanghai in 2019 and featured DOTA 2 , ranked first , with a total templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . templateTitle[1] templateYLabel[0] templateYLabel[1] – additional information The entire templateTitle[1] market is expected to grow over the coming years .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] from templateTitleDate[min] to 2019 , templateTitle[8] National templateTitle[2] Insurance Program templateTitle[9] . The templateTitle[9] of the National templateTitle[2] Insurance Program as a consequence of damage caused templateTitle[8] floods following templateXValue[0] templateXValue[1] in 2017 , amounted to almost templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Leading eSports tournaments worldwide in the November from 2019 to , pool National tournaments Insurance Program pool .  The pool of the National tournaments Insurance Program as a consequence of damage caused pool floods following The_International_2019 The_International_2018 in 2017 , amounted to almost 25.53 million U.S. dollars .

Example 251:
titleEntities: {'Subject': ['Uganda'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Uganda 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']: ['50.93', '44.57', '41.02', '37.1', '33.93', '30.67', '28.12', '26.46', '24.75', '22.96', '26.72', '25.71', '24.59', '21.11', '20.21', '18.58', '17.28', '13.5', '10.85', '9.6', '8.29', '6.72', '6.49', '6.04', '5.98', '6.03', '6.34', '6.48', '5.94', '5.76', '4.81', '3.32', '2.91', '3.0', '5.74', '7.04', '8.69', '8.38', '5.2', '5.22', '5.7']

gold: The statistic shows gross domestic product ( GDP ) in Uganda 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 Uganda 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 252:
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 approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[idxmin(Y)] .
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 approximately 5.1 pounds in 2000 .

Example 253:
titleEntities: {'Subject': ['Flat'], 'Date': ['1998', '2016']}
title: Flat glass product manufacturing gross output 1998 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998']
Y_Axis['Gross', 'output', 'in', 'million', 'U.S.', 'dollars']: ['26699', '26080', '24410', '23853', '23263', '21799', '20684', '20173', '23297', '23310', '23779', '23291', '22595', '22078', '22426', '22682', '23914', '23066', '22530']

gold: This statistic shows the glass and glass product manufacturing gross output in the United States from 1998 to 2016 . In 2016 , the gross output in this sector came to around 26.7 billion U.S. dollars .
gold_template: This statistic shows the templateTitle[1] and templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] in the country from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in this sector came to around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitle[6] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . templateYLabel[0] templateYLabel[1] brought a total of over 135 templateScale British pounds ( templateYLabel[6] ) in revenue to the templateTitle[6] during this period . The peak was in templateXValue[1] when the templateYLabel[4] amounted to approximately templateYValue[max] templateScale pounds .
generated: This statistic shows the total Flat ( UK ) 1998 Gross output million U.S. dollars from fiscal Year 1998 to fiscal Year 2016 .  Gross output brought a total of over 135 million British pounds ( dollars ) in revenue to the 1998 during this period .  The peak was in 2015 when the dollars amounted to approximately 26699 million pounds .

Example 254:
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: The statistic shows sources of templateTitle[2] templateTitle[3] and templateTitle[4] for templateLabel[2][0] users in templateTitle[5] templateTitle[6] in templateTitleDate[0] . Among templateLabel[2][0] users from the templateValue[0][3] , templateValue[1][3] templateScale named templateLabel[1][0] as their templateTitleSubject[0] templateTitle[1] of templateTitle[2] templateTitle[3] and templateTitle[4] , whereas templateValue[2][last] templateScale stated the templateLabel[2][0] was their templateTitleSubject[0] templateTitle[1] of such information .
generated: The statistic shows sources of online dating and services for Twitter users in selected countries in 2014 .  Among Twitter users from the Vietnam , 85 % named Facebook as their February popular of online dating and services , whereas 15 % stated the Twitter was their February popular of such information .

Example 255:
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[8] templateXLabel[0] templateYLabel[2] templateYValue[8] 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 Arts_entertainment_recreation_accommodation_and_food_services Industry added 61.7 billion chained 2012 U.S. dollars of value to the state GDP .

Example 256:
titleEntities: {'Subject': ['Germany'], 'Date': ['2019', '2020']}
title: Monthly inflation rate in Germany 2019 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", "Dez_'18", "Nov_'18"]
Y_Axis['Changes', 'compared', 'to', 'the', 'same', 'month', 'last', 'year']: ['1.7', '1.5', '1.1', '1.1', '1.2', '1.4', '1.7', '1.6', '1.4', '2', '1.3', '1.5', '1.4', '1.7', '2.3']

gold: The timeline shows the current monthly inflation rate in Germany from January 2019 to January 2020 . In January 2020 , the consumer prices in Germany increased by 1.7 percent compared to the same month of the previous year , based on preliminary figures . The inflation rate is calculated using the price increase of a product basket defined by the German Federal Statistical Office .
gold_template: The timeline shows the current templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from 2019 to 2020 . In 2020 , the consumer prices in templateTitleSubject[0] templatePositiveTrend by templateYValue[0] templateScale templateYLabel[1] to the templateYLabel[2] templateXLabel[0] of the previous templateYLabel[5] , based on preliminary figures . The templateTitle[1] templateTitle[2] is calculated using the price templatePositiveTrend of a product basket defined by the German Federal Statistical Office .

generated_template: This statistic shows the templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] , based on user figures . In that year , the templateTitle[1] templateTitle[2] templateTitle[3] was estimated to templatePositiveTrend by templateYValue[min] templateScale of the templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the compared of the inflation rate Germany 2019 in , based on user figures .  In that year , the inflation rate Germany was estimated to increase by 1.1 % of the compared same .

Example 257:
titleEntities: {'Subject': ['United States'], 'Date': ['2014', '2020']}
title: Number of executions in the United States 2014 to 2020
Data:
           0        1         2        3        4             5         6          7            8     9        10              11     12        13        14       15        16
State  Texas  Georgia  Arkansas  Alabama  Florida  South_Dakota  Missouri  Tennessee  Mississippi  Ohio  Virginia  South_Carolina  Idaho  Oklahoma  Nebraska  Arizona  Delaware
2014      10        2         0        0        8             0        10          0            0     1         0               0      0         3         0        1         0
2015      13        5         0        0        2             0         6          0            0     0         1               0      0         1         0        0         0
2016       7        9         0        2        1             0         1          0            0     0         0               0      0         0         0        0         0
2017       7        1         4        3        3             0         1          0            0     2         2               0      0         0         0        0         0
2018      13        2         0        2        2             1         0          3            0     1         0               0      0         0         1        0         0
2019       9        3         0        3        2             1         1          3            0     0         0               0      0         0         0        0         0
2020       2        1         0        0        0             0         0          0            0     0         0               0      0         0         0        0         0 

gold: This statistic shows the number of executions in the United States from 2014 to 2020 , by state . As of February 10 , 2020 , two executions were carried out in Texas in 2020 . Death penalty Texas and Missouri both performed ten executions in 2014 .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , by templateLabel[0][0] . As of 10 , templateTitleDate[max] , templateValue[1][1] templateTitle[1] were carried out in templateValue[0][0] in templateTitleDate[max] . Death penalty templateValue[0][0] and templateValue[0][6] both performed templateValue[1][max] templateTitle[1] in templateTitleDate[min] .

generated_template: templateValue[0][0] was the European templateLabel[0][0] with the largest stock of templateTitle[1] vehicles in all three years here recorded . The total number of templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] stood at 286.8 templateScale units in templateTitleDate[max] , of which templateValue[0][0] accounted for 46.5 templateScale . With the greatest population among all European countries and home to a prominent number of automobile manufacturers , this was unsurprising .
generated: Texas was the European State with the largest stock of executions vehicles in all three years here recorded .  The total number of executions United in United States stood at 286.8 million units in 2020 , of which Texas accounted for 46.5 % .  With the greatest population among all European countries and home to a prominent number of automobile manufacturers , this was unsurprising .

Example 258:
titleEntities: {'Subject': ['New England Patriots'], 'Date': ['2006', '2019']}
title: Average ticket price of the New England Patriots 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']: ['127.04', '127.04', '130.73', '122.0', '122.0', '117.84', '117.84', '117.84', '117.84', '117.84', '117.84', '90.89', '90.89']

gold: This graph depicts the average ticket price for New England Patriots games in the National Football League from 2006 to 2019 . In 2019 , the average ticket price was 127.04 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] Patriots games in the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was 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 England Patriots games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 127.04 U.S. dollars .

Example 259:
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
Latest available time period in brackets  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 templateLabel[0][2] 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 templateTitle[0] amount of templateLabel[0][2] 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 Time 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 260:
titleEntities: {'Subject': ['Paris'], 'Date': ['2007', '2013']}
title: Number of visitors to the Centre Pompidou in Paris from 2007 to 2013
X_Axis['Year']: ['2007', '2008', '2009', '2010', '2011', '2012', '2013']
Y_Axis['Number', 'of', 'visitors', 'in', 'millions']: ['2.7', '2.75', '3.53', '3.13', '3.61', '3.8', '3.75']

gold: This statistic shows the number of visitors to the Centre Pompidou in Paris from 2007 to 2013 . There were approximately 3.75 million visitors to the Centre Pompidou in Paris in 2013 , down from 3.8 million in 2012 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] to the templateTitle[2] templateTitle[3] in templateTitleSubject[0] templateTitle[5] templateXValue[min] to templateXValue[max] . There were approximately templateYValue[idxmax(X)] templateScale templateYLabel[1] to the templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateXValue[max] , down templateTitle[5] templateYValue[max] templateScale in templateXValue[idxmax(Y)] .

generated_template: The statistic shows the templateYLabel[0] of 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[1] templateScale in the previous templateXLabel[0] .
generated: The statistic shows the Number of visitors in Paris from 2007 to 2013 .  In 2013 , the Number of Paris visitors in Paris is expected to reach 3.8 3.75 , up from 2.75 millions in the previous Year .

Example 261:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2020']}
title: Number of immigrants in Denmark 2020 , by country of origin
X_Axis['Country']: ['Poland', 'Syria', 'Turkey', 'Germany', 'Romania', 'Iraq', 'Iran', 'Bosnia_and_Herzegovina', 'Norway', 'United_Kingdom', 'Pakistan', 'Sweden', 'Afghanistan', 'Lithuania', 'Lebanon', 'India', 'China', 'Ukraine', 'Thailand', 'Somalia']
Y_Axis['Number', 'of', 'immigrants']: ['41529', '35536', '33111', '30639', '29443', '21840', '17195', '16755', '15670', '15510', '14471', '14387', '13864', '13233', '12990', '12764', '12452', '12054', '11915', '11282']

gold: What were the top 20 countries of origin of immigrants living in Denmark as of 2020 ? The biggest group of immigrants living in Denmark were from Poland , with more than 41 thousand people living in Denmark on January 1 , 2020 . The second and third biggest immigrant groups were from Syria and Turkey , amounting to almost 36 thousand and 33 thousand people respectively . Only one Nordic country was represented within the ten biggest group of immigrants in Denmark this year , namely Norway , with close to 16 thousand Norwegians living in Denmark .
gold_template: What were the top 20 countries of templateTitle[6] of templateYLabel[1] living in templateTitleSubject[0] as of templateTitleDate[0] ? The biggest group of templateYLabel[1] living in templateTitleSubject[0] were from templateXValue[0] , with more than 41 thousand people living in templateTitleSubject[0] on 1 , templateTitleDate[0] . The second and third biggest immigrant groups were from templateXValue[1] and templateXValue[2] , amounting to almost templateYValue[1] thousand and templateYValue[2] thousand people respectively . Only one Nordic templateXLabel[0] was represented within the ten biggest group of templateYLabel[1] in templateTitleSubject[0] this year , namely templateXValue[8] , with close to templateYValue[8] thousand Norwegians living in templateTitleSubject[0] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] to templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] top 20 countries of templateTitle[6] . Most of the templateYLabel[1] came to templateTitleSubject[0] from templateXValue[0] , amounting to templateYValue[max] people . The second and third most popular countries of templateTitle[6] were the neighbouring countries , templateXValue[1] with templateYValue[1] people , and templateXValue[2] with nearly templateYValue[2] thousand people .
generated: The statistic shows the Number of immigrants to Denmark in 2020 , by top 20 countries of origin .  Most of the immigrants came to Denmark from Poland , amounting to 41529 people .  The second and third most popular countries of origin were the neighbouring countries , Syria with 35536 people , and Turkey with nearly 33111 thousand people .

Example 262:
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 the templateScale templateYLabel[3] in the previous templateYLabel[0] of the templateTitle[1] templateTitle[0] in the third templateXLabel[0] of templateXValue[0] to the third templateXLabel[0] of templateXValue[0] . In the third templateXLabel[0] of templateXValue[0] , templateYLabel[0] reached a total amount of almost templateYValue[max] templateScale British pounds .
generated: This statistic shows the million GBP in the previous Expenditure of the plants Garden in the third Quarter of Q2_2019 to the third Quarter of Q2_2019 .  In the third Quarter of Q2_2019 , Expenditure reached a total amount of almost 1484 million British pounds .

Example 263:
titleEntities: {'Subject': ['Germany'], 'Date': ['2000']}
title: Number of hospitals in Germany 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2000']
Y_Axis['Number', 'of', 'hospitals']: ['1942', '1951', '1956', '1980', '1996', '2017', '2045', '2064', '2084', '2083', '2087', '2104', '2139', '2166', '2197', '2221', '2242']

gold: The number of hospitals in Germany has been decreasing every year , amounting to 1,942 in 2017 . In comparison , there were 2,242 hospitals in 2000 . The number of hospital beds has been dropping also , from 571,600 in 2000 to 497,200 recorded in 2017 .
gold_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_template: According to a survey conducted by the Organization for Economic Cooperation and Development ( OECD ) , the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] steadily templateNegativeTrend between the years templateXValue[min] and templateXValue[4] , going from templateYValue[idxmin(X)] to templateYValue[min] within twelve years . Nevertheless , this trend was abruptly reverted during the first templateXLabel[0] of the Hollande Presidency : the templateYLabel[0] of templateYLabel[1] jumped from templateYValue[min] in templateXValue[idxmin(Y)] to templateYValue[max] in templateXValue[idxmax(Y)] . The templateYLabel[0] of Public templateYLabel[1] in templateTitleSubject[0] have templatePositiveTrend during Hollande 's presidency During the first templateXLabel[0] of the former French president François Hollande Presidency , the templateYLabel[0] of publicly owned templateYLabel[1] in templateTitleSubject[0] also templatePositiveTrend at an abrupt pace , going from 928 in templateXValue[4] to 1,458 templateYLabel[1] in templateXValue[max] .
generated: According to a survey conducted by the Organization for Economic Cooperation and Development ( OECD ) , the Number of hospitals in Germany steadily decreased between the years 2000 and 2013 , going from 2242 to 1942 within twelve years .  Nevertheless , this trend was abruptly reverted during the first Year of the Hollande Presidency : the Number of hospitals jumped from 1942 in 2017 to 2242 in 2000 .  The Number of Public hospitals in Germany have increased during Hollande 's presidency During the first Year of the former French president François Hollande Presidency , the Number of publicly owned hospitals in Germany also increased at an abrupt pace , going from 928 in 2013 to 1,458 hospitals in 2017 .

Example 264:
titleEntities: {'Subject': ['Instagram'], 'Date': ['2019', '2019']}
title: Global follower growth rate on Instagram 2019 , by profile size
X_Axis['Year']: ['0-1k', '1k-10k', '10k-100k', '100k-1m', '1m-10m', '10m+']
Y_Axis['Growth']: ['9.4', '12.6', '15.9', '16', '14', '14.6']

gold: This statistic presents the worldwide Instagram follower growth from January to June 2019 , sorted by profile size . During the measured period , accounts with 1,000 to 10,000 followers increased their followers on average by 12.6 percent .
gold_template: This statistic presents the worldwide templateTitleSubject[0] templateTitle[1] templateYLabel[0] from to 2019 , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During the measured period , accounts with 1,000 to 10,000 followers templatePositiveTrend their followers on average templateTitle[6] templateYValue[1] templateScale .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] hotels templateTitle[4] from templateXValue[last] to templateXValue[0] . According to the report , there were templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] in templateTitleDate[0] .
generated: This statistic shows the Growth of Instagram hotels from 10m+ to 0-1k .  According to the report , there were 9.4 % Growth in Instagram 2019 .

Example 265:
titleEntities: {'Subject': ['India'], 'Date': ['2012', '2019']}
title: Foreign exchange earnings from tourism India 2012 - 2019 by month
Data:
          0     1     2     3     4     5     6     7     8     9    10    11
Month   Dec   Nov   Oct   Sep   Aug  July   Jun   May   Apr   Mar   Feb   Jan
2012   1.93  1.78  1.54  1.22  1.31  1.51  1.16  1.02  1.31  1.56  1.73  1.68
2013   1.94   1.7   1.4  1.22  1.33  1.44  1.23  1.21  1.33  1.76  1.91  1.98
2014   2.07  1.85  1.64  1.49  1.71  1.71  1.47  1.34  1.52  1.72  1.85  1.88
2015   2.13  1.91  1.62  1.57  1.75  1.88   1.5  1.49  1.61  1.78  1.88  1.95
2016   2.44  2.11  1.81  1.74  1.88  2.13  1.59  1.53  1.73  1.94   2.0  2.03
2017   3.02  2.55  2.18  2.15  2.15   2.3  2.01   1.9  2.21  2.22  2.29  2.32
2018   2.76   2.3   2.0   2.1  2.37  2.47  2.12  1.89  2.38  2.65  2.76  2.79
2019      0     0     0     0     0     0   2.3  1.97  2.46  2.32  2.52  2.56 

gold: In June 2019 , foreign exchange earnings from tourism in India amounted to approximately 2.3 billion U.S. dollars . An exponential rise in the foreign exchange earnings was seen since 2009 in the country .
gold_template: In 2019 , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] amounted to approximately templateValue[6][5] templateScale U.S. dollars . An exponential rise in the templateTitle[0] templateTitle[1] templateTitle[2] was seen since 2009 in the country .

generated_template: This statistic shows the average templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitle[5] in the four templateTitle[6] templateTitle[7] in the templateTitleSubject[0] from 2014 to 2016 ( in euros ) . In 2016 , a house in templateLabel[4][0] would cost approximately templateValue[4][last] euros templateTitle[2] templateTitle[3] templateTitle[4] .
generated: This statistic shows the average Foreign exchange earnings from tourism of India in the four 2012 2019 in the India from 2014 to 2016 ( in euros ) .  In 2016 , a house in 2015 would cost approximately 1.95 euros earnings from tourism .

Example 266:
titleEntities: {'Subject': ['Levi Strauss'], 'Date': ['2008', '2019']}
title: Global net sales of Levi Strauss by region 2008 to 2019
Data:
               0       1       2       3       4       5       6       7       8       9      10      11
Year        2019    2018    2017    2016    2015    2014    2013    2012    2011    2010    2009    2008
Americas  3057.0  3042.7  2774.0  2682.9  2726.5  2862.9  2851.0  2749.3  2715.9  2549.1  2357.7  2476.4
Europe    1768.1  1646.2  1312.3  1091.4  1016.4  1143.3  1103.5  1103.2  1174.2  1105.2  1042.1  1195.6
Asia       938.0   886.5   817.7   778.4   751.6   747.8   727.2   757.7   871.5   756.3   706.0   728.9 

gold: This statistic depicts the net sales of Levi Strauss worldwide from 2008 to 2019 , by region . In 2019 , Levi Strauss ' net sales in the Americas region amounted to about 3.1 billion U.S. dollars . In total , Levi Strauss generated net sales of 5.76 billion U.S. dollars that year .
gold_template: This statistic depicts the templateTitle[1] templateTitle[2] of templateTitleSubject[0] worldwide from templateValue[0][last] to templateValue[0][0] , templateTitle[5] templateTitle[6] . In templateValue[0][0] , templateTitleSubject[0] ' templateTitle[1] templateTitle[2] in the templateLabel[1][0] templateTitle[6] amounted to about 3.1 templateScale U.S. dollars . In total , templateTitleSubject[0] generated templateTitle[1] templateTitle[2] of 5.76 templateScale U.S. dollars that templateLabel[0][0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] in templateTitleSubject[1] from templateValue[0][last] to templateValue[0][0] , templateTitle[8] templateTitle[9] templateTitle[10] . In templateValue[0][0] , the templateTitleSubject[0] Corporation generated templateValue[1][last] templateScale of its total templateTitle[0] templateTitle[5] its templateLabel[1][0] templateLabel[1][1] templateTitle[9] .
generated: This statistic shows the Global net of Levi Strauss in from 2008 to 2019 , .  In 2019 , the Levi Strauss Corporation generated 2476.4 % of its total Global by its Americas 2019 .

Example 267:
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: templateTitle[6] templateTitle[4] of most classes of medium and heavy trucks have been templatePositiveTrend year-on-year since templateValue[0][9] , with the notable exception of templateTitle[0] templateTitle[2] heavy trucks , which have fallen sharply in recent years ; in templateValue[0][1] a total of 192,000 templateTitle[0] templateTitle[2] trucks were sold in the templateTitle[6] , compared to the 249,000 sold in templateValue[0][3] . In templateValue[0][0] , templateTitle[0] templateTitle[2] trucks templateTitle[4] recovered , with 251,000 units sold . Commercial vehicle classifications Commercial vehicles vehicles in the templateTitleSubject[0] are divided into templateTitle[2] different classes based on weight .
generated: size 2018 of most classes of medium and heavy trucks have been increasing year-on-year since 2009 , with the notable exception of Number Germany heavy trucks , which have fallen sharply in recent years ; in 2017 a total of 192,000 Number Germany trucks were sold in the size , compared to the 249,000 sold in 2015 .  In 2018 , Number Germany trucks 2018 recovered , with 251,000 units sold .  Commercial vehicle classifications Commercial vehicles in the Germany are divided into Germany different classes based on weight .

Example 268:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['1950', '2018']}
title: Average age at marriage in the Netherlands 1950 - 2018 , by gender
Data:
          0     1     2     3     4     5     6     7     8     9    10    11    12    13    14
Year   2018  2015  2010  2005  2000  1995  1990  1985  1980  1975  1970  1965  1960  1955  1950
Men    38.2  37.5  36.6  35.8  34.1  32.4  30.7  29.1  27.4  26.5  26.2  27.1  28.3  29.2  30.0
Women  35.2  34.4  33.4  32.7  31.1  29.6  28.0  26.3  24.5  23.8  23.7  24.3  25.4  26.3  26.9 

gold: Between the 1950s and today , the average age to marry in the Netherlands increased significantly for both genders . Whereas in 1950 , men were on average 30 years old when they married and women were nearly 27 years old , in 2018 this was 38.2 and 35.2 years respectively . This raise in the average age at marriage however meant a decrease in the number of marriages per person , which decreased by over 0.10 in the last decade alone .
gold_template: Between the 1950s and today , the templateTitle[0] templateTitle[1] to marry in the templateTitleSubject[0] templatePositiveTrend significantly for both genders . Whereas in templateValue[0][last] , templateLabel[1][0] were on templateTitle[0] templateValue[1][last] years old when they married and templateLabel[2][0] were nearly templateValue[1][8] years old , in templateValue[0][0] this was templateValue[1][0] and templateValue[2][0] years respectively . This raise in the templateTitle[0] templateTitle[1] at templateTitle[2] however meant a templateNegativeTrend in the number of marriages per person , which templateNegativeTrend templateTitle[6] over 0.10 in the last decade alone .

generated_template: In the last couple decades , the price of templateTitleSubject[0] templateTitle[1] used for electricity generation has templatePositiveTrend slightly to templateValue[1][0] years old per templateScale British thermal units ( Btu ) in templateValue[0][0] . The price of templateTitle[3] was about templateValue[2][0] U.S. dollars per templateScale Btu . The cost of using some fossil fuels to generate electricity has been recently found found to be more expensive than renewable energy .
generated: In the last couple decades , the price of Netherlands age used for electricity generation has increased slightly to 38.2 years old per million British thermal units ( Btu ) in 2018 .  The price of Netherlands was about 35.2 U.S. dollars per million Btu .  The cost of using some fossil fuels to generate electricity has been recently found to be more expensive than renewable energy .

Example 269:
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: The statistic shows the templateTitle[3] of templateTitleSubject[1] templateTitle[5] templateTitle[6] of the NBA franchise templateTitleSubject[0] Lakers from 2012 to 2019 . In 2019 , the templateTitleSubject[1] page of the templateTitleSubject[0] Lakers basketball team had more than templateValue[1][1] templateScale templateLabel[1][1] .
generated: The statistic shows the 2019 of The 2019 of the NBA franchise The Lakers from 2012 to 2019 .  In 2019 , the page of the Lakers basketball team had more than 8.3 million money .

Example 270:
titleEntities: {'Subject': ['Cyber'], 'Date': ['2018']}
title: Cyber crime : average company loss in selected countries 2018
X_Axis['Country']: ['United_States', 'Japan', 'Germany', 'United_Kingdom', 'France', 'Singapore', 'Canada', 'Spain', 'Italy', 'Brazil', 'Australia']
Y_Axis['Average', 'cyber', 'crime', 'cost', 'in', 'million', 'US', 'dollars']: ['27.37', '13.57', '13.12', '11.46', '9.72', '9.32', '9.25', '8.16', '8.01', '7.24', '6.79']

gold: In 2018 , the average annualized cost of cyber crime attacks in the United States amounted to 27.37 million U.S. dollars . Second-ranked Japan averaged 13.57 million U.S. dollars in damages to attacked companies per year . Overall , the financial service sector had the highest annual costs caused by cyber crime .
gold_template: In templateTitleDate[0] , the templateYLabel[0] annualized templateYLabel[3] of templateYLabel[1] templateYLabel[2] attacks in the templateXValue[0] amounted to templateYValue[max] templateScale U.S. templateYLabel[6] . Second-ranked templateXValue[1] averaged templateYValue[1] templateScale U.S. templateYLabel[6] in damages to attacked companies per year . Overall , the financial service sector had the highest annual costs caused by templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the distribution of templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[5] region in templateTitleDate[0] . During this year , the templateXValue[0] templateXValue[2] consumed approximately templateYValue[max] templateScale of the world 's templateTitle[2] . This drug can be used as an analgesic and is about 100 times more potent than morphine .
generated: This statistic shows the distribution of crime average cyber selected region in 2018 .  During this year , the United_States Germany consumed approximately 27.37 million of the world 's average .  This drug can be used as an analgesic and is about 100 times more potent than morphine .

Example 271:
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 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: This statistic shows the Share of American book reading among ever young people UK to a 2015 in by demographic , sorted demographic .  The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the Comic , in 2010 .  34 % of respondents aged 25 to 19.3 stated they among young people UK to a 2015 at some time during by life .

Example 272:
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 templateTitleSubject[0] hotels templateTitle[4] from templateXValue[min] to templateXValue[max] . There were templateYValue[5] templateTitleSubject[0] hotels templateTitle[4] as of 1 , templateXValue[5] , up from templateYValue[4] at the same time the previous templateXLabel[0] .
generated: This statistic shows the Number of Starwood Hotels hotels from 2009 to 2015 .  There were 1161 Starwood Hotels hotels as of 1 , 2014 up from 1121 at the same time the previous Year .

Example 273:
titleEntities: {'Subject': ['U.S. January'], 'Date': ['2017', '2017']}
title: Most expensive prescription drugs in the U.S. January 2017
X_Axis['Drug', '(condition', 'treated)']: ['Harvoni_(hepatitis_C)', 'Sovaldi_(hepatitis_C)', 'Epclusa_(hepatitis_C)', 'Zepatier_(hepatitis_C)', 'Mytesi_(hereditary_angioedema)', 'Bexarotene/Targretin_(', 'Daklinza_(hepatitis_C)', 'Orkambi_(cystic_fibrosis)', 'HP_Acthar_(multiple_sclerosis)', 'Viekira_Pak_(hepatitis_C)']
Y_Axis['Price', 'per', 'month', 'in', 'U.S.', 'dollars']: ['87800', '73800', '73300', '52600', '51700', '49800', '49400', '41200', '40800', '34600']

gold: This statistic shows the most expensive prescription drugs in the U.S. as of January 2017 . As of this time , Sovaldi , a drug used to treat Hepatitis C , was the second most expensive prescription drug in the U.S. , costing nearly 74,000 dollars per month .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateYLabel[3] as of templateTitleSubject[0] templateTitleDate[0] . As of this time , templateXValue[1] , a templateXLabel[0] used to treat Hepatitis C , was the second templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] in the templateYLabel[3] , costing nearly 74,000 templateYLabel[4] templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the ten most popular television templateTitle[5] in the templateTitle[0] based on their templateTitle[2] of templateYLabel[1] . In 2016 , templateXValue[0] was ranked first with a templateTitle[1] templateTitle[2] of templateYValue[max] templateScale of total templateYLabel[1] .
generated: The statistic shows the ten most popular television January in the Most based on their prescription of per .  In 2016 , Harvoni_(hepatitis_C) was ranked first with a expensive prescription of 87800 % of total per .

Example 274:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2014']}
title: Average vehicle speed on roads in Great Britain 2014
Data:
                                                      0                      1          2                  3                    4
Vehicle type                      30_mph_built-up_roads  40_mph_built-up_roads  Motorways  Dual_carriageways  Single_carriageways
Motorcycles                                          30                     36         68                 67                   51
Cars                                                 30                     35         68                 67                   47
Cars towing                                          29                     33         57                 56                   43
Light Goods                                          30                     35         68                 57                   48
Buses/coaches                                        27                     33         58                 56                   46
Rigid heavy goods vehicles                           29                     34         60                 59                   46
Articulated heavy goods vehicles                     29                     35         54                 53                   44 

gold: This graph shows the average speed of all vehicles on all roads in Great Britain in 2014 . Motorbikes , cars and light goods vehicles always have the highest speed limits which is why their average speeds are higher . Motorcycles tended to be the fastest vehicles especially on dual and single carriageways and 40 mph built-up roads .
gold_template: This graph shows the templateTitle[0] templateTitle[2] of all templateLabel[6][3] on all templateValue[0][0] in templateTitleSubject[0] in templateTitleDate[0] . Motorbikes , templateLabel[2][0] and templateLabel[4][0] templateLabel[4][1] templateLabel[6][3] always have the highest templateTitle[2] limits which is why their templateTitle[0] speeds are higher . templateLabel[1][0] tended to be the fastest templateLabel[6][3] especially on templateValue[0][3] and templateValue[0][4] templateValue[0][3] and templateValue[0][1] templateValue[0][0] roads .

generated_template: This statistic shows the templateTitle[0] change to templateValue[0][4] templateTitle[3] in the templateTitle[4] as of 2017 , broken down templateTitle[6] templateValue[0][4] templateTitle[7] . During the survey , templateValue[1][0] templateScale of respondents reported intending to templateLabel[1][0] spending on templateValue[0][0] templateValue[0][4] and that templateValue[1][2] templateScale intended to templateLabel[1][0] spending on templateValue[0][2] ads .
generated: This statistic shows the Average change to Single_carriageways roads in the Great as of 2017 , broken down 2014 Single_carriageways .  During the survey , 30 % of respondents reported intending to Motorcycles spending on 30_mph_built-up_roads Single_carriageways and that 68 % intended to Motorcycles spending on Motorways ads .

Example 275:
titleEntities: {'Subject': ['Asia'], 'Date': ['2019']}
title: Distribution of internet users in Asia 2019 , by country
X_Axis['Country']: ['China', 'India', 'Indonesia', 'Japan', 'Bangladesh', 'Philippines', 'Vietnam', 'Thailand', 'Korea_South', 'Pakistan']
Y_Axis['Percentage', 'of', 'users']: ['37.7', '25.4', '6.5', '5.4', '4.3', '3', '2.9', '2.6', '2.2', '2']

gold: A geographic analysis of the audience found that China accounted for 37.7 percent of internet users in Asia as of June 2018 , followed by India with 25.4 percent . China 's internet penetration reached 58.4 percent in that year .
gold_template: A geographic analysis of the audience found that templateXValue[0] accounted for templateYValue[max] templateScale of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] as of 2018 , followed templateTitle[5] templateXValue[1] with templateYValue[1] templateScale . templateXValue[0] 's templateTitle[1] penetration reached 58.4 templateScale in that year .

generated_template: The templateXValue[0] was the templateTitle[0] templateTitle[1] heavy goods templateXLabel[0] ( templateTitleSubject[0] ) on the road in the templateTitleSubject[1] during templateTitleDate[0] . As of the fourth quarter of templateTitleDate[0] , a total of templateYValue[max] units had been templateYLabel[1] . This was followed by the templateXValue[1] and templateXValue[2] Fa templateXValue[2] .
generated: The China was the Distribution internet heavy goods Country ( Asia ) on the road in the Asia during 2019 .  As of the fourth quarter of 2019 , a total of 37.7 units had been users .  This was followed by the India and Indonesia Fa .

Example 276:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Top U.S. states for hop production 2019
Data:
                  0        1
Year           2018     2019
Washington  77727.7  82014.9
Oregon      12936.2  13023.2
Idaho       16242.8  17003.1 

gold: This statistic shows the three U.S. states with the highest hop production in 2019 . In that year , Washington produced about 82.01 million pounds of hops . The total U.S. production of hops amounted to about 112.04 million pounds .
gold_template: This statistic shows the three templateTitleSubject[0] templateTitle[2] with the highest templateTitle[4] templateTitle[5] in templateValue[0][last] . In that templateLabel[0][0] , templateLabel[1][0] produced about 82.01 templateScale pounds of hops . The total templateTitleSubject[0] templateTitle[5] of hops amounted to about 112.04 templateScale pounds .

generated_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: This statistic shows the Top of U.S. states in U.S. 2019 and 2018 , 2019 .  In 2019 , Washington automobiles and Washington generated a Top of 82014.9 million in the total states in U.S. .

Example 277:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['1975', '2014']}
title: Attitudes towards gay marriage in Great Britain from 1975 to 2014
Data:
               0     1
Year        2014  1975
Agree         69    16
Disagree      28    53
Don't know     3    31 

gold: This survey shows the shift in attitude towards homosexual marriages in Great Britain from 1975 to 2014 . The prevailing attitude towards same sex marriages has shifted from negative to positive in this time period . There was also a stark change in the number of people who were undecided on the issue .
gold_template: This survey shows the shift in attitude templateTitle[1] homosexual marriages in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . The prevailing attitude templateTitle[1] same sex marriages has shifted templateTitle[6] negative to positive in this time period . There was also a stark change in the number of people who were undecided on the issue .

generated_template: This statistic shows the templateLabel[1][0] templateTitle[2] of the templateTitleSubject[1] templateTitle[1] to the templateTitleSubject[1] templateTitle[3] in templateValue[0][last] and templateValue[0][0] , according to the total contribution of the templateTitle[1] to the templateTitle[3] including templateLabel[2][1] demand , such as via the supply chain industries and induced spending of employee 's wages . In templateValue[0][0] , the templateLabel[1][0] templateTitle[2] of the templateTitle[0] templateTitle[1] was measured at templateValue[1][0] templateScale British pounds ( GBP ) , with a total contribution of templateValue[2][0] templateScale .
generated: This statistic shows the Agree gay of the Great Britain towards to the Great Britain marriage in 1975 and 2014 , according to the total contribution of the towards to the marriage including Disagree demand , such as via the supply chain industries and induced spending of employee 's wages .  In 2014 , the Agree gay of the Attitudes towards was measured at 69 million British pounds ( GBP ) , with a total contribution of 28 % .

Example 278:
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: templateValue[0][0] is the largest templateTitle[2] producer in templateTitleSubject[0] in templateTitleDate[max] with a templateTitle[0] of approximately templateValue[4][idxmax(4)] templateScale metric tons . The second largest templateTitle[2] producer was the second largest share of people in templateTitleSubject[0] in templateTitleDate[max] . The second biggest brand based on the various market , templateValue[0][0] generated a templateLabel[2][0] templateTitle[2] of approximately templateValue[4][2] templateScale metric tons in templateTitleDate[max] .
generated: Australia is the largest lithium producer in Major 2019 with a Major of approximately 42000 million metric tons .  The second largest lithium producer was the second largest share of people in Major 2019 .  The second biggest brand based on the various market , Australia generated a 2016 lithium of approximately 7500 million metric tons in 2019 .

Example 279:
titleEntities: {'Subject': ['Saudi Arabia'], 'Date': ['2024']}
title: Growth of the real gross domestic product in Saudi Arabia in 2024
X_Axis['Year']: ['2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022', '2023', '2024']
Y_Axis['Growth', 'of', 'the', 'GDP', 'compared', 'to', 'previous', 'year']: ['3.68', '4.11', '1.67', '-0.74', '2.43', '0.17', '2.18', '2.22', '2.4', '2.5', '2.47']

gold: This statistic shows the growth of the real gross domestic product ( GDP ) in Saudi Arabia from 2014 to 2018 , with projections up until 2024 . In 2018 , the GDP in Saudi Arabia grew by approximately 2.43 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] ( templateYLabel[1] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[4] , with projections up until templateXValue[max] . In templateXValue[4] , the templateYLabel[1] in templateTitleSubject[0] templatePositiveTrend by approximately templateYValue[4] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_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: This forecast shows the number of Growth GDP in Saudi Arabia from 2014 to 2024 .  For 2018 , the number of Growth GDP in Saudi Arabia is estimated to reach 2.43 billion .  Growth GDP in Saudi Arabia – additional information Smartphones are mobile phones that have more advanced computing capabilities and connectivity than regular mobile phones .

Example 280:
titleEntities: {'Subject': ['Latin America Caribbean'], 'Date': ['2019', '2024']}
title: Latin America & Caribbean : unemployment rate 2019 - 2024 , 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
Country  Venezuela  Nicaragua  Brazil  Puerto_Rico  Jamaica  Barbados  Belize  Argentina  Colombia  Costa_Rica  Bahamas  Uruguay  El_Salvador  Peru  Chile  Panama  Paraguay  Dominican_Republic  Trinidad_and_Tobago  Ecuador  Bolivia  Mexico  Honduras
2019          44.3       23.4    11.4           11       11      10.4    10.1        9.9       9.7         9.7        9      8.1          6.7   6.6    6.5     6.1       5.7                 5.2                  4.8      4.3        4     3.5       3.4
2024            56       12.5       9         11.4      8.6       9.3    13.7        8.8         9         9.3      8.6      7.4          6.9   6.5      6       6       5.8                 5.2                  4.4      4.5        4     3.4       3.7 

gold: The statistic presents a comparison of the unemployment rate in Latin American and Caribbean countries in 2019 , and a forecast for 2024 . In April 2019 , the Latin American country with the highest unemployment rate among the nations shown was Venezuela , with around 44.3 percent of its economically active population being unemployed . The unemployment rate in Mexico was expected to be the lowest in the region by 2024 , amounting to 3.4 percent .
gold_template: The statistic presents a comparison of the templateTitle[4] templateTitle[5] in templateTitleSubject[0] American and templateTitleSubject[0] countries in templateTitleDate[min] , and a forecast for templateTitleDate[max] . In 2019 , the templateTitleSubject[0] American templateLabel[0][0] with the highest templateTitle[4] templateTitle[5] among the nations shown was templateValue[0][0] , with around templateValue[1][max] templateScale of its economically active population being unemployed . The templateTitle[4] templateTitle[5] in templateValue[0][21] was expected to be the lowest in the region templateTitle[8] templateTitleDate[max] , amounting to templateValue[1][min] templateScale .

generated_template: templateValue[0][0] was the European templateLabel[0][0] with the largest templateTitle[3] of templateTitle[4] cars . Although figures had templateNegativeTrend since templateTitleDate[min] , templateTitle[4] cars still made up templateValue[1][9] templateScale of all new passenger cars bought in templateValue[0][0] in templateTitleDate[max] . templateTitle[8] comparison , templateValue[0][2] had the same templateTitle[3] of new templateTitle[4] cars as templateValue[0][0] in templateTitleDate[min] , but was able to cut said templateTitle[3] templateTitle[8] eleven templateScale within the following four years .
generated: Venezuela was the European Country with the largest Caribbean of unemployment cars .  Although figures had dropped since 2019 , unemployment cars still made up 9.7 % of all new passenger cars bought in Venezuela 2024 .  by comparison , Brazil had the same Caribbean of new unemployment cars as Venezuela in 2019 , but was able to cut said Caribbean by eleven percent within the following four years .

Example 281:
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: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged templateYValue[9] years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Vended of volume in machines sales in the volume vended 1999 to 2010 .  In 2010 , the Vended of volume ( aged 45.6 years and older ) in machines sales amounted to approximately 42.2 billion .

Example 282:
titleEntities: {'Subject': ['General Electric', 'R D'], 'Date': ['2010', '2018']}
title: General Electric : R & D expenditure 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Research', 'and', 'development', 'costs', 'in', 'billion', 'U.S.', 'dollars']: ['4.9', '5.5', '5.4', '5.3', '4.2', '4.6', '4.5', '4.6', '3.9']

gold: This statistic represents General Electric 's research and development costs from the fiscal year of 2010 to the fiscal year of 2018 . In the fiscal year of 2018 , the company incurred around 4.9 billion U.S. dollars in research and development costs .
gold_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] and templateYLabel[1] templateYLabel[2] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the company incurred around templateYValue[idxmax(X)] templateScale templateYLabel[4] templateYLabel[5] in templateYLabel[0] and templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows templateTitleSubject[0] templateYLabel[1] in templateYLabel[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateYLabel[2] templateTitle[3] templateYLabel[3] templateNegativeTrend by 0.6 templateScale compared to the previous templateXLabel[0] . templateTitle[3] templateTitle[4] were forecasted to templatePositiveTrend by templateYValue[idxmax(X)] templateScale in templateXValue[max] .
generated: This statistic shows General Electric development in costs & D from 2010 to 2018 .  In 2014 , the costs & billion decreased by 0.6 billion compared to the previous Year .  & D were forecasted to increase by 4.9 billion in 2018 .

Example 283:
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: The statistic shows the templateXLabel[0] templateTitle[1] the highest templateYLabel[0] of templateTitle[3] templateTitle[4] templateYLabel[1] templateYLabel[2] templateTitle[6] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateXLabel[0] in the templateTitle[2] , with a templateYLabel[0] templateYLabel[1] templateYLabel[2] of approximately templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Company baby the highest Million of vendors U.S. dollars based in the United Kingdom ( U.S. ) in 2016 .  In that year , Private_label was the baby wipes vendors U.S. Company in the wipes , with a Million U.S. dollars of approximately 494.4 million dollars .

Example 284:
titleEntities: {'Subject': ['The adidas Group'], 'Date': ['2008', '2018']}
title: The adidas Group 's global apparel production from 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Apparel', 'production', 'in', 'million', 'units']: ['457', '404', '382', '364', '309', '292', '262', '267', '301', '239', '284']

gold: In 2018 , the adidas Group produced approximately 457 million units of apparel worldwide , an increase of 53 million units on the previous year . The company 's global apparel production has increased by about 75 percent since 2012 . Asia at the center of global operations network The adidas Group 's apparel production has risen year-on-year since 2012 .
gold_template: In templateXValue[max] , the templateTitleSubject[0] produced approximately templateYValue[max] templateYValue[idxmax(X)] templateYLabel[3] of templateYLabel[0] worldwide , an templatePositiveTrend of 53 templateScale templateYLabel[3] on the previous templateXLabel[0] . The company templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] has templatePositiveTrend by about 75 templateScale since templateXValue[6] . Asia at the center of templateTitle[3] operations network The templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] has risen year-on-year since templateXValue[6] .

generated_template: This statistic shows the templateScale of the templateYLabel[1] templateYLabel[2] living in urban areas in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] was living in cities and urban areas .
generated: This statistic shows the million of the production million living in urban areas in The adidas Group from 2008 to 2018 .  In 2018 , 457 million of the production million of The adidas Group was living in cities and urban areas .

Example 285:
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: 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 public at primary establishments in France from 2005 to 2018 .  In 2018 , the Number of public in travel primary ( including both international and domestic tourists ) amounted to approximately 44902 million .

Example 286:
titleEntities: {'Subject': ['Global'], 'Date': ['2015', '2020']}
title: Global virtual reality video gaming revenue 2015 - 2020 , by segment
Data:
            0     1     2     3     4
Year     2020  2019  2018  2017  2016
PC        8.5   5.4   3.8   2.5   1.6
Mobile    8.2   5.0   3.2   1.4   0.7
Console   6.1   4.6   2.7   1.9   1.2 

gold: The graph presents data on the virtual reality video gaming sales revenue worldwide from 2016 to 2020 , broken down by segment . The source estimates that the global VR gaming market in 2016 will be worth 3.6 billion U.S. dollars , out of which 1.6 billion will be attributed to PC VR software and hardware . VR gaming – additional information Europe and North America represent the two biggest markets for virtual reality video gaming .
gold_template: The graph presents data on the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] sales templateTitle[5] worldwide from templateValue[0][last] to templateValue[0][0] , broken down templateTitle[8] templateTitle[9] . The source estimates that the templateTitleSubject[0] VR templateTitle[4] market in templateValue[0][last] will be worth 3.6 templateScale U.S. dollars , out of which templateValue[1][last] templateScale will be attributed to templateLabel[1][0] VR software and hardware . VR templateTitle[4] – additional information Europe and North America represent the templateValue[1][last] biggest markets for templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] .

generated_template: This forecast shows the templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , templateTitle[6] templateTitle[7] templateTitle[8] . In templateValue[0][0] , the templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] is estimated to reach templateValue[1][0] templateScale .
generated: This forecast shows the virtual of reality video gaming in Global from 2016 to 2020 , 2015 2020 by .  In 2020 , the virtual of reality video gaming in Global is estimated to reach 8.5 % .

Example 287:
titleEntities: {'Subject': ['Number'], 'Date': ['2016']}
title: Number of deaths caused by majors droughts worldwide up to 2016
X_Axis['Country', 'and', 'Year']: ['China_(1928)', 'Bangladesh_(1943)', 'India_(1942)', 'India_(1965)', 'India_(1900)', 'Soviet_Union_(1921)', 'China_(1920)', 'Ethiopia_(May_1983)', 'Sudan_(April_1983)', 'Ethiopia_(December_1973)']
Y_Axis['Number', 'of', 'deaths']: ['3000000', '1900000', '1500000', '1500000', '1250000', '1200000', '500000', '300000', '150000', '100000']

gold: This statistic illustrates deaths due to drought worldwide from 1900 to 2016  . The dry period of April 1983 in Sudan caused around 150,000 deaths . Deaths due to drought worldwide The 1928 drought in the People 's Republic of China was the deadliest drought during the period between 1900 and 2016 , causing the death of an estimated three million people .
gold_template: This statistic illustrates templateYLabel[1] due to drought templateTitle[6] from 1900 to templateTitleDate[0] . The dry period of 1983 in templateXValue[8] templateTitle[2] around templateYValue[8] templateYLabel[1] . templateYLabel[1] due to drought templateTitle[6] The 1928 drought in the People 's Republic of templateXValue[0] was the deadliest drought during the period between 1900 and templateTitleDate[0] , causing the death of an estimated templateYValue[max] templateScale people .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] the world 's templateTitle[4] templateTitle[5] templateTitle[3] 1900 to templateTitleDate[0] . The templateXLabel[0] in templateXValue[2] in 1973 claimed templateYValue[2] lives . Natural disasters Natural disasters , such as earthquakes , volcanic eruption , tsunamis , floods , tornados or templateTitle[5] affect people templateTitle[6] .
generated: This statistic shows the Number of deaths caused by the world 's majors droughts by 1900 to 2016 .  The Country in India_(1942) 1973 claimed 1500000 lives .  Natural disasters Natural , such as earthquakes , volcanic eruption , tsunamis floods , tornados or droughts affect people worldwide .

Example 288:
titleEntities: {'Subject': ['Semiconductor'], 'Date': ['2018']}
title: Semiconductor equipment manufacturers ' market share worldwide 1Q'17 to 2018
Data:
                         0       1       2
Year                  2018  1Q_'18  1Q_'17
Applied Materials     18.8    17.7    18.4
ASML                  17.6    12.1     9.9
Lam Research          16.8      14    13.5
Tokyo Electron        16.7      15    13.7
KLA-Tencor             6.4     4.4     5.1
Screen Semiconductor   2.1     2.2     2.1
Hitachi High Tech      2.1     1.8     1.9
ASMI                     0     0.9     0.9
Nova Measuring           0     0.3     0.3
Nanometrics              0     0.3     0.3
Rudolph Technology       0     0.4     0.4
Others                19.4    31.1    33.5 

gold: The statistic shows manufacturers ' share of the global semiconductor equipment market revenue from the first quarter of 2017 to 2018 . In 2018 , Applied Materials ' share of the global semiconductor equipment market was 18.8 percent .
gold_template: The statistic shows templateTitle[2] templateTitle[3] templateTitle[5] of the global templateTitleSubject[0] templateTitle[1] templateTitle[4] revenue from the first quarter of 2017 to templateValue[0][0] . In templateValue[0][0] , templateLabel[1][0] templateLabel[1][1] templateTitle[3] templateTitle[5] of the global templateTitleSubject[0] templateTitle[1] templateTitle[4] was templateValue[1][0] templateScale .

generated_template: This statistic shows the templateTitle[1] amount of templateTitle[2] and templateTitle[3] imported and exported by the templateTitle[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][1] , around templateValue[1][3] templateScale pounds of templateTitle[2] and templateTitle[3] were exported from the templateTitle[0] .
generated: This statistic shows the equipment amount of manufacturers and ' imported and exported by the Semiconductor from 1Q_'17 to 2018 .  In 1Q_'18 , around 18.4 million pounds of manufacturers and ' were exported from the Semiconductor .

Example 289:
titleEntities: {'Subject': ['Leading'], 'Date': ['2016', '2018']}
title: Leading countries ranked by mobile app store downloads 2016 to 2018
Data:
             0      1              2       3          4
Country  China  India  United_States  Brazil  Indonesia
2016      53.1    6.5           11.7     5.7        3.3
2017      79.3   12.1           11.3     6.3        4.0
2018      89.7   17.2           11.9     7.3        5.1 

gold: In 2018 , China was ranked first among the leading countries based on mobile app store downloads with 89.7 billion downloads . This represents a 70 percent growth from 2016 . Second-ranked India generated 17.2 billion mobile app downloads in 2018 .
gold_template: In templateTitleDate[max] , templateValue[0][0] was templateTitle[2] first among the templateTitleSubject[0] templateTitle[1] based on templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] with templateValue[3][max] templateScale templateTitle[7] . This represents a 70 templateScale growth from templateTitleDate[min] . Second-ranked templateValue[0][1] generated templateValue[3][1] templateScale templateTitle[4] templateTitle[5] templateTitle[7] in templateTitleDate[max] .

generated_template: templateValue[0][0] and templateValue[0][1] ( Outlook.com ) were still the most used templateTitle[1] services in the templateTitleSubject[0] , according to survey respondents in templateTitleDate[max] . Compared to earlier years , the use of both Google 's as well as Microsoft 's free e-mail service saw an templatePositiveTrend in their usage among Dutch consumers , whilst e-mail services provided by domestic templateTitle[2] ( such as KPN and Ziggo ) saw much less use . Sending and receiving e-mails is a popular online communication method across all age groups in the country .
generated: China and India ( Outlook.com ) were still the most used countries services in the Leading , according to survey respondents in 2018 .  Compared to earlier years , the use of both Google 's as well Microsoft 's free e-mail service saw an increase in their usage among Dutch consumers , whilst e-mail services provided by domestic ranked ( such as KPN and Ziggo ) saw much less use .  Sending and receiving e-mails is a popular online communication method across all age groups in the country .

Example 290:
titleEntities: {'Subject': ['DDoS'], 'Date': ['2018']}
title: Leading source countries of DDoS attack traffic 2018
X_Axis['Country']: ['United_States', 'China', 'United_Kingdom', 'India', 'Spain', 'Russia', 'Brazil', 'Korea', 'Japan', 'Ecuador']
Y_Axis['Percentage', 'of', 'attack', 'traffic']: ['30', '16', '5', '4', '3', '3', '3', '3', '3', '2']

gold: The statistic shows the percentage of worldwide denial of service attack traffic between November 2017 and April 2018 , sorted by originating countries . During that period of time , 30 percent of DDoS attack traffic originated from IP addresses in the United States .
gold_template: The statistic shows the templateScale of worldwide denial of service templateYLabel[1] templateYLabel[2] between 2017 and 2018 , sorted by originating templateTitle[2] . During that period of time , templateYValue[max] templateScale of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] originated from IP addresses in the templateXValue[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[6] during the first quarter of templateTitleDate[0] . During that quarter , it was found that templateYValue[max] templateScale of internet templateYLabel[3] in templateXValue[0] averaged a connection speed of templateTitle[3] templateTitleSubject[0] or more , placing the templateXLabel[0] also in first place in a global ranking of highest average internet speeds . The global templateYLabel[1] templateYLabel[2] templateTitle[6] rate is 45 templateScale .
generated: The statistic shows the Leading source the countries Percentage of attack traffic 2018 during the first quarter of 2018 .  During that quarter , it was found that 30 percentage of internet traffic in United_States averaged a connection speed of DDoS or more , placing the Country also in first place in a global ranking of highest average internet speeds .  The global attack traffic 2018 rate is 45 percentage .

Example 291:
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: In templateXValue[max] , approximately a third of the total templateYLabel[2] in templateTitleSubject[0] lived in cities . The trend shows an templatePositiveTrend of templateTitle[0] by almost 4 templateScale in the last decade , meaning people have moved away from rural areas to find work and make a living in the cities . Leaving the field Over the last decade , templateTitle[0] in templateTitleSubject[0] has templatePositiveTrend by almost 4 templateScale , as more and more people leave the agricultural sector to find work in services .
generated: In 2018 , approximately a third of the total population in Canada lived in cities .  The trend shows an increase of Urbanization by almost 4 % in the last decade , meaning people have moved away from rural areas to find work and make a living in the cities .  Leaving the field Over the last decade , Urbanization in Canada has increased by almost 4 % , as more and people leave the agricultural sector to find work in services .

Example 292:
titleEntities: {'Subject': ['Global'], 'Date': ['2014', '2018']}
title: Global wearable technology unit sales by category 2014 to 2018
Data:
              0     1     2     3     4
Year       2018  2017  2016  2015  2014
Watch        80    65    50    35     5
Wristband    15    15    20    15    15
Glass         6     4     2     1     0
Other        22    15     7     5     3 

gold: The statistic shows a breakdown of global wearable technology sales by category from 2014 to 2018 . In 2014 , 15 million smart wristbands were sold .
gold_template: The statistic shows a breakdown of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[4] templateTitle[5] templateTitle[6] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][last] , templateValue[2][last] templateScale smart wristbands were sold .

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] , the templateTitleSubject[0] carried out 1.62 templateScale of its templateTitle[0] templateTitle[1] . templateTitleSubject[0] is owned by Bloomin ' Brands , Inc. , a U.S.-based templateTitle[0] company .
generated: This statistic shows the Global wearable of Global in the sales by 2014 to 2018 , by ownership type .  In 2018 , the Global carried out 1.62 % of its Global wearable .  Global is owned by Bloomin ' Brands , Inc. a U.S.-based Global company .

Example 293:
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 templateTitle[0] templateTitle[1] the largest templateYLabel[0] of templateTitle[3] templateTitle[4] templateYLabel[2] templateTitle[6] in the templateTitle[7] in templateTitleDate[0] . According to the source , Connecticut was the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[2] templateTitle[6] in templateTitleDate[0] templateTitle[1] templateYValue[max] templateYLabel[1] to every templateYLabel[3] thousand templateYLabel[5] .
generated: This statistic shows the Leading U.S. the largest Number of based total physicians active in the physicians in 2019 .  According to the source , Connecticut was the State U.S. the states based total physicians active in 2019 U.S. 112906 specialist to every physicians thousand .

Example 294:
titleEntities: {'Subject': ['World Warcraft Horde'], 'Date': ['2019']}
title: Distribution of World of Warcraft Horde characters by class in 2019
Data:
                0        1      2        3             4     5        6       7       8      9    10            11
unknown    Hunter  Warrior  Druid  Paladin  Death_Knight  Mage  Warlock  Priest  Shaman  Rogue  Monk  Demon_Hunter
US realms      11      9.6    9.2      8.9           8.6   8.6      8.2       8       8    7.9   6.5           5.4
EU realms    10.2     10.2    9.6      8.6           8.4   8.7      8.1       8     8.3      8   6.5           5.6 

gold: The statistic shows a distribution of the Horde classes in the online computer game World of Warcraft as of December 2019 . Approximately 8.6 percent of all Horde characters in U.S. realms were Death Knights . Overall in terms of class the majority of players chose Hunters , and as for race , the most popular choice in the series was Human .
gold_template: The statistic shows a templateTitle[0] of the templateTitleSubject[0] classes in the online computer game templateTitleSubject[0] of templateTitleSubject[0] as of 2019 . Approximately templateValue[1][4] templateScale of all templateTitleSubject[0] templateTitle[4] in U.S. templateLabel[1][1] were templateValue[0][4] Knights . Overall in terms of templateTitle[6] the majority of players chose Hunters , and as for race , the most popular choice in the series was Human .

generated_template: The statistic shows a templateTitle[0] of the templateTitleSubject[0] classes in the online computer game templateTitleSubject[0] of templateTitleSubject[0] as of 2019 . Approximately templateValue[1][0] templateScale of all templateTitleSubject[0] templateTitle[4] in U.S. templateLabel[1][1] were Hunters . Overall in terms of templateTitle[7] the majority of players chose Hunters , and as for race , the most popular choice in the series was Human .
generated: The statistic shows a Distribution of the World Warcraft Horde classes in the online computer game World Warcraft Horde of as 2019 .  Approximately 11 % of all World Warcraft Horde characters in U.S. realms were Hunters .  Overall in terms of 2019 the majority of players chose Hunters , and as for race , the most popular choice in the series was Human .

Example 295:
titleEntities: {'Subject': ['Projected'], 'Date': ['2020']}
title: Projected world population distribution , by age group 2020 to 2100
Data:
                      0     1     2
Year               2100  2050  2020
0 - 14 years       17.4  21.1  25.4
15 - 59 years      54.3  57.5  61.1
60 years and over  28.2  21.4  13.5 

gold: This statistic shows the distribution of the projected world population from 2020 to 2100 . By 2100 , about 28.2 percent of the world population is estimated to be 60 years and older . This would be an increase from 13.5 percent in 2020 .
gold_template: This statistic shows the templateTitle[3] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] from templateValue[0][last] to templateValue[0][0] . templateTitle[4] templateValue[0][0] , about templateValue[3][max] templateScale of the templateTitle[1] templateTitle[2] is estimated to be templateLabel[3][0] templateLabel[1][2] and older . This would be an templatePositiveTrend from templateValue[3][min] templateScale in templateValue[0][idxmin(3)] .

generated_template: This statistic shows the number of templateTitle[0] templateTitle[1] to templateTitle[2] templateTitle[3] the templateTitleSubject[0] templateTitle[5] from templateValue[0][0] to templateValue[0][last] , distinguished templateTitle[3] templateTitle[7] . As of 24 , templateValue[0][last] , the templateTitleSubject[0] templateTitle[5] templateTitle[1] templateValue[1][last] men and templateValue[2][last] women to templateTitle[2] .
generated: This statistic shows the number of Projected world to population distribution the Projected age from 2100 to 2020 , distinguished distribution 2020 .  As of 24 , 2020 the Projected age world 25.4 men and 61.1 women to population .

Example 296:
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 templateYLabel[0] of migrant worker templateYLabel[1] templatePositiveTrend up away from their parents in templateTitleSubject[0] in templateXValue[min] and templateXValue[max] . The 6th National Population Census of the Republic of templateTitleSubject[0] estimated that templateYValue[max] templateScale templateTitle[1] templateYLabel[1] until the age of 17 templatePositiveTrend up without their parents .
generated: This statistic shows the Market of migrant worker share growing up away from their parents in Amazon 2016 and 2020 .  The 6th National Population Census of the Republic of Amazon estimated that 50 % Amazon share until the age of 17 growing up without their parents .

Example 297:
titleEntities: {'Subject': ['Total'], 'Date': ['2008', '2016']}
title: Total consumption of lithium worldwide 2008 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Consumption', 'in', 'metric', 'tons', 'of', 'lithium', 'carbonate', 'equivalent']: ['212719', '194000', '182903', '165484', '151048', '138306', '125726', '101613', '120968']

gold: This statistic depicts the total consumption of lithium worldwide from 2008 to 2016 . In 2016 , the total global consumption of lithium reached 212,719 metric tons of lithium carbonate equivalent . Increases in battery demand will be a strong driver of lithium consumption in the near future , but producers are currently limited in their ability to match demands .
gold_template: This statistic depicts the templateTitleSubject[0] templateYLabel[0] of templateYLabel[3] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] global templateYLabel[0] of templateYLabel[3] reached templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] of templateYLabel[3] templateYLabel[4] templateYLabel[5] . Increases in battery demand will be a strong driver of templateYLabel[3] templateYLabel[0] in the near future , but producers are currently limited in their ability to match demands .

generated_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: This statistic shows the Consumption of tons to universities in the Total ( ) from 2008 to 2016 .  The Consumption of tons peaked in 2014 .  The lower figures in 2010 and 2011 may be connected to the rise of the tuition fee limit in 2010 to 9,000 British pounds per Year .

Example 298:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Top selling apparel categories for shops in the U.S. based on generated revenue 2013
X_Axis['Category']: ['T-shirts', 'Polo/sport_shirts', 'Sweatshirts', 'Headwear', 'Bags', 'Outerwear', 'Teamwear/school_uniforms']
Y_Axis['Share', 'of', 'respondents']: ['25', '17', '11', '10', '6.6', '5.7', '5.1']

gold: This statistic depicts the leading selling apparel categories for shops in the United States in 2013 , based on generated revenue . The survey revealed that t-shirts generated about 25 percent of apparel shops revenue .
gold_template: This statistic depicts the leading templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] in templateTitleDate[0] , templateTitle[7] on templateTitle[8] templateTitle[9] . The survey revealed that templateXValue[0] templateTitle[8] about templateYValue[max] templateScale of templateTitle[2] templateTitle[5] templateTitle[9] .

generated_template: This statistic depicts the results of a survey , conducted in 2016 in Canada , on templateXValue[5] templateTitle[3] templateTitle[4] templateTitle[5] . According to templateYValue[max] templateScale of surveyed templateTitleSubject[0] , their top resolution templateTitle[6] templateTitleDate[0] was to templateXValue[0] fitness and templateXValue[0] .
generated: This statistic depicts the results of a survey , conducted in 2016 Canada , on Outerwear categories for shops .  According to 25 % of surveyed U.S. , their top resolution U.S. 2013 was to T-shirts fitness and T-shirts .

Example 299:
titleEntities: {'Subject': ['Bridgestone'], 'Date': ['2008', '2018']}
title: Bridgestone - worldwide net sales 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Net', 'sales', 'in', 'billion', 'yen']: ['3650.11', '3643.43', '3337.02', '3790.25', '3673.97', '3568.09', '3039.74', '3024.4', '2861.6', '2597.0', '3234.4']

gold: The statistic shows Bridgestone 's worldwide net sales between the 2008 and 2018 fiscal years . Bridgestone 's worldwide net sales reached around 3.65 trillion yen ( or about 33 billion U.S. dollars ) in its 2018 fiscal year .
gold_template: The statistic shows templateTitleSubject[0] 's templateTitle[1] templateYLabel[0] templateYLabel[1] between the templateXValue[min] and templateXValue[max] fiscal years . templateTitleSubject[0] 's templateTitle[1] templateYLabel[0] templateYLabel[1] reached around templateYValue[0] templateScale templateYLabel[3] ( or about 33 templateScale U.S. dollars ) in its templateXValue[max] fiscal templateXLabel[0] .

generated_template: This statistic shows the templateScale of the templateYLabel[1] templateYLabel[2] living in urban areas in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] was living in cities and urban areas .
generated: This statistic shows the billion of the sales billion living in urban areas in Bridgestone from 2008 to 2018 .  In 2018 , 3650.11 billion of the sales billion of Bridgestone was living in cities and urban areas .

Example 300:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Snapchat usage penetration in the United States 2019 , by gender
X_Axis['Sex']: ['Male', 'Female']
Y_Axis['Reach']: ['16', '31']

gold: According to a third quarter 2019 survey , 16 percent of male internet users in the United States used Snapchat . The photo sharing app had a usage reach of 31 percent among female internet users . In total , 74 percent of Snapchat users in the United States stated that they used Snapchat to keep in contact with friends and family .
gold_template: According to a third quarter templateTitleDate[0] survey , templateYValue[min] templateScale of templateXValue[0] internet users in the templateTitleSubject[0] used templateTitle[0] . The photo sharing app had a templateTitle[1] templateYLabel[0] of templateYValue[max] templateScale among templateXValue[last] internet users . In total , 74 templateScale of templateTitle[0] users in the templateTitleSubject[0] stated that they used templateTitle[0] to keep in contact with friends and family .

generated_template: According to a third quarter templateTitleDate[0] survey , templateYValue[max] templateScale of templateXValue[0] internet users in the templateTitleSubject[0] used templateTitle[0] . The video platform had a templateTitle[1] templateYLabel[0] of templateYValue[min] templateScale among templateXValue[last] templateTitle[4] internet users . Overall , 38 templateScale of templateTitle[4] templateTitle[0] users accessed the platform several times a day .
generated: According to a third quarter 2019 survey , 31 % of Male internet users in the United States used Snapchat .  The video platform had a usage Reach of 16 % among Female States internet users .  Overall , 38 % of States Snapchat users accessed the platform several times a day .

Example 301:
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 number of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] among consumers templateTitle[5] as of 2014 , templateTitle[7] templateXLabel[0] . Respondents from templateXValue[0] templateYLabel[2] the most templateTitleSubject[0] templateYLabel[3] at an templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[4] templateYLabel[5] . templateYLabel[3] - additional information In the broadest sense , templateYLabel[3] is the process of preparing food for consumption , generally using heat .
generated: This statistic shows the number of U.S. dollars per ton among consumers 2019 as of 2014 , 2019 Country .  Respondents from China dollars the most Global per at an Price of 17400 U.S. ton .  per - additional information In the broadest sense , per is the process of preparing food for consumption , generally using heat .

Example 302:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2011']}
title: Camping equipment sales in the U.S. - sleeping bags 2011
X_Axis['Year']: ['2010', '2011']
Y_Axis['Equipment', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['191.76', '210.38']

gold: The statistic shows the total sales of sleeping bags in the United States from 2010 to 2011 . In 2010 , sales of sleeping bags amounted to 191.76 million U.S. dollars .
gold_template: The statistic shows the total templateYLabel[1] of templateTitle[4] templateTitle[5] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYLabel[1] of templateTitle[4] templateTitle[5] amounted to templateYValue[idxmin(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of migrant worker templateYLabel[1] templatePositiveTrend up away from their parents in templateTitleSubject[0] in templateXValue[min] and templateXValue[max] . The 6th National Population Census of the Republic of templateTitleSubject[0] estimated that templateYValue[max] templateScale templateTitle[1] templateYLabel[1] until the age of 17 templatePositiveTrend up without their parents .
generated: This statistic shows the Equipment of migrant worker sales growing up away from their parents in U.S. 2010 and 2011 .  The 6th National Population Census of the Republic of U.S. estimated that 210.38 million equipment sales until the age of 17 grew up without their parents .

Example 303:
titleEntities: {'Subject': ['HSBC'], 'Date': ['2010', '2019']}
title: Number of employees at HSBC 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Number', 'of', 'employees', 'in', 'thousands']: ['235', '235', '229', '241', '264', '266', '263', '270', '298', '307']

gold: The statistic presents the number of employees at HSBC from 2010 to 2019 . The data include both full-time and part-time employees . In 2019 , there were 235,000 employees at HSBC .
gold_template: The statistic presents the templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The data include both full-time and part-time templateYLabel[1] . In templateXValue[max] , there were templateYValue[0] templateYLabel[1] at templateTitleSubject[0] .

generated_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . There were templateYValue[idxmax(X)] thousand full-time equivalent templateYLabel[1] at templateTitleSubject[0] in templateXValue[max] templateXValue[idxmin(Y)] templateTitleSubject[0] - additional information templateTitleSubject[0] is one of four big banks in the country which include JPMorgan Chase , Bank of America and Citigroup .
generated: This statistic illustrates the Number of employees at HSBC from 2010 to 2019 .  There were 235 thousand full-time equivalent employees at HSBC in 2019 2017 HSBC - additional information HSBC is one of four big banks in the country which include JPMorgan Chase , Bank of America and Citigroup .

Example 304:
titleEntities: {'Subject': ['Portugal'], 'Date': ['2024']}
title: National debt of Portugal 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'billion', 'U.S.', 'dollars']: ['268.43', '270.33', '266.42', '268.98', '274.23', '272.3', '269.53', '267.19', '265.16', '254.79', '248.73']

gold: This statistic shows the national debt of Portugal from 2014 to 2018 , with projections up until 2024 . In 2018 , the national debt in Portugal was around 269.53 billion U.S. dollars .
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] in templateTitleSubject[0] was around templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The 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] amounted to around templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the National debt of Portugal from 2014 to 2018 , with projections up until 2024 .  In 2018 , the National debt of Portugal amounted to around 269.53 billion U.S. dollars .

Example 305:
titleEntities: {'Subject': ['Number'], 'Date': ['2015', '2018']}
title: Number of paying virtual reality users worldwide 2015 to 2018
Data:
                                0     1     2     3
Year                         2018  2017  2016  2015
Early Majority/KT&T          14.0   6.0   2.0   0.2
Early Adopters/Light Gamers   7.0   3.0   1.6  0.16
Innovators/Hardcore Gamers    7.0   3.0   1.2  0.21 

gold: The statistic depicts a forecast regarding the number of paying virtual reality users from 2015 to 2018 . The total number of paying virtual reality users is forecast to reach 28 million by 2018 .
gold_template: The statistic depicts a forecast regarding the templateTitleSubject[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] from templateValue[0][last] to templateValue[0][0] . The total templateTitleSubject[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] is forecast to reach 28 templateScale by templateValue[0][0] .

generated_template: The statistic shows the forecast templateTitleSubject[0] templateTitle[1] from templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] . For templateValue[0][1] , the source projects the templateTitle[2] templateTitle[3] templateTitleSubject[0] templateTitle[1] to reach a total amount of around 2.57 templateScale U.S. dollars . templateTitle[2] templateTitle[3] - additional information A templateValue[0][last] Statista survey conducted in the country has asked the question `` Have you ever heard of the term 'virtual templateTitle[3] ' _ ? '' A total of 92 templateScale of respondents have answered positively , awareness being strongest among 30 to 39 year-olds .
generated: The statistic shows the forecast Number paying from virtual reality from 2015 to 2018 .  For 2017 , the source projects the virtual reality Number paying to reach a total amount of around 2.57 billion U.S. dollars .  virtual reality - additional information A 2015 Statista survey conducted in the country has asked the question `` Have you ever heard of the term 'virtual reality ' _ ? '' A total of 92 % of respondents have answered positively , awareness being strongest among 30 to 39 year-olds .

Example 306:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Leading trends in food items on restaurant menus in the U.S. 2018
X_Axis['Category']: ['New_cuts_of_meat_(e.g._shoulder_tender_oyster_steak_Vegas_Strip_Steak_Merlot_cut)', 'African_flavors', 'Doughnuts_with_non-traditional_filling_(e.g._liqueur_Earl_Grey_cream)', "Ethnic-inspired_kids'_dishes_(e.g._tacos_teriyaki_sushi)", 'Street_food-inspired_dishes_(e.g._tempura_kabobs_dumplings_pupusas)', 'House-made_condiments', "Gourmet_items_in_kids'_meals", 'Ethnic-inspired_breakfast_items_(e.g._Asia-flavored_syrups_Chorizo_scrambled_eggs_coconut_milk_pancakes)', 'Ethnic_condiments_(e.g._sriracha_sambal_chimichurri_gochujang_zhug)', 'Thai-rolled_ice_cream', 'Sustainable_seafood', 'Ancient_grains_(e.g._kamut_spelt_amaranth_lupin)', 'Uncommon_herbs_(e.g._chervil_lovage_lemon_balm_papalo)', 'Authentic_ethnic_cuisine', "Healthful_kids'_meals", 'Vegetable_carb_substitutes_(e.g._cauliflower_rice_zucchini_spaghetti)', 'Ethnic_spices_(e.g._harissa_curry_peri_peri_ras_el_hanout_shichimi)', 'Peruvian_cuisine', 'House-made/artisan_pickles', 'Heritage-breed_meats']
Y_Axis['Share', 'of', 'respondents']: ['69', '69', '64', '64', '64', '64', '63', '63', '62', '62', '62', '61', '61', '61', '61', '61', '60', '60', '60', '60']

gold: This statistic shows the leading 20 trends in food items on restaurant menus in the United States as forecasted by professional chefs for 2018 . During the survey , 69 percent of the respondents stated that new cuts of meat ( e.g . shoulder tender , oyster steak ) would be a `` hot trend '' for restaurant menus in 2018 .
gold_template: This statistic shows the templateTitle[0] 20 templateTitle[1] in templateTitle[2] templateXValue[6] on templateTitle[4] templateTitle[5] in the templateTitle[6] as forecasted by professional chefs for templateTitleDate[0] . During the survey , templateYValue[max] templateScale of the templateYLabel[1] stated that templateXValue[0] of templateXValue[0] ( e.g . templateXValue[0] , templateXValue[0] ) would be a `` hot trend '' for templateTitle[4] templateTitle[5] in templateTitleDate[0] .

generated_template: This statistic provides information on the templateYLabel[0] of internet users in the templateTitle[4] who watch templateTitle[1] videos every day as of 2018 . During the survey , it was found that templateYValue[7] templateScale of U.S. internet users watched templateTitle[1] templateTitle[2] content on a templateTitleSubject[0] basis . Additionally , more than half of the internet users in the templateTitle[4] .
generated: This statistic provides information on the Share of internet users in the restaurant who watch trends videos every day as of 2018 .  During the survey , it was found that 63 % of U.S. internet users watched trends food content on a U.S. basis .  Additionally , more than half of the internet users in the restaurant .

Example 307:
titleEntities: {'Subject': ['EU'], 'Date': ['2016']}
title: EU : number of enterprises in the restaurants & mobile food industry by country 2016
X_Axis['Country']: ['France', 'Italy', 'Germany', 'United_Kingdom', 'Spain', 'Czechia', 'Greece', 'Portugal', 'Netherlands', 'Belgium', 'Poland', 'Austria', 'Sweden', 'Hungary', 'Bulgaria', 'Slovakia', 'Romania', 'Denmark', 'Finland', 'Ireland', 'Croatia', 'Slovenia', 'Cyprus', 'Lithuania', 'Latvia', 'Estonia', 'Luxembourg', 'Malta']
Y_Axis['Number', 'of', 'enterprises']: ['168338', '146089', '133555', '83622', '75591', '48241', '43766', '29893', '28750', '28029', '27829', '27754', '24953', '16514', '11346', '10692', '9355', '8537', '7848', '7570', '6455', '4478', '2914', '2474', '2360', '1551', '1430', '1160']

gold: This statistic displays the number of enterprises in the restaurants and mobile food service activities industry in the European Union ( EU-28 ) in 2016 , by country . In 2016 , there were approximately 168,300 enterprises in the restaurants and mobile food service activities industry in France .
gold_template: This statistic displays the templateYLabel[0] of templateYLabel[1] in the templateTitle[3] and templateTitle[5] templateTitle[6] service activities templateTitle[7] in the European Union ( EU-28 ) in templateTitleDate[0] , templateTitle[8] templateXLabel[0] . In templateTitleDate[0] , there were approximately 168,300 templateYLabel[1] in the templateTitle[3] and templateTitle[5] templateTitle[6] service activities templateTitle[7] in templateXValue[0] .

generated_template: Among member countries of the Organization of Economic Co-operation and Development ( OECD ) , the templateXValue[0] have one of the highest densities of magnetic resonance imaging ( templateYLabel[0] ) templateYLabel[1] . Nearly templateYValue[max] such templateYLabel[1] are available templateYLabel[2] every templateScale of its templateYLabel[4] . templateXValue[1] and South templateXValue[2] follow with rates of some templateYValue[1] and templateYValue[2] templateYLabel[2] one templateScale of its inhabitants .
generated: Among member countries of the Organization of Economic Co-operation and Development ( OECD ) , the France have one of the highest densities of magnetic resonance imaging ( Number ) enterprises .  Nearly 168338 such enterprises are available enterprises every million of its enterprises .  Italy and South Germany follow with rates of some 146089 and 133555 enterprises one percent of its inhabitants .

Example 308:
titleEntities: {'Subject': ['Daimler AG'], 'Date': ['2007', '2019']}
title: Daimler AG - net income ( loss ) 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Net', 'income', '/', 'loss', 'in', 'billion', 'euros']: ['2.71', '7.6', '10.6', '8.8', '8.71', '7.29', '8.72', '6.83', '6.03', '4.67', '-2.64', '1.41', '3.99']

gold: Daimler AG reported net income of around 2.7 billion euros ( or about three billion U.S. dollars ) in 2019 , a drop of almost five billion euros year-on-year . Daimler 's earnings were hit by expenses related to recalls of diesel engine-equipped cars and vans .
gold_template: templateTitleSubject[0] reported templateYLabel[0] templateYLabel[1] of around templateYValue[0] templateScale templateYLabel[5] ( or about templateYValue[0] templateScale U.S. dollars ) in templateXValue[max] , a drop of almost templateYValue[9] templateScale templateYLabel[5] year-on-year . templateTitleSubject[0] 's earnings were hit by expenses related to recalls of diesel engine-equipped cars and vans .

generated_template: This statistic shows the total templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale templateTitle[1] templateTitle[2] templateYLabel[1] were living in templateTitleSubject[0] , compared to templateYValue[8] templateScale in templateXValue[8] .
generated: This statistic shows the total Net of AG net income in Daimler AG from 2007 to 2019 .  In 2019 , about 2.71 billion AG net income were living in Daimler AG , compared to 6.03 billion in 2011 .

Example 309:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015', '2020']}
title: Forecast major categories of U.S. healthcare expenditures 2015 to 2020
Data:
                0                      1                   2              3                4
unknown  U.S._GDP  Total_health_spending  Prescription_drugs  Hospital_care  MD_&_outpatient
2015     17722000                3544400              442050        1134200           673000
2020     22145000                5204100              702550        1665300           936700 

gold: This statistic shows a projection in selected major categories regarding U.S. healthcare expenditures in 2015 and 2020 . In 2020 , the U.S. is expected to spend more than 700 billion U.S. dollars on prescription drugs . The United States is expected to experience a rise in total pharmaceutical prescriptions and continues to be one of the largest markets for prescription drugs in the world .
gold_template: This statistic shows a projection in selected templateTitle[1] templateTitle[2] regarding templateValue[0][0] templateTitle[4] templateTitle[5] in templateTitleDate[min] and templateTitleDate[max] . In templateTitleDate[max] , the templateValue[0][0] is expected to spend more than 700 templateScale templateValue[0][0] dollars on templateValue[0][2] . The templateTitle[3] is expected to experience a rise in templateValue[0][1] pharmaceutical prescriptions and continues to be one of the largest markets for templateValue[0][2] in the world .

generated_template: The statistic gives information on the most popular digital templateTitle[4] templateTitle[5] in templateTitleSubject[0] in templateTitleDate[0] with forecast regarding templateLabel[2][0] . In templateTitleDate[0] , templateValue[0][0] on templateValue[0][0] accounted for templateValue[1][max] templateScale of all digital shopping transactions . Mobile wallet is set to templatePositiveTrend from templateValue[1][min] to templateValue[1][idxmin(1)] templateScale of payments in templateLabel[2][0] .
generated: The statistic gives information on the most popular digital healthcare expenditures in U.S. 2015 with forecast regarding 2020 .  In 2015 , U.S._GDP on accounted for 17722000 % of all digital shopping transactions .  Mobile wallet is set to increase from 442050 to millions of payments in 2020 .

Example 310:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2017', '2023']}
title: Brazil : internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['78', '75', '73', '71', '68', '66', '63']

gold: This statistic provides information on internet user penetration in Brazil from 2017 to 2023 . In 2016 , 55.65 percent of the Brazilian population were accessing the internet . These figures are projected to grow to 78 percent in 2023 .
gold_template: This statistic provides information on templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In 2016 , 55.65 templateScale of the Brazilian templateYLabel[1] were accessing the templateTitle[1] . These figures are projected to grow to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .

generated_template: The statistic shows the projected templateTitle[2] templateTitle[3] templateTitle[4] among the templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateYValue[5] templateScale of the total templateTitle[1] templateYLabel[1] accessed the templateTitle[2] from anywhere via any device .
generated: The statistic shows the projected user penetration 2017 among the population in the Brazil from 2017 to 2023 .  In 2018 , 66 % of the total internet population accessed the user from anywhere via any device .

Example 311:
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 gives information on the consolidated number of monthly templateYLabel[0] templateYLabel[2] templateYLabel[3] ( templateTitleSubject[1] ) across templateYLabel[1] 's online shopping properties from the fourth templateXLabel[0] of 2016 to the fourth templateXLabel[0] of templateTitleDate[0] . As of the last templateXLabel[0] of templateTitleDate[0] , templateYLabel[1] 's Chinese e-commerce properties had been accessed by around templateYValue[max] templateScale templateYLabel[2] templateTitleSubject[1] .
generated: This statistic gives information on the consolidated number of monthly Revenue yen ( LINE ) across billion 's online shopping properties from the fourth Quarter of 2016 to the fourth Quarter of 2012 .  As of the last Quarter of 2012 , billion 's Chinese e-commerce properties had been accessed by around 30.0 billion yen LINE .

Example 312:
titleEntities: {'Subject': ['Celgene'], 'Date': ['2013', '2018']}
title: Celgene 's top product revenues 2013 to 2018
Data:
                                          0                          1                   2                  3      4
Drug name (therapy area)  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 templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[min] and templateTitleDate[max] . As of templateTitleDate[max] , templateValue[2][max] templateScale of respondents said their templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] technology was templateValue[0][0] modeling ( FDM ) .
generated: The statistic shows the Celgene 's top product revenues in 2013 and 2018 .  As of 2018 , 4980 % of respondents said their Celgene 's top product technology was Revlimid_(cancer) modeling ( FDM ) .

Example 313:
titleEntities: {'Subject': ['June'], 'Date': []}
title: Leading hotel companies worldwide as of June 2019 , by number of properties
X_Axis['Month']: ['Wyndham_Hotel_Group', 'Choice_Hotels_International', 'Marriott_International', 'Hilton_Worldwide', 'InterContinental_Hotels_Group_(IHG)', 'Best_Western_Hotels_&_Resorts', 'G6_Hospitality', 'Radisson_Hotel_Group', 'RLH_Corporation', 'Hyatt_Hotels_Corp.', 'Aimbridge_Hospitality', 'Westmont_Hospitality_Group', 'Red_Roof', 'Extended_Stay_America', 'Interstate_Hotels_&_Resorts']
Y_Axis['Number', 'of', 'properties']: ['9157', '7045', '7003', '5872', '5656', '4008', '1391', '1179', '1167', '865', '834', '692', '637', '627', '479']

gold: Global hotel chain , Wyndham Hotel Group , leads the ranking of the top hotel companies by number properties . As of June 2019 , Wyndham accounted for over nine thousand units in its worldwide portfolio , which ranged from economy to upscale accommodation . Its closest competitors were Choice Hotels International and Marriot International , who reported 7,045 and 7,003 properties , respectively .
gold_template: Global templateXValue[0] chain , templateXValue[0] Group , leads the ranking of the top templateXValue[0] templateTitle[2] templateTitle[6] templateYLabel[0] templateYLabel[1] . As of templateTitleSubject[0] templateTitleDate[0] , templateXValue[0] accounted for over templateYValue[max] thousand units in its templateXValue[3] portfolio , which ranged from economy to upscale accommodation . Its closest competitors were templateXValue[1] International and Marriot templateXValue[1] , who reported templateYValue[1] and templateYValue[2] templateYLabel[1] , respectively .

generated_template: This statistic provides information on the most popular templateTitle[2] worldwide as of 2020 , templateTitle[3] templateTitle[4] templateYLabel[0] of templateYLabel[1] accounts . Market leader templateXValue[0] was the first templateTitle[1] network to surpass templateYValue[3] templateScale registered accounts and currently sits at almost templateYValue[max] templateScale monthly templateYLabel[1] templateYLabel[2] . Sixth-ranked photo-sharing app templateXValue[5] had templateYValue[3] templateScale monthly templateYLabel[1] accounts .
generated: This statistic provides information on the most popular companies worldwide as of 2020 , worldwide June Number of properties accounts .  Market leader Wyndham_Hotel_Group was the first hotel network to surpass 5872 million registered accounts and currently sits at almost 9157 million monthly properties .  Sixth-ranked photo-sharing app Best_Western_Hotels_&_Resorts had 5872 million monthly properties accounts .

Example 314:
titleEntities: {'Subject': ['Amazon Web Services'], 'Date': ['2014', '2019']}
title: Amazon Web Services : year-on-year growth 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", "Q1_'14"]
Y_Axis['Year-on-year', 'growth']: ['34', '35', '37', '42', '46', '46', '49', '48', '44', '42', '42', '43', '47', '55', '58', '64', '69', '78', '81', '49', '47', '43', '43', '69']

gold: In the fourth quarter of 2019 , revenues of AWS grew 34 percent compared to the preceding quarter . AWS is one of Amazon 's strongest revenue segments , generating 25.66 billion U.S. dollars in 2018 net sales , up from 17.46 billion U.S. dollars in 2017 . AWS ranks as one of the most popular public cloud infrastructure and platform services running applications worldwide in 2018 , ahead off Azure and Google cloud services .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[max] , revenues of AWS templatePositiveTrend templateYValue[min] templateScale compared to the preceding templateXLabel[0] . AWS is one of templateTitleSubject[0] 's strongest revenue segments , generating 25.66 templateScale U.S. dollars in 2018 net sales , up from 17.46 templateScale U.S. dollars in 2017 . AWS ranks as one of the most popular public cloud infrastructure and platform templateTitleSubject[0] running applications worldwide in 2018 , ahead off Azure and Google cloud templateTitleSubject[0] .

generated_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] at the templateTitle[2] templateXLabel[0] of templateTitleSubject[0] from the fourth templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . Automated transaction templateYLabel[2] ( ATMs ) are banking outlets designed for maximum access to banking transactions and information , outside of bank working hours and without the assistance of a clerk . It can be seen that the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] templatePositiveTrend between the first templateXLabel[0] of templateTitleDate[min] and the second templateXLabel[0] of 2016 , reaching a total of more than templateYValue[9] thousand as of the second templateXLabel[0] of 2016 .
generated: This statistic illustrates the Year-on-year of growth at the Services Quarter of Amazon Web Services from the fourth Quarter of 2014 to the fourth Quarter of 2019 .  Automated transaction growth ( ATMs ) are banking outlets designed for maximum access to banking transactions and information , outside of bank working hours and without the assistance of a clerk .  It can be seen that the total Year-on-year of growth increased between the first Quarter of 2014 and the second Quarter of 2016 , reaching a total of more than 42 thousand as of the second Quarter of 2016 .

Example 315:
titleEntities: {'Subject': ['Walt Disney Company'], 'Date': ['2019']}
title: Total assets of the Walt Disney Company 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Total', 'assets', 'in', 'billion', 'U.S.', 'dollars']: ['193.98', '98.6', '95.79', '92.03', '88.18', '84.14', '81.24', '74.9', '72.12', '69.21', '63.12', '62.5', '60.93', '60.0']

gold: This statistic contains data on the total assets of the Walt Disney Company from the fiscal year 2006 to the fiscal year 2019 . In 2019 , the Walt Disney Company held assets worth a total of over 193.98 billion U.S. dollars . In the same year , the American media company generated global revenue of 69.57 billion U.S. dollars , marking the highest figure to-date .
gold_template: This statistic contains data on the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Company from the fiscal templateXLabel[0] templateXValue[min] to the fiscal templateXLabel[0] templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] Company held templateYLabel[1] worth a templateYLabel[0] of over templateYValue[max] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] . In the same templateXLabel[0] , the American media templateTitleSubject[0] generated global revenue of 69.57 templateScale templateYLabel[3] templateYLabel[4] , marking the highest figure to-date .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Holding templateTitle[5] templateTitle[6] FY2006 to FY2019 . In FY2019 , the templateTitle[5] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was approximately templateYValue[max] templateScale templateYLabel[4] dollars .
generated: This statistic shows the Total assets of the Walt Disney Company Holding 2006 2019 FY2006 to FY2019 .  In FY2019 , the 2006 Total assets of Walt Disney Company was approximately 193.98 billion dollars .

Example 316:
titleEntities: {'Subject': ['Colombia'], 'Date': ['2019']}
title: Unemployment rate in Colombia 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']: ['9.19', '9.09', '8.87', '8.69', '8.3', '8.57', '9.05', '9.74', '10.11', '10.98', '12.07', '11.27', '11.2', '11.51', '11.87', '13.72', '14.19', '15.63', '15.04', '20.52', '20.06']

gold: This statistic shows the unemployment rate in Colombia from 1999 to 2019 . In 2019 , the unemployment rate in Colombia was at approximately 9.19 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 Colombia from 1999 to 2019 .  In 2019 , the Unemployment rate in Colombia was at approximately 9.19 % .

Example 317:
titleEntities: {'Subject': ['South Africa'], 'Date': ['2018']}
title: Urbanization in South Africa 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['66.36', '65.85', '65.34', '64.83', '64.31', '63.79', '63.27', '62.75', '62.22', '61.69', '61.15']

gold: In 2018 , over 66 percent of South Africa 's total population lived in urban areas and cities . Urbanization defines the share of urban population from the total population of a country . Just like urbanization , the population density within the nation has risen , reaching 46 inhabitants per square kilometer , meaning more people are sharing less space .
gold_template: In templateXValue[max] , over templateYValue[max] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities . templateTitle[0] defines the templateYLabel[0] of templateYLabel[1] templateYLabel[2] from the templateYLabel[3] templateYLabel[2] of a country . Just like templateTitle[0] , the templateYLabel[2] density within the nation has risen , reaching 46 inhabitants per square kilometer , meaning more people are sharing less space .

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 South Africa from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 66.36 % of South Africa 's total population lived in urban areas and cities .

Example 318:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2018']}
title: Literacy rate in Brazil 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
Adult total   93.23  93.08  92.81  92.05  91.73  91.48  91.34  91.41  90.38   90.3  90.04
Adult male    93.01  92.89  92.58  91.73  91.37  91.18  91.03  91.17  90.06  90.17  89.84
Adult female  93.43  93.24  93.02  92.34  92.06  91.76  91.62  91.63  90.68  90.41  90.22 

gold: The statistic depicts the literacy rate in Brazil from 2008 to 2018 . The literacy rate measures the percentage of people aged 15 and above who can read and write . In 2018 , Brazil 's literacy rate was around 93.23 percent .
gold_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_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 Brazil from 2008 to 2018 .  The Literacy rate measures the percentage of people aged 15 and above who can read and write .  In 2018 , Brazil 's Literacy rate was around 93.23 % .

Example 319:
titleEntities: {'Subject': ['Ubisoft'], 'Date': ['2019']}
title: Ubisoft : revenue by region 2019
X_Axis['Country']: ['United_States_/_Canada', 'Rest_of_Europe', 'Asia_/_Pacific', 'United_Kingdom', 'France', 'Germany', 'Rest_of_the_world']
Y_Axis['Sales', 'in', 'million', 'euros']: ['896', '340', '342', '126', '116', '135', '74']

gold: The graph depicts the sales of Ubisoft in the financial year 2018/2019 , by region . In the United Kingdom , the sales of Ubisoft amounted to 126 million euros in the presented period .
gold_template: The graph depicts the templateYLabel[0] of templateTitleSubject[0] in the financial year 2018/2019 , templateTitle[2] templateTitle[3] . In the templateXValue[0] templateXValue[3] , the templateYLabel[0] of templateTitleSubject[0] amounted to templateYValue[3] templateScale templateYLabel[2] in the presented period .

generated_template: This statistic shows the global templateYLabel[0] of the templateTitle[1] templateTitle[2] in templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . In templateXValue[1] , templateXValue[1] accounted for some templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[1] templateTitle[2] were produced that year .
generated: This statistic shows the global Sales of the revenue by in 2019 , sorted 2019 .  In Rest_of_Europe , accounted for some 340 million euros of revenue by were produced that year .

Example 320:
titleEntities: {'Subject': ['ABB Group'], 'Date': ['2010', '2018']}
title: Number of ABB Group employees 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Number', 'of', 'employees']: ['146600', '134800', '132300', '135800', '140400', '147700', '146100', '133600', '116500']

gold: This statistic represents the number of ABB Group employees from the fiscal year of 2010 to the fiscal year of 2018 . In its 2018 fiscal year , the Switzerland-based electronics company employed 146,600 people worldwide .
gold_template: This statistic represents the templateYLabel[0] of templateTitleSubject[0] employees from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , the Switzerland-based electronics company employed templateYValue[idxmax(X)] people worldwide .

generated_template: This statistic depicts the total templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] is a health care company headquartered in Minnetonka , Minnesota . The total templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] the templateTitleSubject[0] , as of 31 , templateXValue[max] , was about templateYValue[idxmax(X)] templateTitle[1] worldwide templateYValue[idxmax(X)]
generated: This statistic depicts the total Number of ABB Group employees ABB Group from 2010 to 2018 .  The ABB Group is a health care company headquartered in Minnetonka , Minnesota .  The total Number of employees Group the ABB Group , as of 31 , 2018 was about 146600 ABB worldwide 146600 . 

Example 321:
titleEntities: {'Subject': ['Average'], 'Date': ['2012']}
title: Average wages by country , in purchasing power parity dollars , 2012
X_Axis['Country']: ['Luxembourg', 'Norway', 'Austria', 'United_States', 'United_Kingdom', 'Belgium', 'Sweden', 'Ireland', 'Finland', 'Korea_(Republic_of)', 'France', 'Canada', 'Germany', 'Singapore', 'Australia', 'Cyprus', 'Japan', 'Italy', 'Iceland', 'Spain', 'Greece', 'New_Zealand', 'South_Africa', 'Malta', 'Israel', 'Czech_Republic', 'Croatia', 'Turkey', 'Qatar', 'Hong_Kong_(China)', 'Poland', 'Slovakia', 'Hungary', 'Macedonia', 'Bosnia_&_Herzegovina', 'Estonia', 'Russian_Federation', 'Jamaica', 'Lithuania', 'Argentina', 'Latvia', 'Serbia', 'Chile', 'Botswana', 'Malaysia', 'Belarus', 'Romania', 'Bahrain', 'Panama', 'Mauritius', 'Brazil', 'Macau_(China)', 'Kazakhstan', 'Bulgaria', 'Colombia', 'Ukraine', 'China', 'Mexico', 'Georgia', 'Azerbaijan', 'Egypt', 'Thailand', 'Armenia', 'Dominican_Republic', 'Moldova_(Republic_of)', 'Mongolia', 'Syrian_Arab_Republic', 'Kyrgyzstan_Republic', 'India', 'Philippines', 'Pakistan', 'Tajikistan']
Y_Axis['Average', 'monthly', 'wage', 'in', 'U.S.', 'dollars']: ['4089', '3678', '3437', '3263', '3065', '3035', '3023', '2997', '2925', '2903', '2886', '2724', '2720', '2616', '2610', '2605', '2522', '2445', '2431', '2352', '2300', '2283', '1838', '1808', '1804', '1786', '1756', '1731', '1690', '1545', '1536', '1385', '1374', '1345', '1338', '1267', '1215', '1135', '1109', '1108', '1098', '1058', '1021', '996', '961', '959', '954', '917', '831', '783', '778', '758', '753', '750', '692', '686', '656', '609', '603', '596', '548', '489', '471', '462', '438', '415', '364', '336', '295', '279', '255', '227']

gold: This graph shows average wages around the world in 2012 as calculated by purchasing power parity . In 2012 the highest average wage was earned in Luxembourg at 4,089 purchasing power parity dollars . Wages and salaries Wages and salaries in the United States have increased during the last decades .
gold_template: This graph shows templateYLabel[0] templateTitle[1] around the world in templateTitleDate[0] as calculated templateTitle[2] templateTitle[4] templateTitle[5] templateTitle[6] . In templateTitleDate[0] the highest templateYLabel[0] templateYLabel[2] was earned in templateXValue[0] at templateYValue[max] templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[4] . templateTitle[1] and salaries templateTitle[1] and salaries in the templateXValue[3] have templatePositiveTrend during the last decades .

generated_template: The statistic reflects the templateYLabel[0] of templateTitle[0] templateYLabel[1] in the templateTitleSubject[0] in templateTitleDate[0] , with a breakdown templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , there were about templateYValue[max] templateScale templateYLabel[1] living in templateXValue[2] .
generated: The statistic reflects the Average of monthly in the Average in 2012 , with a breakdown power Country .  In 2012 , there were about 4089 thousand monthly living in Austria .

Example 322:
titleEntities: {'Subject': ['Iraq'], 'Date': ['2013']}
title: Civilian deaths per day by suicide bombings in Iraq war 2003 to 2013
X_Axis['Year']: ['2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Average', 'number', 'of', 'civilian', 'deaths', 'per', 'day']: ['6.4', '7.3', '6.6', '7.3', '8.4', '10.0', '22.0', '16.0', '10.0', '5.4', '1.5']

gold: The statistics shows the average number of civilian deaths per day by car bombs and suicide attacks in Iraq war from 2003 to 2013  . As of January 19 , 2013 , there were an average of 6.4 civilian deaths per day caused by suicide attacks and car bombs in 2013 . Iraq war The Iraq war was launched in March 2003 upon the invasion of Iraq by U.S. forces .
gold_template: The statistics shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateTitle[4] car bombs and templateTitle[5] attacks in templateTitleSubject[0] templateTitle[8] from templateXValue[min] to templateXValue[max] . As of 19 , templateXValue[max] , there were an templateYLabel[0] of templateYValue[idxmax(X)] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] templateYLabel[5] caused templateTitle[4] templateTitle[5] attacks and car bombs in templateXValue[max] . templateTitleSubject[0] templateTitle[8] The templateTitleSubject[0] templateTitle[8] was launched in 2003 upon the invasion of templateTitleSubject[0] templateTitle[4] U.S. forces .

generated_template: This statistic shows the proportion of metal and metal products that are produced from templateTitleSubject[0] templateTitle[1] and other low-grade residues worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[2] templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] templateTitle[1] amounted to templateYValue[3] templateScale .
generated: This statistic shows the proportion of metal and products that are produced from Iraq deaths and other low-grade residues worldwide from 2003 to 2013 .  In 2007 , the per Average number for Iraq deaths amounted to 7.3 million .

Example 323:
titleEntities: {'Subject': ['Rocket League'], 'Date': ['2016', '2020']}
title: Rocket League peak concurrent player number on Steam 2016 to 2020
X_Axis['Month']: ["Jan_'20", "Dec_'19", "Nov_'19", "Oct_'19", "Sept_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sept_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Aprl_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sept_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17", "Dec_'16", "Nov_'16", "Oct_'16", "Sept_'16", "Aug_'16", "Jul_'16", "Jun_'16", "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16"]
Y_Axis['Number', 'of', 'players', 'in', 'thousands']: ['81.64', '80.21', '60.08', '68.39', '59.53', '57.62', '65.91', '69.62', '69.47', '67.49', '61.24', '62.02', '64.26', '67.95', '63.28', '67.22', '75.88', '55.82', '82.3', '64.31', '66.08', '60.63', '63.58', '62.49', '60.54', '65.28', '60.12', '73.68', '70.23', '52.61', '68.23', '64.08', '82.25', '65.38', '74.33', '61.65', '69.67', '70.35', '55.53', '59.53', '79.3', '48.14', '58.68', '55.03', '58.21', '102.68', '45.88', '50.36', '46.11']

gold: The statistic shows information on the monthly number of peak concurrent players of Rocket League on Steam worldwide as of January 2020 . In January 2020 , Rocket League reached 81.64 thousand concurrent players on Steam . In the measured period , the game reached its record high peak concurrent player number in April 2016 , with over 102.68 thousand gamers playing at the same time .
gold_template: The statistic shows information on the monthly templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] of templateTitleSubject[0] on templateTitle[6] worldwide as of 2020 . In 2020 , templateTitleSubject[0] reached templateYValue[0] thousand templateTitle[3] templateYLabel[1] on templateTitle[6] . In the measured period , the game reached its record high templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] in 2016 , with over templateYValue[max] thousand gamers playing at the same time .

generated_template: The statistic shows information on the monthly templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] of Grand Theft Auto templateTitleSubject[0] on templateTitleSubject[0] worldwide as of 2020 . In 2020 , templateTitle[0] templateTitleSubject[0] reached templateYValue[max] thousand templateTitle[3] templateYLabel[1] on templateTitleSubject[0] .
generated: The statistic shows information on the monthly Number of peak concurrent players of Grand Theft Auto Rocket League on worldwide as of 2020 .  In 2020 , Rocket Rocket League reached 102.68 thousand concurrent players on Rocket League .

Example 324:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2017']}
title: Fatality rate per 100,000 drivers licensed in the 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['Fatalities', 'per', '100,000', 'licensed', 'drivers']: ['16.48', '17.05', '16.27', '15.29', '15.5', '15.95', '15.33', '15.71', '16.16', '17.96', '20.05', '21.06', '21.7', '22.0', '23.68', '26.7']

gold: The timeline shows the fatality rate per 100,000 drivers licensed to operate a motor vehicle in the United States from 1990 to 2017 . The fatality rate stood at 16.5 deaths per 100,000 licensed drivers in 2017 .
gold_template: The timeline shows the templateTitle[0] templateTitle[1] templateYLabel[1] 100,000 templateYLabel[4] templateYLabel[3] to operate a motor vehicle in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] stood at templateYValue[0] deaths templateYLabel[1] 100,000 templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic represents the annual templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] was around templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic represents the annual Fatality Fatalities of per 100,000 drivers in the U.S. from 1990 to 2017 .  In 2017 , the average Fatality Fatalities of per 100,000 drivers was around 16.48 % 100,000 licensed .

Example 325:
titleEntities: {'Subject': ['Uber U.S.'], 'Date': ['2017', '2019']}
title: Driver satisfaction with Uber U.S. 2017 to 2019
Data:
                         0                  1                           2               3               4
unknown  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: templateValue[0][0] and templateValue[0][1] ( Outlook.com ) were still the most used templateTitle[1] services in the templateTitleSubject[0] , according to survey respondents in templateTitleDate[max] . Compared to earlier years , the use of both Google 's as well as Microsoft 's free e-mail service saw an templatePositiveTrend in their usage among Dutch consumers , whilst e-mail services provided by domestic templateTitle[2] ( such as KPN and Ziggo ) saw much less use . Sending and receiving e-mails is a popular online communication method across all age groups in the country .
generated: Strongly_disagree and Somewhat_disagree ( Outlook.com ) were still the most used satisfaction services in the Uber U.S. , according to survey respondents in 2019 .  Compared to earlier years , the use of both Google 's as well Microsoft 's free e-mail service saw an increase in their usage among Dutch consumers , whilst e-mail services provided by domestic Uber ( such as KPN and Ziggo ) saw much less use .  Sending and receiving e-mails is a popular online communication method across all age groups in the country .

Example 326:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2012', '2019']}
title: Twitter : quarterly net income 2012 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"]
Y_Axis['Net', 'income/loss', 'in', 'million', 'U.S.', 'dollars']: ['118.77', '36.52', '1119.56', '190.8', '255.3', '789.18', '100.12', '61.0', '91.08', '-21.1', '-116.49', '-61.56', '-167.05', '-102.87', '-107.22', '-79.73', '-90.24', '-131.69', '-136.66', '-162.44', '-125.35', '-175.46', '-144.64', '-132.36', '-511.47', '-64.6', '-42.23', '-27.03', '-8.71', '-21.59', '-27.58', '-21.52']

gold: This statistic contains data on the quarterly net loss of Twitter from the first quarter of 2012 to the fourth quarter of 2019 . In the last reported quarter , the social network generated a total net income of 118.77 million U.S. dollars . The social platform has only been reporting positive income since the fourth quarter of 2017 .
gold_template: This statistic contains data on the templateTitle[1] templateYLabel[0] loss of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the last reported templateXLabel[0] , the social network generated a total templateYLabel[0] templateTitle[3] of templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] . The social platform has only been reporting positive templateTitle[3] since the fourth templateXLabel[0] of 2017 .

generated_template: The templateYLabel[0] of Tencent 's templateTitleSubject[0] templateTitle[1] templateYLabel[1] has been templatePositiveTrend by 20 templateScale each templateXLabel[0] . In the most recently reported templateXLabel[0] , Tencent 's templateTitleSubject[0] had over templateYValue[max] templateScale monthly templateTitle[1] users from a wide range of age groups . templateTitleSubject[0] users – additional information First released in 2011 , templateTitleSubject[0] is a mobile messaging app developed by the Chinese company Tencent .
generated: The Net of Tencent 's Twitter quarterly income/loss has been increased by 20 million each Quarter .  In the most recently reported Quarter , Tencent 's Twitter had over 1119.56 million monthly quarterly users from a wide range of age groups .  Twitter users – additional information First released in 2011 , Twitter is a mobile messaging app developed by the Chinese company Tencent .

Example 327:
titleEntities: {'Subject': ['FDI', 'Canada'], 'Date': ['2018']}
title: Foreign Direct investment ( FDI ) in Canada , by country 2018
X_Axis['Country']: ['United_States', 'Netherlands', 'Luxembourg', 'United_Kingdom', 'Switzerland', 'Japan', 'Hong_Kong', 'Germany', "People's_Republic_of_China", 'Bermuda', 'Brazil', 'France', 'Australia', 'Cayman_Islands', 'Ireland', 'Belgium', 'Spain', 'Norway', 'United_Arab_Emirates', 'British_Virgin_Islands', 'Poland', 'Mexico', 'Sweden', 'India', 'South_Korea', 'Hungary', 'Kuwait', 'Russian_Federation', 'Italy', 'Chile', 'Uruguay', 'Barbados', 'Nigeria', 'Austria', 'Cyprus', 'Finland', 'South_Africa', 'Denmark', 'Kyrgyzstan', 'Yemen', 'Malaysia', 'Israel', 'New_Zealand', 'Singapore', 'Indonesia', 'Taiwan', 'Portugal', 'Thailand', 'Liberia', 'Guyana', 'Jamaica', 'Philippines', 'Iceland', 'Panama', 'Costa_Rica', 'Turkey', 'Colombia', 'Bahamas', 'Argentina']
Y_Axis['FDI', 'in', 'million', 'Canadian', 'dollars']: ['406051', '106706', '55828', '50353', '46147', '28871', '21802', '17008', '16959', '16604', '14628', '13509', '9682', '8912', '8094', '8015', '4823', '4563', '3441', '2928', '2830', '2730', '2585', '2561', '2398', '2289', '2158', '1581', '1510', '1439', '1329', '1256', '934', '922', '898', '776', '754', '728', '693', '348', '241', '181', '127', '116', '84', '76', '70', '56', '49', '1', '1', '-1', '-1', '-1', '-1', '-2', '-7', '-39', '-60']

gold: This statistic shows the amount of foreign direct investment in Canada in 2018 , distinguished by country . In 2018 , direct investors from the United States invested 406.05 billion Canadian dollars in Canada .
gold_template: This statistic shows the amount of templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[1] in templateTitleDate[0] , distinguished templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateTitle[1] investors from the templateXValue[0] invested templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateTitleSubject[1] .

generated_template: The statistic shows the degree of templateTitle[3] templateTitleSubject[0] in templateTitle[5] templateTitle[6] worldwide . According to the templateTitleSubject[0] Index , templateXValue[last] occupied the last place in templateTitle[3] templateTitleSubject[0] with templateYValue[min] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . templateXValue[1] and templateXValue[0] were ranked first and second with templateYValue[max] and templateYValue[1] out of 100 templateYLabel[0] templateYLabel[1] respectively .
generated: The statistic shows the degree of FDI in by country worldwide .  According to the FDI Index , Argentina occupied the last place in FDI with -60 FDI million in 2018 .  Netherlands and United_States were ranked first and second with 406051 and 106706 out of 100 FDI million respectively .

Example 328:
titleEntities: {'Subject': ['Most'], 'Date': ['2018']}
title: Most expensive housing markets worldwide 2018
X_Axis['Country']: ['Hong_Kong', 'Singapore', 'Shanghai', 'Vancouver', 'Shenzhen', 'Los_Angeles', 'New_York', 'London', 'Beijing', 'Paris']
Y_Axis['Average', 'price', 'of', 'property', 'in', 'thousand', 'U.S.', 'dollars']: ['1235.22', '874.37', '872.56', '815.32', '680.28', '679.22', '674.5', '646.97', '629.28', '625.3']

gold: In 2018 , Hong Kong had the most expensive residential property market worldwide , with an average property price of 1.24 million U.S. dollars . The government of Hong Kong provide public housing for lower-income residents and almost 45 percent of the Hong Kong population lived in public permanent housing in 2017 .
gold_template: In templateTitleDate[0] , templateXValue[0] had the templateTitleSubject[0] templateTitle[1] residential templateYLabel[2] market templateTitle[4] , with an templateYLabel[0] templateYLabel[2] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] . The government of templateXValue[0] provide public templateTitle[2] for lower-income residents and almost 45 templateScale of the templateXValue[0] population lived in public permanent templateTitle[2] in 2017 .

generated_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 the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] , templateTitle[1] templateYLabel[0] templateYLabel[1] , followed by templateXValue[1] and templateXValue[2] . The overall quarterly templateYLabel[2] templateYLabel[3] in the country can be found here .
generated: This statistic shows the 872.56 Most expensive the housing markets worldwide in the world 2018 to .  Over the past decade , Hong_Kong has the Country expensive the housing markets worldwide , expensive Average price , followed by Singapore and Shanghai .  The overall quarterly property thousand in the country can be found here .

Example 329:
titleEntities: {'Subject': ['ARPU', 'UK'], 'Date': ['2007', '2016']}
title: Monthly ARPU through mobile subscribers in the UK 2007 - 2016 , by pay type
Data:
               0       1       2       3       4       5       6      7       8       9
Year        2016    2015    2014    2013    2012    2011    2010   2009    2008    2007
Post-pay  21.25£  21.99£  22.97£  24.45£  27.35£  29.05£  28.15£    30£  32.78£  34.58£
Blended   15.19£  15.02£  15.38£  15.89£  16.87£  16.94£   15.6£    16£  17.15£   17.4£
Pre-pay    4.74£   4.73£   5.01£   5.58£   6.16£   6.41£   6.23£  7.07£   7.24£   7.43£ 

gold: This statistic illustrates the monthly average revenue per user ( ARPU ) generated through mobile subscribers in the United Kingdom ( UK ) from 2007 to 2016 , by payment type . In 2016 , the average across all subscribers ( blended ARPU ) amounted to 15.19 British pounds .
gold_template: This statistic illustrates the templateTitle[0] average revenue per user ( templateTitleSubject[0] ) generated templateTitle[2] templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[1] ) from templateValue[0][last] to templateValue[0][0] , templateTitle[8] payment templateTitle[10] . In templateValue[0][0] , the average across all templateTitle[4] ( templateLabel[2][0] templateTitleSubject[0] ) amounted to 15.19 British pounds .

generated_template: This statistic displays three different templateTitle[3] scenarios for the templateTitle[1] rough templateTitleSubject[0] templateTitle[2] between templateValue[0][last] and templateValue[0][0] . templateLabel[3][0] estimates templateTitle[3] that templateTitle[1] templateTitleSubject[0] templateTitle[2] will total around templateValue[3][3] templateScale carats in templateValue[0][3] .
generated: This statistic displays three different mobile scenarios for the ARPU rough through between 2007 and 2016 .  Pre-pay estimates mobile that ARPU through will total around 5.58£ million carats in 2013 .

Example 330:
titleEntities: {'Subject': ['Illinois'], 'Date': ['1990', '2018']}
title: Illinois - median household income 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['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['70145', '64609', '61386', '60413', '54916', '57196', '51738', '50637', '50728', '52870', '53254', '52506', '48671', '48398', '46077', '45153', '42710', '46171', '46064', '46330', '43178', '41283', '39554', '38071', '35081', '32857', '31551', '31884', '32542']

gold: This statistic shows the median household income in Illinois from 1990 to 2018 . In 2018 , the median household income in Illinois amounted to 70,145 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[4] .
generated: This statistic shows the household income in Illinois from 1990 to 2018 .  In 2018 , the household income in Illinois amounted to 70145 dollars .

Example 331:
titleEntities: {'Subject': ['LG Electronics'], 'Date': ['2014', '2019']}
title: LG Electronics revenue by segment 2014 - 2019 , by quarter
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
unknown                              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_'14_old  3Q_'14_old  2Q_'14_old  1Q_'14_old  4Q_'13  3Q_'13  2Q_'13  1Q_'13  4Q_'12  3Q_'12  2Q_'12  1Q_'12  4Q_'11  3Q_'11  2Q_'11  1Q_'11
Home Entertainment                     3.81    3.21    3.11    3.47    4.08    3.27     3.4    2.03    2.75    2.52    2.41    2.81    4.51    3.89    3.91    4.07    4.46    4.03     3.7    4.17     5.1    4.24     4.5    4.38        4.94         4.1        4.35         5.1    4.25    4.49    4.22    5.94    5.06    5.06    4.93    4.78    6.23     5.4    5.47    5.38
Mobile Communications                   1.1    1.26    1.37     1.3    1.53     1.8    1.85    3.87    4.58    3.82    3.45    3.61     2.8    2.45    3.22    2.84    3.55    3.17    3.43    3.38    3.57     4.0    3.41    3.22        3.44        3.86        3.29         3.1    3.38    2.86    2.94    3.01    2.64     2.3    2.18    2.35    2.61     2.6    3.05    2.73
Home Appliance & Air Solution          3.84    4.43    5.17    4.71    3.87    4.27    4.68    4.63    3.94    4.49    4.74    4.23    3.81    4.02    4.43    3.98     3.6     3.9    4.22    3.82    3.56    3.74    4.58    3.88           0           0           0           0       0       0       0       0       0       0       0       0       0       0       0       0
Vehicle Components                     1.13    1.11    1.21    1.16    1.25    1.04    0.78    0.79    0.77    0.78    0.79     0.8    0.81    0.63     0.6    0.56    0.49    0.45    0.42    0.36       0       0       0       0           0           0           0           0       0       0       0       0       0       0       0       0       0       0       0       0
B2B                                       0       0       0       0    0.54    0.51    0.52     0.6    0.63     0.6     0.5    0.49       0       0       0       0       0       0       0       0       0       0       0       0           0           0           0           0       0       0       0       0       0       0       0       0       0       0       0       0
Others                                    0    2.44    1.82    1.68    0.86    0.89    1.07    0.92    0.87    0.86    0.79    0.55    0.31    0.41    0.24    0.31    0.42    0.47    0.29     0.3    0.76     0.7    0.59    0.63        0.86        0.81        0.77        0.79    0.75    0.76    0.84    0.81    0.71    0.67    0.77     0.7     0.7    0.65    0.81    0.65
Home Appliance                            0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0        2.62        2.65        2.76        2.47    2.67    2.79       3    2.64    2.77     2.7     2.7    2.38     2.8    2.53    2.63    2.45
Air-Conditioning & Energy Solutions       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0        0.71        0.84        1.49        1.11    0.68    0.91    1.63    1.14    0.63     0.9    1.38    1.13    0.65    0.95    1.56    1.16
Business Solutions                     0.56    0.58    0.57    0.54       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0           0           0           0           0       0       0       0       0       0       0       0       0       0       0       0       0 

gold: The statistic shows the quarterly revenue of LG Electronics from 2014 to 2019 , broken down by business segment . In the fourth quarter of 2019 , LG Electronic 's revenue from its home entertainment segment amounted to about 3.81 billion U.S. dollars .
gold_template: The statistic shows the quarterly templateTitle[2] of templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , broken down templateTitle[3] templateLabel[9][0] templateTitle[4] . In the fourth templateTitle[8] of templateTitleDate[max] , templateTitleSubject[0] Electronic 's templateTitle[2] from its templateLabel[1][0] templateLabel[1][1] templateTitle[4] amounted to about templateValue[1][0] templateScale U.S. dollars .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the leading operating systems for smartphones in templateTitleSubject[0] from templateValue[0][0] to templateValue[0][last] . In the templateLabel[1][0] operating operating system had a templateTitle[0] templateTitle[1] of templateValue[1][0] templateScale in templateTitleSubject[0] .
generated: This statistic shows the LG Electronics revenue by the leading operating systems for smartphones in LG Electronics from 4Q_'19 to 1Q_'11 .  In the Home operating system had a LG Electronics of 3.81 million in LG Electronics .

Example 332:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2018']}
title: United Kingdom ( UK ) : suicide rate 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
Male    17.2  15.5  16.0  16.6  16.8  17.8  16.8  16.8  15.8  16.1  16.3  15.6  16.2  16.4  16.9  16.9  17.1  17.9  18.4
Female   5.4   4.9   5.0   5.4   5.2   4.8   4.9   5.3   4.9   4.9   5.0   4.7   5.0   5.5   5.8   5.5   5.6   5.5   5.9 

gold: In 2018 , the rate of suicides among males was 17.2 per 100,000 population and among females it was 5.4 per 100,000 . The rate of suicide has slightly decreased for both genders since the beginning of given time period of the statistic , although the 2018 rate is an increase from the preceding few years . However , the rate of suicide for men has remained significantly higher than for women .
gold_template: In templateValue[0][0] , the templateTitle[4] of suicides among males was templateValue[1][0] per 100,000 population and among females it was templateValue[2][0] per 100,000 . The templateTitle[4] of templateTitle[3] has slightly templateNegativeTrend for both genders since the beginning of given time period of the statistic , although the templateValue[0][0] templateTitle[4] is an templatePositiveTrend from the preceding few years . However , the templateTitle[4] of templateTitle[3] for men has remained significantly higher than for women .

generated_template: The templateTitle[1] in templateTitleSubject[0] has been templatePositiveTrend in recent years . In templateValue[0][0] , there were templateValue[1][0] templateLabel[1][0] and templateValue[2][0] templateLabel[2][0] and templateValue[2][0] templateLabel[1][0] and templateValue[2][0] templateLabel[2][0] templateTitle[1] in templateTitleSubject[0] .
generated: The Kingdom in United Kingdom has been growing in recent years .  In 2018 , there were 17.2 Male and 5.4 Female and 5.4 Male and 5.4 Female Kingdom in United Kingdom .

Example 333:
titleEntities: {'Subject': ['U.S. Q1'], 'Date': ['2014', '2014']}
title: Average trip length in the U.S. in Q1 2014
X_Axis['Year']: ['Less_than_one_to_two_days', 'Three_to_four_days', 'Five_to_six_days', 'Seven_to_eight_days', 'Nine_to_14_days', '15_to_20_days', '21_to_30_days', 'More_than_30_days']
Y_Axis['Share', 'of', 'trip', 'requests']: ['9', '27', '25', '22', '14', '3', '2', '1']

gold: This statistic shows the average trip length in the United States during the first quarter of 2014 . In the first quarter of 2014 , 25 percent of trip requests made on tripology.com were for trips lengths of five to six days .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] templateTitle[2] in the templateTitle[3] during the first quarter of templateTitleDate[0] . In the first quarter of templateTitleDate[0] , templateYValue[2] templateScale of templateYLabel[1] templateYLabel[2] made on tripology.com were for trips lengths of templateXValue[2] to templateXValue[2] templateXValue[0] .

generated_template: This statistic displays a forecast of the templateTitle[0] of templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] up to templateXValue[max] . By templateXValue[2] , it was predicted that templateYValue[2] templateScale people will be accessing templateYLabel[0] templateYLabel[1] services on their templateYLabel[0] phones . templateYLabel[0] templateYLabel[1] – additional information The templateTitle[0] of smartphone templateYLabel[2] in the templateTitleSubject[0] increases each templateXLabel[0] and with templatePositiveTrend penetration , smartphones are changing the way we do just about everything – including templateYLabel[1] .
generated: This statistic displays a forecast of the Average of Share trip requests in the U.S. Q1 up to Five_to_six_days .  By Five_to_six_days , it was predicted that 25 million people will be accessing Share trip services on their Share phones .  Share trip – additional information The Average of smartphone requests in the U.S. Q1 increases each Year and with increasing penetration , smartphones are changing the way we do just about everything – including trip .

Example 334:
titleEntities: {'Subject': ['Netherlands', 'Buzz'], 'Date': ['2018']}
title: Leading brands in the Netherlands 2018 , ranked by Buzz score
X_Axis['Platform']: ['Samsung', 'Albert_Heijn', 'Philips', 'Google', 'Lidl', 'Jumbo', 'YouTube', 'Sony', 'Wikipedia', 'Bose']
Y_Axis['Buzz', 'score']: ['47.7', '46.8', '38.6', '37.9', '34.8', '33.8', '33.5', '28.9', '25.4', '21.9']

gold: In 2018 , Samsung was the brand with the highest Buzz score in the Netherlands , followed by two Dutch brands : food retailer Albert Heijn and Philips . A brand 's Buzz score indicates how positive a brand is perceived in a certain country . The survey tracks how many people have heard anything about a certain brand in the last two weeks , through advertising , news or word of mouth .
gold_template: In templateTitleDate[0] , templateXValue[0] was the brand with the highest templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] , followed templateTitle[5] two Dutch templateTitle[1] : food retailer templateXValue[1] and templateXValue[2] . A brand 's templateYLabel[0] templateYLabel[1] indicates how positive a brand is perceived in a certain country . The survey tracks how many people have heard anything about a certain brand in the last two weeks , through advertising , news or word of mouth .

generated_template: This statistic shows the most popular templateXValue[5] templateTitle[3] templateTitle[4] in the templateTitle[1] as of 2018 , ranked templateTitle[6] templateYLabel[0] . As of 2018 , templateXValue[0] was the most popular templateXValue[5] templateTitle[3] service , with a templateYLabel[0] of templateYValue[max] templateScale , whereas templateXValue[1] had a templateYLabel[0] of templateYValue[1] templateScale .
generated: This statistic shows the most popular Jumbo 2018 ranked in the brands as of 2018 , ranked Buzz .  As of 2018 , Samsung was the most popular Jumbo 2018 service , with a Buzz of 47.7 % , whereas Albert_Heijn had a Buzz of 46.8 % .

Example 335:
titleEntities: {'Subject': ['Global'], 'Date': []}
title: Global feed conversion ratio of selected meat and fish
X_Axis['Animal', 'Type']: ['Cattle', 'Pork', 'Chicken', 'Salmon']
Y_Axis['Feed', 'conversion', 'ratio']: ['7.0', '3.0', '1.9', '1.1']

gold: This statistic describes the global feed conversion ratio of selected meat and fish . The feed conversion ratio is defined as the amount of feed needed to increase the animal 's bodyweight by one kilogram . The highest feed conversion rate was reported for cattle with an average ratio of seven .
gold_template: This statistic describes the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] and templateTitle[6] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] is defined as the amount of templateYLabel[0] needed to templatePositiveTrend the templateXLabel[0] 's bodyweight by templateYValue[min] kilogram . The highest templateYLabel[0] templateYLabel[1] rate was reported for templateXValue[0] with an average templateYLabel[2] of templateYValue[max] .

generated_template: This statistic shows the templateScale of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitleDate[0] , by the templateXLabel[0] of templateXLabel[1] . templateYValue[max] templateScale of templateYLabel[1] with templateXValue[last] and templateXValue[last] templateXLabel[1] used templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] .
generated: This statistic shows the percentage of Global conversion ratio selected meat fish Type in , by the Animal of Type .  7.0 % of conversion with Salmon and Type used selected meat fish in .

Example 336:
titleEntities: {'Subject': ['Chicago Blackhawks'], 'Date': ['2019']}
title: Franchise value of the Chicago Blackhawks 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1085', '1050', '1000', '925', '925', '825', '625', '350', '306', '300', '258', '205', '179', '168']

gold: This graph depicts the value of the Chicago Blackhawks franchise of the National Hockey League from 2006 to 2019 . In 2019 , the franchise had an estimated value of 1.09 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] franchise of the National Hockey League 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] 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)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Franchise value million for Chicago Blackhawks value games in Major League Baseball from 2006 to 2019 .  In 2019 , the Franchise value million was at 1085 million U.S. dollars .

Example 337:
titleEntities: {'Subject': ['Global'], 'Date': ['2015']}
title: Global music market value 2015 to 2020
X_Axis['Year']: ['2015', '2020']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['33.87', '43.49']

gold: The timeline shows teh value of the music market worldwide in 2015 as well as a forecast thereof for 2020 . The source projected the value would increase from 33.87 to 43.49 billion U.S. dollars in the presented period .
gold_template: The timeline shows teh templateTitle[3] of the templateTitle[1] templateTitle[2] worldwide in templateXValue[min] as well as a forecast thereof for templateXValue[max] . The source projected the templateTitle[3] would templatePositiveTrend from templateYValue[idxmin(X)] to templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in the presented period .

generated_template: This timeline presents information on the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] templateTitle[3] in templateXValue[min] and templateXValue[max] . According to the calculations , the templateTitleSubject[0] templateTitle[1] is expected to grow from templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] to nearly templateYValue[max] templateScale in templateXValue[idxmax(Y)] .
generated: This timeline presents information on the Revenue of the Global music value in 2015 and 2020 .  According to the calculations , the Global music is expected to grow from 33.87 billion U.S. dollars in 2015 to nearly 43.49 billion in 2020 .

Example 338:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Share of U.S. middle class population 2015 , by gender and marital status
Data:
                   0            1                                 2                            3              4
Month  Married_women  Married_men  Unmarried_women_children_at_home  Unmarried_women_no_children  Unmarried_men
1971              65           65                                41                           49             57
2001              56           56                                43                           49             54
2015              52           52                                41                           46             50 

gold: This statistic shows the percentage of the U.S. adult population that belong to the middle class from 1971 to 2015 , by gender and marital status . In 2015 , about 41 percent of unmarried women with children in their household were part of the U.S. middle class . This study defined middle class income households as those with an income between 67 and 200 percent of the U.S. median household income , after adjustment for household size .
gold_template: This statistic shows the templateScale of the templateTitleSubject[0] adult templateTitle[4] that belong to the templateTitle[2] templateTitle[3] from templateLabel[1][0] to templateTitleDate[0] , templateTitle[6] templateTitle[7] and templateTitle[8] templateTitle[9] . In templateTitleDate[0] , about templateValue[2][idxmin(1)] templateScale of templateValue[0][2] templateValue[0][0] with templateValue[0][2] in their household were part of the templateTitleSubject[0] templateTitle[2] templateTitle[3] . This study defined templateTitle[2] templateTitle[3] income households as those with an income between 67 and 200 templateScale of the templateTitleSubject[0] median household income , after adjustment for household size .

generated_template: This statistic outlines the templateScale of templateTitleSubject[0] that caught a templateTitle[5] or a templateTitle[6] in the templateTitle[1] from templateValue[0][0] to templateValue[0][6] . As of templateValue[0][0] templateValue[0][4] , templateValue[1][4] templateScale of the respondents reported to have been templateTitle[3] templateTitle[4] a templateTitle[5] , while templateValue[2][1] templateScale reported to have been templateTitle[3] templateTitle[4] a templateTitle[6] on any given day in templateValue[0][0] .
generated: This statistic outlines the percentage of U.S. that caught a 2015 or a by in the U.S. from Married_women to Unmarried_men .  As of Married_women Unmarried_men , 57 % of the respondents reported to have been class population a 2015 , while 56 % reported to have been class population a by on any given day in Married_women .

Example 339:
titleEntities: {'Subject': ['UK'], 'Date': ['2010', '2018']}
title: Price of oil per metric ton : manufacturing industry in the UK 2010 to 2018
Data:
                      0       1       2       3      4      5      6      7      8
Year               2018    2017    2016    2015   2014   2013   2012   2011   2010
Small consumer   572.49  519.71  415.19  406.28  558.8  675.4  651.8  625.6  506.9
Medium consumer  522.95   413.2   385.5  411.49  519.4  581.7  592.8  537.5  461.0
Large consumer   447.91  400.49  327.99  313.02  519.1  570.5  605.8  581.8  496.6
Average          480.41  413.81  359.49   359.6  524.4  588.2  607.3  572.0  471.5 

gold: This statistic shows the price of heavy fuel oil per metric ton for the manufacturing industry in the United Kingdom ( UK ) between 2010 and 2018 in British pounds per metric ton . The average price of all consumers was 480.41 British pounds in 2018 . This was an increase from the previous year .
gold_template: This statistic shows the templateTitle[0] of heavy fuel templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] for the templateTitle[5] templateTitle[6] in the United Kingdom ( templateTitleSubject[0] ) between templateValue[0][last] and templateValue[0][0] in British pounds templateTitle[2] templateTitle[3] templateTitle[4] . The templateLabel[4][0] templateTitle[0] of all consumers was templateValue[4][0] British pounds in templateValue[0][0] . This was an templatePositiveTrend from the previous templateLabel[0][0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] inhabitants from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][0] templateScale of inhabitants were aged 0 to 14 years , while approximately templateValue[2][0] templateScale were aged 15 to 64 , and templateValue[3][0] templateScale of templateTitleSubject[0] inhabitants were aged templateLabel[3][1] or older .
generated: This statistic shows the Price oil of UK inhabitants from 2010 to 2018 .  In 2018 , about 572.49 % of inhabitants were aged 0 to 14 years , while approximately 522.95 % were aged 15 to 64 , and 447.91 % of UK inhabitants were aged consumer or older .

Example 340:
titleEntities: {'Subject': ['Australia'], 'Date': ['2018']}
title: Population growth in Australia 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Population', 'growth', 'compared', 'to', 'previous', 'year']: ['1.57', '1.68', '1.56', '1.44', '1.49', '1.72', '1.75', '1.39', '1.56', '2.06', '2']

gold: This statistic shows the population growth in Australia from 2008 to 2018 , compared to the previous year . In 2018 , Australia 's population grew by approximately 1.57 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templatePositiveTrend by approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] has declined in this time period from templateYValue[max] children per woman in templateXValue[8] to templateYValue[idxmax(X)] in templateXValue[idxmin(Y)] .
generated: This statistic shows the Population growth in Australia from 2008 to 2018 .  The Population growth has declined in this time period from 2.06 children per woman in 2010 to 1.57 in 2011 .

Example 341:
titleEntities: {'Subject': ['Estée Lauder'], 'Date': ['2024']}
title: Global revenue of Estée Lauder 2013 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['23.88', '22.34', '20.88', '19.5', '18.19', '16.14', '14.24', '12.95', '11.81', '11.27', '10.8', '10.39']

gold: This statistic depicts the global revenue of Estée Lauder from 2013 to 2024 . For 2018 , it is forecasted that the Estée Lauder Companies global revenue will amount to USD 14.24 billion . 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 templateTitle[0] templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . For templateXValue[6] , it is forecasted that the templateTitleSubject[0] Companies templateTitle[0] templateYLabel[0] will amount to USD templateYValue[6] templateScale . templateTitleSubject[0] Companies is headquartered in Midtown Manhattan , New York City and was founded in 1946 by Joseph templateTitleSubject[0] and his wife templateTitleSubject[0] .

generated_template: This timeline shows templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[4] templateTitle[5] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[4] templateTitle[5] amounted to about templateYValue[6] templateScale templateYLabel[2] templateYLabel[3] .
generated: This timeline shows Estée Lauder revenue Estée 2013 2024 to .  In 2018 , Estée Lauder revenue Estée 2013 2024 amounted to about 14.24 billion U.S. dollars .

Example 342:
titleEntities: {'Subject': ['Ghana'], 'Date': ['2019']}
title: Unemployment rate in Ghana 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']: ['6.78', '6.71', '6.63', '6.77', '6.81', '6.48', '6.43', '6.01', '5.91', '5.32', '5.17', '4.66', '4.57', '4.64', '5.54', '6.47', '7.57', '8.58', '9.29', '10.36', '10.1']

gold: In 2019 , the unemployment rate in Ghana was at approximately 6.78 percent of the total labor force . The unemployment rate is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . Ghana 's unemployment rate is above the worldwide unemployment rate , and compared to other Sub-Saharan African countries and other regions , Ghana has a relatively average rate of unemployment .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale of the total labor force . The templateYLabel[0] templateYLabel[1] is the templateScale of a country 's labor force that are without jobs but are available to work and actively seeking employment . templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is above the worldwide templateYLabel[0] templateYLabel[1] , and compared to other Sub-Saharan African countries and other regions , templateTitleSubject[0] has a relatively average templateYLabel[1] of templateYLabel[0] .

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 Ghana from 1999 to 2019 .  In 2019 , the Unemployment rate in Ghana was at approximately 6.78 % .

Example 343:
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 344:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017', '2019']}
title: U.S. music industry - revenue distribution 2017 - 2019 , by source
Data:
                       0                  1         2          3
unknown  Synchronization  Digital_downloads  Physical  Streaming
2017                   3                 15        17         65
2018                   3                 11        12         75
2019                   2                  8        10         79 

gold: Streaming accounted for 79 percent of the U.S. music industry 's revenue in 2019 , up from 75 percent a year earlier and marking an increase of ten percent from 2017 . During the same time period , the share of revenue generated by digital downloads almost halved .
gold_template: templateValue[0][3] accounted for templateValue[3][max] templateScale of the templateTitleSubject[0] templateTitle[1] templateTitle[2] 's templateTitle[3] in templateTitleDate[max] , up from templateValue[2][idxmax(3)] templateScale a year earlier and marking an templatePositiveTrend of templateValue[3][2] templateScale from templateTitleDate[min] . During the same time period , the share of templateTitle[3] generated templateTitle[7] templateValue[0][1] almost halved .

generated_template: templateValue[0][0] and templateValue[0][1] ( Outlook.com ) were still the most used templateTitle[1] in the templateTitleSubject[0] , according to survey respondents in templateTitleDate[max] . Compared to the survey period , the use of both Google 's as well as Microsoft 's free e-mail service saw an templatePositiveTrend in their usage among Dutch consumers , whilst e-mail services provided by domestic templateTitle[2] ( such as KPN and Ziggo ) saw much less use . Sending and receiving e-mails is a popular online communication method across all age groups in the country .
generated: Synchronization and Digital_downloads ( Outlook.com ) were still the most used music in the U.S. , according to survey respondents in 2019 .  Compared to the survey period , the use of both Google 's as well Microsoft 's free e-mail service saw an increase in their usage among Dutch consumers , whilst e-mail services provided by domestic industry ( such as KPN and Ziggo ) saw much less use .  Sending and receiving e-mails is a popular online communication method across all age groups in the country .

Example 345:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2011', '2019']}
title: Production of silicon alloys and metals in U.S. 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Production', 'in', 'thousand', 'metric', 'tons', 'of', 'silicon', 'content']: ['320', '430', '415', '384', '411', '401', '392', '390', '326']

gold: This statistic represents the total silicon production in the United States from 2011 to 2019 . In 2019 , 320,000 metric tons of silicon content was produced . Silicon alloys and metals are important in various industries .
gold_template: This statistic represents the total templateYLabel[4] templateYLabel[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[min] templateYLabel[2] templateYLabel[3] of templateYLabel[4] templateYLabel[5] was produced . templateYLabel[4] templateTitle[2] and templateTitle[3] are important in various industries .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] .
generated: This statistic shows the Production of thousand metric in U.S. from 2011 to 2019 .  In 2019 , there were a total of 320 thousand metric in U.S. .

Example 346:
titleEntities: {'Subject': ['Eastman Chemical'], 'Date': ['2008', '2018']}
title: Eastman Chemical 's revenue 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['10151', '9549', '9008', '9648', '9527', '9350', '8102', '7178', '5842', '4396', '5936']

gold: This statistic shows the revenues of Eastman Chemical from 2007 to 2018 . United States-based Eastman Chemical Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In 2018 , the company generated approximately 10.15 billion U.S. dollars of sales revenues .
gold_template: This statistic shows the revenues of templateTitleSubject[0] from 2007 to templateXValue[max] . United States-based templateTitleSubject[0] Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In templateXValue[max] , the company generated approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of sales revenues .

generated_template: The statistic depicts templateTitleSubject[0] templateTitle[1] templateYLabel[0] between templateXValue[min] and templateXValue[max] . templateTitleSubject[0] , Inc. is the largest industrial gases company in North and South America and one of the largest worldwide . In templateXValue[max] , the corporation generated around templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] .
generated: The statistic depicts Eastman Chemical Chemical Revenue between 2008 and 2018 .  Eastman Chemical , Inc. is the largest industrial gases company in North and South America and one of the largest worldwide .  In 2018 , the corporation generated around 10151 million U.S. dollars in Revenue .

Example 347:
titleEntities: {'Subject': ['Airbus', 'Boeing'], 'Date': ['2019']}
title: Airbus and Boeing - key figures 2019
Data:
                    0           1           2
unknown  Gross_orders  Net_orders  Deliveries
Airbus           1131         768         863
Boeing            250         -87         380 

gold: Airbus reported 863 aircraft deliveries in 2019 , meanwhile Boeing delivered 380 aircraft that year . The two manufacturers form a de-facto duopoly in the market for large jet airliners . With only 380 deliveries , Boeing experienced its worst year in more than a decade .
gold_template: templateTitleSubject[0] reported templateValue[1][last] aircraft templateValue[0][2] in templateTitleDate[0] , meanwhile templateTitleSubject[1] delivered templateValue[2][last] aircraft that year . The two manufacturers form a de-facto duopoly in the market for large jet airliners . With only templateValue[2][last] templateValue[0][2] , templateTitleSubject[1] experienced its worst year in more than a decade .

generated_template: This statistic depicts the number of templateTitleSubject[0] templateTitle[1] templateTitle[3] and templateTitle[2] templateTitle[4] that were in operation as of year-end templateTitleDate[0] . At that moment , the company templateTitle[2] templateValue[1][last] templateValue[0][2] templateValue[0][1] . templateTitleSubject[0] , which is short for the full name Petróleo Brasileiro S.A. , is a Brazilian multinational energy corporation .
generated: This statistic depicts the number of Airbus Boeing figures and key 2019 that were in operation as of year-end 2019 .  At that moment , the company key 863 Deliveries Net_orders .  Airbus , which is short for the full name Petróleo Brasileiro S.A. , is a Brazilian multinational energy corporation .

Example 348:
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: 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 minutes by UK number in the 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 , 59 new UK number had been minutes , a decrease of roughly ten % in comparison to 53 Number as of 2018 .

Example 349:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2017']}
title: Most popular mobile payment services in Sweden 2017
X_Axis['Response']: ['Swish', 'PayPal_Mobile', 'WyWallet', 'Other', 'None']
Y_Axis['Share', 'of', 'respondents']: ['75', '14', '3', '5', '21']

gold: Swish was the most popular mobile payment service in Sweden in 2017 . The share of respondents who used Swish amounted to 75 percent . It was followed by PayPal Mobile , which was used by 14 percent of respondents that year .
gold_template: templateXValue[0] was the templateTitle[0] templateTitle[1] templateXValue[1] templateTitle[3] service in templateTitleSubject[0] in templateTitleDate[0] . The templateYLabel[0] of templateYLabel[1] who used templateXValue[0] amounted to templateYValue[max] templateScale . It was followed by templateXValue[1] , which was used by templateYValue[1] templateScale of templateYLabel[1] that year .

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 Most popular an active Sweden or subscription in the 2017 as of 2017 , sorted 2017 .  According to the source , 75 % of PayPal_Mobile who subscribe to online video or music subscriptions had a Sweden or subscription as of 2017 .

Example 350:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2018']}
title: Infant mortality rate in Argentina 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['8.8', '9.3', '9.7', '10.2', '10.7', '11.3', '11.8', '12.4', '12.9', '13.3', '13.7']

gold: The statistic shows the infant mortality rate in Argentina from 2008 to 2018 . In 2018 , the infant mortality rate in Argentina was at about 8.8 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 Argentina from 2008 to 2018 .  In 2018 , the Infant mortality rate in Argentina was at about 8.8 Deaths per 1,000 live births .

Example 351:
titleEntities: {'Subject': ['Passenger'], 'Date': ['2018']}
title: Passenger cars - major producing countries 2018
X_Axis['Country']: ['China', 'Japan', 'Germany', 'India', 'South_Korea', 'U.S.', 'Brazil', 'Spain', 'France', 'Mexico', 'Russia', 'UK', 'Czechia', 'Slovakia', 'Indonesia']
Y_Axis['Production', 'in', 'million', 'units']: ['23.53', '8.36', '5.12', '4.06', '3.66', '2.8', '2.39', '2.27', '1.76', '1.58', '1.56', '1.52', '1.35', '1.09', '1.06']

gold: China was the leading country in terms of passenger car production : 23.53 million units were produced here in 2018 . This compares to about 8.36 million units in runner-up Japan . Light vehicle production by country China remained the top light vehicle producing country in 2018 .
gold_template: templateXValue[0] was the leading templateXLabel[0] in terms of templateTitleSubject[0] car templateYLabel[0] : templateYValue[max] templateScale templateYLabel[2] were produced here in templateTitleDate[0] . This compares to about templateYValue[1] templateScale templateYLabel[2] in runner-up templateXValue[1] . Light vehicle templateYLabel[0] by templateXLabel[0] templateXValue[0] remained the top light vehicle templateTitle[3] templateXLabel[0] in templateTitleDate[0] .

generated_template: This statistic shows the templateTitle[2] of templateYLabel[1] templateTitleSubject[0] templateTitle[5] in selected countries in templateTitleDate[0] . That year , the templateTitle[2] of templateYLabel[1] templateYLabel[2] in templateXValue[6] 's electricity templateTitle[1] amounted to approximately templateYValue[6] templateScale .
generated: This statistic shows the major of million Passenger 2018 in selected countries in 2018 .  That year , the major of million units in Brazil 's electricity cars amounted to approximately 2.39 million .

Example 352:
titleEntities: {'Subject': ['Nordic'], 'Date': ['2017']}
title: Surface area of the Nordic countries 2017
X_Axis['Country']: ['Sweden', 'Greenland', 'Norway', 'Finland', 'Iceland', 'Denmark', 'Faroe_Islands']
Y_Axis['Surface', 'area', 'in', 'square', 'kilometers']: ['447420', '410450', '385178', '338420', '103000', '42922', '1396']

gold: This statistic shows the surface area of the Nordic countries in 2017 . The largest of all Nordic countries is Sweden , with a surface of roughly 447 thousand square kilometers . Its neighboring country Norway has a size of approximately 385 thousand square kilometers , which includes the arctic islands of Svalbard and Jan Mayen .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] in templateTitleDate[0] . The largest of all templateTitleSubject[0] templateTitle[3] is templateXValue[0] , with a templateYLabel[0] of roughly templateYValue[max] thousand templateYLabel[2] templateYLabel[3] . Its neighboring templateXLabel[0] templateXValue[2] has a size of approximately templateYValue[2] thousand templateYLabel[2] templateYLabel[3] , which includes the arctic templateXValue[last] of Svalbard and Jan Mayen .

generated_template: This statistic shows the distribution of templateYLabel[1] templateTitle[1] templateTitle[2] in templateTitleDate[0] . In templateTitleDate[0] , the templateYLabel[0] of templateYLabel[1] templateXValue[4] was at around templateYValue[max] templateScale . templateYLabel[1] templateXValue[4] templateTitle[1] templateTitle[2] The Population Reference Bureau released data on global templateYLabel[1] templateTitle[1] templateTitle[2] in templateTitleDate[0] .
generated: This statistic shows the distribution of area Nordic in 2017 .  In 2017 , the Surface of area Iceland was at around 447420 % .  area Iceland area Nordic The Population Reference Bureau released data on global area Nordic in 2017 .

Example 353:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['1996', '2018']}
title: Unions : Collective agreement coverage in the United Kingdom ( UK ) 1996 - 2018 , by sector
Data:
                   0     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19    20    21    22
Year            2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  2000  1999  1998  1997  1996
Private sector  14.7  15.3    15  16.1  15.4  16.6  16.1    17  16.9  17.8  18.7    20  19.6  20.6  20.5  21.9  21.1  21.9  22.5    23  21.7    22  23.2
Public sector   58.9  57.6    59  60.7  60.7  63.7  63.7  67.8  64.5  68.2  70.5    72    69  70.9  71.2  71.6  73.6  72.6  74.2  72.7  75.1  74.9  74.4 

gold: This statistic shows the percentage of employees whose pay and conditions were agreed in negotiations between the employer and a trade union ( collective agreement coverage ) in the United Kingdom from 1996 to 2018 , broken down by sector . In the most recent surveyed time period , 58.9 percent of employees in the public sector had their pay and conditions negotiated for them by a union . In comparison , this figure was 14.7 percent for the private sector .
gold_template: This statistic shows the templateScale of employees whose pay and conditions were agreed in negotiations between the employer and a trade union ( templateTitle[1] templateTitle[2] templateTitle[3] ) in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , broken down templateTitle[9] templateTitle[10] . In the most recent surveyed time period , templateValue[2][0] templateScale of employees in the templateLabel[2][0] templateTitle[10] had their pay and conditions negotiated for them templateTitle[9] a union . In comparison , this figure was templateValue[1][0] templateScale for the templateLabel[1][0] templateTitle[10] .

generated_template: The cost of templateTitle[2] templateTitle[3] in the electric power industry can vary depending on the source that is used . In general , templateTitle[2] templateTitle[3] cost about templateValue[4][0] templateTitleSubject[0] dollars per templateScale British thermal units ( Btu ) but can range from templateValue[1][0] templateTitleSubject[0] dollars per templateScale Btu templateTitle[4] templateLabel[1][0] to templateValue[2][0] templateTitleSubject[0] dollars per templateScale Btu templateTitle[4] templateLabel[2][0] . templateLabel[2][0] and oil prices In general , templateLabel[1][0] and oil prices have been the most volatile , while templateLabel[3][0] templateLabel[3][1] prices have remained relatively stable in comparison .
generated: The cost of agreement coverage in the electric power industry can vary depending on the source that is used .  In general , agreement coverage cost about 58.9 United Kingdom dollars per million British thermal units ( Btu ) but can range from 14.7 United Kingdom dollars per million Btu United Private to 58.9 United Kingdom dollars per million Btu United Public .  Public and oil prices In general , Private and oil prices have been the most volatile , while Public sector prices have remained relatively stable in comparison .

Example 354:
titleEntities: {'Subject': ['Iran'], 'Date': ['2024']}
title: Gross domestic product per capita of Iran 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']: ['6552.33', '6062.62', '5785.16', '5604.66', '5503.08', '5506.23', '5416.53', '5289.8', '5026.65', '4723.48', '5395.79', '5152.04', '5118.47', '7680.86', '6504.87', '5608.98', '5621.05', '4929.06', '3834.73', '3288.37', '2734.03', '2272.5', '1957.43', '5119.72', '5698.41', '4387.75', '3364.22', '3041.22', '2620.05', '1954.87', '1412.71', '1131.19', '872.34', '5417.51', '10556.09', '7128.94', '6137.68', '5465.15', '4222.51', '3776.61', '3495.22']

gold: This statistic shows the gross domestic product ( GDP ) per capita of Iran from 1984 to 2018 , with projections up until to 2024 . In 2018 , Iran 's GDP amounted to approximately 5,416.53 U.S. dollars per capita .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until to templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] amounted to approximately templateYValue[6] templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] .

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] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was around templateYValue[7] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] 's economy templateYLabel[0] templateYLabel[1] templateYLabel[2] is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the templateYLabel[0] and dividing it by the total population in the country .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in Iran from 1984 to 2018 , with projections up until 2024 .  In 2018 , the Gross domestic product per capita in Iran was around 5289.8 U.S. dollars .  Iran 's economy GDP per capita is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the GDP and dividing it by the total population in the country .

Example 355:
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: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateTitle[1] templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . In templateTitleDate[0] , templateYValue[max] templateScale of the templateXValue[2] community was templateYLabel[1] templateTitle[2] .
generated: This statistic shows the total Number of employed average working in the hours in 2019 , .  In 2019 , 7282.5 thousands of the 30_to_34_hours community was employed working .

Example 356:
titleEntities: {'Subject': ['Debenhams', 'United Kingdom', 'UK'], 'Date': ['2015']}
title: Debenhams customers in the United Kingdom ( UK ) 2015 , by demographic group
X_Axis['NRS', 'social', 'classification']: ['AB', 'C1', 'C2', 'DE']
Y_Axis['Share', 'of', 'respondents']: ['35', '31', '19', '15']

gold: This statistic displays the demographic distribution of customers at Debenhams in the United Kingdom as of 2015 . According to the annual report , 35 percent of Debenhams customers were classed under the demographic group AB ( middle class and upper middle class ) . This group represented the second largest customer group behind C1 ( lower middle class ) .Debenhams is a British retailer selling clothing , furniture and other household items in a department store format .
gold_template: This statistic displays the templateTitle[7] distribution of templateTitle[1] at templateTitleSubject[0] in the templateTitleSubject[1] as of templateTitleDate[0] . According to the annual report , templateYValue[max] templateScale of templateTitleSubject[0] templateTitle[1] were classed under the templateTitle[7] templateTitle[8] templateXValue[0] ( middle class and upper middle class ) . This templateTitle[8] represented the second largest customer templateTitle[8] behind templateXValue[1] ( lower middle class ) .Debenhams is a British retailer selling clothing , furniture and other household items in a department store format .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] in the templateTitle[1] templateTitle[3] were templateTitleSubject[0] users as of 2015 , sorted templateTitle[7] templateTitle[8] and templateTitle[9] group . During that period of time , templateYValue[max] templateScale of female templateTitleSubject[0] teens aged 15 to 17 years used the social networking app .
generated: This statistic shows the Share of United in the customers Kingdom were Debenhams users as of 2015 , sorted demographic group and .  During that period of time , 35 % of female Debenhams teens aged 15 to 17 years used the social networking app .

Example 357:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: Average daily trading volume of U.S. treasury securities 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2010', '2005', '2000']
Y_Axis['Volume', 'in', 'billion', 'U.S.', 'dollars']: ['547.8', '505.2', '519.1', '490.1', '504.2', '528.2', '554.5', '206.5']

gold: In 2018 , the average total volume of treasury securities traded per day was over 547 billion U.S. dollars . This means that every day the market was open , the average amount of U.S. government securities bought and sold amounted to half a trillion U.S. dollars in that year . What are treasury securities ? Treasury securities are U.S. government debt , bonds sold to finance the United States government .
gold_template: In templateXValue[max] , the templateTitle[0] total templateYLabel[0] of templateTitle[5] templateTitle[6] traded per day was over 547 templateScale templateYLabel[2] templateYLabel[3] . This means that every day the market was open , the templateTitle[0] amount of templateYLabel[2] government templateTitle[6] bought and sold amounted to half a templateScale templateYLabel[2] templateYLabel[3] in that templateXLabel[0] . What are templateTitle[5] templateTitle[6] ? templateTitle[5] templateTitle[6] are templateYLabel[2] government debt , bonds sold to finance the templateTitle[4] government .

generated_template: The templateYLabel[0] of the templateTitleSubject[0] luxury templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] S.p.A. amounted to templateYValue[min] templateScale templateYLabel[2] in templateXValue[max] . This figure represents a templateNegativeTrend compared to the peak reached by the templateTitle[3] in templateXValue[3] , when the templateYLabel[0] reported amounted to templateYValue[2] templateScale templateYLabel[2] . The reduction in templateYLabel[0] coincided with lower profits for the templateTitle[3] during the same period .
generated: The Volume of the U.S. luxury trading volume U.S. treasury S.p.A. amounted to 206.5 billion U.S. in 2018 .  This figure represents a decrease compared to the peak reached by the volume in 2015 , when the Volume reported amounted to 519.1 billion U.S. The reduction in Volume coincided with lower profits for the volume during the same period .

Example 358:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Leading diversified financial service companies in the U.S. 2018 , by revenue
X_Axis['Month']: ['Fannie_Mae', 'Freddie_Mac', 'American_Express', 'INTL_FCStone', 'Icahn_Enterprises', 'Synchrony_Financial', 'Marsh_&_McLennan', 'Ameriprise_Financial', 'Ally_Financial', 'Voya_Financial']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['120.1', '73.6', '43.28', '27.66', '18.98', '18.25', '14.95', '12.92', '10.47', '8.93']

gold: The statistic displays the leading diversified financial service companies in the United States in 2018 , by revenue . In that year , Fannie Mae was ranked first with revenue of around 120.1 billion U.S. dollars .
gold_template: The statistic displays the templateTitle[0] templateTitle[1] templateXValue[5] templateTitle[3] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] , templateTitle[7] templateYLabel[0] . In that year , templateXValue[0] was ranked first with templateYLabel[0] of around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic presents the ten templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , ranked templateTitle[5] templateYLabel[0] . According to the source , templateXValue[0] was the top templateTitle[1] templateTitle[2] market in templateTitleDate[0] , with a templateYLabel[0] of approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . With over third time , with a templateYLabel[0] of approximately templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic presents the ten Leading diversified financial service in 2018 , ranked U.S. Revenue .  According to the source , Fannie_Mae was the top diversified financial market in 2018 , with a Revenue of approximately 120.1 billion U.S. dollars .  With over third time , with a Revenue of approximately 73.6 billion U.S. dollars .

Example 359:
titleEntities: {'Subject': ['Interpublic Group'], 'Date': ['2006', '2019']}
title: Interpublic Group 's revenue 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['10.22', '9.71', '9.05', '7.85', '7.61', '7.54', '7.12', '6.96', '7.01', '6.51', '6.01', '6.94', '6.54', '6.19']

gold: This statistic presents the global revenue generated by the U.S. advertising giant Interpublic Group from 2006 to 2019 . In 2019 , Interpublic 's global revenue was 10.2 billion U.S. dollars , up from 9.7 billion a year earlier .
gold_template: This 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_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of PricewaterhouseCoopers from templateXValue[min] to templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , templateTitleSubject[0] generated approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateTitle[0] templateTitle[1] templateTitle[2] . templateTitleSubject[0] - additional information templateTitleSubject[0] is one of the four largest accounting and audit firms in the world .
generated: This statistic shows the Interpublic Group 's of PricewaterhouseCoopers from 2006 to 2019 .  In the fiscal Year of 2019 , Interpublic Group generated approximately 10.22 billion U.S. dollars in Interpublic Group 's .  Interpublic Group - additional information Interpublic Group is one of the four largest accounting and audit firms in the world .

Example 360:
titleEntities: {'Subject': ['United States'], 'Date': ['18', '19']}
title: Monthly job openings in the United States 2018/2019
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"]
Y_Axis['Job', 'openings', 'in', 'millions']: ['6.42', '6.79', '7.36', '7.03', '7.3', '7.17', '7.25', '7.38', '7.37', '7.47', '7.14', '7.63', '7.48']

gold: This statistic shows the monthly number of job openings in the United States from December 2018 to December 2019 . The data are seasonally adjusted . There were about 6.42 million job openings on the last business day of December 2019 .
gold_template: This statistic shows the templateTitle[0] number of templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] from 2018 to 2019 . The data are seasonally adjusted . There were about templateYValue[min] templateScale templateYLabel[0] templateYLabel[1] on the last business day of 2019 .

generated_template: This statistic shows the templateTitleSubject[0] Business templateTitleSubject[0] templateYLabel[0] from 2019 to 2020 . In 2020 , the templateYLabel[0] amounted to templateYValue[0] . The templateYLabel[0] consists of 10 indicators derived from questions addressing templateTitleSubject[0] owners : Plans to create employment ; plans to make capital outlays ; plans to templatePositiveTrend inventories ; expect economy to improve ; expect real sales higher ; current inventory ; current job openings ; expected credit conditions ; now a good time to expand ; earnings trends .
generated: This statistic shows the United States Business Job from 2019 to 2020 .  In 2020 , the Job amounted to 6.42 .  The Job consists of 10 indicators derived from questions addressing United States owners : Plans to create employment ; plans to make capital outlays ; plans to increase inventories ; expect economy to improve ; expect real sales higher ; current inventory ; current job openings ; expected credit conditions ; now a good time to expand ; earnings trends .

Example 361:
titleEntities: {'Subject': ['UK', 'BMW'], 'Date': ['2016', '2019']}
title: UK : monthly BMW motorcycle sales 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", 'Jun_´16', "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16"]
Y_Axis['Units', 'sold']: ['515', '398', '431', '1490', '302', '675', '1154', '773', '811', '2176', '183', '403', '439', '384', '535', '1199', '363', '627', '1358', '924', '674', '1938', '233', '339', '323', '343', '522', '1305', '370', '682', '1297', '907', '902', '2101', '272', '263', '400', '322', '337', '1510', '226', '596', '1148', '856', '807', '1688', '215', '320']

gold: This statistic shows the total number of BMW motorcycles sold in the United Kingdom ( UK ) between January 2016 to December 2019 . March and September recorded the highest sales , which were the months when the Driver and Vehicle Licensing Agency issued new registration plates for cars and motorcycles . In December 2019 , BMW sold 515 motorcycles in the United Kingdom .
gold_template: This statistic shows the total number of templateTitleSubject[1] motorcycles templateYLabel[1] in the United Kingdom ( templateTitleSubject[0] ) between 2016 to 2019 . and recorded the highest templateTitle[4] , which were the months when the Driver and Vehicle Licensing Agency issued new registration plates for cars and motorcycles . In 2019 , templateTitleSubject[1] templateYLabel[1] templateYValue[0] motorcycles in the United Kingdom .

generated_template: templateTitle[2] templateYLabel[1] templateYValue[0] motorcycles in the United Kingdom ( templateTitleSubject[0] ) in 2019 . This was slightly higher than in the corresponding templateXLabel[0] in the previous year . Across all years recorded , templateTitle[4] figures were highest in , as this is the templateXLabel[0] when the Driver & Vehicle Licensing Agency ( DVLA ) issues new registration plates .
generated: BMW sold 515 motorcycles in the United Kingdom ( UK ) in 2019 .  This was slightly higher than in the corresponding Month in the previous year .  Across all years recorded , sales figures were highest in , as this is the Month when the Driver & Vehicle Licensing Agency ( DVLA ) issues new registration plates .

Example 362:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1965', '2018']}
title: U.S. house prices : average sales price of new homes sold 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['Sales', 'price', 'in', 'thousand', 'U.S.', 'dollars']: ['385.0', '384.9', '360.9', '360.6', '345.8', '319.3', '292.2', '267.9', '272.9', '297.0', '207.0', '158.7', '149.8', '100.8', '76.4', '42.6', '26.6', '21.0']

gold: In 2018 , new homes in the United States were , on average , sold for 385,000 U.S. dollars . Home prices have steadily risen since 2011 , after a drop between 2005 and 2011 , which corresponds with the 2007 - 2009 recession . Prices have more or less plateaued since 2017 , so the bubble which began to form in 2011 may be about to burst .
gold_template: In templateXValue[max] , templateTitle[6] templateTitle[7] in the templateTitle[0] were , on templateTitle[3] , templateTitle[8] for templateYValue[max] templateYLabel[3] templateYLabel[4] . Home templateTitle[2] have steadily risen since templateXValue[7] , after a drop between templateXValue[9] and templateXValue[7] , which corresponds with the 2007 - 2009 recession . templateTitle[2] have more or less plateaued since templateXValue[1] , so the bubble which began to form in templateXValue[7] may be about to burst .

generated_template: This statistic shows the templateTitleSubject[0] templateYLabel[0] of pumpkins from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around 12.36 templateScale templateYLabel[2] of pumpkins were produced in the templateTitle[0] . Pumpkins are especially popular around Halloween .
generated: This statistic shows the U.S. Sales of pumpkins from 1965 to 2018 .  In 2018 , around 12.36 thousand of pumpkins were produced in the U.S. Pumpkins are especially popular around Halloween .

Example 363:
titleEntities: {'Subject': ['Russia'], 'Date': ['2019']}
title: Distribution of the workforce across economic sectors in Russia 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   5.77   5.84    5.9   6.72   6.71   6.72   6.99   7.33   7.69   7.92   8.43
Industry     26.93  26.95  26.97  26.95  27.18  27.52  27.73  27.77  27.45  27.69  27.46
Services      67.3  67.22  67.13  66.34  66.11  65.76  65.28  64.91  64.86  64.39  64.11 

gold: The statistic shows the distribution of the workforce across economic sectors in Russia from 2009 to 2019 . In 2019 , 5.77 percent of the workforce in Russia was employed in agriculture , 26.93 percent in industry and 67.3 percent in services . Russia 's economy The majority of Russia 's labor force works in the services sector , which accounts for more than half of the jobs in the country .
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] was employed in templateLabel[1][0] , templateValue[2][0] templateScale in templateLabel[2][0] and templateValue[3][0] templateScale in templateLabel[3][0] . templateTitleSubject[0] 's economy The majority of templateTitleSubject[0] 's labor force works in the templateLabel[3][0] sector , which accounts for more than half of the jobs in the country .

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 Russia workforce across economic from 2009 to 2019 .  In 2019 , 5.77 % of the employees in Russia were active in the agricultural economic , 26.93 % in Industry and 67.3 % in the service economic .

Example 364:
titleEntities: {'Subject': ['United States'], 'Date': ['2019/20', '2019/20']}
title: United States - public debt by month 2019/20
X_Axis['Year']: ["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['National', 'debt', 'in', 'billion', 'U.S.', 'dollars']: ['23223.81', '23201.38', '23076.2', '23008.41', '22719.4', '22460.47', '22022.37', '22023.28', '22026.42', '22027.67', '22027.88', '22115.53', '21982.42']

gold: In January 2020 , the public debt of the United States was around 23.2 trillion U.S. dollars , over 1.2 trillion more than a year earlier , when it was around 21.9 trillion U.S. dollars . The U.S. public debt has become one of the most prominent political issues in the States in recent years , with debate over how to handle it causing political turmoil between Democrats and Republicans . The public debt The public debt of the United States has risen quickly since 2000 , almost quadrupling until 2018 .
gold_template: In 2020 , the templateTitle[2] templateYLabel[1] of the templateTitleSubject[0] was around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , over 1.2 templateScale more than a templateXLabel[0] earlier , when it was around 21.9 templateScale templateYLabel[3] templateYLabel[4] . The templateYLabel[3] templateTitle[2] templateYLabel[1] has become one of the most prominent political issues in the templateTitleSubject[0] in recent years , with debate over how to handle it causing political turmoil between Democrats and Republicans . The templateTitle[2] templateYLabel[1] The templateTitle[2] templateYLabel[1] of the templateTitleSubject[0] has risen quickly since 2000 , almost quadrupling until 2018 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] templateTitle[1] games in Major League Baseball from templateXValue[last] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the National debt billion of United States States games in Major League Baseball from Jan_'19 to 2019/20 .  In 2019/20 , the National debt billion was at 23223.81 U.S. dollars .

Example 365:
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: In 2019 , templateTitleSubject[0] templateTitle[1] templateTitleSubject[1] templateYLabel[0] templateYLabel[1] stood at templateYValue[0] templateScale . Year-to-date , some 948,000 units were sold to templateTitleSubject[1] customers by the templateTitleSubject[0] Motor Company , which is counted among the Detroit Big Three automakers . The United Kingdom and Germany were among templateTitleSubject[0] templateTitle[1] four most important sales markets in 2018 .
generated: In 2019 , Mercedes market EU share stood at 5.9 % .  Year-to-date , some 948,000 units were sold to EU customers by the Mercedes Motor Company , which is counted among the Detroit Big Three automakers .  The United Kingdom and Germany were among Mercedes market four most important sales markets in 2018 .

Example 366:
titleEntities: {'Subject': ['Georgia'], 'Date': ['2000', '2018']}
title: Georgia - 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']: ['522.42', '509.42', '495.23', '479.24', '464.06', '450.77', '444.13', '439.89', '433.03', '426.26', '443.42', '453.06', '450.58', '445.53', '429.31', '410.59', '400.09', '396.88', '393.2']

gold: This statistic shows the development of Georgia 's real GDP from 2000 to 2018 . In 2018 , the real GDP of Georgia was about 522.42 billion U.S. dollars . The annual real GDP growth of the U.S. can be accessed here .
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[0] templateYLabel[1] of templateTitleSubject[0] was about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . The annual templateYLabel[0] templateYLabel[1] growth of the templateYLabel[3] can be accessed here .

generated_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[0] templateYLabel[1] of templateTitleSubject[0] was 2.67 templateScale templateYLabel[3] templateYLabel[4] . The annual templateYLabel[0] templateYLabel[1] growth of the templateYLabel[3] can be accessed here .
generated: This statistic shows the development of Georgia 's Real GDP from 2000 to 2018 .  In 2018 , the Real GDP of Georgia was 2.67 billion U.S. dollars .  The annual Real GDP growth of the U.S. can be accessed here .

Example 367:
titleEntities: {'Subject': ['Luxembourg national football team'], 'Date': ['1993', '2019']}
title: World ranking of Luxembourg national football team 1993 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']
Y_Axis['FIFA', 'World', 'Ranking', 'position']: ['96', '85', '83', '130', '142', '128', '123', '148', '128', '134', '128', '121', '149', '186', '150', '155', '153', '148', '142', '139', '124', '143', '138', '123', '100', '128', '111']

gold: As of 2019 , the Luxembourg national men 's football team was ranked 96th on the FIFA World Ranking and had an average position of 133 since the World Ranking creation . The best position the country held was in 2017 with an 83rd position , whereas the lowest ranking was in 2006 when the national team was placed 186th . The Luxembourg national women 's team was ranked 113th in 2019 .
gold_template: As of templateXValue[max] , the templateTitleSubject[0] men 's templateTitleSubject[0] was ranked 96th on the templateYLabel[0] templateYLabel[1] templateYLabel[2] and had an average templateYLabel[3] of 133 since the templateYLabel[1] templateYLabel[2] creation . The best templateYLabel[3] the country held was in templateXValue[2] with an 83rd templateYLabel[3] , whereas the lowest templateYLabel[2] was in templateXValue[13] when the templateTitleSubject[0] was placed 186th . The templateTitleSubject[0] women 's templateTitleSubject[0] was ranked 113th in templateXValue[max] .

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 football team 1993 from to 2019 .  The highest position ever reached was 83 in 2017 .  Rank 186 was the lowest result of the 1993 , which was reached in 2006 .

Example 368:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005', '2019']}
title: Thanksgiving holiday travelers in the U.S. from 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', 'travelers', 'in', 'millions']: ['55.3', '54.3', '51.71', '49.29', '47.7', '46.6', '44.4', '44.0', '43.3', '40.9', '37.9', '37.8', '50.6', '47.6', '58.6']

gold: This statistic shows the number of Thanksgiving holiday travelers in the United States from 2005 to 2019 . According to the source , the number of people traveling over the Thanksgiving holiday period is forecast to amount to 55.3 million in 2019 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . According to the source , the templateYLabel[0] of people traveling over the templateTitle[0] templateTitle[1] period is forecast to amount to templateYValue[idxmax(X)] templateScale in templateXValue[max] .

generated_template: This statistic represents the development in the templateYLabel[0] templateYLabel[1] of templateYLabel[2] at templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] templateTitle[5] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] had an templateYLabel[0] of templateYValue[idxmax(X)] templateYLabel[2] templateTitle[5] . templateTitleSubject[0] templateTitle[3] MH templateTitle[3] templateTitleSubject[0] is a leading global fashion company with strong values and a clear business concept .
generated: This statistic represents the development in the Number travelers of millions at U.S. 2005 2019 to .  In 2019 , U.S. had an Number of 55.3 millions 2005 .  U.S. U.S. MH is a leading global fashion company with strong values and a clear business concept .

Example 369:
titleEntities: {'Subject': ['Canada'], 'Date': ['2015', '2019']}
title: Monthly retail sales of grocery 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   7.33  7.41  7.19  7.31  7.29  7.25  7.27  7.25  7.26  7.31   7.2  7.14
2016   7.39  7.48  7.48   7.4  7.48  7.38  7.41  7.47  7.37  7.39  7.34  7.34
2017    7.7  7.59  7.67  7.54   7.6  7.76  7.66  7.67  7.57  7.62  7.66  7.66
2018   7.99  7.82  7.93  8.09  7.93  7.98  7.86  7.74  7.81  7.61  7.62  7.65
2019      0  8.09     8  8.02  7.91  7.95   7.9  7.86  8.02  8.04  8.03  7.92 

gold: Monthly sales of grocery stores in Canada reached approximately 8.09 billion Canadian dollars in November 2019 . This figure has gradually increased over the given survey period – in 2015 the average monthly sales value was 7.27 billion dollars . Canadians prefer shopping in-store As of December 2018 , there were over 15,600 grocery stores located across Canada , signaling an ongoing demand for brick-and-mortar grocery stores , despite the rise of e-commerce in the Canadian retail sector .
gold_template: templateTitle[0] templateTitle[2] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] reached approximately templateValue[4][max] templateScale Canadian dollars in 2019 . This figure has gradually templatePositiveTrend over the given survey period – in templateTitleDate[min] the average templateTitle[0] templateTitle[2] value was templateValue[1][6] templateScale dollars . Canadians prefer shopping in-store As of 2018 , there were over 15,600 templateTitle[3] templateTitle[4] located across templateTitleSubject[0] , signaling an ongoing demand for brick-and-mortar templateTitle[3] templateTitle[4] , despite the rise of e-commerce in the Canadian templateTitle[1] sector .

generated_template: The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] one-kilogram templateTitle[4] templateTitle[5] templateTitle[6] was templateValue[5][max] Canadian dollars in 2019 in templateTitleSubject[0] . This templateTitle[2] is an all-time high templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . templateTitle[4] templateTitle[5] templateTitle[6] is a relatively expensive option when compared to other cuts of beef , such as ground beef , which retailed at a templateTitle[2] of 11.3 Canadian dollars per kilogram in templateValue[0][7] templateTitleDate[max] .
generated: The Monthly retail sales grocery one-kilogram stores Canada 2015 was 8.09 Canadian dollars in 2019 Canada .  This sales is an all-time high grocery stores Canada 2015 .  stores Canada 2015 is a relatively expensive option when compared to other cuts of beef , such as ground beef , which retailed at a sales of 11.3 Canadian dollars per kilogram in May 2019 .

Example 370:
titleEntities: {'Subject': ['Iraq'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Iraq 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.08', '2.07', '2.32', '3.73', '4.68', '3.4', '-0.56', '-2.5', '15.2', '2.54', '0.74']

gold: The statistic shows the growth in real GDP in Iraq between 2014 to 2017 , with projections up until 2024 . In 2017 , Iraq 's real gross domestic product decreased by around 2.5 percent compared to the previous year . GDP is a reliable tool used to indicate the shape of a national economy .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] between templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templateNegativeTrend by around templateYValue[9] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . templateYLabel[0] is a reliable tool used to indicate the shape of a national economy .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from between 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 Iraq from between 2014 to 2018 , with projections up until 2024 .  In 2018 , Iraq 's real Gross domestic product increased by around -0.56 % compared to the previous Year .

Example 371:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2008', '2018']}
title: Number of straight and same-sex marriages in the Netherlands 2008 to 2018
Data:
                           0      1      2      3      4      5      6      7      8      9     10
Year                    2008   2009   2010   2011   2012   2013   2014   2015   2016   2017   2018
Between man and woman  74030  72119  74045  70217  69030  63327  64074  62912  63813  63027  62813
Same-sex marriages      1408   1358   1354   1355   1285   1222   1259   1396   1436   1375   1502 

gold: This statistic shows the number of straight vs. same-sex marriages in the Netherlands from 2008 to 2018 . The number of heterosexual marriages decreased between 2006 and 2015 , but saw an increase again in 2016 to 2018 . In 2018 , there were nearly 63,000 straight marriages in the Netherlands .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] vs. templateTitle[2] templateTitle[3] in the templateTitleSubject[0] from templateValue[0][0] to templateValue[0][last] . The templateTitle[0] of heterosexual templateTitle[3] templateNegativeTrend templateLabel[1][0] 2006 and templateValue[0][7] , but saw an templatePositiveTrend again in templateValue[0][8] to templateValue[0][last] . In templateValue[0][last] , there were nearly 63,000 templateTitle[1] templateTitle[3] in the templateTitleSubject[0] .

generated_template: This statistic shows the share of templateTitle[0] that participated in templateTitle[3] templateTitle[4] in the last templateLabel[0][0] in templateTitleSubject[0] , according to annual surveys conducted between templateTitleDate[min] and 2014 . The share of 11 - 15 templateLabel[0][0] olds participating in templateValue[0][0] was measured at templateValue[1][5] templateScale , down from templateValue[2][max] templateScale in templateValue[0][idxmax(2)] . The share of 5 - 10 templateLabel[1][1] olds participating in templateTitle[3] templateTitle[4] this templateLabel[0][0] was slightly higher at templateValue[1][0] templateScale .
generated: This statistic shows the share of Number that participated in marriages Netherlands in the last Year in Netherlands , according to annual surveys conducted between 2008 and 2014 .  The share of 11 - 15 Year olds participating in 2008 was measured at 63327 % , down from 1502 % in 1502 .  The share of 5 - 10 man olds participating in marriages Netherlands this Year was slightly higher at 74030 % .

Example 372:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Most popular domestic summer city destinations for travelers in the U.S. 2015
X_Axis['Response']: ['Orlando', 'Las_Vegas', 'San_Francisco', 'New_York_City', 'Miami', 'Los_Angeles', 'Anaheim', 'San_Diego', 'New_Orleans', 'Washington_DC']
Y_Axis['Share', 'of', 'respondents']: ['17', '13', '4', '3', '3', '2', '2', '2', '2', '2']

gold: This statistic shows the most popular domestic summer city destinations for travelers in the United States in 2015 , according to travel agent members of the ASTA Research Family . During the survey , 17 percent of the respondents forecasted that Orlando would be a popular domestic city destination for U.S. travelers in summer 2015 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateXValue[3] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[8] in templateTitleDate[0] , according to travel agent members of the ASTA Research Family . During the survey , templateYValue[max] templateScale of the templateYLabel[1] forecasted that templateXValue[0] would be a templateTitle[1] templateTitle[2] templateXValue[3] destination templateTitle[6] templateTitleSubject[0] templateTitle[7] in templateTitle[3] templateTitleDate[0] .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] problems templateTitle[4] templateTitle[5] templateXValue[4] in the templateTitleSubject[0] in 2020 . During the survey , about templateYValue[max] templateScale of the templateYLabel[1] stated that the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateXValue[4] was templateXValue[0] .
generated: This statistic shows the popular domestic problems city destinations Miami in the U.S. in 2020 .  During the survey , about 17 % of the respondents stated that the popular domestic summer city destinations Miami was Orlando .

Example 373:
titleEntities: {'Subject': ['NAND'], 'Date': ['2017']}
title: Market share held by NAND and DRAM manufacturers 2017 and Q1'18
Data:
             0        1
Year      2017  Q1_2018
SK Hynix  27.8     29.2
Micron    25.1     26.4
Sony      10.5     10.7
Toshiba   11.5      8.6
Samsung    8.4      6.8
TI           6      5.9
Intel      3.7      3.7
Other        7        7 

gold: This statistic shows the market share held by the leading NAND and DRAM manufacturers worldwide in 2017 and the first quarter of 2018 . In the first quarter of 2018 , SK Hynix held a market share of 29.2 percent . The overall NAND and DRAM market revenue amounted to around 96 billion U.S. dollars in that same year .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the leading templateTitleSubject[0] and templateTitle[5] templateTitle[6] worldwide in templateValue[0][0] and the first quarter of templateValue[0][1] . In the first quarter of templateValue[0][1] , templateLabel[1][0] templateLabel[1][1] templateTitle[2] a templateTitle[0] templateTitle[1] of templateValue[1][last] templateScale . The overall templateTitleSubject[0] and templateTitle[5] templateTitle[0] revenue amounted to around 96 templateScale U.S. dollars in that same templateLabel[0][0] .

generated_template: This statistic shows the templateTitle[4] templateTitle[5] of the templateTitle[1] and templateTitle[2] templateTitle[3] templateTitle[4] worldwide in templateValue[0][0] and the first quarter of templateValue[0][1] , templateTitle[6] manufacturer . In the first quarter of templateValue[0][1] , with its templateTitle[4] templateTitle[5] of templateValue[1][last] templateScale , templateLabel[1][0] templateLabel[1][1] was the leader of the templateTitle[1] and templateTitle[2] templateTitle[3] templateTitle[4] .
generated: This statistic shows the NAND DRAM of the share and held by NAND worldwide in 2017 and the first quarter of Q1_2018 , manufacturers manufacturer .  In the first quarter of Q1_2018 , with its NAND DRAM of 29.2 % , SK Hynix was the leader of the share and held by NAND .

Example 374:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: U.S. wage and salary workers : weekly earnings by education and gender 2019
Data:
             0                                1                       2                                   3                  4                5
unknown  Total  Less_than_a_high_school_diploma  High_school_no_college  Some_college_or_associate's_degree  Bachelor's_degree  Advanced_degree
Men       1070                              644                     844                                 991               1442             1878
Women      865                              494                     633                                 737               1100             1365 

gold: This statistic shows the median weekly earnings of full-time wage and salary workers in the U.S. by educational attainment and gender in 2019 . In that year , women holding a Bachelor 's degree earned on average 1,100 U.S. dollars per week .
gold_template: This statistic shows the median templateTitle[4] templateTitle[5] of full-time templateTitle[1] and templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitle[6] educational attainment and templateTitle[8] in templateTitleDate[0] . In that year , templateLabel[2][0] holding a Bachelor 's templateValue[0][3] earned on average templateValue[2][4] templateTitleSubject[0] dollars per week .

generated_template: This statistic shows the share of American templateTitle[6] in the templateTitle[4] who templateTitle[0] templateTitle[1] templateTitle[2] by method , sorted templateTitle[5] templateTitle[6] templateTitle[7] and templateTitle[8] . According to the survey , templateValue[1][last] templateScale of templateValue[0][2] student templateValue[0][2] reported that they had voted for templateLabel[1][0] templateLabel[1][1] , compared with templateValue[2][1] templateScale of templateLabel[2][0] respondents reported that they had voted to templateLabel[2][0] .
generated: This statistic shows the share of American by in the weekly who U.S. wage salary by method , sorted earnings by education and gender .  According to the survey , 1878 % of High_school_no_college student reported that they had voted for Men , compared with 494 % of Women respondents reported that they had voted to Women .

Example 375:
titleEntities: {'Subject': ['Pennsylvania'], 'Date': ['2000', '2018']}
title: Pennsylvania - 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']: ['712.1', '697.18', '685.46', '677.87', '664.79', '651.32', '641.32', '631.37', '622.47', '605.5', '623.82', '612.02', '592.03', '588.05', '578.03', '562.43', '550.25', '544.79', '537.22']

gold: This statistic shows the development of Pennsylvania 's real GDP from 2000 to 2018 . In 2018 , the real GDP of Pennsylvania was 712.1 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[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_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[0] templateYLabel[1] of templateTitleSubject[0] was 2.67 templateScale templateYLabel[3] templateYLabel[4] . The annual templateYLabel[0] templateYLabel[1] growth of the templateYLabel[3] can be accessed here .
generated: This statistic shows the development of Pennsylvania 's Real GDP from 2000 to 2018 .  In 2018 , the Real GDP of Pennsylvania was 2.67 billion U.S. dollars .  The annual Real GDP growth of the U.S. can be accessed here .

Example 376:
titleEntities: {'Subject': ['Australia'], 'Date': ['2015', '2022']}
title: Number of social network users Australia 2015 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['19.27', '18.94', '18.58', '18.17', '17.7', '17.19', '16.62', '15.99']

gold: This statistic shows the number of social network users in Australia from 2015 to 2022 . In 2022 , it is estimated that there will be around 19.27 million social network users in Australia , up from around 17.19 million in 2017 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] 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[1] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] , up from around templateYValue[5] templateScale in templateXValue[5] .

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 Australia users in Australia from 2015 to 2022 .  In 2022 , the Number of Australia users in Australia is expected to reach 19.27 millions , up from 17.19 millions in 2017 .

Example 377:
titleEntities: {'Subject': ['Ukraine'], 'Date': ['2019']}
title: Unemployment rate of Ukraine 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['Share', 'of', 'unemployed', 'persons', 'in', 'total', 'labor', 'force']: ['9.31', '9.38', '9.51', '9.35', '9.14', '9.27', '7.17', '7.53', '7.86', '8.1', '8.84', '6.36', '6.35', '6.81', '7.18', '8.59', '9.06', '10.14', '11.06', '11.71', '11.86']

gold: This statistic shows the unemployment rate of the Ukraine from 1999 to 2019 . In 2019 , the unemployment rate of the Ukraine amounted to approximately 9.31 percent of the total labor force . The economic situation in Ukraine Amid the political and economic crisis , Ukraine 's unemployment is rising .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateScale of the templateYLabel[3] templateYLabel[4] templateYLabel[5] . The economic situation in templateTitleSubject[0] Amid the political and economic crisis , templateTitleSubject[0] 's templateTitle[0] is templatePositiveTrend .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total templateYValue[idxmax(X)] templateYLabel[1] reported in the templateTitle[0] .
generated: This statistic shows the Share of unemployed in the Unemployment from 1999 to 2019 .  In 2019 , there were a total 9.31 unemployed reported in the Unemployment .

Example 378:
titleEntities: {'Subject': ['Latin America', 'Big Mac', 'Argentina'], 'Date': ['2020']}
title: Latin America : Big Mac index in Argentina 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Average', 'price', 'in', 'U.S.', 'dollars']: ['2.85', '2.0', '3.96', '3.47', '2.39', '3.25', '3.03', '3.82', '4.64', '4.84', '1.84']

gold: Argentina is one of the Latin American countries with the lowest Big Mac prices . In January 2020 , it was estimated that a Big Mac burger would cost an average of 2.85 U.S. dollars in Argentina , up from only two dollars a year earlier . The Big Mac Index in Argentina The Big Mac Index is an indicator that measures an economy 's purchasing power .
gold_template: templateTitleSubject[2] is one of the templateTitleSubject[0] American countries with the lowest templateTitleSubject[1] prices . In 2020 , it was estimated that a templateTitleSubject[1] burger would cost an templateYLabel[0] of templateYValue[0] templateYLabel[2] templateYLabel[3] in templateTitleSubject[2] , up from only templateYValue[1] templateYLabel[3] a templateXLabel[0] earlier . The templateTitleSubject[1] Index in templateTitleSubject[2] The templateTitleSubject[1] Index is an indicator that measures an economy 's purchasing power .

generated_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: The statistic shows the Average price of Big at U.S. in Latin America from 2010 to 2020 .  In 2020 , the average price of Big at U.S. in Latin America was about 2.85 dollars .

Example 379:
titleEntities: {'Subject': ['Thailand'], 'Date': ['2005', '2017']}
title: Population density of Thailand 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Population', 'density', 'in', 'people', 'per', 'square', 'kilometer']: ['135.13', '134.79', '134.39', '133.92', '133.38', '132.8', '132.18', '131.55', '130.91', '130.25', '129.57', '128.84', '128.06']

gold: This statistic displays the population density of Thailand from 2005 to 2017 . In 2017 , the population density of Thailand was around 135 people per square kilometer of land area , an increase from the previous year .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of land area , an templatePositiveTrend from the previous templateXLabel[0] .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of land area , an templatePositiveTrend from the previous templateXLabel[0] .
generated: This statistic displays the Population density of Thailand from 2005 to 2017 .  In 2017 , the Population density of Thailand was around 135.13 people per square kilometer of land area , an increase from the previous Year .

Example 380:
titleEntities: {'Subject': ['IDPs'], 'Date': ['2000', '2018']}
title: Refugees and IDPs - worldwide 2000 to 2018
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
Refugees  20360562  19941347  17187488  16121427  14385316  11699279  10497957  10404804  10549681  10396538  10489812   9679646   9877703  8661988  9568144  9592795  10594055  12031996  12064599
IDPs      41425147  39118516  36627127  37494172  32274619  23925555  17670368  15473378  14697804  15628057  14442227  13740317  12794268  6616791  5426539  4181701   4646641   5096502   5998501 

gold: The statistic shows the number of refugees and internally displaced persons worldwide from 2000 to 2018 . The United Nations registered about 20.36 million refugees worldwide and 41.43 million IDPs in 2018 .
gold_template: The statistic shows the number of templateTitle[0] and internally displaced persons templateTitle[2] from templateValue[0][last] to templateValue[0][0] . The United Nations registered about templateValue[1][0] templateScale templateTitle[0] templateTitle[2] and templateValue[2][0] templateScale templateTitleSubject[0] in templateValue[0][0] .

generated_template: This statistic shows the templateTitle[0] of homicides in templateTitleSubject[0] and the templateTitleSubject[1] from templateValue[0][last] to templateValue[0][0] . 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: This statistic shows the Refugees of homicides in IDPs and the IDPs from 2000 to 2018 .  There were roughly 5998501 homicides in the 2018 and 12064599 homicides in IDPs per 100,000 residents in 2000 .

Example 381:
titleEntities: {'Subject': ['Oakland Athletics'], 'Date': ['2019']}
title: Oakland Athletics all-time home run leaders 2019
X_Axis['Month']: ['Mark_McGwire', 'Jimmie_Foxx', 'Reggie_Jackson', 'Jose_Canseco', 'Bob_Johnson', 'Eric_Chavez', 'Al_Simmons', 'Jason_Giambi', 'Sal_Bando', 'Gus_Zernial']
Y_Axis['Number', 'of', 'home', 'runs']: ['363', '302', '269', '254', '252', '230', '209', '198', '192', '191']

gold: This statistic shows the Oakland Athletics all-time home run leaders as of October 15 , 2019 . Mark McGwire has hit the most home runs in Oakland Athletics franchise history with 363 home runs .
gold_template: This statistic shows the templateTitleSubject[0] all-time templateYLabel[1] templateTitle[4] templateTitle[5] as of October 15 , 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_template: This graph shows the templateTitle[0] templateTitleSubject[0] all-time templateYLabel[1] templateTitle[5] templateTitle[6] as of October 14 , templateTitleDate[0] . templateXValue[0] has hit the most templateYLabel[1] templateYLabel[2] in templateTitle[0] templateTitleSubject[0] franchise history with templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This graph shows the Oakland Oakland Athletics all-time home leaders 2019 as of October 14 , 2019 .  Mark_McGwire has hit the most home runs in Oakland Oakland Athletics franchise history with 363 home runs .

Example 382:
titleEntities: {'Subject': ['Royal Dutch Shell'], 'Date': ['2008', '2018']}
title: Number of Royal Dutch Shell shares 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'shares', 'in', 'millions']: ['8283', '8223', '7834', '6320', '6311', '6291', '6261', '6213', '6133', '6125', '6159']

gold: Royal Dutch Shell 's number of shares amounted to nearly 8.3 billion in 2018 . This is a large increase from the number of shares available in 2008 ; which equaled about 6.2 billion shares . Shell is one of the leading oil and gas companies worldwide .
gold_template: templateTitleSubject[0] Shell 's templateYLabel[0] of templateYLabel[1] amounted to nearly templateYValue[max] templateScale in templateXValue[idxmax(Y)] . This is a large templatePositiveTrend from the templateYLabel[0] of templateYLabel[1] available in templateXValue[min] ; which equaled about templateYValue[7] templateScale templateYLabel[1] . templateTitleSubject[0] is one of the leading oil and gas companies worldwide .

generated_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: This statistic shows the Number of shares millions in Royal Dutch Shell from 2008 to 2018 .  The Number of millions has declined during the period , from the peak of roughly 8283 thousand in 2018 to around 6125 thousand in 2009 .

Example 383:
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 total templateTitle[0] of templateTitle[1] and templateTitle[2] templateTitle[3] in the templateTitle[4] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][last] , there were around 9,172,000 templateTitle[2] templateTitle[3] ( including templateTitle[3] and heifers that have calved ) in the templateTitle[4] .
generated: This statistic shows the total Deep of surface and mining coal in the production from 1970 to 2018 .  In 1970 , there were around 9,172,000 mining coal ( including coal and heifers that have calved ) in the production .

Example 384:
titleEntities: {'Subject': ['Jordan'], 'Date': ['2018']}
title: Literacy rate in Jordan 2018
Data:
                  0      1      2      3      4
Year           2018   2012   2011   2010   2007
Adult total   98.23  97.89   95.9  92.55   92.2
Adult male    98.61  98.44  97.75  95.77  95.49
Adult female  97.83  97.37  93.93  89.21   88.9 

gold: The statistic depicts the literacy rate in Jordan from 2007 to 2018 . The literacy rate measures the percentage of people aged 15 and above who can read and write . In 2018 , Jordan 's literacy rate was around 98.23 percent .
gold_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_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 Jordan from 2007 to 2018 .  The Literacy rate measures the percentage of people aged 15 and above who can read and write .  In 2018 , Jordan 's Literacy rate was around 98.23 % .

Example 385:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Public views on U.S. favorability , by country 2014
X_Axis['Country']: ['Jordan', 'Pakistan', 'Egypt', 'Turkey', 'Russia', 'Palestinian_territory', 'Greece', 'Argentina', 'Lebanon', 'Tunisia', 'China', 'Germany', 'Malaysia', 'Czech_Republic', 'India', 'Mexico', 'Ukraine', 'Indonesia', 'Spain', 'Britain', 'Uganda', 'Venezuela', 'Mexico', 'Colombia', 'Brazil', 'Peru', 'Japan', 'United_Kingdom', 'South_Africa', 'Nigeria', 'Nicaragua', 'Chile', 'Poland', 'Thailand', 'Senegal', 'France', 'Tanzania', 'Bangladesh', 'Vietnam', 'Ghana', 'Italy', 'El_Salvador', 'Kenya', 'South_Korea', 'Israel', 'Philippines']
Y_Axis['Share', 'of', 'respondents']: ['12', '14', '10', '19', '23', '30', '34', '36', '41', '42', '50', '51', '51', '54', '55', '56', '57', '59', '60', '60', '62', '62', '63', '64', '65', '65', '66', '66', '68', '69', '71', '72', '73', '73', '74', '75', '75', '76', '76', '77', '78', '80', '80', '82', '84', '92']

gold: This statistic shows the share of people from different countries around the world in 2014 , who hold a favorable opinion of the United States . In 2014 , 78 percent of respondents in Italy held a favorable view of the United States .
gold_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_template: This statistic displays the templateYLabel[0] of internet users in templateTitle[5] templateTitle[6] visiting templateTitleSubject[0] networking sites as of 2020 . Based on a comparison of the number of templateTitle[2] accounts on the top templateTitleSubject[0] network in each templateXLabel[0] to the templateYLabel[1] , templateXValue[2] ranked third with a templateTitleSubject[0] templateTitle[1] templateTitle[3] templateTitle[4] of templateYValue[2] templateScale . templateTitleSubject[0] templateTitle[1] templateTitle[3] has also become increasingly mobile , in large part thanks to templateTitleSubject[0] apps .
generated: This statistic displays the Share of internet users in country 2014 visiting U.S. networking sites as of 2020 .  Based on a comparison of the number of U.S. accounts on the top U.S. network in each Country to the respondents , Egypt ranked third with a U.S. views favorability by of 10 % .  U.S. views favorability has also become increasingly mobile , in large part thanks to U.S. apps .

Example 386:
titleEntities: {'Subject': ['Percentage'], 'Date': ['2007']}
title: Percentage of population with a university degree , by country 2007
X_Axis['Country']: ['Canada', 'New_Zealand', 'Japan', 'United_States', 'Korea_Republic_of', 'Norway', 'Australia', 'Ireland', 'United_Kingdom', 'Denmark', 'Sweden', 'Netherlands', 'Spain', 'France', 'Germany', 'Austria', 'Mexico', 'Portugal', 'Italy']
Y_Axis['Percent', 'with', 'a', 'university', 'degree']: ['48', '41', '41', '40', '35', '34', '34', '32', '32', '32', '31', '31', '29', '27', '24', '18', '15', '14', '13']

gold: In 2007 , Canada had the highest share of adults with a university degree , at 48 percent of those between the ages of 25 and 64 . Italy had the least amount of people with a university degree , at 13 percent of people between the ages of 25 and 64 . University around the world Deciding which university to attend can be a difficult decision for some and in today 's world , people are not left wanting for choice .
gold_template: In templateTitleDate[0] , templateXValue[0] had the highest share of adults templateYLabel[1] a templateYLabel[2] templateYLabel[3] , at templateYValue[max] templateScale of those between the ages of 25 and 64 . templateXValue[last] had the least amount of people templateYLabel[1] a templateYLabel[2] templateYLabel[3] , at templateYValue[min] templateScale of people between the ages of 25 and 64 . templateYLabel[2] around the world Deciding which templateYLabel[2] to attend can be a difficult decision for some and in today 's world , people are not left wanting for choice .

generated_template: This statistic shows the distribution of templateTitle[2] templateTitle[3] templateYLabel[2] between templateTitleDate[min] and templateTitleDate[max] . During the survey period , templateYValue[max] templateScale of the Peruvian population accessed the templateYLabel[3] , up from templateYValue[2] templateScale attacks in templateXValue[8] .
generated: This statistic shows the distribution of university degree between 2007 and .  During the survey period , 48 percent of the Peruvian population accessed the degree , up from 41 percent attacks in United_Kingdom .

Example 387:
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 international templateTitle[2] team of templateTitleSubject[0] templateTitle[4] a total of templateYValue[max] templateYLabel[0] templateYLabel[1] , followed by templateXValue[1] with templateYValue[1] templateYLabel[0] . templateXValue[0] retired from the templateTitle[1] team back in 2002 after a templateYValue[14] year career for templateTitleSubject[0] . templateXValue[1] on the other hand took part in the 2018 World Cup in Russia and helped play a phenomenal tournament for templateTitleSubject[0] making second place after losing in the final against France 4:2 .
generated: As of 23rd 2020 , Henrik_Larsson is the 2019 leader in Goals scored for the international League team of UEFA Europa League top a total of 40 Goals scored , followed by Klaas_Jan_Huntelaar with 34 Goals .  Henrik_Larsson retired from the Europa team back in 2002 after a 23 year career for UEFA Europa League .  Klaas_Jan_Huntelaar on the other hand took part in the 2018 World Cup in Russia and helped play a phenomenal tournament for UEFA Europa League making second place after losing in the final against France 4:2 .

Example 388:
titleEntities: {'Subject': ['Countries'], 'Date': []}
title: Countries ranked by number of ice hockey rinks 2018/19
Data:
              0              1       2       3        4        5      6               7      8       9        10     11           12       13          14
Country  Canada  United_States  Russia  Sweden  Finland  Germany  China  Czech_Republic  Japan  France  Slovakia  Italy  Switzerland  Austria  Kazakhstan
Indoor     3300           1535     612     360      274      218    213             200    117     117        71     58           48       42          24
Outdoor    5000            500    2710     136       53       45    197               8     50       9        28     12          111       35         215 

gold: The statistics ranks countries by the number of ice hockey rinks in 2018/19 . In the 2018/19 season , 3,300 indoor and 5,000 outdoor ice hockey rinks were located in Canada according to the International Ice Hockey Federation .
gold_template: The statistics ranks templateTitleSubject[0] templateTitle[2] the templateTitle[3] of templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . In the templateTitle[7] season , templateValue[1][0] templateLabel[1][0] and templateValue[2][0] templateLabel[2][0] templateTitle[4] templateTitle[5] templateTitle[6] were located in templateValue[0][0] according to the International templateTitle[4] templateTitle[5] Federation .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in the templateTitle[2] in templateTitleDate[0] , templateTitle[5] templateTitle[6] templateTitle[7] . In templateTitleDate[0] , templateValue[1][0] templateScale people were aged between 0 and templateValue[3][5] templateLabel[1][1] .
generated: The statistic shows the Countries ranked in the by in , hockey rinks 2018/19 .  In , 3300 % people were aged between 0 and 45 Indoor .

Example 389:
titleEntities: {'Subject': ['Buffalo Bills', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Buffalo Bills ( 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']: ['1900', '1600', '1600', '1500', '1400', '935', '870', '805', '792', '799', '909', '885', '821', '756', '708', '637', '564', '458']

gold: This graph depicts the franchise value of the Buffalo Bills of the National Football League from 2002 to 2019 . In 2019 , the franchise value came to 1.9 billion U.S. dollars . The Buffalo Bills are owned by Terry and Kim Pegula .
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 templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Terry and Kim Pegula .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitleSubject[0] franchise 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 templateTitle[2] templateTitleSubject[0] are owned by William DeWitt Jr. , who bought the templateYLabel[0] for 150 templateScale templateYLabel[3] templateYLabel[4] in 1996 .
generated: This graph depicts the value of the Buffalo Buffalo Bills franchise of Major League Baseball from 2002 to 2019 .  In 2019 , the Franchise had an estimated value of 1900 million U.S. dollars .  The Buffalo Buffalo Bills are owned by William DeWitt Jr. , who bought the Franchise for 150 million U.S. dollars in 1996 .

Example 390:
titleEntities: {'Subject': ['Russia'], 'Date': ['2000', '2018']}
title: Direct investment position of the U.S. in Russia 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['Direct', 'investments', 'in', 'billion', 'U.S.', 'dollars']: ['17.8', '13.88', '14.52', '10.26', '9.76', '13.28', '13.39', '11.29', '10.04', '20.76', '19.78', '15.03', '11.37', '9.36', '6.09', '2.51', '1.14', '0.88', '1.15']

gold: This statistic shows the direct investment position of the United States in Russia from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in Russia were valued at approximately 17.8 billion U.S. dollars . U.S. direct investment abroad is defined as ownership by a U.S. investor of at least 10 percent of a foreign business .
gold_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] templateTitle[1] abroad is defined as ownership by a templateYLabel[3] investor of at least templateYValue[3] templateScale of a foreign business .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] templateTitle[5] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] templateTitle[5] was valued at approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] templateTitle[1] abroad is defined as ownership by a templateYLabel[3] investor of at least 10 templateScale of a foreign business .
generated: This statistic shows the Direct investment position of the U.S. in Russia 2000 from to 2018 , on a historical-cost basis .  In 2018 , the U.S. investments made in Russia 2000 was valued at approximately 17.8 billion U.S. dollars .  U.S. Direct investment abroad is defined as ownership by a U.S. investor of at least 10 billion of a foreign business .

Example 391:
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 templateXValue[0] in the templateTitle[1] templateYLabel[3] were templateYLabel[5] using templateTitle[5] templateTitle[6] templateTitle[7] as of 2019 , sorted templateTitle[7] templateTitle[8] . According to the survey , templateYValue[max] templateScale of voters stated that they had a templateXValue[0] templateYLabel[5] of templateXValue[0] .
generated: This statistic shows the Share of February_2019 in the ownership adults were using 2008 2019 as of 2019 , sorted 2019 .  According to the survey , 78 % of voters stated that they had a February_2019 adults of February_2019 .

Example 392:
titleEntities: {'Subject': ['Alabama'], 'Date': ['1992', '2018']}
title: Alabama - 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', '4.4', '5.8', '6.1', '6.8', '7.2', '8', '9.7', '10.5', '11', '5.7', '4', '4', '4.5', '5.7', '6', '5.9', '5.1', '4.6', '4.7', '4.4', '5', '5.2', '6', '6.2', '7.3', '7.6']

gold: This statistic shows the unemployment rate in Alabama from 1992 to 2018 . In 2018 , the unemployment in Alabama was 3.9 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the 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] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] templateScale . You can access the monthly templateYLabel[0] templateYLabel[1] for the country here .
generated: This statistic displays the Unemployment rate in Alabama from 1992 to 2018 .  In 2018 , Unemployment rate in Alabama was 3.9 % .  You can access the monthly Unemployment rate for the country here .

Example 393:
titleEntities: {'Subject': ['Steam'], 'Date': ['16', '20']}
title: DOTA 2 peak concurrent player number on Steam 2016 to 2020
X_Axis['Month']: ["Jan_'20", "Dec_'19", "Nov_'19", "Oct_'19", "Sept_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sept'_18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sept_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17", "Dec_'16", "Nov_'16", "Oct_'16", "Sept_'16", "Aug_'16", "Jul_'16", "Jun_'16", "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16"]
Y_Axis['Number', 'of', 'players', 'in', 'thousands']: ['614.83', '685.17', '708.52', '741.52', '753.37', '826.69', '779.16', '894.33', '995.43', '968.76', '1036.05', '964.92', '876.87', '765.42', '826.05', '739.64', '826.17', '829.28', '701.58', '796.89', '844.71', '733.21', '773.9', '778.37', '777.7', '862.96', '859.1', '832.55', '829.56', '874.98', '824.3', '923.12', '972.89', '921.32', '956.23', '1040.39', '1007.45', '1016.44', '1004.97', '1135.83', '1063.58', '1114.22', '1084.78', '1096.25', '1077.11', '1128.85', '1286.62', '1244.18', '1068.25']

gold: First released in July 2013 , DOTA 2 is a multiplayer online battle arena that was created as a sequel to Defense of the Ancients ( DotA ) . Published by Valve Corporation , which is also the creator of the gaming platform Steam , DOTA 2 reached its record high peak concurrent player number in March 2016 at nearly 1.29 million gamers playing at the same time . In the last measured month , January 2020 , the game peaked at over 614 thousand concurrent players .
gold_template: First released in 2013 , templateTitle[0] templateTitle[1] is a multiplayer online battle arena that was created as a sequel to Defense of the Ancients ( templateTitle[0] ) . Published by Valve Corporation , which is also the creator of the gaming platform templateTitleSubject[0] , templateTitle[0] templateTitle[1] reached its record high templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] in 2016 at nearly templateYValue[max] templateScale gamers playing at the same time . In the last measured templateXLabel[0] , 2020 , the game peaked at over 614 thousand templateTitle[3] templateYLabel[1] .

generated_template: The statistic shows information on the monthly templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] of Grand Theft Auto templateTitleSubject[0] on templateTitleSubject[0] worldwide as of 2020 . In 2020 , templateTitle[0] templateTitleSubject[0] reached templateYValue[max] thousand templateTitle[3] templateYLabel[1] on templateTitleSubject[0] .
generated: The statistic shows information on the monthly Number of peak concurrent players of Grand Theft Auto Steam on worldwide as of 2020 .  In 2020 , DOTA Steam reached 1286.62 thousand concurrent players on Steam .

Example 394:
titleEntities: {'Subject': ['UK'], 'Date': ['2009', '2018']}
title: Number of high net worth individuals in the 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
United Kingdom   556.0   575.0   568.3   552.8   549.6   527.5   465.1   441.3   454.3   448.1
Europe          4800.0  4800.0  4497.0  4176.3  3985.2  3832.1  3407.6  3168.8  3134.6  2947.8 

gold: high net worth individuals are those that have more than one million U.S dollars in investable wealth . The number of high net worth individuals ( HNWI 's ) in the United Kingdom ( UK ) and in Europe from 2009 to 2018 . Although the total number of HNWI 's in Europe remained stable between 2017 and 2018 , the UK saw a fall of almost 20 thousand individuals .
gold_template: templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] are those that have more than one templateScale U.S dollars in investable wealth . The templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] ( HNWI 's ) in the templateLabel[1][0] templateLabel[1][1] ( templateTitleSubject[0] ) and in templateLabel[2][0] from templateValue[0][last] to templateValue[0][0] . Although the total templateTitle[0] of HNWI 's in templateLabel[2][0] remained stable between templateValue[0][1] and templateValue[0][0] , the templateTitleSubject[0] saw a fall of almost 20 thousand templateTitle[4] .

generated_template: The templateTitle[0] of templateTitle[3] at templateTitleSubject[0] has been steadily templateNegativeTrend in recent years , with templateValue[1][0] templateLabel[1][0] templateTitle[3] and templateValue[2][0] templateLabel[2][0] templateTitle[3] in templateValue[0][0] . This compares to templateValue[1][2] templateLabel[1][0] templateTitle[3] and templateValue[2][2] templateLabel[2][0] templateTitle[3] in templateValue[0][2] . templateTitleSubject[0] Sonic templateTitleSubject[0] is the operating company of the templateTitle[4] drive-through quick service chain templateTitleSubject[0] .
generated: The Number of worth at UK has been steadily decreasing in recent years , with 556.0 United worth and 4800.0 Europe worth in 2018 .  This compares to 568.3 United worth and 4497.0 Europe worth in 2016 .  UK Sonic UK is the operating company of the individuals drive-through quick service chain UK .

Example 395:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2018']}
title: Population growth in Malaysia 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Population', 'growth', 'compared', 'to', 'previous', 'year']: ['1.35', '1.36', '1.36', '1.34', '1.34', '1.37', '1.45', '1.56', '1.69', '1.82', '1.91']

gold: This statistic shows the population growth in Malaysia from 2008 to 2018 . In 2018 , Malaysia 's population increased by approximately 1.35 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templatePositiveTrend by approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . In templateXValue[max] , the Bangladeshi templateYLabel[0] templatePositiveTrend by approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Population growth in Malaysia from 2008 to 2018 , compared to the previous Year .  In 2018 , the Bangladeshi Population increased by approximately 1.35 % .

Example 396:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2018']}
title: Countries with biggest Facebook user share 2018
X_Axis['Country']: ['India', 'United_States', 'Brazil', 'Indonesia', 'Mexico', 'Philippines', 'Vietnam', 'Thailand', 'Turkey', 'United_Kingdom']
Y_Axis['Share', 'of', 'global', 'monthly', 'active', 'Facebook', 'users']: ['12', '11', '6', '6', '4', '3', '3', '2', '2', '2']

gold: The statistic shows a ranking on the leading countries based on their share of Facebook users worldwide as of January 2018 . During the measured period , the United States accounted for 11 percent of the global Facebook population . India accounted for 12 percent of total Facebook users worldwide , having finally overtaken the United States in terms of Facebook user figures .
gold_template: The statistic shows a ranking on the leading templateTitle[0] based on their templateYLabel[0] of templateYLabel[4] templateYLabel[5] worldwide as of 2018 . During the measured period , the templateXValue[1] accounted for templateYValue[1] templateScale of the templateYLabel[1] templateYLabel[4] population . templateXValue[0] accounted for templateYValue[max] templateScale of total templateYLabel[4] templateYLabel[5] worldwide , having finally overtaken the templateXValue[1] in terms of templateYLabel[4] templateTitle[4] figures .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[1] farming templateTitle[3] templateYLabel[1] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . The templateXValue[0] and templateXValue[1] accounted for 48 templateScale of the templateTitle[2] templateTitle[3] templateYLabel[1] in this year , though templateXValue[2] in third place is one of the fastest templatePositiveTrend markets .
generated: This statistic shows the Share of the biggest farming user global in 2018 , .  The India and United_States accounted for 48 % of the Facebook user global in this year , though Brazil in third place is one of the fastest growing markets .

Example 397:
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 398:
titleEntities: {'Subject': ['Iceland'], 'Date': ['2020']}
title: Iceland : national football players with most goals scored of all time as of 2020
X_Axis['Month']: ['Eidur_Gudjohnsen', 'Kolbeinn_Sigthórsson', 'Gylfi_Sigurdsson', 'Rikhardur_Jonsson', 'Arnor_Gudjohnsen', 'Rikhardur_Dadason', 'Alfred_Finnbogason', 'Birkir_Bjarnason', 'Heidar_Helguson', 'Tryggvi_Gudmundsson', 'Thordur_Gudjonsson', 'Matthias_Hallgrimsson', 'Petur_Petursson', 'Helgi_Sigurdsson', 'Eyjolfur_Sverrisson', 'Thordur_Thordarson', 'Teitur_Thordarson', 'Atli_Edvaldsson', 'Gudmundur_Thorbjornsson', 'Marteinn_Geirsson']
Y_Axis['Goals', 'scored']: ['26', '24', '22', '19', '15', '14', '14', '13', '12', '12', '11', '11', '11', '10', '10', '9', '8', '8', '8', '8']

gold: As of 23rd January 2020 , Eiður Guðjohnsen is the all-time leader in goals scored for the national football team of Iceland with a total of 26 goals scored , followed by Kolbeinn Sigþórsson with 24 goals and 22 goals by Gylfi Sigurðsson , who played at the 2018 World Cup and , at the time , had the highest market value among Iceland players with 30 million euros . First World Cup appearance 2018 12 times had Iceland failed to qualify themselves before finally doing so for the World Cup in 2018 . In doing so Iceland became smallest nation to reach the World Cup , having a population of roughly 340 thousand people .
gold_template: As of 23rd 2020 , Eiður Guðjohnsen is the all-time 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 by templateXValue[1] Sigþórsson templateTitle[4] templateYValue[1] templateYLabel[0] and templateYValue[2] templateYLabel[0] by templateXValue[2] Sigurðsson , who played at the 2018 World Cup and , at the templateTitle[9] , had the highest market value among templateTitleSubject[0] templateTitle[3] templateTitle[4] 30 templateScale euros . First World Cup appearance 2018 templateYValue[8] times had templateTitleSubject[0] failed to qualify themselves before finally doing so for the World Cup in 2018 . In doing so templateTitleSubject[0] became smallest nation to reach the World Cup , having a population of roughly 340 thousand people .

generated_template: As of 23rd 2020 , templateXValue[0] is the templateTitle[8] templateTitle[9] leader in templateYLabel[0] templateYLabel[1] for the international 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] and France ) , it came as a surprise to many when templateTitleSubject[0] did not qualify for the World Cup 2018 .
generated: As of 23rd 2020 , Eidur_Gudjohnsen is the time 2020 leader in Goals scored for the international football team of Iceland most a total of 26 Goals scored , followed rather closely by Kolbeinn_Sigthórsson with 24 Goals .  Kolbeinn_Sigthórsson has passed away back in 1979 and Eidur_Gudjohnsen retired from the national already back in 1974 so these records are exceptionally old .  Iceland not at World Cup 2018 As Iceland is generally a very prominent country for football and even part of the Big Five , which are the biggest football league market countries ( England , Germany Spain , Iceland and France ) , it came as a surprise to many when Iceland did not qualify for the World Cup 2018 .

Example 399:
titleEntities: {'Subject': ['NCAA'], 'Date': ['1939', '2019']}
title: NCAA men 's college basketball championships by team 1939 to 2019
X_Axis['State']: ['UCLA', 'Kentucky', 'North_Carolina', 'Indiana', 'Duke', 'Connecticut', 'Kansas', 'Villanova', 'Louisville', 'Cincinnati', 'Florida', 'Michigan_St.', 'North_Carolina_State', 'Oklahoma_A&M', 'San_Francisco']
Y_Axis['Number', 'of', 'titles']: ['11', '8', '6', '5', '5', '4', '3', '3', '3', '2', '2', '2', '2', '2', '2']

gold: The statistic shows the men 's college basketball teams with the most NCAA Division I championships from 1939 to 2019 . UCLA has won the championship a record eleven times . NCAA college basketball championship - additional information NCAA Men 's Division I Basketball tournament , also known as `` March Madness '' , is the most important competition for collegiate basketball , which is the second most followed college sport in the U.S .
gold_template: The statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] teams with the most templateTitleSubject[0] Division I templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . templateXValue[0] has won the championship a record templateYValue[max] times . templateTitleSubject[0] templateTitle[3] templateTitle[4] championship - additional information templateTitleSubject[0] templateTitle[1] templateTitle[2] Division I templateTitle[4] tournament , also known as `` Madness '' , is the most important competition for collegiate templateTitle[4] , which is the second most followed templateTitle[3] sport in the U.S .

generated_template: The statistic shows the 15 templateTitle[2] with the highest templateTitleSubject[0] templateTitle[1] in the period 2009/2010 . With an templateYLabel[0] templateYLabel[1] of templateYValue[max] templateXValue[0] was the templateXLabel[0] with the world 's largest templateTitleSubject[0] templateTitle[1] in 2009/2010 .
generated: The statistic shows the 15 's with the highest NCAA men in the period 2009/2010 .  With an Number titles of 11 UCLA was the State with the world 's largest NCAA men in 2009/2010 .

Example 400:
titleEntities: {'Subject': ['Grand Canyon National Park U.S.'], 'Date': ['2008', '2019']}
title: Number of visitors to the Grand Canyon 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']: ['5.97', '6.38', '6.25', '5.97', '5.52', '4.76', '4.56', '4.42', '4.3', '4.39', '4.35', '4.43']

gold: The number of recreational visitors to the Grand Canyon National Park ( GCNP ) in the U.S. reached a total of 5.97 million in 2019 . This shows a drop when compared to previous year 's total of 6.38 million visitors . The number of visitors has increased by about 2 million people per year in the last seven years .
gold_template: The templateYLabel[0] of recreational templateYLabel[1] to the templateTitleSubject[0] National templateTitleSubject[0] ( GCNP ) in the templateTitleSubject[0] reached a total of templateYValue[0] templateScale in templateXValue[max] . This shows a drop when compared to previous templateXLabel[0] 's total of templateYValue[max] templateScale templateYLabel[1] . The templateYLabel[0] of templateYLabel[1] has templatePositiveTrend by about 2 templateScale people per templateXLabel[0] in the last seven years .

generated_template: In templateXValue[max] , templateTitleSubject[0] Park saw nearly templateYValue[0] and a half templateScale templateYLabel[1] during the templateXLabel[0] . In templateXValue[3] , the templateTitleSubject[0] saw its largest volume of templateYLabel[1] accounting for about templateYValue[max] templateScale . templateTitleSubject[0] Park templateTitleSubject[0] Park is a large templateTitleSubject[0] forest located in central California .
generated: In 2019 , Grand Canyon National Park U.S. Park saw nearly 5.97 and a half millions visitors during the Year .  In 2016 , the Grand Canyon National Park U.S. saw its largest volume of visitors accounting for about 6.38 millions .  Grand Canyon National Park U.S. Park Grand Canyon National Park U.S. is a large Grand Canyon National Park U.S. forest located in central California .

Example 401:
titleEntities: {'Subject': ['Texas Rangers'], 'Date': ['2006', '2019']}
title: Texas Rangers 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']: ['25.75', '26.94', '23.64', '23.64', '23.54', '22.54', '20.49', '18.6', '20.65', '19.41', '18.01', '16.47', '15.81']

gold: This graph depicts the average ticket price for Texas Rangers games in Major League Baseball from 2006 to 2019 . In 2019 , the average ticket price was at 25.75 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[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 Texas Rangers games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 25.75 U.S. dollars .

Example 402:
titleEntities: {'Subject': ['Boot FIFA World Cup'], 'Date': []}
title: Golden Boot at the 2018 FIFA World Cup
X_Axis['Month']: ['Harry_Kane_(England)', 'Antoine_Griezmann_(France)', 'Romelu_Lukaku_(Belgium)', 'Denis_Cheryshev_(Russia)', 'Cristiano_Ronaldo_(Portugal)', 'Kylian_Mbappe_(France)', 'Artem_Dzyuba_(Russia)', 'Eden_Hazard_(Belgium)', 'Mario_Mandzukic_(Croatia)', 'Ivan_Perisic_(Croatia)', 'Yerry_Mina_(Columbia)', 'Diego_Costa_(Spain)', 'Edinson_Cavani_(Uruguay)', 'Wahbi_Khazri_(Tunisia)', 'Philippe_Coutinho_(Brazil)', 'Neymar_(Brazil)', 'Takashi_Inui_(Japan)', 'Luis_Suarez_(Uruguay)', 'Luka_Modric_(Croatia)', 'Mohamed_Salah_(Egypt)', 'Sergio_Agüero_(Argentina)', 'Ahmed_Musa_(Nigeria)', 'Heung-Min_Son_(South_Korea)', 'Mile_Jedinak_(Australia)', 'Andreas_Granqvist_(Sweden)', 'John_Stones_(England)']
Y_Axis['Number', 'of', 'goals']: ['6', '4', '4', '4', '4', '4', '3', '3', '3', '3', '3', '3', '3', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2']

gold: England captain , Harry Kane , won the Golden Boot at the 2018 World Cup in Russia , having scored a total of six goals . Three of the Tottenham Hotspur striker 's goals came in England 's 6 - 1 defeat of Panama in the group stages . His final goal of the tournament came in England 's round of 16 game against Columbia where he opened the scoring from the penalty spot .
gold_template: England captain , templateXValue[0] , won the templateTitle[0] templateTitleSubject[0] at the templateTitleDate[0] templateTitleSubject[0] in Russia , having scored a total of templateYValue[max] templateYLabel[1] . templateYValue[6] of the Tottenham Hotspur striker 's templateYLabel[1] came in England 's templateYValue[max] - 1 defeat of Panama in the group stages . His final goal of the tournament came in England 's round of 16 game against Columbia where he opened the scoring from the penalty spot .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] at the Norwegian templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[5] templateTitle[8] . On 1 , templateXValue[0] templateYLabel[2] templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Number of goals at the Norwegian Boot 2018 FIFA in the United Kingdom ( Boot FIFA World Cup ) in , Cup .  On 1 , Harry_Kane_(England) goals 6 .

Example 403:
titleEntities: {'Subject': ['MAU', 'Europe'], 'Date': ['2019']}
title: Facebook : quarterly MAU in Europe Q4 2012-Q2 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', 'Q4_2012']
Y_Axis['Number', 'of', 'monthly', 'active', 'users', '(MAU)', 'in', 'millions']: ['385', '384', '381', '375', '376', '377', '370', '364', '360', '354', '349', '342', '338', '333', '323', '315', '311', '307', '301', '296', '292', '289', '282', '276', '272', '269', '261']

gold: More people are using Facebook in Europe than ever before , with the company recording 385 million monthly active users ( MAUs ) in the second quarter of 2019 . That represents a growth of six million active users from the previous quarter , and 121 million users from the fourth quarter of 2012 . It also marks the third consecutive period of growth , after the company lost millions of MAU at the same time news of the Cambridge Analytica scandal broke .
gold_template: More people are using templateTitle[0] in templateTitleSubject[1] than ever before , with the company recording templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] ( MAUs ) in the second templateXLabel[0] of templateXValue[0] . That represents a growth of six templateScale templateYLabel[2] templateYLabel[3] from the previous templateXLabel[0] , and 121 templateScale templateYLabel[3] from the fourth templateXLabel[0] of templateXValue[last] . It also marks the third consecutive period of growth , after the company lost templateScale of templateTitleSubject[0] at the same time news of the Cambridge Analytica scandal broke .

generated_template: This statistic shows the cumulative templateYLabel[0] of templateYLabel[2] templateYLabel[3] to project templateTitle[3] on templateTitleSubject[0] from the fourth templateXLabel[0] of templateXValue[26] to the third templateXLabel[0] of templateXValue[0] . In the templateXLabel[0] of templateXValue[0] , a total of templateYLabel[2] templateTitle[5] templateYLabel[4] amounted to templateYValue[max] templateScale templateYLabel[2] .
generated: This statistic shows the cumulative Number of active users to project Europe on MAU from the fourth Quarter of Q4_2012 to the third Quarter of Q2_2019 .  In the Quarter of Q2_2019 , a total of active 2012-Q2 (MAU) amounted to 385 millions active .

Example 404:
titleEntities: {'Subject': ['Global'], 'Date': ['2017']}
title: Global mango 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']: ['50.65', '46.51', '46.33', '45.22', '42.66', '40.44', '39.26', '37.14', '34.88', '35.62', '34.86', '33.85', '31.63', '29.72', '29.73', '26.22', '24.92', '24.72']

gold: Mangos are native to South and Southeast Asia and are now enjoyed all over the world . The global production volume of mangos , mangosteens , and guavas reached 50.65 million metric tons in 2017 , an increase from 46.5 million metric tons in the 2016 . Mango production in Asia Much of the world 's mangos come from the Asia Pacific region , specifically India and China .
gold_template: Mangos are native to South and Southeast Asia and are now enjoyed all over the world . The templateTitleSubject[0] templateTitle[2] volume of mangos , mangosteens , and guavas reached templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[idxmax(Y)] , an templatePositiveTrend from templateYValue[1] templateScale templateYLabel[1] templateYLabel[2] in the templateXValue[1] . templateTitle[1] templateTitle[2] in Asia Much of the world 's mangos come from the Asia Pacific region , specifically India and China .

generated_template: The templateYLabel[0] of templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] has undergone a decline since the templateXLabel[0] templateXValue[last] . Whereas in templateXValue[last] , there were over templateYValue[max] thousand templateYLabel[1] in the templateTitleSubject[1] , by templateXValue[0] this figure was approximately templateYValue[min] thousand . This means over this sixteen-year period there were over 70 thousand fewer templateTitle[1] templateYLabel[1] in the templateTitleSubject[1] .
generated: The Million of mango metric in the Global has undergone a decline since the Year 2000 .  Whereas in 2000 , there were over 50.65 thousand metric in the Global , by 2017 this figure was approximately 24.72 thousand .  This means over this sixteen-year period there were over 70 thousand fewer mango metric in the Global .

Example 405:
titleEntities: {'Subject': ['Grammy Awards'], 'Date': ['1959', '2020']}
title: Number of Grammy Awards categories 1959 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '1959']
Y_Axis['Number', 'of', 'categories']: ['84', '84', '84', '84', '83', '83', '82', '81', '78', '109', '109', '110', '28']

gold: When the Grammy Awards were introduced in 1959 , 28 different categories were available in which to win an award . In 2009 , the number reached its highest level with 110 categories , and in 2019 , awards were presented in 84 categories . Grammy Awards categories - additional information The first edition of the Grammy Awards ( originally called the Gramophone Award ) was held on May 4 , 1959 simultaneously on both American coasts : one ceremony took place in the Grand Ballroom of the Beverly Hilton , in Los Angeles , while another function was held in New York City .
gold_template: When the templateTitleSubject[0] were introduced in templateXValue[min] , templateYValue[idxmin(X)] different templateYLabel[1] were available in which to win an award . In templateXValue[11] , the templateYLabel[0] reached its highest level with templateYValue[max] templateYLabel[1] , and in templateXValue[1] , templateTitleSubject[0] were presented in templateYValue[0] templateYLabel[1] . templateTitleSubject[0] categories - additional information The first edition of the templateTitleSubject[0] ( originally called the Gramophone Award ) was held on 4 , templateXValue[min] simultaneously on templateXValue[idxmin(Y)] American coasts : one ceremony took place in the Grand Ballroom of the Beverly Hilton , in Los Angeles , while another function was held in New York City .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[idxmax(X)] templateScale templateTitle[1] templateTitle[2] templateYLabel[1] were living in the templateTitleSubject[0] , a U.S.-based templatePositiveTrend from the previous templateXLabel[0] .
generated: This statistic shows the Number of Grammy Awards categories in the Grammy Awards from 1959 to 2020 .  In 2020 , around 84 million Grammy Awards categories were living in the Grammy Awards , a U.S.-based increase from the previous Year .

Example 406:
titleEntities: {'Subject': ['Witcher'], 'Date': ['2015', '2015']}
title: Witcher game series unit sales worldwide as of August 2015
X_Axis['Game', 'release', '(year', 'of', 'release)']: ['The_Witcher_3:_Wild_Hunt_(2015)', 'The_Witcher_2:_Assasins_of_Kings_(2011)', 'The_Witcher_(2007)']
Y_Axis['Number', 'of', 'units', 'sold', 'in', 'millions']: ['6.01', '1.7', '1.0']

gold: The statistic shows global first year unit sales of the Witcher game series . As of August 2015 , six weeks after its release date , the third part of the Witcher series had already sold 6 million units worldwide .
gold_template: The statistic shows global first year templateTitle[3] templateTitle[4] of the templateXValue[0] templateXLabel[0] templateTitle[2] . As of templateTitle[6] templateTitleDate[0] , templateYValue[max] weeks after its templateXLabel[1] date , the third part of the templateXValue[0] templateTitle[2] had already templateYLabel[2] templateYValue[max] templateScale templateYLabel[1] templateTitle[5] .

generated_template: This statistic depicts the templateScale of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitleDate[0] , by the templateXLabel[0] of templateXValue[0] . templateYValue[max] templateScale of templateYLabel[1] with templateXValue[last] and templateXValue[last] templateXLabel[1] used templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] .
generated: This statistic depicts the millions of Witcher units unit sales worldwide August 2015 release in 2015 , by the Game of The_Witcher_3:_Wild_Hunt_(2015) .  6.01 millions of units with The_Witcher_(2007) and release used sales worldwide August in 2015 .

Example 407:
titleEntities: {'Subject': ['PwC'], 'Date': ['2018']}
title: Aggregated revenues of PwC by industry sector 2018
X_Axis['Industry', 'sector']: ['Financial_services', 'Industrial_manufacturing_and_automotive', 'Consumer_markets', 'Technology_media_and_telecommunications', 'Energy_utilities_and_resources', 'Health_industries', 'Government_and_public_services', 'Private_equity_and_sovereign_investment_funds']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['10.73', '6.53', '6.47', '5.47', '3.91', '3.18', '2.64', '2.37']

gold: PricewaterhouseCoopers ( PwC ) generated 10.73 billion U.S. dollars from the financial services industry in 2018 , around four billion U.S. dollars more than the next ranked industry sector . Income from the financial services industry accounted for around 25 percent of the firm 's aggregated gross revenue worldwide in 2018 . What drives demand from financial services ? Financial institutions such as banks and insurance companies face frequent changes in their line of work and , consequently , accounting firms can be confident that revenue from this industry generates a significant share of their annual revenue .
gold_template: PricewaterhouseCoopers ( templateTitleSubject[0] ) generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] from the templateXValue[0] industry in templateTitleDate[0] , around templateYValue[4] templateScale templateYLabel[2] templateYLabel[3] more than the next ranked templateXLabel[0] templateXLabel[1] . Income from the templateXValue[0] industry accounted for around 25 templateScale of the firm 's templateTitle[0] gross templateYLabel[0] worldwide in templateTitleDate[0] . What drives demand from templateXValue[0] ? templateXValue[0] institutions such as banks and insurance companies face frequent changes in their line of work and , consequently , accounting firms can be confident that templateYLabel[0] from this templateXLabel[0] generates a significant share of their annual templateYLabel[0] .

generated_template: This statistic shows the age-standardized templateTitle[2] templateTitle[3] of templateTitle[0] stays in templateTitleSubject[0] from templateXLabel[0] templateXLabel[1] in templateTitleDate[0] . In templateXValue[2] , the templateXValue[2] 's templateTitle[0] templateTitle[1] templateTitle[2] was approximately templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the age-standardized PwC by of Aggregated stays in PwC from Industry sector in 2018 .  In Consumer_markets , the Consumer_markets 's Aggregated revenues PwC was approximately 6.47 billion U.S. dollars .

Example 408:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2008', '2018']}
title: Average age at first marriage in Sweden 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
Men    36.3  36.2  36.2  36.0  35.7  35.6  35.9  35.6  35.5  35.1  35.1
Women  33.9  33.6  33.6  33.5  33.2  33.0  33.4  33.1  32.9  32.5  32.5 

gold: The average age at first marriage in Sweden increased among both men and women during the period from 2008 to 2018 . The average age at first marriage among women increased by 1.5 years during the ten-year period and was 33.9 years in 2018 . Among men , the age increase was not as large as for women .
gold_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_template: In templateValue[0][0] , just over one fifth of the Dutch population smoked . templateValue[2][0] templateScale were templateLabel[2][0] templateLabel[1][0] templateLabel[1][0] , defined by the source as people templateTitle[0] more than templateValue[2][0] cigarettes a day . templateTitle[0] is becoming less and less popular in the templateTitleSubject[0] .
generated: In 2018 , just over one fifth of the Dutch population smoked .  33.9 % were Women Men , defined by the source as people Average more than 33.9 cigarettes a day .  Average is becoming less and popular in the Sweden .

Example 409:
titleEntities: {'Subject': ['Japan'], 'Date': ['2000']}
title: Japan : internet penetration 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000']
Y_Axis['Percentage', 'of', 'individuals', 'using', 'internet']: ['90.87', '93.18', '91.06', '89.11', '88.22', '79.5', '79.05', '78.21', '78', '75.4', '74.3', '68.69', '66.92', '29.99']

gold: This statistic gives information on the internet penetration in Japan from 2000 to 2017 . That year , almost 91 percent of the Japanese population accessed the internet , down from over 93 percent in the previous year .
gold_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . That templateXLabel[0] , almost templateYValue[0] templateScale of the Japanese population accessed the templateYLabel[3] , down from over templateYValue[max] templateScale in the previous templateXLabel[0] .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] , an templatePositiveTrend compared to the previous templateXLabel[0] .
generated: This statistic presents the Percentage of individuals using in the Japan from 2000 to 2017 .  In 2017 , there were a total of 90.87 percentage individuals using , an increase compared to the previous Year .

Example 410:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2015']}
title: Median age of the population in Argentina 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']: ['38.2', '37.1', '36.0', '34.9', '33.8', '32.7', '31.5', '30.5', '29.6', '28.5', '27.6', '27.1', '27.0', '27.1', '27.2', '27.2', '27.1', '26.9', '26.6', '26.0', '25.4']

gold: This statistic shows the median age of the population in Argentina 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 Argentina 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 411:
titleEntities: {'Subject': ['Italy'], 'Date': ['2006', '2018']}
title: Weekly church attendance in Italy 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'individuals', 'in', 'thousands']: ['14264', '15234', '15752', '16597', '16506', '17323', '16871', '17124', '18058', '18270', '18267', '18470', '18366']

gold: Every year the number of Italian believers attending religious services becomes lower and lower . In 2018 , there were about 14.3 million citizens attending house of prayer at least once every week , around four million worshippers less in comparison with the figures from a decade earlier . The most prayerful were the citizens aged over 75 years , whereas the lowest number of individuals attending religious services at least once a week was recorded in the age group between 18 and 24 years .
gold_template: Every templateXLabel[0] the templateYLabel[0] of Italian believers attending religious services becomes lower and lower . In templateXValue[max] , there were about templateYValue[min] templateScale citizens attending house of prayer at least once every week , around four templateScale worshippers less in comparison with the figures from a decade earlier . The most prayerful were the citizens aged over 75 years , whereas the lowest templateYLabel[0] of templateYLabel[1] attending religious services at least once a week was recorded in the age group between 18 and 24 years .

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 attendance individuals at Italy establishments in Italy from 2006 to 2018 .  In 2018 , the Number of individuals in travel Italy ( including both international and domestic tourists ) amounted to approximately 14264 thousands .

Example 412:
titleEntities: {'Subject': ['S'], 'Date': ['2019']}
title: Number of employees at S & P\xa0Global 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'employees', 'in', 'thousands']: ['23.0', '21.0', '20.4', '20.0', '20.4', '17.0', '16.4', '15.9']

gold: This statistic presents the number of employees at S & P Global from 2012 to 2019 . In 2019 , S & P Global , formerly McGraw-Hill Financial , employed approximately 23,000 persons .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] templateTitle[3] P Global from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[3] P Global , formerly McGraw-Hill Financial , employed approximately templateYValue[max] persons .

generated_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: The statistic shows the Number of employees S , and S for the fiscal years 2012 to 2019 .  The Number of employees at S , and S reached a high in 2017 with 20.4 employed at the S that Year .

Example 413:
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 templateYLabel[0] of templateYLabel[2] in the templateTitle[1] templateYLabel[3] were templateYLabel[5] using templateTitle[5] templateTitle[6] templateTitle[7] . During the 2019 survey period , templateYValue[1] templateScale of respondents stated that they had templateYLabel[5] accessed templateXValue[1] , either via desktop or mobile internet connection .
generated: This statistic shows the Share of shoppers in the online shoppers were using worldwide 2017 .  During the 2019 survey period , 39 % of respondents stated that they had shoppers accessed Electronic_payment_(PayPal_if_available) , either via desktop or mobile internet connection .

Example 414:
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 415:
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: As per recent data , in 2020 , templateTitle[1] templateYLabel[0] came to a total of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] , down from the templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] seen in 2019 . The templateYLabel[0] figures for the first quarter of 2020 represent a third templateXLabel[0] of successive decline since October templateTitleDate[max] .
generated: As per recent data , in 2020 , clothing Percentage came to a total of 6.6 percentage change , down from the 8.5 percentage change seen in 2019 .  The Percentage figures for the first quarter of 2020 represent a third Month of successive decline since October 2019 .

Example 416:
titleEntities: {'Subject': ['Activision Blizzard'], 'Date': ['2007', '2018']}
title: Activision Blizzard 's annual revenue from 2007 to 2018 , by region
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
Americas      3880  3607  3423  2409  2190  2414  2436  2405  2409  2217  1494   620
EMEA          2618  2464  2221  1741  1824  1826  1968  1990  1743  1798  1288   555
Asia Pacific  1002   946   964   514   394   343   452   360   295   263   227   164 

gold: This timeline depicts Activision Blizzard 's net revenue worldwide from 2007 to 2018 , broken down by region . In 2018 , revenue in the Asia Pacific region amounted to slightly over one billion U.S. dollars .
gold_template: This timeline depicts templateTitleSubject[0] 's net templateTitle[4] worldwide templateTitle[5] templateValue[0][last] to templateValue[0][0] , broken down templateTitle[8] templateTitle[9] . In templateValue[0][0] , templateTitle[4] in the templateLabel[3][0] templateLabel[3][1] templateTitle[9] amounted to slightly over one templateScale U.S. dollars .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] in templateTitleSubject[1] from templateValue[0][last] to templateValue[0][0] , templateTitle[8] templateTitle[9] templateTitle[10] . In templateValue[0][0] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] generated templateValue[1][0] years olds .
generated: This statistic shows the Activision Blizzard of Activision Blizzard in from 2007 to 2018 , by region .  In 2018 , the Activision Blizzard of Activision Blizzard generated 3880 years olds .

Example 417:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2019']}
title: Public opinion of important issues facing the Netherlands 2019
X_Axis['Issue']: ['The_environment_climate_and_energy_issues', 'Health_and_social_security', 'The_education_system', 'Crime', 'Housing', 'Pensions', 'Immigration', 'Rising_prices/inflation/cost_of_living', 'Economic_situation', 'Terrorism', 'Taxation', 'Unemployment', 'Government_debt']
Y_Axis['Share', 'of', 'respondents']: ['66', '31', '25', '15', '15', '14', '13', '9', '5', '4', '1', '1', '0']

gold: When asked what they think are the most important issues the Netherlands is currently facing , 66 percent of survey respondents stated to worry about the climate and environment . Another 31 percent was concerned about health and social security . By comparison , relatively few people named the government debt or unemployment .
gold_template: When asked what they think are the most templateTitle[2] templateXValue[0] the templateTitleSubject[0] is currently templateTitle[4] , templateYValue[max] templateScale of survey templateYLabel[1] stated to worry about the templateXValue[0] and templateXValue[0] . Another templateYValue[1] templateScale was concerned about templateXValue[1] and templateXValue[1] . By comparison , relatively few people named the templateXValue[last] or templateXValue[11] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] internet users who have used templateTitle[2] templateTitle[3] templateTitle[4] as of templateTitleDate[0] , sorted templateTitle[6] templateXLabel[0] . During the survey period , templateYValue[max] templateScale of templateYLabel[1] from templateXValue[0] said that they had used templateTitle[2] templateTitle[3] templateTitle[4] in the past six months . In the templateXValue[5] , templateYValue[5] templateScale of internet users used templateTitle[2] templateTitle[3] templateTitle[4] .
generated: This statistic shows the Share of Netherlands internet users who have used important issues facing as of 2019 , sorted 2019 Issue .  During the survey period , 66 % of respondents from The_environment_climate_and_energy_issues said that they had used important issues facing in the past six months .  In the Pensions , 14 % of internet users used important issues facing .

Example 418:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000', '2019']}
title: Canada : number of internet users 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', 'internet', 'users', 'in', 'millions']: ['34.56', '33.22', '33.0', '33.0', '31.56', '31.0', '30.23', '28.94', '28.63', '27.4', '27.1', '25.59', '24.15', '23.61', '23.11', '21.05', '20.28', '19.27', '18.65', '15.75']

gold: In 2019 , Canada had an estimated 34.56 million internet users . That year it was calculated that online services reached almost 96 percent of the population .
gold_template: In templateXValue[max] , templateTitleSubject[0] had an estimated templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] . That templateXLabel[0] it was calculated that online services reached almost 96 templateScale of the population .

generated_template: In templateXValue[max] , close to templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] accessed the web from the templateTitleSubject[0] , up from nearly templateYValue[min] templateScale in templateXValue[2] . The templateTitleSubject[0] are one of the largest online markets worldwide , ranking only behind China and India in terms of online audience size . templateYLabel[1] usage in the templateTitleSubject[0] Overall , 90 templateScale of templateTitle[1] adults were reported to use the templateYLabel[1] at least occasionally , up from 76 templateScale in templateXValue[9] .
generated: In 2019 , close to 34.56 millions internet users accessed the web from the Canada , up from nearly 15.75 millions in 2017 .  The Canada are one of the largest online markets worldwide , ranking only behind China and India in terms of online audience size .  internet usage in the Canada Overall , 90 millions of number adults were reported to use the internet at least occasionally , up from 76 millions in 2010 .

Example 419:
titleEntities: {'Subject': ['Disney+ U.S.'], 'Date': ['2019']}
title: Likelihood of subscribing to Disney+ in the U.S. 2019
X_Axis['Response']: ['Very_likely', 'Somewhat_likely', 'Not_that_likely', 'Not_likely_at_all', "Don't_know/no_opinion"]
Y_Axis['Share', 'of', 'respondents']: ['9', '20', '21', '39', '11']

gold: The graph shows the likelihood of subscribing to Disney 's new online video streaming service ( Disney+ ) among adults in the United States as of February 2019 . The data reveals that just nine percent of surveyed U.S. adults said that they were very likely to sign up to Disney+ upon its launch in 2019 , and 39 percent said that they were not at all likely to subscribe to the streaming service .
gold_template: The graph shows the templateTitle[0] of templateTitle[1] to Disney 's new online video streaming service ( templateTitleSubject[0] ) among adults in the templateTitle[3] as of 2019 . The data reveals templateXValue[2] just templateYValue[min] templateScale of surveyed templateTitleSubject[0] adults said templateXValue[2] they were templateXValue[0] to sign up to templateTitleSubject[0] upon its launch in templateTitleDate[0] , and templateYValue[max] templateScale said templateXValue[2] they were templateXValue[2] at templateXValue[3] templateXValue[0] to subscribe to the streaming service .

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 Likelihood subscribing an active Disney+ U.S. or subscription in the 2019 as of 2017 , sorted 2019 .  According to the source , 39 % of Somewhat_likely who subscribe to online video or music subscriptions had a Disney+ U.S. or subscription as of 2017 .

Example 420:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1960', '2018']}
title: Median age of the U.S. population 1960 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2000', '1990', '1980', '1970', '1960']
Y_Axis['Median', 'age', 'in', 'years']: ['38.2', '38.0', '37.9', '37.8', '37.7', '37.6', '37.5', '37.3', '37.2', '35.3', '32.9', '30.0', '28.1', '29.5']

gold: In 2018 , the median age of the population of the United States was 38.2 years . While this may seem quite young , the median age in 1960 was even younger , at 29.5 years . The aging population The year 2035 is expected to be a turning point for the population of the United States .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[3] of the templateTitle[2] was templateYValue[max] templateYValue[idxmax(X)] . While this may seem quite young , the templateYLabel[0] templateYLabel[1] in templateXValue[min] was even younger , at templateYValue[idxmin(X)] templateYLabel[2] . The aging templateTitle[3] The templateXLabel[0] 2035 is expected to be a turning point for the templateTitle[3] of the templateTitle[2] .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[2] templateTitle[3] templateTitle[4] was approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic presents the Median age years of the U.S. population 1960 from to 2018 .  In 2018 , the Median age years of the U.S. population 1960 was approximately 38.2 % .

Example 421:
titleEntities: {'Subject': ['Turkey'], 'Date': ['2019']}
title: Turkey : social network penetration Q3 2019
X_Axis['Platform']: ['Youtube', 'Instagram', 'Whatsapp', 'Facebook', 'Twitter', 'FB_Messenger', 'Pinterest', 'LinkedIn', 'Snapchat', 'TikTok', 'Skype', 'Twitch', 'Tumblr', 'Reddit', 'WeChat', 'Line']
Y_Axis['Share', 'of', 'population']: ['90', '83', '81', '76', '61', '53', '34', '32', '29', '27', '27', '24', '20', '16', '15', '14']

gold: This statistic presents the social network , voip service , and messenger app penetration rate in Turkey as of the 3rd quarter of 2019 . The most used social network was YouTube with a 90 percent penetration rate followed by Instagram with 83 percent . A The information presented is an extrapolation of data gained during a survey and represents users ' own claimed activity .
gold_template: This statistic presents the templateTitle[1] templateTitle[2] , voip service , and templateXValue[5] app templateTitle[3] rate in templateTitleSubject[0] as of the 3rd quarter of templateTitleDate[0] . The most used templateTitle[1] templateTitle[2] was templateXValue[0] with a templateYValue[max] templateScale templateTitle[3] rate followed by templateXValue[1] with templateYValue[1] templateScale . A The information presented is an extrapolation of data templatePositiveTrend during a survey and represents users ' own claimed activity .

generated_template: templateXValue[0] is the most active templateTitle[0] templateTitle[1] among internet users in the templateTitleSubject[0] ( templateTitleSubject[1] ) , with templateYValue[max] templateScale of people reporting use of the service . templateXValue[1] was very close behind , with a rate of templateYValue[1] templateScale . templateXValue[0] 's broad audience in the templateTitleSubject[0] YouTube reaches a broad audience in the templateTitleSubject[1] , making it an attractive partner for advertisers of almost any product .
generated: Youtube is the most active Turkey social among internet users in the Turkey ( ) , with 90 % of people reporting use of the service .  Instagram was very close behind , with a rate of 83 % .  Youtube 's broad audience in the Turkey YouTube reaches a broad audience in the Turkey , making it an attractive partner for advertisers of almost any product .

Example 422:
titleEntities: {'Subject': ['Cambodia'], 'Date': ['2018']}
title: Urbanization in Cambodia 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['23.39', '22.98', '22.58', '22.19', '21.8', '21.42', '21.04', '20.66', '20.29', '19.93', '19.57']

gold: This statistic shows the degree of urbanization in Cambodia from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 23.39 percent of Cambodia '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 Cambodia from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 23.39 % of Cambodia 's total population lived in urban areas and cities .

Example 423:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Snapchat : age distribution of U.S. users 2016
X_Axis['Year']: ['13-17', '18-24', '25-34', '35-54', '55+']
Y_Axis['Share', 'of', 'active', 'users']: ['23', '37', '26', '12', '2']

gold: This statistic provides information on the distribution of U.S. Snapchat users as of February 2016 , sorted by age group . During the survey period it was found that 23 percent of Snapchat users in the United States were aged between 13 and 17 years . As of spring 2017 , Snapchat ranks as the most important social network of teens in the United States , ahead of other platforms such as Instagram , Twitter or Facebook .
gold_template: This statistic provides information on the templateTitle[2] of templateTitleSubject[0] templateTitle[0] templateYLabel[2] as of 2016 , sorted by templateTitle[1] group . During the survey period it was found that templateYValue[0] templateScale of templateTitle[0] templateYLabel[2] in the templateTitle[3] were aged between 13 and 17 years . As of spring 2017 , templateTitle[0] ranks as the most important social network of teens in the templateTitle[3] , ahead of other platforms such as Instagram , Twitter or Facebook .

generated_template: This statistic shows the templateYLabel[0] of French templateYLabel[1] who have already practiced naturism on the templateTitle[4] or in a nudist camp in templateTitleDate[0] , templateTitle[7] templateTitle[8] group . We can see that more than 10 templateScale of templateYLabel[1] aged templateXValue[3] to templateXValue[3] had already practiced templateTitle[3] at the templateTitle[4] or in a naturist camp . Discover also the level of interest of the French for naturism .
generated: This statistic shows the Share of French active who have already practiced naturism on the users or in a nudist camp in 2016 , group .  We can see that more than 10 % of active aged 35-54 to had already practiced U.S. at the users or in a naturist camp .  Discover also the level of interest the French for naturism .

Example 424:
titleEntities: {'Subject': ['Starwood Hotels Resorts'], 'Date': ['2015']}
title: Number of Starwood Hotels & Resorts properties worldwide in 2015 , by brand
X_Axis['Brand']: ['St._Regis', 'The_Luxury_Collection', 'W', 'Sheraton', 'Westin', 'Le_Méridien', 'Four_Points', 'Aloft', 'Element', 'Tribute', 'Independent_/_Other']
Y_Axis['Number', 'of', 'hotels']: ['36', '99', '46', '446', '209', '103', '210', '104', '20', '6', '18']

gold: This statistic shows the number of Starwood Hotels & Resorts properties worldwide in 2015 , by brand . There were 210 Four Points properties within the Starwood Hotels & Resorts portfolio 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] templateYLabel[1] templateTitle[3] templateTitleSubject[0] templateTitle[5] templateTitle[6] in templateTitleDate[0] , templateTitle[8] templateXLabel[0] . There were templateYValue[6] templateXValue[6] properties within the templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitleSubject[0] portfolio in templateTitleDate[0] . templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitleSubject[0] was founded in 1969 and now has more than 1,200 templateTitle[5] templateTitle[6] .

generated_template: This statistic shows the most popular templateXValue[5] templateTitle[3] templateTitle[4] in the templateTitle[1] as of 2018 , ranked templateTitle[6] templateTitle[7] templateTitle[8] templateYLabel[1] . The templateXValue[0] was the most common templateTitle[1] templateTitle[2] templateXLabel[0] templateXLabel[1] in the templateTitleSubject[0] , with a templateYLabel[0] of templateYValue[max] templateScale .
generated: This statistic shows the most popular Le_Méridien & Resorts in the Starwood as of 2018 , ranked worldwide 2015 by hotels .  The St._Regis was the most common Starwood Hotels Brand in the Starwood Hotels Resorts , with a Number of 446 % .

Example 425:
titleEntities: {'Subject': ['Norway'], 'Date': ['2019']}
title: Leading companies in Norway 2019 , by number of employees
X_Axis['Month']: ['Helse_Sør-Øst_RHF', 'Telenor_ASA', 'Aker_ASA', 'Equinor_ASA_/_Statoil', 'Posten_Norge_AS', 'Orkla_ASA', 'Yara_International_ASA', 'Aker_Solutions_ASA', 'Tallyman_AS', 'Norges_Statsbaner_AS', 'Norsk_Hydro_ASA', 'Marine_Harvest_ASA', 'Strawberry_Holding_AS', 'Nordic_Choice_Hospitality_Group_AS', 'Kongsberg_Automotive_ASA', 'DNB_ASA', 'Hfn_Group_AS', 'Evry_ASA', 'Hospitality_Invest_AS', 'Nokas_AS']
Y_Axis['Number', 'of', 'employees']: ['60368', '31000', '20753', '20245', '18327', '18154', '14736', '14300', '13760', '13006', '12911', '12717', '10412', '10320', '9791', '9561', '9172', '9100', '9001', '8273']

gold: This statistic shows the 20 biggest companies in Norway as of March 2019 , by number of employees . Helse Sør-Øst RHF was ranked first with over 60 thousand employees , while Telenor ASA was ranked second with 31 thousand employees .
gold_template: This statistic shows the 20 biggest templateTitle[1] in templateTitleSubject[0] as of 2019 , templateTitle[4] templateYLabel[0] of templateYLabel[1] . templateXValue[0] RHF was ranked first with over templateYValue[max] thousand templateYLabel[1] , while templateXValue[1] was ranked second with templateYValue[1] thousand templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] the templateTitle[3] templateXValue[1] templateTitle[5] templateTitle[6] in templateTitleDate[0] . The templateXValue[1] templateXLabel[1] templateXValue[0] employs the most people among the templateTitle[3] templateXValue[1] templateTitle[5] , with templateYValue[max] templateYLabel[1] templateTitle[6] in templateTitleDate[0] .
generated: This statistic shows the Number of employees Norway the 2019 Telenor_ASA number employees in 2019 .  The Telenor_ASA Month Helse_Sør-Øst_RHF employs the most people among the 2019 Telenor_ASA number , with 60368 employees in 2019 .

Example 426:
titleEntities: {'Subject': ['Lithuania'], 'Date': ['2018']}
title: Distribution of gross domestic product ( GDP ) across economic sectors Lithuania 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   2.91   3.51   3.11   3.42   3.42   3.55   3.99    3.5   3.03   2.53   3.28
Industry     25.52  25.88  25.91  26.65  27.43  27.35  27.98  28.04  26.19  25.06  29.19
Services     61.44  60.45  60.98  59.97  59.57  59.75  58.48  58.48   60.7  62.63  57.29 

gold: This statistic shows the distribution of the gross domestic product ( GDP ) across economic sectors in Lithuania from 2008 to 2018 . In 2018 , agriculture contributed around 2.91 percent to the GDP of Lithuania , 25.52 percent came from the industry and 61.44 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 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_template: In templateValue[0][0] , templateLabel[1][0] contributed the most to templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) , with a share of just over templateValue[3][0] templateScale . Having an economy based on the templateLabel[3][0] sector is a widely recognized marker of an advanced economy . What are the attractions in the templateLabel[3][0] sector ? templateTitleSubject[0] 's economy was about 2.7 templateScale U.S. dollars , and its templateTitle[4] is projected to templatePositiveTrend through 2024 .
generated: In 2018 , Agriculture contributed the most to Lithuania 's gross domestic product ( GDP ) , with a share of just over 61.44 % .  Having an economy based on the Services sector is a widely recognized marker of an advanced economy .  What are the attractions in the Services sector ? Lithuania 's economy was about 2.7 billion U.S. dollars , and its GDP is projected to increase through 2024 .

Example 427:
titleEntities: {'Subject': ['HSBC'], 'Date': ['2009', '2019']}
title: Return on average ordinary shareholders ' equity at HSBC 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Return', 'on', 'equity']: ['3.6', '7.7', '5.9', '0.8', '7.2', '7.3', '9.2', '8.4', '10.9', '9.5', '5.1']

gold: The statistic shows the return on average ordinary shareholders ' equity at HSBC from 2009 to 2019 . The return on average ordinary shareholders ' equity at HSBC amounted to 3.6 percent in 2019 .
gold_template: The statistic shows the templateYLabel[0] on templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] on templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] at templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale in templateXValue[max] .

generated_template: The templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] templatePositiveTrend gradually between templateXValue[min] and templateXValue[max] . The templateYLabel[0] templateYLabel[1] of the Icelandic was templateYValue[idxmax(X)] templateYLabel[2] old as of templateXValue[idxmax(Y)] . The largest Icelandic templateYLabel[1] group that templateXLabel[0] , however , was 20 to 39 templateXLabel[0] olds .
generated: The Return equity of the ordinary in HSBC increased gradually between 2009 and 2019 .  The Return equity of the Icelandic was 3.6 equity old as of 2011 .  The largest Icelandic equity group that Year , however was 20 to 39 Year olds .

Example 428:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Facebook usage reach in the United States 2019 , by education
X_Axis['Response']: ['High_school_or_less', 'Some_college', 'College_+']
Y_Axis['Share', 'of', 'respondents']: ['61', '75', '74']

gold: This statistic shows the share of adults in the United States who were using Facebook as of February 2019 , sorted by educational background . During that period of time , 74 percent of respondents who had attained a college degree 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] educational background . During that period of time , templateYValue[last] templateScale of templateYLabel[1] who had attained a templateXValue[1] degree used the social networking site .

generated_template: This statistic shows the templateYLabel[0] of internet users in the templateTitleSubject[1] who were using templateTitleSubject[0] as of 2019 , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] templateScale of templateYLabel[1] who had attained a templateXValue[1] degree used the photo sharing app .
generated: This statistic shows the Share of internet users in the United States who were using United States as of 2019 , sorted by education .  During that period of time , 75 % of respondents who had attained a Some_college degree used the photo sharing app .

Example 429:
titleEntities: {'Subject': ['Cincinnati Reds'], 'Date': ['2019']}
title: Cincinnati Reds all-time home run leaders 2019
X_Axis['Player']: ['Johnny_Bench', 'Frank_Robinson', 'Tony_Perez', 'Joey_Votto', 'Adam_Dunn', 'Ted_Kluszewski', 'George_Foster', 'Jay_Bruce', 'Ken_Griffey_Jr.', 'Eric_Davis']
Y_Axis['Number', 'of', 'home', 'runs']: ['389', '324', '287', '284', '270', '251', '244', '233', '210', '203']

gold: This statistic shows the Cincinnati Reds all-time home run leaders as of October 15 , 2019 . Johnny Bench has hit the most home runs in Cincinnati Reds franchise history with 389 home runs .
gold_template: This statistic shows the templateTitleSubject[0] all-time templateYLabel[1] templateTitle[4] templateTitle[5] as of October 15 , 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_template: This statistic shows the templateTitle[0] templateTitleSubject[0] all-time templateYLabel[1] templateTitle[5] templateTitle[6] as of October 14 , templateTitleDate[0] . templateXValue[0] has hit the most templateYLabel[1] templateYLabel[2] in templateTitle[0] templateTitleSubject[0] franchise history with templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Cincinnati Cincinnati Reds all-time home leaders 2019 as of October 14 , 2019 .  Johnny_Bench has hit the most home runs in Cincinnati Cincinnati Reds franchise history with 389 home runs .

Example 430:
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: In 2019 , templateTitleSubject[0] templateTitle[1] templateTitleSubject[1] templateYLabel[0] templateYLabel[1] stood at templateYValue[0] templateScale . Year-to-date , some 948,000 units were sold to templateTitleSubject[1] customers by the templateTitleSubject[0] Motor Company , which is counted among the Detroit Big Three automakers . The United Kingdom and Germany were among templateTitleSubject[0] templateTitle[1] four most important sales markets in 2018 .
generated: In 2019 , Volkswagen 's EU Market share stood at 10.7 % .  Year-to-date , some 948,000 units were sold to EU customers by the Volkswagen Motor Company , which is counted among the Detroit Big Three automakers .  The United Kingdom and Germany were among Volkswagen 's four most important sales markets in 2018 .

Example 431:
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 shows the templateYLabel[0] of templateXValue[0] in the templateTitle[1] templateYLabel[3] were templateYLabel[5] using templateTitle[5] templateTitle[6] templateTitle[7] . During the 2019 survey , templateYValue[1] templateScale of responding templateXValue[0] stated that they had templateYLabel[5] accessed templateXValue[1] , either via desktop or mobile internet connection .
generated: This statistic shows the Net of Department_stores in the sales share were using worldwide 2017 by .  During the 2019 survey , 14 % of responding Department_stores stated that they had share accessed Travel_retail , either via desktop or mobile internet connection .

Example 432:
titleEntities: {'Subject': ['WhatsApp'], 'Date': ['2012', '2014']}
title: WhatsApp : annual revenue 2012 to 2014
X_Axis['Year']: ['2012', '2013', 'H1_2014']
Y_Axis['Annual', 'revenue', 'in', '1,000', 'U.S.', 'dollars']: ['3.82', '10.21', '15.29']

gold: This statistic presents the annual revenue of WhatsApp from 2012 to the first half of 2014 . In the most recently reported period , the chat app 's revenue amounted to 15.29 million U.S. dollars , up from 10.21 million U.S. dollars for the preceding fiscal year .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[0] to the first half of templateXValue[last] . In the most recently reported period , the chat app 's templateYLabel[1] amounted to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] for the preceding fiscal templateXLabel[0] .

generated_template: This statistic displays the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateXValue[min] , templateXValue[1] and templateXValue[max] . According to the source , approximately 240 thousand templateYLabel[1] are templateTitle[0] to have templateTitle[4] by templateXValue[idxmax(Y)] in templateTitleSubject[0] .
generated: This statistic displays the WhatsApp Annual of revenue 2012 2014 in WhatsApp 2012 , 2013 and .  According to the source , approximately 240 thousand revenue are WhatsApp to have 2014 by H1_2014 in WhatsApp .

Example 433:
titleEntities: {'Subject': ['Grand Slam'], 'Date': []}
title: Male tennis players by number of Grand Slam tournament titles up to 2020
X_Axis['Month']: ['Roger_Federer_(SUI)', 'Rafael_Nadal_(ESP)', 'Novak_Djokovic_(SRB)', 'Pete_Sampras_(USA)', 'Roy_Emerson_(AUS)', 'Björn_Borg_(SWE)', 'Rodney_Laver_(AUS)', 'William_Tilden_(USA)', 'Frederick_Perry_(ENG)', 'Andre_Agassi_(USA)', 'James_Connors_(USA)', 'Kenneth_Rosewall_(AUS)', 'Ivan_Lendl_(CZE/USA)', 'Henri_Cochet_(FRA)', 'Max_Décugis_(FRA)']
Y_Axis['Number', 'of', 'Grand', 'Slam', 'victories']: ['20', '19', '17', '14', '12', '11', '11', '10', '8', '8', '8', '8', '8', '8', '8']

gold: The statistic depicts the tennis players with the most Grand Slam tournament titles . Roger Federer has won the most Grand Slam titles , most recently the Australian Open in 2018 , with a total of 20 . He has also consistently ranked amongst the top tennis players in the ATP Rankings throughout his career .
gold_template: The statistic depicts the templateTitle[1] templateTitle[2] with the most templateYLabel[1] templateYLabel[2] templateTitle[7] templateTitle[8] . templateXValue[0] has won the most templateYLabel[1] templateYLabel[2] templateTitle[8] , most recently the Australian Open in 2018 , with a total of templateYValue[max] . He has also consistently ranked amongst the top templateTitle[1] templateTitle[2] in the ATP Rankings throughout his career .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[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: This statistic presents the Number Grand Slam players on Grand Slam in the tennis as of the third quarter of , titles up .  According to the findings , the Rafael_Nadal_(ESP) industry had an Number Grand Slam of 19 % to communicating back to their consumers on Grand Slam , while the Novak_Djokovic_(SRB) industry reported in 17 % .

Example 434:
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 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: 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 .

Example 435:
titleEntities: {'Subject': ['San Francisco Giants'], 'Date': ['2009', '2019']}
title: Regular season average attendance of the San Francisco Giants 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Average', 'attendance']: ['33429', '38965', '40785', '41546', '41677', '41588', '41584', '41695', '41818', '37499', '35322']

gold: This graph depicts the average regular season home attendance of the San Francisco Giants from 2009 to 2019 . In 2019 , the average regular season home attendance of the San Francisco Giants was 33,429 . • San Francisco Giants 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] Giants from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateTitle[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] Giants was templateYValue[min] templateYValue[idxmax(X)] • templateTitleSubject[0] Giants 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[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateTitle[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[1] was templateYValue[idxmax(X)] . • templateTitleSubject[0] templateTitleSubject[1] total home templateYLabel[1] • Major League Baseball templateYLabel[0] per game templateYLabel[1] • Major League Baseball total templateYLabel[1]
generated: This graph depicts the Average Regular season home attendance of the San Francisco Giants from 2009 to 2019 .  In 2019 , the Average Regular season home attendance of the San Francisco Giants was 33429 .  • San Francisco Giants San Francisco Giants total home attendance • Major League Baseball Average per game attendance • Major League Baseball total attendance . 

Example 436:
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 templateYLabel[0] of templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , there were a total of templateYValue[idxmax(X)] templateYLabel[1] reported in the templateTitleSubject[0] .
generated: This statistic shows the Number of Luxembourg inhabitants in the Luxembourg from 2019 to .  In 2019 , there were a total of 32592 inhabitants reported in the Luxembourg .

Example 437:
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: The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] one-kilogram templateTitle[4] templateTitle[5] templateTitle[6] was templateValue[5][max] Canadian dollars in 2019 in templateTitleSubject[0] . This templateTitle[2] is an all-time high templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . templateTitle[4] templateTitle[5] templateTitle[6] is a relatively expensive option when compared to other cuts of beef , such as ground beef , which retailed at a templateTitle[2] of 11.3 Canadian dollars per kilogram in templateValue[0][7] templateTitleDate[max] .
generated: The Average retail price for one-kilogram regular unleaded gasoline was 130.7 Canadian dollars in 2019 Canada .  This price is an all-time high for regular unleaded gasoline .  regular unleaded gasoline is a relatively expensive option when compared to other cuts of beef , such as ground beef , which retailed at a price of 11.3 Canadian dollars per kilogram in May 2019 .

Example 438:
titleEntities: {'Subject': ['Share'], 'Date': ['2018']}
title: Share of internet users who watch online videos 2018 , by country
X_Axis['Country']: ['Saudi_Arabia', 'Turkey', 'China', 'New_Zealand', 'Mexico', 'Australia', 'Philippines', 'Spain', 'India', 'Brazil', 'United_States', 'Canada', 'France', 'Indonesia', 'Germany', 'South_Korea', 'Japan', 'Nigeria', 'South_Africa']
Y_Axis['Share', 'of', 'internet', 'users']: ['95', '95', '92', '91', '88', '88', '87', '86', '85', '85', '85', '83', '81', '80', '76', '71', '69', '65', '64']

gold: This statistic gives information on the share of internet users who watch online videos as of January 2018 , by country . During the survey , it was found that 85 percent of U.S. internet users watched online video content on any device . Saudi Arabia was ranked first with a 95 percent online video penetration among the online population .
gold_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] as of 2018 , templateTitle[8] templateXLabel[0] . During the survey , it was found that templateYValue[8] templateScale of U.S. templateYLabel[1] templateYLabel[2] watched templateTitle[5] video content on any device . templateXValue[0] was ranked first with a templateYValue[max] templateScale templateTitle[5] video penetration among the templateTitle[5] population .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in selected countries in templateTitleDate[0] . During the reported period , templateXValue[0] had almost templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] . The templateXValue[1] was ranked second , as templateYValue[1] templateScale Indians accessed the templateYLabel[1] via computers or mobile devices .
generated: This statistic gives information on the Share of internet users in selected countries in 2018 .  During the reported period , Saudi_Arabia had almost 95 million internet users .  The Turkey was ranked second , as 95 % Indians accessed the internet via computers or mobile devices .

Example 439:
titleEntities: {'Subject': ['Peru'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Peru 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']: ['298.84', '282.82', '267.69', '253.44', '240.18', '228.99', '225.37', '214.19', '194.39', '191.32', '202.31', '202.12', '192.93', '170.84', '148.82', '121.36', '121.78', '102.19', '87.89', '74.97', '66.71', '59.05', '54.45', '51.61', '51.01', '49.38', '54.6', '56.91', '54.09', '52.14', '43.79', '34.33', '35.38', '33.99', '28.33', '40.7', '32.98', '41.68', '25.24', '16.82', '19.44']

gold: The statistic shows gross domestic product ( GDP ) in Peru 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 Peru 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 440:
titleEntities: {'Subject': ['Manchester City'], 'Date': ['2009']}
title: Value of Manchester City 's jersey sponsorship 2009 to 2020
X_Axis['Year']: ['2019/20_(Etihad)', '2018/19_(Etihad)', '2017/18_(Etihad)', '2016/17_(Etihad)', '2015/16_(Etihad)', '2014/15_(Etihad)', '2013/14_(Etihad)', '2012/13_(Etihad)', '2011/12_(Etihad)', '2010/11_(Etihad)', '2009/10_(Etihad)']
Y_Axis['Jersey', 'sponsorship', 'revenue', 'in', 'million', 'GBP']: ['45.0', '45.0', '35.0', '20.0', '20.0', '20.0', '20.0', '20.0', '20.0', '7.5', '7.34']

gold: The statistic shows the revenue Manchester City generated from its jersey sponsorship deal from the 2009/10 season to the 2019/20 season . In the 2019/20 season , Manchester City received 45 million GBP from its jersey sponsor Etihad .
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 Etihad .

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 Standard Chartered .
generated: The statistic shows the revenue Manchester City generated from its Jersey sponsorship deal from the 2009/10_(Etihad) season to the 2019/20_(Etihad) season .  In the 2019/20_(Etihad) season , Manchester City received 45.0 million GBP from its Jersey sponsor Standard Chartered .

Example 441:
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 represents the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[4] in templateXValue[min] and templateXValue[max] . According to the calculations , the templateTitleSubject[0] templateTitle[1] templateTitle[2] is expected to grow from templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] to nearly templateYValue[max] templateScale in templateXValue[idxmax(Y)] .
generated: This statistic represents the Revenue of the Birkenstock revenue 2013 in 2012 and 2013 .  According to the calculations , the Birkenstock revenue is expected to grow from 14109.4 million euros in 2012 to nearly 14525.69 million in 2013 .

Example 442:
titleEntities: {'Subject': ['South Africa'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in South Africa 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']: ['434.42', '416.43', '399.7', '383.9', '369.85', '358.84', '368.14', '349.43', '296.27', '317.58', '350.9', '366.82', '396.33', '416.88', '375.3', '297.22', '287.1', '299.03', '271.81', '257.67', '228.93', '175.25', '115.75', '121.6', '136.45', '136.55', '137.69', '152.61', '147.7', '155.46', '139.8', '134.35', '134.56', '123.96', '115.53', '99.04', '95.21', '88.57', '67.56', '59.1', '77.32']

gold: This statistic shows the gross domestic product ( GDP ) in South Africa 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: templateTitle[0] templateTitle[1] templateTitle[2] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered an important indicator of the economic strength of a country . In templateXValue[6] , templateYLabel[0] in templateTitleSubject[0] amounted to around templateYValue[6] templateScale templateYLabel[2] templateYLabel[3] .
generated: Gross domestic product is the total value of all goods and services produced in a country in a Year .  It is considered an important indicator of the economic strength of a country .  In 2018 , GDP in South Africa amounted to around 368.14 billion U.S. dollars .

Example 443:
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: The statistic shows the templateTitle[2] templateYLabel[0] of templateTitleSubject[0] in the period from the first templateXLabel[0] of templateTitleDate[min] to the first templateXLabel[0] of templateTitleDate[max] . In the most recently reported templateXLabel[0] , templateTitleSubject[0] generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateScale a in the previous templateXLabel[0] .
generated: The statistic shows the revenue of Twitter in the period from the first Quarter of 2011 to the first Quarter of 2019 .  In the most recently reported Quarter , Twitter generated 1007.43 million U.S. dollars , up from 823.72 million a in the previous Quarter .

Example 444:
titleEntities: {'Subject': ['South Africa'], 'Date': ['2019']}
title: Unemployment rate in South Africa 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']: ['27.32', '26.96', '27.33', '26.55', '25.16', '24.9', '24.57', '24.73', '24.65', '24.69', '23.54', '22.43', '26.67', '28.49', '29.25', '29.58', '32.46', '33.47', '30.9', '30.23', '30.2']

gold: This statistic shows the unemployment rate in South Africa from 1999 to 2019 . In 2019 , the unemployment rate in South Africa was around 27.32 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 around templateYValue[0] templateYValue[idxmax(X)] .

generated_template: In templateXValue[max] , templateTitleSubject[0] 's estimated templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[0] templateYValue[idxmax(X)] . This templatePositiveTrend is up .03 templateScale from the templateXLabel[0] before . The templateYLabel[0] templateYLabel[1] is defined as the templateScale of unemployed workers in the total labor force .
generated: In 2019 , South Africa 's estimated Unemployment rate amounted to approximately 27.32 .  This increase is up .03 % from the Year before .  The Unemployment rate is defined as the percentage of unemployed workers in the total labor force .

Example 445:
titleEntities: {'Subject': ['PwC'], 'Date': ['2013', '2019']}
title: Number of PwC employees worldwide by region 2013 to 2019
Data:
                                     0      1      2      3      4      5      6
Year                              2019   2018   2017   2016   2015   2014   2013
Americas                         73801  71222  70982  70883  66517  62803  59103
Asia                             73856  68963  60255  53010  47090  43370  38863
Australasia and Pacific Islands  10444   8966   8308   7639   7339   6967   6760
Central and Eastern Europe       13695  11538  10451   9273   8432   7746   7490
Middle East and Africa           16145  15090  13974  13036  12861  12486  11578
Western Europe                   88064  75151  72265  69627  65870  62061  60441 

gold: This statistic shows the number of PricewaterhouseCoopers employees worldwide from 2013 to 2019 , by region . In the fiscal year of 2019 , the company employed 88,064 people in Western Europe . PricewaterhouseCoopers ( PwC ) PricewaterhouseCoopers ( PwC ) is a professional services company , which has its headquarters in London , United Kingdom .
gold_template: This statistic shows the templateTitle[0] of PricewaterhouseCoopers templateTitle[2] templateTitle[3] from templateValue[0][last] to templateValue[0][0] , templateTitle[4] templateTitle[5] . In the fiscal templateLabel[0][0] of templateValue[0][0] , the company employed templateValue[6][0] people in templateLabel[6][0] templateLabel[4][2] . PricewaterhouseCoopers ( templateTitleSubject[0] ) PricewaterhouseCoopers ( templateTitleSubject[0] ) is a professional services company , which has its headquarters in London , United Kingdom .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] in templateTitleSubject[1] from templateValue[0][0] to templateValue[0][last] , templateTitle[8] templateTitle[9] templateTitle[10] . In templateValue[0][last] , the templateTitleSubject[0] Corporation generated templateValue[1][last] templateScale of its total templateTitle[0] templateTitle[5] its templateLabel[1][0] templateLabel[1][1] templateTitle[9] .
generated: This statistic shows the Number PwC of in PwC from 2019 to 2013 , 2019 .  In 2013 , the PwC Corporation generated 59103 % of its total Number region its Americas 2019 .

Example 446:
titleEntities: {'Subject': ['Europe'], 'Date': ['2018']}
title: Registration of buses and coaches in Europe by country 2018
X_Axis['Country']: ['United_Kingdom', 'Germany', 'France', 'Italy', 'Spain', 'Austria', 'Romania', 'Poland', 'Czechia', 'Norway', 'Belgium', 'Sweden', 'Lithuania', 'Hungary', 'Switzerland', 'Ireland', 'Denmark', 'Portugal', 'Finland', 'Netherlands', 'Slovakia', 'Croatia', 'Luxembourg', 'Slovenia', 'Greece', 'Estonia', 'Latvia', 'Iceland']
Y_Axis['Registrations', 'in', 'units']: ['7236', '6669', '6230', '4348', '3327', '1060', '1029', '1026', '1009', '1001', '989', '872', '696', '662', '614', '547', '503', '500', '497', '429', '302', '242', '221', '187', '183', '142', '108', '65']

gold: More than 7,000 buses and coaches with a weight of over 3.5 metric tons were registered the United Kingdom in 2018 . The UK accounted for the largest share of medium and heavy bus and coach registrations in the European Union in 2018 . Great Britain and Northern Ireland officially withdrew from the EU on January 31 , 2020
gold_template: More than 7,000 templateTitle[1] and templateTitle[2] with a weight of over 3.5 metric tons were registered the templateXValue[0] in templateTitleDate[0] . The UK accounted for the largest share of medium and heavy bus and coach templateYLabel[0] in the European Union in templateTitleDate[0] . Great Britain and Northern templateXValue[15] officially withdrew from the EU on 31 , 2020

generated_template: This statistic shows the proportion of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] only ( excludes templateYLabel[1] templateYLabel[2] templateYLabel[3] both templateYLabel[4] templateYLabel[5] and eyeglasses ) in templateTitle[6] templateTitleSubject[0] templateTitle[8] in templateTitleDate[0] . In this year , templateXValue[0] , templateXValue[1] and templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] wearing templateYLabel[4] templateYLabel[5] with approximately templateYValue[max] templateScale doing so . This was followed by templateXValue[3] and templateXValue[4] with templateYValue[3] templateScale of the respective populations wearing templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the proportion of units only ( excludes units both and eyeglasses ) in 2018 Europe in 2018 .  In this year , United_Kingdom Germany and United_Kingdom had the highest Registrations of units wearing with approximately 7236 million doing so .  This was followed by Italy and Spain with 4348 % of the respective populations wearing units .

Example 447:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Countries with highest direct investment position received from the U.S. 2018
X_Axis['Country']: ['Netherlands', 'United_Kingdom', 'Luxembourg', 'Ireland', 'Canada', 'Switzerland', 'Bermuda', 'Singapore', 'Australia', 'Germany', 'Japan', 'China', 'Mexico', 'France', 'Hong_Kong', 'Brazil', 'Belgium', 'India', 'Republic_of_Korea', 'Sweden']
Y_Axis['Direct', 'investments', 'in', 'billion', 'U.S.', 'dollars']: ['866.33', '757.78', '713.83', '442.17', '401.87', '278.04', '249.39', '218.84', '162.98', '140.33', '125.49', '116.52', '114.88', '86.86', '82.55', '70.88', '64.05', '45.98', '41.53', '39.31']

gold: The Netherlands received the most direct investment from the United States in 2018 , an amount exceeding 866 billion U.S. dollars . This measurement was based on a historical-cost basis , meaning that the original cost of investment has been adjusted for inflation . What is foreign direct investment ? Foreign direct investment ( FDI ) is the amount that foreign speculators invests in firms in another country .
gold_template: The templateXValue[0] templateTitle[6] the most templateYLabel[0] templateTitle[4] templateTitle[7] the templateXValue[1] States in templateTitleDate[0] , an amount exceeding templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . This measurement was based on a historical-cost basis , meaning that the original cost of templateTitle[4] has been adjusted for inflation . What is foreign templateYLabel[0] templateTitle[4] ? Foreign templateYLabel[0] templateTitle[4] ( FDI ) is the amount that foreign speculators invests in firms in another templateXLabel[0] .

generated_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: This statistic shows the U.S. highest direct in the investment as rated according to the Direct investments for 2018 .  In 2018 , the U.S. highest Country in the investment was considered to be Netherlands with a Direct investments billion of 866.33 .  Quality of life around the investment economic indicator , Direct investments was created by Arthur Okun .

Example 448:
titleEntities: {'Subject': ['U.S. SNAP Food Stamps'], 'Date': ['2011']}
title: Percentage of U.S. state population receiving SNAP benefits ( Food Stamps ) 2011
X_Axis['State']: ['District_of_Columbia', 'Mississippi', 'Tennessee', 'New_Mexico', 'Michigan', 'Oregon', 'Louisiana', 'Maine', 'Alabama', 'Kentucky', 'West_Virginia', 'Georgia', 'South_Carolina', 'Arkansas', 'Florida', 'North_Carolina', 'Missouri', 'Arizona', 'Oklahoma', 'Washington', 'Texas', 'Vermont', 'New_York', 'Rhode_Island', 'Delaware', 'Ohio', 'Wisconsin', 'Idaho', 'Illinois', 'Pennsylvania', 'Indiana', 'Nevada', 'Montana', 'South_Dakota', 'Iowa', 'Alaska', 'Hawaii', 'Massachusetts', 'Connecticut', 'Virginia', 'Maryland', 'Kansas', 'California', 'Utah', 'North_Dakota', 'New_Hampshire', 'Colorado', 'New_Jersey', 'Minnesota', 'Nebraska', 'Wyoming']
Y_Axis['Number', 'of', 'households', 'in', 'millions']: ['22', '21', '20', '20', '20', '20', '19', '19', '19', '19', '19', '18', '18', '17', '16', '16', '16', '16', '16', '15', '15', '15', '15', '15', '15', '15', '14', '14', '14', '13', '13', '12', '12', '12', '12', '12', '12', '12', '11', '11', '11', '10', '10', '10', '9', '9', '9', '9', '9', '9', '6']

gold: The statistic shows the percentage of U.S. population receiving benefits through the Supplemental Nutrition Assistance Program ( SNAP , formerly called Food Stamps ) in 2011 , by state . About 20 percent of the population in Oregon is receiving benefits through the Supplemental Nutrition Assistance Program .
gold_template: The statistic shows the templateScale of templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program ( templateTitleSubject[0] , formerly called templateTitleSubject[0] ) in templateTitleDate[0] , by templateXLabel[0] . About templateYValue[2] templateScale of the templateTitle[3] in templateXValue[5] is templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program .

generated_template: This statistic shows the templateScale of templateYLabel[1] in the templateXValue[26] of America as of templateTitleDate[0] , in the last 30 days templateTitle[5] templateXLabel[0] . As of that year , templateYValue[22] templateScale of templateTitle[4] in templateXValue[22] consumed more than 4 ( women ) or 5 ( men ) alcoholic beverages on a single occasion within the preceding 30 days .
generated: This statistic shows the millions of households in the Wisconsin of America as of 2011 , in the last 30 days SNAP State .  As of that year , 15 millions of receiving in New_York consumed more than 4 ( women ) or 5 ( men ) alcoholic beverages on a single occasion within the preceding 30 days .

Example 449:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2010', '2017']}
title: Average biking distance per person per day in the Netherlands 2010 - 2017 , by gender
Data:
          0     1     2     3     4     5     6     7
Year   2017  2016  2015  2014  2013  2012  2011  2010
Men    2.56  2.63  2.72  3.05  2.68  2.69  2.79  2.52
Women  2.31  2.31  2.38  2.53  2.27  2.35  2.34  2.22 

gold: In 2017 , the average biking distance per person per day amounted to approximately 2.5 and 2.3 kilometers for men and women respectively . In terms of the average biking distance by age , children between 12 to 18 years old cycled 5.6 kilometers , the longest distance in 2017 . Furthermore , people aged 25 to 35 years had the lowest average kilometers with roughly two thousand meters a day .
gold_template: In templateValue[0][0] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[3] templateTitle[6] amounted to approximately templateValue[1][last] and templateValue[2][0] kilometers for templateLabel[1][0] and templateLabel[2][0] respectively . In terms of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[10] age , children between 12 to 18 years old cycled 5.6 kilometers , the longest templateTitle[2] in templateValue[0][0] . Furthermore , people aged 25 to 35 years had the lowest templateTitle[0] kilometers with roughly templateValue[0][0] thousand meters a templateTitle[6] .

generated_template: This statistic shows the average templateTitle[3] templateTitle[0] templateTitle[1] templateTitle[5] of individuals in the United Kingdom from templateValue[0][0] to templateValue[0][last] , templateTitle[10] templateTitle[11] . templateLabel[2][0] consumed templateValue[1][2] hours of templateTitle[0] templateTitle[3] and templateLabel[1][0] consumed templateValue[1][last] hours of templateTitle[0] templateTitle[3] in the United Kingdom in templateValue[0][last] .
generated: This statistic shows the average per biking of individuals in the United Kingdom from 2017 to 2010 , by gender .  Women consumed 2.72 hours of Average per and Men consumed 2.52 hours of Average per in the United Kingdom in 2010 .

Example 450:
titleEntities: {'Subject': ['Singapore'], 'Date': ['2017', '2023']}
title: Singapore : 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']: ['4.9', '4.9', '4.8', '4.7', '4.6', '4.5', '4.3']

gold: The statistic shows the number of mobile internet users in Singapore from 2017 to 2023 . In 2017 , 4.3 million people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 4.9 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[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 Singapore from 2017 to 2023 .  In 2017 , 4.3 millions people accessed the internet through their mobile phone .  In 2023 , this figure is projected to amount 4.9 millions mobile phone internet users .

Example 451:
titleEntities: {'Subject': ['National Hockey League Toronto Maple Leafs'], 'Date': ['2005', '2019']}
title: National Hockey League - Toronto Maple Leafs 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     790316   786677   809519   785485   781576   797310   466229   799786   793522   789681   791795   796803   798981   795747
Average attendance    19276    19187    19744    19158    19062    19446    19426    19506    19354    19260    19312    19434    19487    19408 

gold: This graph depicts the total/average regular season home attendance of the Toronto Maple Leafs 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 790,316 .
gold_template: This graph depicts the total/average regular season templateTitle[6] templateTitle[7] of the templateTitleSubject[0] Leafs 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 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: This graph depicts the total/average regular season Leafs home of the Toronto Maple franchise of the National Hockey League Toronto Maple Leafs League from the 2005/06 season to the 2018/19 season .  In 2018/19 , the Total regular season Leafs home of the franchise was 790316 .

Example 452:
titleEntities: {'Subject': ['South Africa'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in South Africa 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['1.78', '1.78', '1.76', '1.44', '1.08', '0.66', '0.79', '1.42', '0.4', '1.19', '1.85']

gold: The statistic depicts South Africa 's real 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 real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateTitle[5] 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] in real templateYLabel[0] in templateTitleSubject[0] from between 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 South Africa from between 2014 to 2018 , with projections up until 2024 .  In 2018 , South Africa 's real Gross domestic product increased by around 0.79 % compared to the previous Year .

Example 453:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2014', '2019']}
title: Average mortgage interest rates in the United Kingdom ( UK ) 2014 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
Month                        Mar_'14  Jun_'14  Sep_'14  Dec_'14  Mar_'15  Jun_'15  Sep_'15  Dec_'15  Mar_'16  Jun_'16  Sep_'16  Dec_'16  Mar_'17  Jun_'17  Sep_'17  Dec_'17  Mar_'18  Jun_'18  Sep_'18  Dec_'18  Mar_'19  Jun_'19  Sep_'19  Dec_'19
2 year fixed rate mortgages     2.37      2.6     2.46     2.08     1.99     1.83     1.91      1.9      1.9     1.75     1.59     1.45     1.37     1.48      1.4     1.57     1.53     1.74     1.71     1.73      1.7     1.64     1.55     1.45
3 year fixed mortgage           2.97     3.01     2.94     2.89     2.63     2.54      2.4     2.26     2.14     2.13     1.92     1.79     1.68      1.7      1.6      1.7     1.77      1.8     1.81     1.81      1.8     1.77     1.66     1.56
5 year fixed mortgage           3.48     3.69     3.61     3.21     2.98     2.82     2.84     2.75      2.7     2.54     2.34     2.26     2.15     1.99      1.9     2.07     2.02     2.03     2.01     2.01     2.03     1.97     1.79     1.69
10 year fixed                      0        0     4.06        4     3.47     3.24     3.35     3.36     3.19      3.1     2.84     2.98      2.8     2.82     2.72     2.66     2.76     2.74     2.68     2.64     2.56     2.59     2.58     2.53
2 year variable                 2.68     2.71     2.44      1.6     1.56     1.66     1.74     1.78     1.67     1.62     1.54     1.48     1.42     1.42     1.46     1.69     1.58     1.56     1.68      1.7     1.61      1.6     2.03     1.94 

gold: As of December 2020 , 10-year fixed mortgage rates were at their lowest since recording began by the Bank of England at 2.53 percent . This is particularly good news for first-time home buyers and those remortgaging their property . Across the fixed date terms , all mortgage rates fell between September and December 2019 .
gold_template: As of 2020 , 10-year templateLabel[1][2] templateTitle[1] templateTitle[3] were at their lowest since recording began by the Bank of England at templateValue[4][last] templateScale . This is particularly good news for first-time home buyers and those remortgaging their property . Across the templateLabel[1][2] date terms , all templateTitle[1] templateTitle[3] templateNegativeTrend between and 2019 .

generated_template: The templateTitle[0] of templateLabel[1][0] templateTitle[1] outnumber templateLabel[2][0] templateTitle[1] in the templateTitleSubject[0] in most specialties . The only major exceptions are found in templateValue[0][30] , templateValue[0][4] and templateValue[0][4] , templateValue[0][23] and templateValue[0][23] , although templateLabel[2][0] templateTitle[1] do slightly outnumber males in a few other specialties . As of templateTitleDate[0] , there were around 68,000 templateLabel[1][0] templateValue[0][9] practice templateTitle[1] in the templateTitleSubject[0] .
generated: The Average of 2 mortgage outnumber 3 mortgage in the United Kingdom in most specialties .  The only major exceptions are found in Dec_'19 , Mar_'15 and , Dec_'19 and , although 3 mortgage do slightly outnumber males in a few other specialties .  As of 2014 , there were around 68,000 2 Jun_'16 practice mortgage in the United Kingdom .

Example 454:
titleEntities: {'Subject': ['Serbia'], 'Date': ['2024']}
title: Inflation rate in Serbia 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']: ['3', '2.98', '2.78', '2.48', '1.88', '2.17', '1.96', '3.13', '1.12', '1.39', '2.08', '7.69', '7.33', '11.14', '6.14', '8.12', '12.41', '6', '10.73', '16.25', '10.6']

gold: This statistic shows the average inflation rate in Serbia from 2004 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Serbia amounted to about 1.96 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: In templateXValue[7] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] , when it was just recovering from a slump below the 0-percent-mark in templateXValue[9] . Political turmoil begets economic turmoil In templateXValue[10] , after a coup d'etat following months of political crisis , the Thai military took over the country , and the senate and government were dissolved . As a result , templateTitleSubject[0] 's economy experienced a sudden downturn , GDP growth and templateYLabel[0] slumped , while unemployment , which is usually delayed in reflecting economic struggles , has been templatePositiveTrend ever since .
generated: In 2017 , the average Inflation rate in Serbia amounted to about 1.96 percent compared to the previous Year , when it was just recovering from a slump below the 0-percent-mark in 2015 .  Political turmoil begets economic turmoil In 2014 , after a coup d'etat following months of political crisis , the Thai military took over the country , and the senate and government were dissolved .  As a result , Serbia 's economy experienced a sudden downturn , GDP growth and Inflation slumped , while unemployment , which is usually delayed in reflecting economic struggles , has been rising ever since .

Example 455:
titleEntities: {'Subject': ['Reasons'], 'Date': ['2014']}
title: Reasons for quitting health club membership 2014
X_Axis['Response']: ['It_was_too_expensive_/_I_could_not_afford_it', "I_wasn't_using_my_membership_/_wasn't_going", 'I_moved_/_the_location_was_no_longer_convenient', 'I_could_exercise_somewhere_else_for_free', 'It_was_too_crowded', 'I_developed_an_injury_/_surgery_/_other_condition', 'I_felt_out_of_place', 'I_lost_my_job', 'I_did_not_like_to_exercise', "I_didn't_reach_my_fitness_goals", 'It_was_too_intimidating', 'There_was_no_one_there_to_guide_me', "I_didn't_know_what_to_do_there", 'I_wanted_to_participate_in_another_type_of_exercise', 'I_met_my_fitness_goals', 'Nobody_I_knew_went_to_my_former_health_club', 'Other']
Y_Axis['Percentage', 'of', 'respondents']: ['40', '30', '20', '19', '17', '11', '9', '8', '7', '5', '4', '4', '4', '3', '2', '2', '1']

gold: The statistic shows the results of a survey concerned with the reasons why people quit their health club membership . 40 percent of respondents said that the membership was too expensive and they could not afford it .
gold_template: The statistic shows the results of a survey concerned with the templateTitleSubject[0] why people quit their templateXValue[15] membership . templateYValue[max] templateScale of templateYLabel[1] said that the templateXValue[1] templateXValue[0] expensive and they templateXValue[0] afford it .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] the templateTitle[5] as a result of the templateTitle[7] .
generated: The statistic shows the Reasons for quitting health club membership 2014 in .  The survey revealed that 40 percentage of the respondents for It_was_too_expensive_/_I_could_not_afford_it the membership as a result of the 2014 .

Example 456:
titleEntities: {'Subject': ['Christian'], 'Date': ['2010']}
title: Largest U.S. Christian denominations 2010 , by number of adherents
X_Axis['Christian', 'grouping']: ['Catholic', 'Evangelical_and_Conservative_Protestant', 'Mainline_Protestant', 'Latter-day_Saints', 'Black_Protestant', 'Orthodox_Christian', 'Other_Faiths', 'Total']
Y_Axis['Number', 'of', 'followers']: ['58928987', '50013803', '22655826', '6267771', '4877067', '1056535', '6667542', '150686156']

gold: This graph shows the largest Christian denominations in the United States in 2010 , by number of adherents . In 2010 , the Lattar-day Saints were among the largest Christian groups with about 6.3 million adherents in the United States .
gold_template: This graph shows the templateTitle[0] templateXValue[5] templateTitle[3] in the templateTitle[1] in templateTitleDate[0] , templateTitle[5] templateYLabel[0] of templateTitle[7] . In templateTitleDate[0] , the Lattar-day templateXValue[3] were among the templateTitle[0] templateXValue[5] groups with about templateYValue[3] templateScale templateTitle[7] in the templateTitle[1] .

generated_template: This statistic shows the age-standardized templateTitleSubject[0] templateTitle[6] where the most tomatoes for templateTitle[1] templateTitle[2] were produced in templateTitleDate[0] . In templateXValue[1] , around templateYValue[max] templateScale templateYLabel[2] of tomatoes for templateTitle[1] templateTitle[2] were produced that year .
generated: This statistic shows the age-standardized Christian number where the most tomatoes for U.S. Christian were produced in 2010 .  In Evangelical_and_Conservative_Protestant , around 150686156 % followers of tomatoes for U.S. Christian were produced that year .

Example 457:
titleEntities: {'Subject': ['Autonomous'], 'Date': ['2015', '2030']}
title: Autonomous driving sensor components - global market size 2015 to 2030
X_Axis['Year']: ['2030', '2025', '2020', '2015']
Y_Axis['Market', 'size', 'in', 'billion', 'U.S.', 'dollars']: ['25.0', '15.0', '4.0', '0.4']

gold: This statistic represents the projected size of the global market for autonomous driving sensor components from 2015 to 2030 . In 2025 , the market for autonomous driving sensor components is projected to be sized at 15 billion U.S. dollars in an optimistic scenario .
gold_template: This statistic represents the projected templateYLabel[1] of the templateTitle[4] templateYLabel[0] for templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateYLabel[0] for templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] is projected to be sized at templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in an optimistic scenario .

generated_template: The statistic shows the templateYLabel[1] of the Disaster Recovery as a Service ( templateTitleSubject[0] ) templateYLabel[0] templateTitle[2] , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateTitleSubject[0] templateYLabel[0] was predicted to reach templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] in templateYLabel[1] . Additional information - Disaster Recovery as a Service ( templateTitleSubject[0] ) Within the field of information technology , disaster recovery is the process of replicating data on servers , either physical or virtual , as a precaution against man-made or natural disasters .
generated: The statistic shows the size of the Disaster Recovery as a Service ( Autonomous ) Market sensor , from 2015 to 2030 .  In 2030 , the global Autonomous Market was predicted to reach 25.0 billion U.S. dollars in size .  Additional information - Disaster Recovery as a Service ( Autonomous ) Within the field of information technology , disaster recovery is the process of replicating data on servers , either physical or virtual , as a precaution against man-made or natural disasters .

Example 458:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2019']}
title: Copper consumption in the U.S. 2006 to 2019
Data:
                             0     1     2     3     4     5     6     7     8     9    10    11    12    13
Year                      2019  2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006
Reported, refined         1850  1820  1800  1800  1810  1760  1830  1760  1760  1760  1650  2020  2140  2110
Apparent, unmanufactured  1800  1830  1860  1880  1840  1780  1750  1760  1730  1760  1580  1990  2270  2200 

gold: This statistic depicts the total copper consumption of the United States from 2006 to 2019 . In 2019 , the United States had an apparent consumption of some 1.8 million metric tons of unmanufactured copper .
gold_template: This statistic depicts the total templateTitle[0] templateTitle[1] of the templateTitle[2] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitle[2] had an apparent templateTitle[1] of some 1.8 templateScale metric tons of templateLabel[2][1] templateTitle[0] .

generated_template: The statistic presents the recorded templateTitle[2] of templateLabel[1][0] templateTitle[3] and templateTitle[4] templateTitle[5] in the country between templateValue[0][last] and templateValue[0][0] . In the last measured period , the templateTitle[2] of templateLabel[1][0] templateTitle[3] in the country amounted to templateValue[1][0] with over templateValue[2][0] templateScale templateTitle[4] templateTitle[5] . templateLabel[1][0] templateTitle[3] and templateTitle[5] templateTitle[4] – additional information templateLabel[1][0] templateTitle[3] have templatePositiveTrend attention with the templatePositiveTrend use of digital files and companies and users large reliance on digital templateLabel[1][0] .
generated: The statistic presents the recorded U.S. of Reported, 2006 and 2019 in the country between 2006 and 2019 .  In the last measured period , the U.S. of Reported, 2006 in the country amounted to 1850 with over 1800 % 2019 .  Reported, 2006 and 2019 – additional information Reported, 2006 have gained attention with the increasing use of digital files and companies users large reliance on digital Reported, .

Example 459:
titleEntities: {'Subject': ['EIFS', 'STUCCO'], 'Date': ['2018']}
title: Most used EIFS and STUCCO brands in the U.S. 2018
X_Axis['Brand']: ['Dryvit', 'STO', 'Omega_Products', 'Senergy', 'Simplex_(Finestone)', 'Parex/La_Habra', 'TEC', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['26.9', '11.5', '6.7', '5.8', '5.8', '4.8', '3.8', '34.6']

gold: This statistic depicts the most used EIFS and STUCCO siding brands by U.S. construction firms in 2018 . The survey revealed that 26.9 percent of the respondents used Dryvit brand the most .
gold_template: This statistic depicts the templateTitle[0] templateTitle[1] templateTitleSubject[0] and templateTitleSubject[1] siding templateTitle[4] by templateTitle[5] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[0] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] the templateTitle[0] .

generated_template: This statistic depicts templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateTitle[2] templateTitle[3] the templateTitle[0] .
generated: This statistic depicts EIFS STUCCO brands used the Most by EIFS construction firms in 2018 .  The survey revealed that 34.6 % of the respondents used Dryvit Brand EIFS STUCCO the Most .

Example 460:
titleEntities: {'Subject': ['Arizona Coyotes'], 'Date': ['2005', '2019']}
title: Arizona Coyotes ' 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']: ['102', '96', '98', '101', '92', '80', '67', '83', '70', '67', '66', '68', '67', '63']

gold: This graph depicts the annual National Hockey League revenue of the Arizona Coyotes from the 2005/06 season to the 2018/19 season . The revenue of the Arizona Coyotes amounted to 102 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] Wild from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] Wild amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .
generated: This graph depicts the annual National Hockey League Revenue of the Arizona Coyotes Wild from the 2005/06 season to the 2018/19 season .  The Revenue of the Arizona Coyotes Wild amounted to 102 million U.S. dollars in the 2018/19 season .

Example 461:
titleEntities: {'Subject': ['UK'], 'Date': ['2018']}
title: Most valuable telecommunications brands in the UK in 2018
X_Axis['Brand']: ['Vodafone', 'BT', 'Sky', 'O2', '3', 'EE']
Y_Axis['Brand', 'value', 'in', 'million', 'U.S.', 'dollars']: ['18744', '11494', '10162', '8637', '7347', '4686']

gold: This statistic displays a ranking of the most valuable telecommunications brands in the United Kingdom ( UK ) as of 2018 . Vodafone ranked first with a brand value of 18.7 billion U.S. dollars . O2 had a brand value of 8.6 billion U.S. dollars .
gold_template: This statistic displays a ranking of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) as of templateTitleDate[0] . templateXValue[0] ranked first with a templateXLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . templateXValue[3] had a templateXLabel[0] templateYLabel[1] of templateYValue[3] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateXLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[7] U.S. templateTitle[8] on templateTitleSubject[0] as of 2011 . templateXValue[1] templateXLabel[0] received templateYValue[1] likes from U.S. templateTitle[8] in the previous year .
generated: This statistic shows the Brand valuable telecommunications brands UK 2018 U.S. on UK as of 2011 .  BT Brand received 11494 likes from U.S. 2018 in the previous year .

Example 462:
titleEntities: {'Subject': ['Southern California Edison'], 'Date': ['2011', '2018']}
title: Southern California Edison 's total assets 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['56574', '51515', '50891', '49795', '49456', '45786', '44034', '40315']

gold: This statistic represents the total assets of Southern California Edison between the fiscal year of 2011 and the fiscal year of 2018 . In the fiscal year of 2018 , the California-based public utility had total assets amounting to over 56.5 billion U.S. dollars .
gold_template: This statistic represents the templateTitle[4] templateTitle[5] of templateTitleSubject[0] Edison 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 California-based public utility had templateTitle[4] templateTitle[5] amounting to over 56.5 templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the annual templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] , templateTitleSubject[1] , and templateTitleSubject[2] for the fiscal years templateXValue[min] to templateXValue[max] . The company 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: The statistic shows the annual Net income of Southern California Edison , and Southern California Edison for the fiscal years 2011 to 2018 .  The company 's Net income amounted to approximately 56574 million U.S. dollars in 2018 .

Example 463:
titleEntities: {'Subject': ['Oil'], 'Date': ['2008', '2018']}
title: Oil production in selected countries 2008 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     24        25         26       27        28     29         30       31                 32                 33            34     35       36      37     38    39                 40     41       42    43          44       45     46     47
Country  United_States  Saudi_Arabia  Russian_Federation  Canada  China  Iraq  Iran  United_Arab_Emirates  Kuwait  Venezuela  Mexico  Brazil  Nigeria  Norway  Qatar  Angola  Kazakhstan  Algeria  Colombia  United_Kingdom  Oman  India  Azerbaijan  Indonesia  Egypt  Malaysia  Argentina  Ecuador  Thailand  Libya  Australia  Vietnam  Equatorial_Guinea  Republic_of_Congo  Turkmenistan  Gabon  Denmark  Brunei  Italy  Peru  Trinidad_&_Tobago  Sudan  Romania  Chad  Uzbekistan  Tunisia  Yemen  Syria
2008              6783         10655                9965    3207   3814  2428  4415                  3113    2781       3228    3165    1887     2172    2458   1432    1876        1485     1951       588            1549   757    818         916       1006    715       727        802      507       368   1875        538      309                369                237           208    240      287     175    108   122                149    457       99   127         102       96    316    406
2018             15311         12287               11438    5208   3798  4614  4715                  3942    3049       1514    2068    2683     2051    1844   1879    1534        1927     1510       866            1085   978    869         795        808    670       682        592      517       485   1010        356      275                190                333           222    194      138     112     97   154                 87    100       74  1534          64       50     68     24 

gold: This statistic depicts the oil production in selected major oil producing countries in 2008 and in 2018 . In 2008 , the United States produced around 6.8 million barrels of oil per day . Ten years later , the production stood at around 15.3 million barrels per day .
gold_template: This statistic depicts the templateTitleSubject[0] templateTitle[1] in templateTitle[2] major templateTitleSubject[0] producing templateTitle[3] in templateTitleDate[min] and in templateTitleDate[max] . In templateTitleDate[min] , the templateValue[0][0] produced around 6.8 templateScale barrels of templateTitleSubject[0] per day . Ten years later , the templateTitle[1] stood at around 15.3 templateScale barrels per day .

generated_template: templateValue[0][0] was the European templateLabel[0][0] with the largest stock of templateTitle[1] vehicles in all three years here recorded . The total number of templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] stood at 286.8 templateScale units in templateTitleDate[max] , of which templateValue[0][0] accounted for 46.5 templateScale . With the greatest population among all European countries and home to a prominent number of automobile manufacturers , this was unsurprising .
generated: United_States was the European Country with the largest stock of production vehicles in all three years here recorded .  The total number of Oil production selected in Oil stood at 286.8 million units in 2018 , of which United_States accounted for 46.5 % .  With the greatest population among all European countries and home to a prominent number of automobile manufacturers , this was unsurprising .

Example 464:
titleEntities: {'Subject': ['French'], 'Date': ['2018', '2018']}
title: Leading French YouTube gaming channels as of June 2018 , by number of subscribers
X_Axis['Name']: ['Bigorneaux_&_Coquillages', "Le_Bled'Art", 'Joueur_du_Grenier', 'LaSalle', 'GOTAGA', 'GameMixTreize', 'Siphano', 'theKAIRI78', 'Furious_Jumper', 'Bob_Lennon']
Y_Axis['Number', 'of', 'subscribers', 'in', 'thousands']: ['5387', '3285', '3073', '2575', '2461', '2205', '2152', '1989', '1973', '1884']

gold: This statistic presents the ranking of French YouTube gaming channels as of June 2018 , by number of subscribers . The gaming channel `` Bigorneaux & Coquillages '' , with approximately 5.3 million followers , was the most subscribed French YouTube gaming channel in June 2018 . It was followed by `` Le Bled'Art '' with around 3.3 million subscribers and `` Le Joueur de Grenier '' with three million subscribers .
gold_template: This statistic presents the ranking of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] as of templateTitle[5] templateTitleDate[0] , templateTitle[7] templateYLabel[0] of templateYLabel[1] . The templateTitle[3] channel `` templateXValue[0] Coquillages '' , with approximately 5.3 templateScale followers , was the most subscribed templateTitleSubject[0] templateTitle[2] templateTitle[3] channel in templateTitle[5] templateTitleDate[0] . It was followed templateTitle[7] `` templateXValue[1] '' with around templateYValue[1] templateScale templateYLabel[1] and `` templateXValue[1] templateXValue[2] de templateXValue[2] '' with templateYValue[1] templateScale templateYLabel[1] .

generated_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: The comedy 2018 French of Bigorneaux_&_Coquillages and Le_Bled'Art des were the two leading 2018 French in France as of October 2018 .  At that time , Bigorneaux_&_Coquillages recorded 5387 thousands subscribers , while Le_Bled'Art des videos ranked in second YouTube 3285 thousands followers .  2018 's success storys Beside music French , most of the 2018 French in this ranking are comedy French moderated by young adults around topics regarding everyday life situations and problems of younger generations , as well joking about the adult life from a Millennial perspective .

Example 465:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2024']}
title: Total population of Argentina 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['47.6', '47.08', '46.56', '46.05', '45.55', '45.05', '44.56', '44.07', '43.59', '43.13', '42.67']

gold: This statistic shows the total population of Argentina from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population of Argentina amounted to approximately 44.56 million inhabitants .
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] 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[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 Argentina from 2014 to 2024 .  In 2018 , the Total population of Argentina was estimated at approximately 44.56 millions Inhabitants .

Example 466:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000', '2017']}
title: GDP growth in Canada 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['Annual', 'gross', 'domestic', 'product', 'growth']: ['3.05', '1.41', '1', '2.86', '2.48', '1.75', '3.14', '3.08', '-2.95', '1', '2.06', '2.62', '3.2', '3.09', '1.8', '3.01', '1.77', '5.18']

gold: This statistic shows the annual growth of the gross domestic product ( GDP ) of Canada from 2000 to 2017 . In 2017 , the GDP of Canada had increased by 3.05 percent over the previous year 's GDP .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[4] of the templateYLabel[1] templateYLabel[2] templateYLabel[3] ( templateTitle[0] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] had templatePositiveTrend by templateYValue[idxmax(X)] templateScale over the previous templateXLabel[0] 's templateTitle[0] .

generated_template: This statistic shows the templateTitleSubject[0] templateYLabel[0] of pumpkins from templateXValue[min] to templateXValue[max] . In templateXValue[6] , around 12.36 templateScale templateYLabel[2] of pumpkins were produced in the templateTitle[0] . Pumpkins are especially popular around Halloween .
generated: This statistic shows the Canada Annual of pumpkins from 2000 to 2017 .  In 2011 , around 12.36 million domestic of pumpkins were produced in the GDP .  Pumpkins are especially popular around Halloween .

Example 467:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014', '2019']}
title: Leading U.S. states for rice yield per acre 2014 to 2019
Data:
                0         1         2            3      4          5
State  California  Arkansas  Missouri  Mississippi  Texas  Louisiana
2014         8580      7560      6830         7420   7360       7130
2015         8890      7340      7020         7110   6900       6940
2016         8840      6920      6650         7180   7360       6630
2017         8410      7490      7440         7400   7260       6710
2018         8620      7520      7770         7350   7970       7130
2019         8450      7480      7370         7350   7350       6380 

gold: This statistic shows the U.S. states with the highest rice yield per harvested acre from 2014 to 2019 . California reported a rice yield of 8,450 pounds per harvested acre in 2019 .
gold_template: This statistic shows the templateTitleSubject[0] templateTitle[2] with the highest templateTitle[4] templateTitle[5] templateTitle[6] harvested templateTitle[7] from templateTitleDate[min] to templateTitleDate[max] . templateValue[0][0] reported a templateTitle[4] templateTitle[5] of templateValue[6][max] pounds templateTitle[6] harvested templateTitle[7] in templateTitleDate[max] .

generated_template: This statistic illustrates the templateTitle[3] templateTitle[4] of templateTitleSubject[0] templateTitle[1] to templateTitle[5] templateTitle[6] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[9] templateTitle[10] . The forecast templateTitle[3] templateTitle[4] of approximately templateValue[2][max] templateScale U.S. dollars in templateTitle[3] to the templateValue[0][0] Asian economy in templateTitleDate[max] .
generated: This statistic illustrates the for rice of U.S. to yield per from 2014 to 2019 , .  The forecast for rice of approximately 8890 million U.S. dollars in for to the California Asian economy in 2019 .

Example 468:
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 templateLabel[1][0] templateTitle[2] of the templateTitleSubject[1] templateTitle[0] templateTitle[1] to the templateTitleSubject[1] templateTitle[3] in templateValue[0][last] and with a forecast for templateXValue[max] . In templateValue[0][0] , the templateTitle[2] of the templateTitleSubject[0] templateTitle[1] is expected to grow to templateYValue[max] templateScale units ( GBP ) . The templateTitleSubject[0] templateTitle[1] templateTitle[2] is predicted to grow to templateValue[2][last] templateScale euros in templateValue[0][0] .
generated: This statistic shows the Urea forecast of the Medium term to the Medium for in 2023 and with a forecast for .  In 2023 , the forecast of the Medium term is expected to grow million units ( GBP ) .  The Medium term forecast is predicted to grow 63.9 billion euros in 2023 .

Example 469:
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: This statistic shows the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] titles templateTitle[5] as of 2019 . With templateYValue[max] templateScale templateYLabel[2] sold templateTitle[5] , templateXValue[0] 7 was the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] game as of 2019 .
generated: This statistic shows the Top selling Nintendo Wii Wii titles worldwide as of 2019 .  With 82.88 million units sold worldwide , Wii_Sports 7 was the Top selling Nintendo Wii Wii game as of 2019 .

Example 470:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016', '2019']}
title: Current bank account switching numbers 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", "Jun_'16", "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16"]
Y_Axis['Number', 'of', 'switches', 'in', 'thousands']: ['72.51', '90.09', '99.51', '78.94', '66.95', '81.86', '83.46', '84.91', '78.61', '92.99', '100.35', '71.85', '61.89', '88.5', '85.52', '62.1', '62.29', '69.24', '79.52', '77.98', '68.84', '102.27', '102.99', '68.21', '64.45', '110.77', '72.47', '60.3', '73.48', '84.04', '77.09', '72.9', '68.16', '95.99', '90.4', '61.91', '60.06', '77.98', '70.35', '71.16', '77.61', '85.59', '84.59', '79.23', '94.18', '124.62', '110.34', '74.72']

gold: This statistic illustrates the monthly number of customers who switched their current bank account provider in the United Kingdom ( UK ) between January 2016 to December 2019 . It can be seen that the number of switches fluctuated during the period under observation , reaching a total of approximately 72.5 thousand as of December 2019 . The largest number of switches in one month occurred in March 2016 , when more than 124.6 thousand switches were recorded .
gold_template: This statistic illustrates the monthly templateYLabel[0] of customers who switched their templateTitle[0] templateTitle[1] templateTitle[2] provider in the templateTitleSubject[0] ( templateTitleSubject[1] ) between 2016 to 2019 . It can be seen that the templateYLabel[0] of templateYLabel[1] fluctuated during the period under observation , reaching a total of approximately templateYValue[0] thousand as of 2019 . The largest templateYLabel[0] of templateYLabel[1] in one templateXLabel[0] occurred in 2016 , when more than templateYValue[max] thousand templateYLabel[1] were recorded .

generated_template: This statistic shows the total number of templateTitleSubject[0] motorcycles templateYLabel[1] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 to 2019 . and recorded the highest templateTitle[3] , which were the months when the Driver and Vehicle Licensing Agency issued new registration plates for cars and motorcycles . In 2019 , templateTitleSubject[0] templateYLabel[1] templateYValue[0] motorcycles in the templateTitleSubject[1] .
generated: This statistic shows the total number of United Kingdom motorcycles switches in the UK ( ) between 2016 to 2019 .  and recorded the highest switching , which were the months when the Driver and Vehicle Licensing Agency issued new registration plates for cars and motorcycles .  In 2019 , United Kingdom switches 72.51 motorcycles in the UK .

Example 471:
titleEntities: {'Subject': ['Life'], 'Date': ['2018']}
title: Life expectancy in industrial and developing countries in 2018
Data:
                                0       1                         2                                       3                          4
unknown  More_Developed_Countries  Global  Less_Developed_Countries  Less_Developed_Countries_(Excl._China)  Least_Developed_Countries
Males                          76      70                        69                                      67                         63
Females                        82      74                        73                                      71                         66 

gold: In 2018 , the average life expectancy for those born in more developed countries was 76 years for males and 82 years for females . Globally , the life expectancy for males was 70 years , and 74 years for females . Life expectancy Life expectancy is the measure of how long a person is expected to live .
gold_template: In templateTitleDate[0] , the average templateTitleSubject[0] templateTitle[1] for those born in templateValue[0][0] countries was templateValue[1][0] years for templateLabel[1][0] and templateValue[2][0] years for templateLabel[2][0] . Globally , the templateTitleSubject[0] templateTitle[1] for templateLabel[1][0] was templateValue[1][1] years , and templateValue[2][1] years for templateLabel[2][0] . templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[1] is the measure of how long a person is expected to live .

generated_template: This statistic shows the average templateTitle[0] templateTitle[1] in templateValue[0][0] for those born in templateTitleDate[0] , by gender and region . The average templateTitle[0] templateTitle[1] in templateValue[0][1] templateValue[0][0] was templateValue[1][1] years for templateLabel[1][0] and templateValue[2][1] years for templateLabel[2][0] in templateTitleDate[0] . Additional information on European templateTitle[0] templateTitle[1] The difference in templateTitle[0] templateTitle[1] seen between men and women across all European regions is in line with the global trends of women outliving men , on average .
generated: This statistic shows the average Life expectancy in More_Developed_Countries for those born in 2018 , by gender and region .  The average Life expectancy in Global More_Developed_Countries was 70 years for Males and 74 years for Females in 2018 .  Additional information on European Life expectancy The difference in Life expectancy seen between men and women across all European regions is in line with the global trends of women outliving men , on average .

Example 472:
titleEntities: {'Subject': ['Rapeseed'], 'Date': []}
title: Rapeseed production volume worldwide by country 2018/2019
X_Axis['Country']: ['European_Union', 'Canada', 'China', 'India', 'Other']
Y_Axis['Rapeseed', 'production', 'in', 'million', 'metric', 'tons']: ['19.6', '21.1', '12.85', '6.6', '10.75']

gold: This statistic provides a forecast of the rapeseed production volume worldwide in 2018/2019 , by country . It was forecasted , that the rapeseed production of Canada would amount to some 21.1 million metric tons . The global production of rapeseed oil is expected to reach nearly 28 million metric tons by that year .
gold_template: This statistic provides a forecast of the templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] in templateTitle[6] , templateTitle[4] templateXLabel[0] . It was forecasted , that the templateYLabel[0] templateYLabel[1] of templateXValue[1] would amount to some templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The global templateYLabel[1] of templateYLabel[0] oil is expected to reach nearly 28 templateScale templateYLabel[3] templateYLabel[4] templateTitle[4] that year .

generated_template: This statistic shows the templateScale of templateYLabel[1] in the templateTitle[3] diagnosed templateTitle[1] templateTitle[2] A , sorted templateTitle[5] templateXLabel[0] templateXLabel[1] , as of templateTitleDate[0] . In that year , templateYValue[min] templateScale of all Americans diagnosed templateTitle[1] templateTitle[2] A were between 0 and 4 templateXValue[0] of templateXLabel[0] .
generated: This statistic shows the million of production in the worldwide diagnosed production volume A , sorted country , as of .  In that year , 6.6 million of all Americans diagnosed production volume A were between 0 and 4 European_Union of Country .

Example 473:
titleEntities: {'Subject': ['Countries'], 'Date': ['2010']}
title: Countries with the lowest proportion of soccer players in 2010
X_Axis['Country']: ['North_Korea', 'South_Korea', 'Japan', 'Ivory_Coast', 'Ghana']
Y_Axis['Proportion', 'of', 'soccer', 'players']: ['2.2', '2.3', '3.8', '3.9', '4.1']

gold: This table shows the countries with the lowest proportion of soccer players in the population . 3.8 percent of Japans 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[2] templateScale of Japans population are templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the population distribution of templateTitleSubject[0] templateYLabel[1] living abroad as of templateTitleDate[0] , templateTitle[5] templateTitle[6] . templateTitle[5] the end of that year , around templateYValue[2] templateScale templateTitleSubject[0] nationals who were living templateTitle[1] were in templateXValue[2] .
generated: This statistic shows the population distribution of Countries soccer living abroad as of 2010 , .  2010 the end of that year , around 3.8 million Countries nationals who were living lowest were in Japan .

Example 474:
titleEntities: {'Subject': ['Glencore'], 'Date': ['2013', '2018']}
title: Glencore 's number of employees 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Number', 'of', 'employees']: ['158000', '145977', '154832', '156468', '181349', '203637']

gold: As of 2018 there were approximately 158,000 employees and contractors globally working for Glencore . This is a significant decrease from 2013 when there were about 204,000 employees working for the multinational commodity trading and mining company . Despite decreasing employment numbers , Glencore remains the second largest mining company worldwide based on employment .
gold_template: As of templateXValue[max] there were approximately templateYValue[idxmax(X)] templateYLabel[1] and contractors globally working for templateTitleSubject[0] . This is a significant templateNegativeTrend from templateXValue[min] when there were about 204,000 templateYLabel[1] working for the multinational commodity trading and mining company . Despite templateNegativeTrend employment numbers , templateTitleSubject[0] remains the second largest mining company worldwide based on employment .

generated_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] .
generated: The statistic shows the Number of employees Glencore , and Glencore for the fiscal years 2013 to 2018 .  The Number of employees at Glencore , and Glencore reached a high in 2016 .

Example 475:
titleEntities: {'Subject': ['Italy'], 'Date': ['2019', '2019']}
title: Mortgage interest rate in Italy Q1 2013- Q2 2019
X_Axis['Quarter']: ["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"]
Y_Axis['Mortgage', 'interest', 'rate']: ['1.77', '1.85', '1.89', '1.8', '1.8', '1.88', '1.9', '2.02', '2.1', '2.11', '2.02', '2.02', '2.2', '2.33', '2.5', '2.67', '2.77', '2.68', '2.84', '2.99', '3.26', '3.44', '3.5', '3.68', '3.64', '3.68']

gold: Mortgage interest rates in Italy decreased by half between the first quarter of 2013 and the second quarter of 2019 . Over the period under consideration , the highest interest rate was found in the first and third quarters of 2013 , when the value recorded was 3.68 percent . After the third quarter of 2013 , mortgage interest rates experienced a steady decrease , reaching a value of 1.77 percent as of the second quarter of 2019 , which represented the lowest interest rate during the period considered .
gold_template: templateYLabel[0] templateYLabel[1] rates in templateTitleSubject[0] templateNegativeTrend by half between the first templateXLabel[0] of 2013 and the second templateXLabel[0] of templateTitleDate[max] . Over the period under consideration , the highest templateYLabel[1] templateYLabel[2] was found in the first and third quarters of 2013 , when the value recorded was templateYValue[max] templateScale . After the third templateXLabel[0] of 2013 , templateYLabel[0] templateYLabel[1] rates experienced a steady templateNegativeTrend , reaching a value of templateYValue[min] templateScale as of the second templateXLabel[0] of templateTitleDate[max] , which represented the lowest templateYLabel[1] templateYLabel[2] during the period considered .

generated_template: This statistic highlights the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[5] templateYLabel[3] in the templateTitle[2] . As of the fourth templateXLabel[0] of templateTitleDate[max] , it was found that templateTitle[0] devices accounted for templateYValue[0] templateScale of templateYLabel[1] templateYLabel[2] templateTitle[5] templateYLabel[3] .
generated: This statistic highlights the Mortgage of interest rate 2013- in the rate .  As of the fourth Quarter of 2019 , it was found that Mortgage devices accounted for 1.77 % of interest rate 2013- .

Example 476:
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 templateYLabel[0] of sentenced templateYLabel[1] under templateTitle[2] jurisdiction in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateTitle[5] . As of 17 , templateTitleDate[0] , templateYValue[last] templateYLabel[1] in the templateXValue[0] were from templateXValue[3] .
generated: This statistic shows the Number of sentenced pets under Europe jurisdiction in the Europe in 2018 , by animal .  As of 17 , 2018 7848 pets in the Cat were from Small_mammal .

Example 477:
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 shows the templateTitle[1] templateTitle[2] problems templateTitle[4] templateTitle[5] templateXValue[4] in the templateTitleSubject[0] in 2020 . During the survey , about templateYValue[max] templateScale of the templateYLabel[1] stated that the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateXValue[4] was templateXValue[0] .
generated: This statistic shows the consumer perception problems company 2017 Amazon_assumes_a_pioneering_role_in_this_day_and_age in the Amazon in 2020 .  During the survey , about 67 % of the respondents stated that the consumer perception Amazon company 2017 Amazon_assumes_a_pioneering_role_in_this_day_and_age was I_like_using_Amazon .

Example 478:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019']}
title: Canada - resident population , by gender and age group 2019
Data:
                       0               1               2               3                  4
Age group  0_to_17_years  18_to_24_years  25_to_44_years  45_to_64_years  65_years_and_over
Male                3.69            1.76            5.16            5.04               3.03
Female              3.53            1.62            5.07            5.12               3.56 

gold: In 2019 , there were about 5.16 million males and 5.07 million females between the ages of 25 and 44 living in Canada , which was the most out of any age group . The next largest age group was between the ages of 45 and 64 , with 5.04 million males and 5.12 million females . Canadian demographics The average age of the Canadian resident population was about 40.8 years in 2019 , with Newfoundland and Labrador having the oldest average population , and Nunavut having the youngest average population .
gold_template: In templateTitleDate[0] , there were about templateValue[1][2] templateScale males and templateValue[2][2] templateScale females between the ages of templateValue[0][2] and templateValue[0][2] living in templateTitleSubject[0] , which was the most out of any templateLabel[0][0] templateLabel[0][1] . The next largest templateLabel[0][0] templateLabel[0][1] was between the ages of templateValue[0][3] and templateValue[0][3] , with templateValue[1][3] templateScale males and templateValue[2][3] templateScale females . Canadian demographics The average templateLabel[0][0] of the Canadian templateTitle[1] templateTitle[2] was about 40.8 templateValue[0][0] in templateTitleDate[0] , with Newfoundland and Labrador having the oldest average templateTitle[2] , and Nunavut having the youngest average templateTitle[2] .

generated_template: This statistic displays the templateTitle[0] of templateTitle[1] that the templateTitle[4] of a templateTitleSubject[0] award templateTitle[5] templateTitle[6] gone on to win an Academy Award in that same year , sorted by category . The Academy Award for templateValue[0][0] has been templateTitle[7] templateValue[1][0] templateTitle[1] by a templateTitleSubject[0] winner in the same year , and templateValue[3][0] templateTitle[1] by templateLabel[3][0] else .
generated: This statistic displays the Canada of resident that the gender of a Canada award age group gone on to win an Academy Award in that same year , sorted by category .  The Academy Award for 0_to_17_years has been 2019 3.69 resident by a Canada winner in the same year , and 3.53 resident by Female else .

Example 479:
titleEntities: {'Subject': ['UK'], 'Date': ['2014', '2018']}
title: Quarterly premiums of general insurance companies in the UK 2014 to 2018
X_Axis['Quarter']: ["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"]
Y_Axis['Value', 'in', 'billion', 'GBP']: ['8.8', '8.8', '9.0', '8.6', '8.9', '8.6', '8.5', '8.9', '8.6', '8.8', '8.7', '8.5', '9.1', '8.3', '9.2', '9.1', '8.6', '8.8', '9.6', '9.1']

gold: This statistic illustrates the quarterly premiums of general insurance companies in the United Kingdom ( UK ) from the first quarter of 2014 to the fourth quarter of 2018 . It can be seen that the total premiums of general insurance companies fluctuated overall during the period under observation , reaching a value of 8.8 billion British pounds as of the fourth quarter of 2018 . The largest value of premiums was found in the second quarter of 2014 , when premiums amounting to 9.6 billion British pounds were recorded .
gold_template: This statistic illustrates the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[0] ) from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . It can be seen that the total templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] fluctuated overall during the period under observation , reaching a templateYLabel[0] of templateYValue[0] templateScale British pounds as of the fourth templateXLabel[0] of templateTitleDate[max] . The largest templateYLabel[0] of templateTitle[1] was found in the second templateXLabel[0] of templateTitleDate[min] , when templateTitle[1] amounting to templateYValue[max] templateScale British pounds were recorded .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[0] templateTitleSubject[0] app store . As of the fourth templateXLabel[0] of templateTitleDate[0] , approximately templateYValue[0] mobile templateTitle[4] templateYLabel[2] were templateYLabel[1] , representing a 4.07 templateScale templatePositiveTrend compared to the previous templateXLabel[0] .
generated: This statistic gives information on the Value of billion GBP in the Quarterly UK app store .  As of the fourth Quarter of 2014 , approximately 8.8 mobile companies GBP were billion , representing a 4.07 billion increase compared to the previous Quarter .

Example 480:
titleEntities: {'Subject': ['Latin America Caribbean'], 'Date': ['2018']}
title: Latin America & Caribbean : GDP per capita 2018 , by country
X_Axis['Country']: ['Bahamas', 'Puerto_Rico', 'St._Kitts_and_Nevis', 'Uruguay', 'Antigua_and_Barbuda', 'Trinidad_and_Tobago', 'Barbados_', 'Chile', 'Panama', 'Costa_Rica', 'Argentina', 'Grenada', 'St._Lucia', 'Mexico', 'Regional_average', 'Brazil', 'Cuba_', 'Dominican_Republic', 'St._Vincent_and_the_Grenadines', 'Dominica', 'Peru', 'Colombia', 'Ecuador', 'Suriname', 'Paraguay', 'Jamaica', 'Belize', 'Guyana', 'Guatemala', 'El_Salvador', 'Bolivia', 'Honduras', 'Nicaragua', 'Haiti']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['31857.89', '31651.35', '19829.43', '17277.97', '16864.38', '16843.7', '16327.61', '15923.36', '15575.07', '12026.55', '11652.57', '10833.66', '10315.03', '9698.08', '9023.5', '8920.76', '8541.21', '7650.07', '7377.68', '7031.71', '6947.26', '6651.29', '6344.87', '5950.21', '5871.47', '5355.58', '5025.18', '4634.68', '4549.01', '4058.24', '3548.59', '2482.73', '2028.9', '868.28']

gold: The Bahamas and Puerto Rico were the countries with the highest gross domestic product ( GDP ) per capita in Latin America and the Caribbean as of 2018 . The average GDP generated per person in the Bahamas amounted to almost 31,900 U.S. dollars in 2017 ( latest year with available data ) , whereas the average wealth created per capita in Puerto Rico was estimated at around 31,700 U.S. dollars in 2018 . In that same year , this region 's lowest GDP per capita was that of Haiti , at nearly 870 U.S. dollars .
gold_template: The templateXValue[0] and templateXValue[1] were the countries with the highest gross domestic product ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] and the templateTitleSubject[0] as of templateTitleDate[0] . The templateXValue[14] templateYLabel[0] generated templateYLabel[1] person in the templateXValue[0] amounted to almost 31,900 templateYLabel[3] templateYLabel[4] in 2017 ( latest year with available data ) , whereas the templateXValue[14] wealth created templateYLabel[1] templateYLabel[2] in templateXValue[1] was estimated at around 31,700 templateYLabel[3] templateYLabel[4] in templateTitleDate[0] . In that same year , this region 's lowest templateYLabel[0] templateYLabel[1] templateYLabel[2] was that of templateXValue[last] , at nearly 870 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in the templateXValue[0] was templateYValue[max] templateYLabel[0] .
generated: The statistic shows the Latin America & Caribbean ( GDP ) per capita in Latin America Caribbean 2018 .  In 2018 , the Latin America & per capita in the Bahamas was 31857.89 GDP .

Example 481:
titleEntities: {'Subject': ['England'], 'Date': ['2010', '2017']}
title: Total household waste in England 2010 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Kilograms', 'per', 'person', 'per', 'year']: ['403', '412', '406', '413', '402', '412', '421', '425']

gold: Household waste volumes per person in England remained at a similar level between 2010 and 2017 . Although there was an overall decrease during this period , the household volumes were still over 400 kilograms per person in 2017 . The region which generated the largest volume of residual waste per household was the North East of England , where an average of 601 kilograms of waste was generated per person in 2017/2018 .
gold_template: templateTitle[1] templateTitle[2] volumes templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] remained at a similar level between templateXValue[min] and templateXValue[max] . Although there was an overall templateNegativeTrend during this period , the templateTitle[1] volumes were still over 400 templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The region which generated the largest volume of residual templateTitle[2] templateYLabel[1] templateTitle[1] was the North East of templateTitleSubject[0] , where an average of 601 templateYLabel[0] of templateTitle[2] was generated templateYLabel[1] templateYLabel[2] in 2017/2018 .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , around templateYValue[max] thousand babies were born in templateTitleSubject[0] in templateXValue[idxmax(Y)] , an templatePositiveTrend from the previous templateXLabel[0] were templateYValue[1] thousand babies were born .
generated: This statistic displays the Kilograms of per in England from 2010 to 2017 .  According to the report , around 425 thousand babies were born in England 2010 , an increase from the previous Year were 412 thousand babies were born .

Example 482:
titleEntities: {'Subject': ['India'], 'Date': ['2015', '2023']}
title: Number of social network users India 2015 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['447.9', '422.7', '400.3', '376.1', '351.4', '326.1', '296.3', '168.1', '142.23']

gold: With the ease of internet access , the number of social media users in India stood at 326.1 million in 2018 . This increase is relatively lower as compared to the growth that occurred between 2016 and 2017 . Nevertheless , the social network users in the country were expected to be almost 448 million in 2023 .
gold_template: With the ease of internet access , the templateYLabel[0] of templateTitle[1] media templateYLabel[1] in templateTitleSubject[0] stood at templateYValue[5] templateScale in templateXValue[5] . This templatePositiveTrend is relatively lower as compared to the growth that occurred between templateXValue[7] and templateXValue[6] . Nevertheless , the templateTitle[1] templateTitle[2] templateYLabel[1] in the country were expected to be almost templateYValue[max] templateScale in templateXValue[idxmax(Y)] .

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] . templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] – additional information templateTitleSubject[1] , headquartered in Menlo Park , California , is by far the leading social network in the world .
generated: This statistic shows the Number of India users in India from 2015 to 2023 .  In 2023 , the Number of India users in India is expected to reach 447.9 millions , up from 326.1 millions in 2018 .  India users in India – additional information India , headquartered in Menlo Park , California is by far the leading social network in the world .

Example 483:
titleEntities: {'Subject': ['Millennials'], 'Date': ['2013']}
title: Most popular leisure activities among Millennials in the U.S. 2013
X_Axis['Activity']: ['Watch_TV', 'Reading', 'Spending_time_with_family_and_friends', 'Computer/internet', 'Playing_video_games_and_computer/internet_games', 'Watching/going_to_the_movies', 'Exercise/working_out', 'Concerts/listening_to/playing_music', 'Walking/running/jogging', 'Eating/going_to_restaurants', 'Cooking/baking', 'Shopping']
Y_Axis['Share', 'of', 'respondents']: ['38', '31', '19', '16', '16', '13', '13', '10', '5', '5', '5', '5']

gold: This statistic shows the most popular leisure activities among Millennials in the United States as of September 2013 . During the survey , 19 percent of Millennials stated spending time with family and friends as a favorite leisure activity .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] in the templateTitle[6] as of 2013 . During the survey , templateYValue[2] templateScale of templateTitleSubject[0] stated templateXValue[2] with templateXValue[2] and templateXValue[2] as a favorite templateTitle[2] templateXLabel[0] .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded online by Harris Interactive in 2014 , asking the templateYLabel[1] where they usually templateTitle[3] their shampoo and/or templateTitle[6] . Some templateYValue[3] templateScale of templateTitleSubject[0] adults indicated that they buy their shampoo/conditioner templateXValue[3] .
generated: This statistic shows the results of a survey among Millennials adult popular .  The survey was fielded online by Harris Interactive in 2014 , asking the respondents where they usually activities their shampoo and/or U.S. Some 16 % of Millennials adults indicated that they buy their shampoo/conditioner Computer/internet .

Example 484:
titleEntities: {'Subject': ['Singapore'], 'Date': ['2024']}
title: Total population of Singapore 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['5.83', '5.8', '5.76', '5.73', '5.7', '5.67', '5.64', '5.61', '5.61', '5.54', '5.47']

gold: In 2018 , the total population of Singapore was approximately 5.64 million inhabitants . Population growth in the country is slow and even dropped below one percent the same year . The youth is fadingSingapore 's population is getting older , with the age bracket of those aged 65 and older getting bigger every year .
gold_template: In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was approximately templateYValue[6] templateScale templateYLabel[0] . templateTitle[1] growth in the country is slow and even templateNegativeTrend below one templateScale the same templateXLabel[0] . The youth is fadingSingapore 's templateTitle[1] is getting older , with the age bracket of those aged 65 and older getting bigger every templateXLabel[0] .

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 Singapore from 2014 to 2024 .  In 2018 , the Total population of Singapore was estimated at approximately 5.64 millions Inhabitants .

Example 485:
titleEntities: {'Subject': ['Game'], 'Date': ['2017']}
title: Game developer distribution worldwide 2017 , by age
X_Axis['Response']: ['25_to_29_years', '30_to_34_years', '40_to_50_years', 'more_than_50_years', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['20', '24', '17', '5', '56']

gold: The statistic shows the distribution of game developers worldwide in 2017 , broken down by age . According to the survey findings , the majority of responding game developers were between 30 to 34 years old , while 17 percent were in their 40s .
gold_template: The statistic shows the templateTitle[2] of templateTitleSubject[0] developers templateTitle[3] in templateTitleDate[0] , broken down templateTitle[5] templateTitle[6] . According to the survey findings , the majority of responding templateTitleSubject[0] developers were between templateXValue[1] to templateXValue[1] templateXValue[0] old , while templateYValue[2] templateScale were in their 40s .

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 Game developer an active Game or subscription in the age as of 2017 , sorted age .  According to the source , 56 % of 30_to_34_years who subscribe to online video or music subscriptions had a Game or subscription as of 2017 .

Example 486:
titleEntities: {'Subject': ['Americans'], 'Date': ['2012', '2017']}
title: Americans who identify as gay , lesbian , bisexual , or transgender 2012 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Share', 'of', 'respondents', 'stating', '"Yes"']: ['4.5', '4.1', '3.9', '3.7', '3.6', '3.5']

gold: This statistic shows the share of adults in the United States who identify as lesbian , gay , bisexual , or transgender from 2012 to 2017 . In 2012 , 3.5 percent of adults surveyed stated they identify as LGBT , while in 2017 , 4.5 percent of respondents said the same .
gold_template: This statistic shows the templateYLabel[0] of adults in the country templateTitle[1] templateTitle[2] as templateTitle[4] , templateTitle[3] , templateTitle[5] , or templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of adults surveyed stated they templateTitle[2] as LGBT , while in templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateYLabel[1] said the same .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] of templateTitleSubject[0] ( LoL ) monthly active templateYLabel[2] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , LoL had templateYValue[idxmax(X)] templateScale templateTitleSubject[0] , up from templateYValue[1] templateScale in templateXValue[1] . Being one of the most prominent eSports games , in templateXValue[1] LoL championship finals attracted 36 templateScale viewers worldwide .
generated: This statistic shows the Share of Americans ( LoL ) monthly active stating worldwide from 2012 to 2017 .  In 2017 , LoL had 4.5 million Americans , up from 4.1 million in 2016 .  Being one of the most prominent eSports games , in 2016 LoL championship finals attracted 36 million viewers worldwide .

Example 487:
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 share of selected templateTitle[3] groups of the templateValue[0][1] templateTitle[1] in templateTitleDate[0] , templateTitle[2] templateTitle[4] . As of mid-2019 , about templateValue[1][1] templateScale of the templateValue[0][1] 's templateTitle[1] were templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] old . templateTitle[1] development Globally , about templateValue[1][1] templateScale of the templateValue[0][1] is templateLabel[1][0] templateLabel[1][1] templateLabel[1][2] of templateTitle[3] and some templateValue[2][1] templateScale is templateLabel[2][0] templateLabel[2][1] templateLabel[1][2] of templateTitle[3] .
generated: This statistic shows the share of selected Vodafone groups of the Germany market in 2018 , share its .  As of mid-2019 , about 33.6 % of the Germany 's market were Mobile market old .  market development Globally , about 33.6 % of the Germany is Mobile market of Vodafone and some 21.4 % is Fixed market of Vodafone .

Example 488:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2011', '2018']}
title: Number of households in Sweden 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'households']: ['4657395', '4593508', '4536214', '4481746', '4432614', '4389007', '4360368', '4345582']

gold: The population in Sweden has been growing annually and reached 10.23 million in 2018 , and these individuals were living in almost 4.7 million households . As with the growing population , the number of households were growing annually as well , from 4.3 households in 2011 , up to the 4.7 in 2018 . Single household without children most common Most of the households in Sweden in 2018 were single households without children and amounted to around 1.8 million .
gold_template: The population in templateTitleSubject[0] has been templatePositiveTrend annually and reached 10.23 templateScale in templateXValue[max] , and these individuals were living in almost templateYValue[max] templateScale templateYLabel[1] . As with the templatePositiveTrend population , the templateYLabel[0] of templateYLabel[1] were templatePositiveTrend annually as well , from 4.3 templateYLabel[1] in templateXValue[min] , up to the 4.7 in templateXValue[idxmax(Y)] . Single household without children most common Most of the templateYLabel[1] in templateTitleSubject[0] in templateXValue[max] were single templateYLabel[1] without children and amounted to around 1.8 templateScale .

generated_template: In templateXValue[max] , the templateYLabel[0] of visitor templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] . The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] has templatePositiveTrend steadily templatePositiveTrend since the period , from templateYValue[max] thousand templateYLabel[1] in templateXValue[idxmax(Y)] . templateNegativeTrend in the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] templatePositiveTrend during this period , with a total templateYLabel[0] of approximately 25 thousand couples in templateXValue[max] .
generated: In 2018 , the Number of visitor households in Sweden amounted to 4657395 .  The Number of households in Sweden has increased steadily since the period , from 4657395 thousand households in 2018 .  decrease in the Number of households in Sweden increased during this period , with a total Number of approximately 25 thousand couples in 2018 .

Example 489:
titleEntities: {'Subject': ['Finland'], 'Date': ['2019']}
title: Employment by economic sector in Finland 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   3.73   3.76   3.87   4.22   4.26   4.13   4.16   4.23   4.44   4.63
Industry     21.81  21.97  22.16  22.16   21.7   21.9  22.85  22.79     23   23.3  23.95
Services      74.5   74.3  74.08  73.98  74.07  73.85  73.02  73.06  72.78  72.26  71.43 

gold: The statistic shows the distribution of employment in Finland by economic sector from 2009 to 2019 . In 2019 , 3.69 percent of the employees in Finland were active in the agricultural sector , 21.81 percent in industry and 74.5 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 Finland by economic sector from 2009 to 2019 .  In 2019 , 3.69 % of the employees in Finland were active in the agricultural sector , 21.81 % in Industry and 74.5 % in the service sector .

Example 490:
titleEntities: {'Subject': ['Regal Entertainment Group'], 'Date': ['2006', '2017']}
title: Regal Entertainment Group 's net income 2006 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['112.3', '170.5', '153.2', '105.2', '157.6', '142.2', '40.1', '77.6', '95.5', '112.2', '360.4', '104.3']

gold: This statistic contains income data for the Regal Entertainment Group from 2006 to 2017 . In 2017 , REG reported a net profit of 112.3 million U.S dollars .
gold_template: This statistic contains templateYLabel[1] data for the templateTitleSubject[0] Group from templateXValue[min] to templateXValue[max] . In templateXValue[max] , REG reported a templateYLabel[0] profit of templateYValue[idxmax(X)] templateScale U.S templateYLabel[4] .

generated_template: This timeline shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] Cheese templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . Family-style restaurant chain templateTitleSubject[0] Cheese templateTitle[5] made a templateYLabel[0] templateYLabel[1] ( loss ) of approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: This timeline shows the Net income of Regal Entertainment Group Cheese income 2006 from to 2017 .  Family-style restaurant chain Regal Entertainment Group Cheese income made a Net income ( loss ) of approximately 112.3 U.S. dollars in 2017 .

Example 491:
titleEntities: {'Subject': ['Share'], 'Date': ['2015', '2018']}
title: Share of mobile phone shipments worldwide 2015 - 2018 , by vendor
Data:
                  0       1       2       3       4       5       6       7       8       9      10      11      12
unknown      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
Samsung          17      15      19      20      20    16.5      18    19.6      21      18      21      19      22
Apple            11      14       9       9      11    14.3     9.2     8.8      12      14      10      10      13
Huawei            8       7       8       8       7     8.2     6.8       7       7       6       6       7       4
OPPO              5       5       6       6       6     5.7     5.2       5       0       0       3       2       0
ZTE               0       0       0       0       0     3.4     3.5     3.8       3       0       3       4       3
vivo              0       4       6       5       5     4.3     4.4     3.6       0       0       0       2       0
itel mobile       5       0       4       4       4     3.3       0       0       0       0       0       0       0
LG                0       0       0       0       3       0       0     3.4       3       3       3       4       4
Nokia/hmd         0       4       0       0       0       0       0       0       4       5       6       6       0
TCL Alcatel       0       0       0       0       0       0     3.2       0       4       4       4       4       3
Xiaomi            6       6       6       5       0       0       0       0       0       4       4       4       3
Lenovo            0       0       0       0       0       0       0       0       0       0       2       2       2
HMD               5       0       0       0       0       0       0       0       0       0       0       0       0
Others           43      45      42      43      44    44.3    49.7    48.8      46      46      38      36      46 

gold: The statistic shows the mobile phone market share by vendor worldwide from the first quarter of 2015 to the first quarter of 2018 . In the third quarter of 2017 , Samsung had a 19 percent share of the global mobile phone market .
gold_template: The statistic shows the templateTitle[1] templateTitle[2] market templateTitleSubject[0] templateTitle[7] templateTitle[8] templateTitle[4] from the first quarter of templateTitleDate[min] to the first quarter of templateTitleDate[max] . In the third quarter of 2017 , templateLabel[1][0] had a templateValue[1][2] templateScale templateTitleSubject[0] of the global templateTitle[1] templateTitle[2] market .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] on their templateTitle[1] templateTitle[2] in the templateTitle[0] in templateTitleDate[0] . In templateTitleDate[0] , about templateValue[1][0] templateScale of templateTitleSubject[0] were templateLabel[1][0] , down from templateValue[1][1] templateScale in 2015 .
generated: This statistic shows the results of a survey among Share on their mobile phone in the Share in 2015 .  In 2015 , about 17 % of Share were Samsung , down from 15 % in 2015 .

Example 492:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2019']}
title: Cartoon Network viewers reached quarterly in the United Kingdom ( UK ) Q1 2012-Q3 2019
X_Axis['Quarter']: ['Q1_2012', 'Q2_2012', 'Q3_2012', 'Q4_2012', 'Q1_2013', 'Q2_2013', 'Q3_2013', 'Q4_2013', 'Q1_2014', 'Q2_2014', 'Q3_2014', 'Q4_2014', 'Q1_2015', 'Q2_2015', 'Q3_2015', 'Q4_2015', 'Q1_2016', 'Q2_2016', 'Q3_2016', 'Q4_2016', 'Q1_2017', 'Q2_2017', 'Q3_2017', 'Q4_2017', 'Q1_2018', 'Q2_2018', 'Q3_2018', 'Q4_2018', 'Q1_2019', 'Q2_2019', 'Q3_2019']
Y_Axis['Viewers', 'in', 'thousands']: ['5377', '5415', '5570', '5537', '5675', '5684', '5826', '6185', '6297', '6075', '6617', '7018', '6359', '6214', '6379', '4035', '6163', '5812', '6322', '5409', '4747', '4782', '4685', '4730', '4558', '4673', '4987', '4549', '3998', '3639', '3998']

gold: This statistic shows the quarterly reach of the Cartoon Network television channel in the United Kingdom ( UK ) from the first quarter of 2012 to the third quarter of 2019 . In the fourth quarter of 2014 , Cartoon Network reached over 7 million viewers . The number of viewers fell to roughly four million in the most recent period in consideration .
gold_template: This statistic shows the templateTitle[4] reach of the templateTitle[0] templateTitle[1] television channel in the templateTitleSubject[0] ( templateTitleSubject[1] ) from the first templateXLabel[0] of templateXValue[0] to the third templateXLabel[0] of templateXValue[28] . In the fourth templateXLabel[0] of templateXValue[8] , templateTitle[0] templateTitle[1] templateTitle[3] over templateYValue[10] templateScale templateYLabel[0] . The number of templateYLabel[0] templateNegativeTrend to roughly templateYValue[15] templateScale in the most recent period in consideration .

generated_template: The templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] and templateTitle[4] templateTitle[5] amounted to approximately templateYValue[max] templateScale templateYLabel[2] 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 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: The Cartoon Network Viewers of reached and quarterly United amounted to approximately 7018 thousands in the third Quarter of Q1_2012 .  Cartoon Network sector in the Kingdom Cartoon Network sector Viewers in the Kingdom has been steadily decreasing 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 Kingdom real estate market was quite stable .

Example 493:
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: This statistic shows the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] titles templateTitle[5] as of 2019 . With templateYValue[max] templateScale templateYLabel[2] sold templateTitle[5] , templateXValue[0] 7 was the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] game as of 2019 .
generated: This statistic shows the Largest cities Ukraine 2016 titles as of 2019 .  With 2.91 millions sold 2016 , Kyïv_(Kiev) 7 was the Largest cities Ukraine 2016 game as of 2019 .

Example 494:
titleEntities: {'Subject': ['Golden State Warriors', 'Facebook'], 'Date': ['2012', '2019']}
title: Golden State Warriors number of Facebook fans/twitter followers 2012 to 2019
Data:
                                0           1               2           3               4           5               6           7               8           9              10          11              12             13              14
Month              September_2019  March_2019  September_2018  March_2018  September_2017  March_2017  September_2016  March_2016  September_2015  March_2015  September_2014  March_2014  September_2013  February_2013  September_2012
Facebook fans               11.72       11.09           11.09       11.08           11.22       10.44            9.12        6.34            4.49        2.78            2.43        1.47            0.52            0.4            0.36
Twitter followers             6.3        5.63            5.63         5.6            4.51        3.24            2.42        1.48            1.09        0.64            0.47        0.37            0.27           0.16            0.11 

gold: The statistic shows the number of Facebook fans/Twitter followers of the NBA franchise Golden State Warriors from September 2012 to September 2019 . In September 2019 , the Facebook page of the Golden State Warriors basketball team had around 11.72 million fans .
gold_template: The statistic shows the templateTitle[3] of templateTitleSubject[1] templateTitle[5] templateTitle[6] of the NBA franchise templateTitleSubject[0] Warriors from templateValue[0][0] templateValue[0][14] to templateValue[0][0] . In templateValue[0][0] , the templateTitleSubject[1] page of the templateTitleSubject[0] Warriors basketball team had around templateValue[1][0] templateScale templateLabel[1][1] .

generated_template: The statistic shows the templateTitle[3] of templateTitleSubject[1] templateTitle[5] templateTitle[6] of the NBA franchise templateTitleSubject[0] Lakers from 2012 to 2019 . In 2019 , the templateTitleSubject[1] page of the templateTitleSubject[0] Lakers basketball team had more than templateValue[1][1] templateScale templateLabel[1][1] .
generated: The statistic shows the number of Facebook fans/twitter followers of the NBA franchise Golden State Warriors Lakers from 2012 to 2019 .  In 2019 , the Facebook page of the Golden State Warriors Lakers basketball team had more than 11.09 million fans .

Example 495:
titleEntities: {'Subject': ['Largest'], 'Date': ['2019']}
title: Largest donors of humanitarian aid worldwide 2019 , by country
X_Axis['Country']: ['United_States', 'Germany', 'United_Kingdom', 'European_commission', 'Saudi_Arabia', 'United_Arab_Emirates', 'Canada', 'Central_Emergency_Response_Fund', 'Sweden', 'EU_Commission_-_EU_facility_for_refugees_in_Turkey']
Y_Axis['Aid', '(in', 'million', 'U.S.', 'dollars)']: ['6496.3', '1483.8', '1439.3', '1168.1', '990.8', '425.0', '406.2', '398.5', '351.2', '349.4']

gold: This statistic shows the top donors of humanitarian aid worldwide in 2019 . In this year , the United States government donated nearly 6.5 billion U.S. dollars in humanitarian aid worldwide .
gold_template: This statistic shows the top templateTitle[1] of templateTitle[2] templateYLabel[0] templateTitle[4] in templateTitleDate[0] . In this year , the templateXValue[0] government donated nearly templateYValue[max] templateScale templateYLabel[3] dollars in templateTitle[2] templateYLabel[0] templateTitle[4] .

generated_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: This statistic shows the 1439.3 Largest donors the humanitarian aid worldwide in the world 2019 to .  Over the past decade , United_States has demonstrated the humanitarian economic U.S. rate donors (in Aid million U.S. sitting as high 6496.3 million .  The overall quarterly million U.S. in the country can be found here .

Example 496:
titleEntities: {'Subject': ['National Basketball Association', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price National Basketball Association ( NBA ) 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['Ticket', 'price', 'in', 'U.S.', 'dollars']: ['55.88', '53.98', '52.5', '50.99', '48.48', '47.66', '48.9', '49.47', '48.83', '46.99']

gold: This graph depicts the average ticket price in the National Basketball Association from the 2006/07 season to the 2015/16 season . In the 2006/07 season , the average ticket price was at 46.99 U.S. dollars .
gold_template: This graph depicts the templateTitle[0] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] Association from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[last] season , the templateTitle[0] templateYLabel[0] templateYLabel[1] was at templateYValue[min] templateYLabel[2] templateYLabel[3] .

generated_template: This graph depicts the total 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 total price U.S. for National Basketball Association games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Ticket price U.S. was 46.99 dollars .

Example 497:
titleEntities: {'Subject': ['Renewable'], 'Date': ['2018']}
title: Renewable energy capacity - country ranking 2018
X_Axis['Country']: ['China', 'U.S.', 'Brazil', 'Germany', 'India', 'Canada', 'Japan', 'Italy', 'Russia', 'France']
Y_Axis['Capacity', 'in', 'gigawatts']: ['695.87', '245.25', '135.67', '120.01', '117.92', '99.04', '90.15', '53.29', '52.22', '50.5']

gold: The leading countries for installed renewable energy in 2018 were China , the U.S. , and Brazil . China was leading in renewable energy installations with a capacity of around 696 gigawatts . The U.S. , in second place , had a capacity of around 245 gigawatts .
gold_template: The leading countries for installed templateTitleSubject[0] templateTitle[1] in templateTitleDate[0] were templateXValue[0] , the templateXValue[1] , and templateXValue[2] . templateXValue[0] was leading in templateTitleSubject[0] templateTitle[1] installations with a templateYLabel[0] of around templateYValue[max] templateYLabel[1] . The templateXValue[1] , in second place , had a templateYLabel[0] of around templateYValue[1] templateYLabel[1] .

generated_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 templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[2] templateYLabel[3] sitting .
generated: This statistic shows the 135.67 Renewable energy the capacity country ranking in the world 2018 to .  Over the past decade , China has demonstrated the Country energy the capacity country ranking gigawatts sitting .

Example 498:
titleEntities: {'Subject': ['Global'], 'Date': ['2015', '2020']}
title: Global in-app advertising and app store revenues 2015 & 2020
Data:
                                                 0     1
Year                                          2020  2015
Apps excl. games - mobile in-app advertising  65.8  19.3
Apps excl. games - mobile app store           18.8   4.5
Games - mobile in-app advertising             51.4  21.1
Games - mobile app store                      53.0  24.8 

gold: This statistic presents the worldwide in-app advertising and app store revenues in 2015 and a forecast regarding 2020 . In 2015 , mobile app store revenues of gaming apps amounted to 24.8 billion U.S. dollars and are projected to grow to 53 billion U.S. dollars in 2020 .
gold_template: This statistic presents the worldwide templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] in templateValue[0][last] and a forecast regarding templateValue[0][0] . In templateValue[0][last] , templateLabel[1][3] templateTitle[3] templateTitle[4] templateTitle[5] of gaming templateLabel[1][0] amounted to templateValue[4][last] templateScale U.S. dollars and are projected to grow to templateValue[4][0] templateScale U.S. dollars in templateValue[0][0] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] of the templateTitleSubject[0] worldwide from templateValue[0][0] to templateValue[0][last] , templateTitle[8] templateTitle[9] . In templateValue[0][1] , the templateTitleSubject[0] operated templateValue[2][1] templateLabel[2][0] templateTitle[9] templateTitle[2] templateTitle[5] .
generated: This statistic shows the Global of in-app advertising of the Global worldwide from 2020 to 2015 , 2020 .  In 2015 , the Global operated 4.5 Apps 2020 advertising revenues .

Example 499:
titleEntities: {'Subject': ['Steam'], 'Date': ['2012', '2019']}
title: Number of Steam users 2012 to 2019
X_Axis['Month']: ['September_2019', 'October_2018', 'January_2018', 'November_2017', 'September_2017', 'July_2017', 'April_2017', 'January_2017', 'October_2016', 'July_2016', 'April_2016', 'January_2016', 'November_2015', 'June_2015', 'May_2015', 'March_2015', 'January_2015', 'November_2014', 'June_2014', 'December_2013', 'November_2012']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['14.15', '18.5', '18.5', '17.68', '15.0', '13.15', '11.83', '14.21', '11.13', '12.28', '12.74', '12.33', '13.48', '10.05', '9.5', '9.1', '8.5', '9.3', '8.0', '7.0', '6.0']

gold: The online gaming platform , Steam , was first released by the Valve Corporation in 2003 . What started off as a small platform for Valve to provide updates to its games has turned into the largest computer gaming platform in the world . As of September 2019 , Steam reached a peak of 14.15 million concurrent users .
gold_template: The online gaming platform , templateTitleSubject[0] , was first released by the Valve Corporation in 2003 . What started off as a small platform for Valve to provide updates to its games has turned into the largest computer gaming platform in the world . As of templateXValue[0] , templateTitleSubject[0] reached a peak of templateYValue[0] templateScale concurrent templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[min] to templateTitleDate[max] . In 2019 , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in the templateTitleSubject[0] reached templateYValue[0] years .
generated: This statistic shows the Number of Steam users in the Steam ( ) from 2012 to 2019 .  In 2019 , the Number of Steam users in the Steam reached 14.15 years .

Example 500:
titleEntities: {'Subject': ['Thailand'], 'Date': ['2008', '2017']}
title: Value of the entertainment and media market in Thailand from 2008 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Market', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['14.79', '13.42', '12.09', '10.98', '9.89', '8.89', '7.93', '7.21', '6.62', '6.43']

gold: This timeline presents the value of the Thai entertainment and media market from 2008 to 2011 and provides a forecast until 2017 . In 2011 , the entertainment and media market in Thailand was worth over 7.93 billion U.S. dollars and PwC expected it to reach 14.79 billion by 2017 . Here you can find a distribution of the value by subsector .
gold_template: This timeline presents the templateYLabel[1] of the Thai templateTitle[1] and templateTitle[2] templateYLabel[0] templateTitle[5] templateXValue[min] to templateXValue[6] and provides a forecast until templateXValue[max] . In templateXValue[6] , the templateTitle[1] and templateTitle[2] templateYLabel[0] in templateTitleSubject[0] was worth over templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] and PwC expected it to reach templateYValue[idxmax(X)] templateScale by templateXValue[idxmax(Y)] . Here you can find a distribution of the templateYLabel[1] by subsector .

generated_template: This statistic depicts the templateTitle[2] templateTitle[3] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company had templateTitle[2] templateTitle[3] worth approximately templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] was an agricultural company specialized on genetically engineered seeds .
generated: This statistic depicts the media market of Thailand from 2008 to 2017 .  In 2017 , the company had media market worth approximately 14.79 billion U.S. Thailand was an agricultural company specialized on genetically engineered seeds .

Example 501:
titleEntities: {'Subject': ['Canada Goose'], 'Date': ['2019']}
title: Revenue of Canada Goose by region 2019
X_Axis['Country']: ['Total', 'Canada', 'United_States', 'Rest_of_world']
Y_Axis['Revenue', 'in', 'million', 'Canadan', 'dollars']: ['830.5', '293.3', '251.1', '286.1']

gold: Canada Goose , originally known as Metro Sportswear Ltd. , is a Canadian owned apparel company focusing on the production and sale of winter clothing . The worldwide revenue of Canada Goose amounted to approximately 830.5 million Canadian dollars in the 2019 financial year . Around 293 million dollars of this total revenue was generated in Canada , and around 251 million dollars in the United States .
gold_template: templateXValue[1] templateTitleSubject[0] , originally known as Metro Sportswear Ltd. , is a Canadian owned apparel company focusing on the production and sale of winter clothing . The worldwide templateYLabel[0] of templateXValue[1] templateTitleSubject[0] amounted to approximately templateYValue[max] templateScale Canadian templateYLabel[3] in the templateTitleDate[0] financial year . Around templateYValue[1] templateScale templateYLabel[3] of this templateXValue[0] templateYLabel[0] was generated in templateXValue[1] , and around templateYValue[min] templateScale templateYLabel[3] in the templateXValue[2] .

generated_template: This statistic shows the templateScale of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitleDate[0] , by the templateXLabel[0] of America . templateYValue[max] templateScale of templateYLabel[1] with templateXValue[last] and templateXValue[last] templateXLabel[1] used templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] .
generated: This statistic shows the million of Canada Goose million by region 2019 Country in 2019 , by the Country of America .  830.5 million of with Rest_of_world and Country used region 2019 in .

Example 502:
titleEntities: {'Subject': ['Austria'], 'Date': ['2019']}
title: Employment by economic sector in Austria 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   3.86    3.9   3.93   4.35   4.53   4.84   4.58   4.68   4.94   5.21   5.32
Industry     24.47  24.68  24.92  25.56  25.76  25.75  25.91  26.08  26.03  24.91  24.97
Services     71.67  71.43  71.15  70.09  69.71  69.41  69.52  69.25  69.04  69.88  69.71 

gold: The statistic shows the distribution of employment in Austria by economic sector from 2009 to 2019 . In 2019 , 3.86 percent of the employees in Austria were active in the agricultural sector , 24.47 percent in industry and 71.67 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 Austria by economic sector from 2009 to 2019 .  In 2019 , 3.86 % of the employees in Austria were active in the agricultural sector , 24.47 % in Industry and 71.67 % in the service sector .

Example 503:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: State unemployment rate in the U.S. 2018
X_Axis['State']: ['Alaska', 'District_of_Columbia', 'West_Virginia', 'New_Mexico', 'Louisiana', 'Arizona', 'Mississippi', 'Nevada', 'Ohio', 'Washington', 'Illinois', 'Kentucky', 'Pennsylvania', 'Oregon', 'California', 'Connecticut', 'Michigan', 'New_Jersey', 'New_York', 'Rhode_Island', 'Wyoming', 'Maryland', 'Texas', 'Alabama', 'North_Carolina', 'Georgia', 'Delaware', 'Arkansas', 'Montana', 'Florida', 'Tennessee', 'Maine', 'Oklahoma', 'South_Carolina', 'Kansas', 'Indiana', 'Colorado', 'Massachusetts', 'Missouri', 'Utah', 'South_Dakota', 'Virginia', 'Wisconsin', 'Minnesota', 'Idaho', 'Nebraska', 'Vermont', 'North_Dakota', 'New_Hampshire', 'Iowa', 'Hawaii']
Y_Axis['Unemployment', 'rate']: ['6.6', '5.6', '5.3', '4.9', '4.9', '4.8', '4.8', '4.6', '4.6', '4.5', '4.3', '4.3', '4.3', '4.2', '4.2', '4.1', '4.1', '4.1', '4.1', '4.1', '4.1', '3.9', '3.9', '3.9', '3.9', '3.9', '3.8', '3.7', '3.7', '3.6', '3.5', '3.4', '3.4', '3.4', '3.4', '3.4', '3.3', '3.3', '3.2', '3.1', '3', '3', '3', '2.9', '2.8', '2.8', '2.7', '2.6', '2.5', '2.5', '2.4']

gold: This table ranks the 50 states of the United States and the District of Columbia by their unemployment rate in 2018 . In 2018 , Alaska had the highest unemployment rate in the United States with 6.6 percent .
gold_template: This table ranks the 50 states of the templateTitle[3] and the templateXValue[1] of templateXValue[1] by their templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] in the templateTitle[3] with templateYValue[max] templateScale .

generated_template: This table ranks the 50 states of the templateTitle[3] and the templateXValue[2] of templateXValue[2] by their templateYLabel[0] templateYLabel[1] . In templateTitleSubject[0] templateTitleDate[0] , about templateYValue[43] templateScale of templateXValue[44] 's population was unemployed . The highest templateYLabel[0] templateYLabel[1] recorded was in templateXValue[0] at templateYValue[max] templateScale .
generated: This table ranks the 50 states of the U.S. and the West_Virginia of by their Unemployment rate .  In U.S. 2018 , about 2.9 % of Idaho 's population was unemployed .  The highest Unemployment rate recorded was in Alaska at 6.6 % .

Example 504:
titleEntities: {'Subject': ['Philadelphia', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Philadelphia 76ers ( 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']: ['38.46', '39.25', '39.25', '39.25', '39.25', '41.0', '43.0', '43.0', '43.0', '46.83']

gold: This graph depicts the average ticket price for Philadelphia 76ers games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 46.83 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] templateTitle[4] templateTitle[6] 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 Philadelphia games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Average ticket price was 46.83 U.S. dollars .

Example 505:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2020', '2024']}
title: Forecast on U.S. petroleum refinery end-use market output 2020 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020']
Y_Axis['Change', 'from', 'year', 'to', 'year']: ['2', '0.5', '0.5', '0.8', '2.2']

gold: This statistic displays a forecast of the petroleum and refinery end-use market output in the United States from 2020 to 2024 . Through 2020 , the petroleum and refinery end-use market output is expected to increase by 2.2 percent . U.S. petroleum refinery market It is projected that the growth of output from the U.S. petroleum refinery end-use market will slow , from a rate of 2.2 percent in 2020 to 0.5 percent in 2023 , and grow again to 2.2 percent in 2024 .
gold_template: This statistic displays a templateTitle[0] of the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[1] templateYLabel[1] templateXValue[min] to templateXValue[max] . Through templateXValue[min] , the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] is expected to templatePositiveTrend by templateYValue[idxmin(X)] templateScale . templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[5] It is projected that the growth of templateTitle[6] templateYLabel[1] the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] will slow , templateYLabel[1] a rate of templateYValue[max] templateScale in templateXValue[idxmax(Y)] to templateYValue[min] templateScale in templateXValue[idxmin(Y)] , and grow again to templateYValue[idxmin(X)] templateScale in templateXValue[max] .

generated_template: The statistic shows the results of a survey about the templateTitleSubject[0] Games from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is expected to grow by templateYValue[idxmax(X)] templateScale from templateXValue[3] to templateXValue[2] .
generated: The statistic shows the results of a survey about the U.S. Games from 2020 to 2024 .  The Change from is expected to grow by 2 % from 2021 to 2022 .

Example 506:
titleEntities: {'Subject': ['Inditex Group', 'Asia', 'Africa'], 'Date': ['2019']}
title: Inditex Group 's number of stores in Asia and Africa 2019 , by country
X_Axis['Country']: ['China', 'Turkey', 'Japan', 'Israel', 'Indonesia', 'Lebanon', 'Qatar', 'Egypt', 'Kazakhstan', 'Hong_Kong_SAR', 'Kuwait', 'Morocco', 'India', 'Thailand', 'Taiwan', 'Singapore', 'Malaysia', 'Jordan', 'Tunisia', 'Azerbaijan', 'Armenia', 'South_Africa', 'Bahrain', 'Macao_SAR', 'Oman']
Y_Axis['Number', 'of', 'stores']: ['589', '228', '147', '79', '67', '45', '37', '36', '36', '35', '29', '28', '25', '23', '22', '21', '21', '18', '15', '14', '12', '10', '9', '9', '5']

gold: This statistic shows Inditex Group 's number of stores in Asia and Africa as of January 31 , 2019 , by country . The number of Inditex stores in China amounted to 589 . Inditex is one of the world 's largest fashion retailers .
gold_template: This statistic shows templateTitleSubject[0] 's templateYLabel[0] of templateYLabel[1] in templateTitleSubject[1] and templateXValue[21] as of 31 , templateTitleDate[0] , templateTitle[8] templateXLabel[0] . The templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] in templateXValue[0] amounted to templateYValue[max] . templateTitleSubject[0] is one of the world templateTitle[2] largest fashion retailers .

generated_template: templateYLabel[1] are a popular choice in Europe when it comes to pet ownership , although we do n't see them outside as often as their canine friends . As shown in this statistic , templateXValue[0] and templateXValue[1] are the two countries leading the list of cat owners in the EU , with the former 's narrow win . While templateXValue[0] also ranks as the top EU templateXLabel[0] with the highest templateYLabel[0] of pet dogs , templateYLabel[1] still win in templateTitleSubject[0] households .
generated: stores are a popular choice in Europe when it comes to pet ownership , although we do n't see them outside as often their canine friends .  As shown in this statistic , China and Turkey are the two countries leading the list of cat owners in the EU , with the former 's narrow win .  While China also ranks as the top EU Country with the highest Number of pet dogs , stores still win in Inditex Group households .

Example 507:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016']}
title: Leading bakery retailers in the United Kingdom ( UK ) 2016
X_Axis['Country']: ['Subway', 'Costa', 'Greggs', 'Starbucks_Coffee_Company', 'Cafe_Nero', 'BP_-_Wild_Bean_Cafe', 'SSPUK_(various_brands)', 'Pret_A_Manger', 'Coffee_Republic', 'Stephens', 'Patisserie_Holdings', 'Sayers_the_Bakers', 'Coopland_and_Son', 'EAT', 'Aulds', "Stuart's_of_Buckhaven", "Greenhalgh's_Craft_Bakery", 'Krispy_Kreme', 'Muffin_Break', 'Coffee_#1', 'Birds_the_Confectioners', 'West_Cornwall_Pasty_Co', "Bayne's", 'Percy_Ingle', 'Warrens_Bakery']
Y_Axis['Number', 'of', 'outlets']: ['2097', '2000', '1698', '792', '574', '337', '331', '303', '230', '177', '175', '170', '144', '118', '79', '73', '61', '61', '60', '57', '56', '54', '53', '51', '50']

gold: This statistic shows the leading 25 retailers selling baked products as the primary element of their food offering in the United Kingdom ( UK ) in 2016 , ranked by number of outlets . In 2016 , Subway ranked highest with 2,097 outlets in the UK , followed closely by Costa with 2,000 outlets . Similar trends are seen in the number of new outlets opened by leading bakery retailers in the past year .
gold_template: This statistic shows the templateTitle[0] 25 templateTitle[2] selling baked products as the primary element of their food offering in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] , ranked by templateYLabel[0] of templateYLabel[1] . In templateTitleDate[0] , templateXValue[0] ranked highest with templateYValue[max] templateYLabel[1] in the templateTitleSubject[1] , followed closely by templateXValue[1] with templateYValue[1] templateYLabel[1] . Similar trends are seen in the templateYLabel[0] of new templateYLabel[1] opened by templateTitle[0] templateXValue[16] templateTitle[2] in the past year .

generated_template: This statistic shows the top templateYValue[16] templateTitle[0] in the world templateTitle[1] the templateTitle[2] number of templateYLabel[1] templateYLabel[2] in templateTitleDate[0] . In templateTitleDate[0] , there were about templateYValue[max] templateScale Muslims living in templateXValue[1] .
generated: This statistic shows the top 61 Leading in the world bakery the retailers number of outlets in 2016 .  In 2016 , there were about 2097 % Muslims living in Costa .

Example 508:
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: This statistic 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[idxmin(Y)] .
generated: This statistic 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 509:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2008', '2018']}
title: Age distribution in Mexico 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   26.56  26.94   27.3  27.64  28.01  28.35   28.7  29.08  29.51  29.96  30.45
Ages 15-64  66.22  66.02  65.82  65.64  65.42   65.2  64.96  64.68  64.35  64.01  63.63
Ages 65+     7.22   7.04   6.87   6.72   6.57   6.45   6.34   6.24   6.14   6.03   5.92 

gold: The statistic depicts the age distribution of Mexico from 2008 to 2018 . In 2018 , around 26.56 percent of Mexico 's population were between 0 and 14 years of age , around 66.22 percent between the ages of 15 and 64 , and about 7.22 percent were 65 years or older .
gold_template: The statistic depicts the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , around 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_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: In 2018 , the distribution of Age in Mexico was six percent – the lowest it had been in a decade .  There were real regional differences within the country though .  The Ages 2018 did particularly well economically , with an Age distribution of 26.56 % in 2018 .

Example 510:
titleEntities: {'Subject': ['Bulgaria'], 'Date': ['2008', '2018']}
title: Share of economic sectors in the GDP in Bulgaria 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   3.65   4.07   4.05   4.14   4.56   4.61   4.56    4.6   4.16   4.24   5.88
Industry     23.76  24.67  24.43  24.09  23.68  23.48     25  25.61  23.81  26.38  24.92
Services     59.24  57.99   57.8   58.2   58.6  57.99  56.97  57.24  58.99  56.19  53.31 

gold: This statistic shows the share of economic sectors in the gross domestic product ( GDP ) in Bulgaria from 2008 to 2018 . In 2018 , the share of agriculture in Bulgaria 's gross domestic product was 3.65 percent , industry contributed approximately 23.76 percent and the services sector contributed about 59.24 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 ( Bulgaria ) 2008 2018 in Bulgaria from 2008 to 2018 .  In 2018 , Agriculture contributed around 3.65 % to the Bulgaria of , 23.76 % came from the Industry and 59.24 % from the Services sector .

Example 511:
titleEntities: {'Subject': ['Major'], 'Date': ['2014', '2019']}
title: Major importing countries of wheat flour and products 2014 to 2019
Data:
               0          1       2            3       4        5      6               7       8        9
Country    Egypt  Indonesia  Brazil  Philippines  Turkey  Algeria  Japan  European_Union  Mexico  Nigeria
2014/2015  11300       7478    5869         5054    5960     7257   5878            5979    4471     4244
2015/2016  11925      10116    5922         4918    4405     8153   5715            6916    4805     4410
2016/2017  11500      10000    7788         5684    4500     8200   5900            5300    5250     4925
2017/2018  12500      12500    7500         6059    6400     7500   5800            5500    5300     5200
2018/2019  12347      10780    7442         7546    6494     7515   5726            5762    4861     4585
2019/2020  12500      11000    7500         7400    7200     7000   5900            5500    5200     5100 

gold: This statistic shows the principal importing countries of wheat , wheat flour and wheat products worldwide from 2014/2015 to 2019/2020 . In 2016/2017 , Egypt imported about 11.5 million metric tons of wheat and wheat-derived products . Wheat Wheat is one the most widely produced grains in the world , leading the grain market along with corn and rice in production and sales .
gold_template: This statistic shows the principal templateTitle[1] templateTitle[2] of templateTitle[3] , templateTitle[3] templateTitle[4] and templateTitle[3] templateTitle[5] worldwide from templateLabel[1][0] to templateLabel[6][0] . In templateLabel[3][0] , templateValue[0][0] imported about 11.5 templateScale metric tons of templateTitle[3] and wheat-derived templateTitle[5] . templateTitle[3] is one the most widely produced grains in the world , leading the grain market along with corn and rice in production and sales .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[7] templateTitle[8] . In 2014 , the templateValue[0][6] templateTitle[3] in the templateValue[0][6] was at templateValue[3][1] templateScale .
generated: This statistic shows the Major importing countries wheat flour products from 2014 to 2019 , .  In 2014 , the Japan wheat in the Japan was at 10000 % .

Example 512:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005', '2018']}
title: Fatalities - U.S. train crashes 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Number', 'of', 'fatalities']: ['878', '823', '760', '749', '768', '702', '669', '681', '735', '695', '804', '851', '903', '884']

gold: This graph shows the number of fatalities in U.S. railroad accidents from 2005 through 2018 . In 2018 , some 878 people died in train accidents in the United States , an increase from 823 fatalities in 2017 .
gold_template: This graph shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] railroad accidents from templateXValue[min] through templateXValue[max] . In templateXValue[max] , some templateYValue[idxmax(X)] people died in templateTitle[2] accidents in the templateTitle[1] , an templatePositiveTrend from templateYValue[1] templateYLabel[1] in templateXValue[1] .

generated_template: In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] was of templateYValue[idxmax(X)] . templateTitleSubject[0] had a divorce rate of 51.2 per 100 marriages in templateXValue[10] . A templateYLabel[0] which was not one of the highest in Europe but that emphasizes the fact that in recent years , divorce is a phenomenon with a significant impact on Western countries .
generated: In 2018 , the Number of fatalities in U.S. was of 878 .  U.S. had a divorce rate of 51.2 per 100 marriages in 2008 .  A Number which was not one of the highest in Europe but that emphasizes the fact that in recent years , divorce is a phenomenon with a significant impact on Western countries .

Example 513:
titleEntities: {'Subject': ['Alaska'], 'Date': ['2000', '2018']}
title: Alaska - 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']: ['52.31', '52.49', '52.61', '53.58', '53.21', '54.75', '57.67', '54.65', '54.15', '55.84', '50.88', '51.11', '48.52', '45.05', '43.74', '42.15', '42.88', '40.97', '39.52']

gold: This statistic shows the development of Alaska 's real GDP from 2000 to 2018 . In 2018 , the real GDP of Alaska was about 52.31 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[0] templateYLabel[1] of templateTitleSubject[0] was about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_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[0] templateYLabel[1] of templateTitleSubject[0] was 2.67 templateScale templateYLabel[3] templateYLabel[4] . The annual templateYLabel[0] templateYLabel[1] growth of the templateYLabel[3] can be accessed here .
generated: This statistic shows the development of Alaska 's Real GDP from 2000 to 2018 .  In 2018 , the Real GDP of Alaska was 2.67 billion U.S. dollars .  The annual Real GDP growth of the U.S. can be accessed here .

Example 514:
titleEntities: {'Subject': ['Natural'], 'Date': ['2018']}
title: Natural disasters - continents with the most fatalities 2018
X_Axis['Country']: ['Asia', 'Africa', 'Americas', 'Europe', 'Oceania']
Y_Axis['Number', 'of', 'fatalities']: ['79.8', '8.7', '8.1', '1.9', '1.6']

gold: This statistic shows the continents with the most fatalities from natural disasters in 2018 . Of the total fatalities from natural disasters worldwide in 2018 , approximately 79.8 percent occurred in Asia .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] the templateTitle[4] templateYLabel[1] from templateTitleSubject[0] templateTitle[1] in templateTitleDate[0] . Of the total templateYLabel[1] from templateTitleSubject[0] templateTitle[1] worldwide in templateTitleDate[0] , approximately templateYValue[max] templateScale occurred in templateXValue[0] .

generated_template: This statistic shows the population distribution of templateTitleSubject[0] templateYLabel[1] living abroad as of templateTitleDate[0] , templateTitle[5] templateTitle[6] . templateTitle[5] the end of that year , around templateYValue[2] templateScale templateTitleSubject[0] nationals who were living templateTitle[1] were in templateXValue[2] .
generated: This statistic shows the population distribution of Natural fatalities living abroad as of 2018 , .  2018 the end of that year , around 8.1 million Natural nationals who were living disasters were in Americas .

Example 515:
titleEntities: {'Subject': ['Latin America Caribbean'], 'Date': ['2019']}
title: Latin America & the Caribbean : corruption perception index in 2019 , by country
X_Axis['Country']: ['Uruguay', 'Chile', 'Bahamas', 'Barbados', 'Saint_Vincent_&_the_Grenadines', 'Costa_Rica', 'Dominica', 'Saint_Lucia', 'Grenada', 'Cuba', 'Argentina', 'Suriname', 'Jamaica', 'Guyana', 'Trinidad_&_Tobago', 'Regional_Median', 'Ecuador', 'Colombia', 'Panama', 'Peru', 'Brazil', 'El_Salvador', 'Bolivia', 'Mexico', 'Dominican_Republic', 'Paraguay', 'Guatemala', 'Honduras', 'Nicaragua', 'Haiti', 'Venezuela']
Y_Axis['Corruption', 'perception', 'index', 'score']: ['71', '67', '64', '62', '59', '56', '55', '55', '53', '48', '45', '44', '43', '40', '40', '39', '38', '37', '36', '36', '35', '34', '31', '29', '28', '28', '26', '26', '22', '18', '16']

gold: According to the Corruption Perception Index , Uruguay was perceived as the least corrupt country in Latin America and the Caribbean in 2019 , with a score of 71 out of 100 . Venezuela , on the other hand , was found to be the Latin American nation with the worst perceived level of corruption , at 16 points . The Corruption Perception Index is a composite indicator that includes the scores attained by each country in areas such as : bribery of public officials , kickbacks in public procurement , embezzlement of state funds , and effectiveness of governments ' anti-corruption efforts .
gold_template: According to the templateYLabel[0] templateYLabel[1] templateYLabel[2] , templateXValue[0] was perceived as the least corrupt templateXLabel[0] in templateTitleSubject[0] and the templateTitleSubject[0] in templateTitleDate[0] , with a templateYLabel[3] of templateYValue[max] out of 100 . templateXValue[last] , on the other hand , was found to be the templateTitleSubject[0] American nation with the worst perceived level of templateYLabel[0] , at templateYValue[min] points . The templateYLabel[0] templateYLabel[1] templateYLabel[2] is a composite indicator that includes the scores attained templateTitle[8] each templateXLabel[0] in areas such as : bribery of public officials , kickbacks in public procurement , embezzlement of state funds , and effectiveness of governments ' anti-corruption efforts .

generated_template: This statistic shows the templateXValue[0] templateYLabel[1] templateTitle[3] templateYLabel[0] of templateTitleSubject[0] as of 2017 in leading online markets . During the measured period , templateTitleSubject[0] accounted for templateYValue[3] templateScale of templateYLabel[1] templateYLabel[2] in templateXValue[3] . The Microsoft-owned platform accounted for templateYValue[0] templateScale of templateYLabel[1] templateYLabel[2] templateXValue[0] .
generated: This statistic shows the Uruguay perception Caribbean Corruption of Latin America Caribbean as of 2017 in leading online markets .  During the measured period , Latin America Caribbean accounted for 62 % of perception index in Barbados .  The Microsoft-owned platform accounted for 71 % of perception index Uruguay .

Example 516:
titleEntities: {'Subject': ['Global'], 'Date': ['2012', '2018']}
title: Global aesthetic medical and surgery market by segment 2012 to 2018
Data:
                                     0     1     2     3
Year                              2018  2014  2013  2012
Body contouring & energy devices  2339  1670  1542  1408
Fillers                           2211  1697  1591  1494
Breast implants                    997   778   734   699
Cosmeceuticals                    1020   764   712   665 

gold: This statistic depicts the value of the global aesthetic medical and surgical market from 2012 to 2018 , by segment . In 2013 , the aesthetic medical and surgical market dedicated to body contouring and energy devices was valued at some 1.5 billion euros , and it was estimated to grow up to 2.3 billion until 2018 . Most of the world including the United States , Latin America , and Asia-Pacific regions are expected to maintain growth in the cosmetic surgical and aesthetic medical market .
gold_template: This statistic depicts the value of the templateTitleSubject[0] templateTitle[1] templateTitle[2] and surgical templateTitle[4] from templateValue[0][last] to templateValue[0][0] , templateTitle[5] templateTitle[6] . In templateValue[0][2] , the templateTitle[1] templateTitle[2] and surgical templateTitle[4] dedicated to templateLabel[1][0] templateLabel[1][1] and templateLabel[1][3] templateLabel[1][4] was valued at some 1.5 templateScale euros , and it was estimated to grow up to 2.3 templateScale until templateValue[0][0] . Most of the world including the country , Latin America , and Asia-Pacific regions are expected to maintain growth in the cosmetic surgical and templateTitle[1] templateTitle[2] templateTitle[4] .

generated_template: This statistic shows 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: This statistic shows the aesthetic medical of surgery market and by in Global between 2012 and 2018 , broken down 2018 .  Over the survey period , the aesthetic medical of surgery market and by designated to families has increased from 1408 percent of the whole aesthetic to 2339 % .  On the contrary , the aesthetic medical of surgery market and by for the Breast industry had been stable around 699 % .

Example 517:
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: This statistic shows the templateYLabel[0] of Scottish templateYLabel[1] templateTitle[3] the rest of the United Kingdom templateTitle[3] templateXValue[min] to templateXValue[max] . With the exception of templateXValue[17] , templateXValue[8] and templateXValue[4] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] has templatePositiveTrend templateTitle[3] templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] to approximately templateYValue[max] templateScale by templateXValue[idxmax(Y)] .
generated: This statistic shows the Foreign of Scottish exchange from the rest of the United Kingdom from 2000 to 2018 .  With the exception of 2001 , 2010 and 2014 , the Foreign of India exchange has increased from 3.46 billion in 2002 to approximately 28.59 billion by 2018 .

Example 518:
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 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 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 519:
titleEntities: {'Subject': ['Turkish'], 'Date': ['2019']}
title: Most valuable Turkish brands 2019
X_Axis['Brand']: ['Turkish_Airlines', 'Ziraat_Bankası', 'Garanti', 'Türk_Telekom', 'Turkcell', 'İş_Bankası', 'Akbank', 'Arçelik', 'Yapı_Kredi', 'Ford_Otosan', 'BSH', 'Halkbank', 'Ülker_Bisküvi', 'VakıfBank', 'DenizBank', 'Opet', 'Anadolu_Efes', 'Bim', 'Tofaş', 'Vestel', 'Şişe_Cam', 'Sütaş', 'Migros', 'TEB', 'Pegasus']
Y_Axis['Brand', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1735', '1637', '1344', '1185', '1182', '1135', '934', '837', '647', '458', '441', '428', '409', '396', '371', '364', '315', '308', '299', '265', '242', '236', '235', '204', '203']

gold: his statistic displays a ranking of the most valuable Turkish brands in 2019 , sorted by brand value . With a brand value of roughly 1.73 billion U.S. dollars , the country 's flag carrier Turkish Airlines is the leading Turkish brand as of 2019 . Ranking just behind Turkish Airlines is Ziraat Bankası , with a value of 1.6 billion U.S. dollars .
gold_template: his statistic displays a ranking of the templateTitle[0] templateTitle[1] templateXValue[0] templateTitle[3] in templateTitleDate[0] , sorted by templateXLabel[0] templateYLabel[1] . With a templateXLabel[0] templateYLabel[1] of roughly 1.73 templateScale templateYLabel[3] templateYLabel[4] , the country 's flag carrier templateXValue[0] is the leading templateXValue[0] templateXLabel[0] as of templateTitleDate[0] . Ranking just behind templateXValue[0] is templateXValue[1] , with a templateYLabel[1] of templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] templateXLabel[0] templateTitle[4] in the templateTitle[5] as of 2019 . templateXValue[0] ranked highest with a templateXLabel[0] templateYLabel[1] of approximately templateYValue[max] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] , followed templateTitle[5] the templateXValue[1] with templateYValue[1] templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the Brand of value Turkish brands Brand 2019 in the 2019 as of 2019 .  Turkish_Airlines ranked highest with a Brand value of approximately 1735 value million in 2019 , followed 2019 the Ziraat_Bankası with 1637 value million .

Example 520:
titleEntities: {'Subject': ['France'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita 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', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['48913.37', '47204.93', '45657.02', '44132.79', '42643.95', '41760.61', '42953.27', '40108.92', '38348.51', '37937.86', '44616.46', '44145.23', '42371.12', '45410.1', '42181.58', '43176.39', '47191.13', '43059.91', '37793.81', '36082.12', '35004.53', '30675.37', '25113.25', '23240.84', '23207.84', '25549.7', '25818.37', '25028.47', '27720.86', '27741.29', '24262.18', '23082.72', '24590.75', '22406.39', '22490.29', '18236.77', '18241.0', '16793.96', '13947.32', '10108.56', '9697.43']

gold: The statistic shows the GDP per capita in France from 1984 to 2018 , with projections up until 2024 . In 2018 , GDP per capita in France was at around 42,953.27 US dollars.Suffering competitiveness GDP is one of the primary indicators that is used to gauge the state of health of a country 's economy . It is the total market value of all completed goods and services that have been produced within a country in a given period of time , usually a year .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at around templateYValue[6] US dollars.Suffering competitiveness templateYLabel[0] is one of the primary indicators that is used to gauge the state of health of a country 's economy . It is the total market value of all completed goods and services that have been produced within a country in a given period of time , usually a templateXLabel[0] .

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] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was around templateYValue[7] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] 's economy templateYLabel[0] templateYLabel[1] templateYLabel[2] is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the templateYLabel[0] and dividing it by the total population in the country .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in France from 1984 to 2018 , with projections up until 2024 .  In 2018 , the Gross domestic product per capita in France was around 40108.92 U.S. dollars .  France 's economy GDP per capita is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the GDP and dividing it by the total population in the country .

Example 521:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2019']}
title: Annual gross salary percentiles in the United Kingdom ( UK ) in 2019
X_Axis['Percentiles']: ['10', '20', '25', '30', '40', '60', '70', '75', '80', '90']
Y_Axis['Average', 'annual', 'pay', 'in', 'GBP']: ['8160', '12970', '15480', '17510', '21000', '29228', '34531', '37715', '41402', '54180']

gold: This statistic shows the average annual gross salary percentiles in the United Kingdom ( UK ) in 2019 . While the annual gross pay at the 10th percentile was approximately 8.2 thousand British pounds in 2019 , the average yearly total income for the bottom 90 percent of earners was approximately 54.2 thousand British pounds . There was a substantial difference between the average incomes of those in the 80th and the 90th percentiles , a disparity of approximately 12.8 thousand pounds .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[1] templateTitle[2] templateXLabel[0] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . While the templateYLabel[1] templateTitle[1] templateYLabel[2] at the 10th percentile was approximately templateYValue[min] thousand British pounds in templateTitleDate[0] , the templateYLabel[0] yearly total income for the bottom templateXValue[last] templateScale of earners was approximately templateYValue[max] thousand British pounds . There was a substantial difference between the templateYLabel[0] incomes of those in the 80th and the 90th templateXLabel[0] , a disparity of approximately 12.8 thousand pounds .

generated_template: The statistic shows the ten most popular television templateTitle[5] in the templateTitle[0] based on their templateTitle[2] of templateYLabel[1] . In 2016 , templateXValue[0] was ranked first with a templateTitle[1] templateTitle[2] of templateYValue[max] templateScale of total templateYLabel[1] .
generated: The statistic shows the ten most popular television Kingdom in the Annual based on their salary of annual .  In 2016 , 10 was ranked first with a gross salary of 54180 % of total annual .

Example 522:
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: As per recent data , in 2020 , templateTitle[1] templateYLabel[0] came to a total of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] , down from the templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] seen in 2019 . The templateYLabel[0] figures for the first templateXLabel[0] of 2020 represent a third templateXLabel[0] of successive decline since October templateTitleDate[max] .
generated: As per recent data , in 2020 , semiconductor Sales came to a total of 2.77 billion U.S. dollars , down from the 2.71 billion U.S. dollars seen in 2019 .  The Sales figures for the first Month of 2020 represent a third Month of successive decline since October 19 .

Example 523:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of yogurt 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', 'per', 'person']: ['13.4', '13.7', '13.7', '14.4', '14.9', '14.9', '14.0', '13.6', '13.5', '12.5', '11.8', '11.6', '11.1', '10.4', '9.2', '8.7', '8.0', '7.0', '6.5']

gold: The timeline shows the per capita consumption of yogurt in the United States from 2000 to 2018 . According to the report , the U.S. yogurt per capita consumption amounted to about 13.4 pounds per person in 2018 .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to about templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[0] templateYLabel[5] in templateXValue[max] .

generated_template: This statistic 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: This statistic shows the Per of consumption in the per pounds permanent person via per from 2000 to 2018 .  In the most recently reported period , close to 13.4 % capita consumption had fixed per person , up from close to 12.5 % in 2009 .  The per are one of the biggest online markets worldwide .

Example 524:
titleEntities: {'Subject': ['PUBG'], 'Date': ['2017']}
title: PUBG player share 2017 , by country
X_Axis['Country']: ['United_States', 'China', 'Germany', 'Russia', 'South_Korea', 'Japan', 'United_Kingdom', 'Other']
Y_Axis['Share', 'of', 'players']: ['24', '19', '6', '6', '5.5', '4.3', '4', '31.2']

gold: First released at the start of 2017 , PlayerUnknown 's Battlegrounds ( PUBG ) has quickly gained a legion of followers across the world , most notably in the United States . Almost one quarter of PUBG 's players base was in the United States , with a further 19 percent originating in China . The Battle Royale game , developed by Bluehole , pits up to 100 online players against each other in a fight to the death until only one player or one team is left standing .
gold_template: First released at the start of templateTitleDate[0] , PlayerUnknown 's Battlegrounds ( templateTitleSubject[0] ) has quickly templatePositiveTrend a legion of followers across the world , most notably in the templateXValue[0] . Almost one quarter of templateTitleSubject[0] 's templateYLabel[1] base was in the templateXValue[0] , with a further templateYValue[1] templateScale originating in templateXValue[1] . The Battle Royale game , developed templateTitle[4] Bluehole , pits up to 100 online templateYLabel[1] against each templateXValue[last] in a fight to the death until only one templateTitle[1] or one team is left standing .

generated_template: This statistic indicates the distribution of templateYLabel[1] templateTitle[1] templateTitle[2] ( mineral sands ) templateYLabel[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . templateXValue[1] , templateXValue[0] , and templateXValue[2] were the leading producers with templateYValue[1] , templateYValue[0] , and templateYValue[2] templateScale , respectively . templateTitle[1] templateTitle[2] is one of the two core product streams of the mineral sands industry .
generated: This statistic indicates the distribution of players player share ( mineral sands ) players in 2017 , by Country .  China , United_States and Germany were the leading producers with 19 , 24 and 6 % , respectively .  player share is one of the two core product streams of the mineral sands industry .

Example 525:
titleEntities: {'Subject': ['South Africa'], 'Date': ['2012', '2023']}
title: Number of tourists in South Africa from 2012 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'tourists', 'in', 'millions']: ['19.6', '18.8', '18.0', '17.3', '16.65', '16.44', '16.0', '15.52', '14.3', '14.86', '14.62', '14.16']

gold: In 2018 , the number of tourists in South Africa amounted to 16.44 million – a figure that steadily increased over the past five years and is expected to see growth in the future . By 2023 , it was forecasted that the number of tourists in South Africa would reach 19.6 million . South Africa tourist arrivals South Africa came second in a ranking of the leading African countries in terms of international tourist arrivals .
gold_template: In templateXValue[5] , the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[5] templateScale – a figure that steadily templatePositiveTrend over the past five years and is expected to see growth in the future . By templateXValue[max] , it was forecasted that the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] would reach templateYValue[max] templateYValue[idxmax(X)] . templateTitleSubject[0] tourist arrivals templateTitleSubject[0] came second in a ranking of the leading African countries in terms of international tourist arrivals .

generated_template: This statistic shows a forecast for the templateYLabel[0] of templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale templateTitle[1] templateYLabel[1] were templateYLabel[2] in the templateTitleSubject[0] .
generated: This statistic shows a forecast for the Number of tourists in the South Africa from 2012 to 2023 .  In 2023 , about 19.6 millions tourists were millions in the South Africa .

Example 526:
titleEntities: {'Subject': ['UK'], 'Date': ['2014', '2017']}
title: Hair and beauty salons : duration between female treatments in the UK 2014 to 2017
Data:
                0         1             2        3       4           5         6
unknown  Pedicure  Manicure  Hair_removal  Tanning  Facial  Hair_color  Haircuts
2014          4.4       3.1           4.3      3.1     4.6         6.2       5.8
2015          6.7       5.2           6.3      7.2     7.8         7.7       7.3
2016          5.6       3.9           4.5      5.0     5.5         6.5       6.0
2017          5.1       3.7           4.4      4.7     5.4         6.6       6.2 

gold: This statistic shows the average number of weeks between hair and beauty salon treatments for females in the United Kingdom in 2014 , 2015 , 2016 and 2017 by type . In 2014 , the time between haircuts averaged 5.8 weeks for women . As of the 2015 survey , this has increased to 7.3 weeks .
gold_template: This statistic shows the average number of weeks templateTitle[4] templateValue[0][2] and templateTitle[1] salon templateTitle[6] for females in the United Kingdom in templateTitleDate[min] , templateLabel[2][0] , templateLabel[3][0] and templateTitleDate[max] by type . In templateTitleDate[min] , the time templateTitle[4] templateValue[0][6] averaged templateValue[1][last] weeks for women . As of the templateLabel[2][0] survey , this has templatePositiveTrend to templateValue[2][last] weeks .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[min] and templateTitleDate[max] . As of templateTitleDate[max] , templateValue[2][max] templateScale of respondents said their templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] technology was templateValue[0][0] modeling ( FDM ) .
generated: The statistic shows the UK beauty salons duration between in 2014 and 2017 .  As of 2017 , 7.8 % of respondents said their UK beauty salons duration technology was Pedicure modeling ( FDM ) .

Example 527:
titleEntities: {'Subject': ['eBay'], 'Date': ['2014', '2019']}
title: eBay : gross merchandise volume 2014 - 2019 , by segment
Data:
                 0      1      2      3      4
Year          2019   2018   2017   2016   2015
Marketplace  85510  89829  83883  79178  77729
StubHub       4700   4751   4520   4310   3575 

gold: This statistic gives information on eBay 's gross merchandise volume from 2014 to 2019 , sorted by segment . In the most recently reported fiscal period , eBay 's Marketplace segment generated a GMV of 85.5 billion U.S. dollars . The company 's overall GMV amounted to 90.21 billion U.S. dollars .
gold_template: This statistic gives information on templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateTitle[3] from templateTitleDate[min] to templateValue[0][0] , sorted templateTitle[6] templateTitle[7] . In the most recently reported fiscal period , templateTitleSubject[0] 's templateLabel[1][0] templateTitle[7] generated a GMV of 85.5 templateScale U.S. dollars . The company 's overall GMV amounted to 90.21 templateScale U.S. dollars .

generated_template: This statistic displays 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: This statistic displays the volume of the eBay automobile production merchandise in 2015 and 2019 , by region .  In 2019 , the Marketplace American merchandise is projected to have volume of about 85510 million units .

Example 528:
titleEntities: {'Subject': ['Limited Brands'], 'Date': ['2010', '2018']}
title: Average store size of Limited Brands worldwide from 2010 to 2018 , by brand
Data:
                             0     1     2     3     4     5     6     7     8
Year                      2018  2017  2016  2015  2014  2013  2012  2011  2010
Victoria's Secret stores  6484  6415  6349  6187  6083  6018  6038  5941  5892
Bath & Body Works         2585  2532  2459  2382  2359  2364  2365  2374  2369
La Senza                     0     0     0     0     0  3185  3219  3312  3343 

gold: In 2018 , the average size of a Victoria 's Secret store was about 6,484 selling square feet . The average size of a Bath & Body Works store was 2,585 selling square feet that year . Limited Brands ( currently known as L Brands , Inc ) is an American fashion retailer based in Columbus , Ohio .
gold_template: In templateValue[0][0] , the templateTitle[0] templateTitle[2] of a Victoria 's templateLabel[1][1] templateTitle[1] was about templateValue[1][0] selling square feet . The templateTitle[0] templateTitle[2] of a templateLabel[2][0] templateLabel[2][1] templateLabel[2][2] templateLabel[2][3] templateTitle[1] was templateValue[2][0] selling square feet that templateLabel[0][0] . templateTitleSubject[0] ( currently known as L templateTitleSubject[0] , Inc ) is an American fashion retailer based in Columbus , Ohio .

generated_template: This statistic shows the templateTitle[0] of directly operated templateTitleSubject[0] stores templateTitle[5] from templateValue[0][last] to templateValue[0][0] , templateTitle[8] templateTitle[9] . In templateValue[0][0] , templateTitleSubject[0] operated templateValue[1][0] templateTitle[1] throughout templateLabel[1][0] templateLabel[1][1] .
generated: This statistic shows the Average of directly operated Limited Brands stores worldwide from 2010 to 2018 , by .  In 2018 , Limited Brands operated 6484 store throughout Victoria's Secret .

Example 529:
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] , there were about templateYValue[0] templateScale templateYLabel[1] in the templateTitle[4] with a templateTitle[2] mother . This is a significant templateNegativeTrend from templateXValue[min] levels , when there were about templateYValue[min] templateScale templateYLabel[1] templateYLabel[2] with a templateTitle[2] mother . templateTitle[2] parenthood The typical family is comprised of two parents and at least one child .
generated: In 2019 , there were about 67.3 % turnout in the 1918 with a UK mother .  This is a significant decrease from 1918 levels , when there were about 57.2 % turnout with a UK mother .  UK parenthood The typical family is comprised of two parents and at least one child .

Example 530:
titleEntities: {'Subject': ['Denver Broncos'], 'Date': ['2019']}
title: Regular season home attendance of the Denver Broncos 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['607497', '611571', '610846', '614193', '615381', '615517', '614977', '613062', '602618', '599264', '600928', '604074', '612888', '610776']

gold: This graph depicts the total regular season home attendance of the Denver Broncos franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 607,497 .
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: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the St. Louis / templateTitleSubject[0] Rams 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)] . The franchise moved from St. Louis to templateTitleSubject[0] before the templateXValue[3] templateTitle[1] .
generated: This graph depicts the total Regular season Home attendance of the St. Louis / Denver Broncos Rams franchise of the National Football League from 2006 to 2019 .  In 2019 , the Regular season Home attendance of the franchise was 607497 .  The franchise moved from St. Louis to Denver Broncos before the 2016 season .

Example 531:
titleEntities: {'Subject': ['Spain'], 'Date': ['2019']}
title: Unemployment rate in Spain 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']: ['14.7', '15.49', '17.22', '19.64', '22.06', '24.44', '26.09', '24.79', '21.39', '19.86', '17.86', '11.25', '8.23', '8.45', '9.15', '11.09', '11.28', '11.15', '10.35', '13.79', '15.48']

gold: This statistic shows the unemployment rate in Spain from 1999 to 2019 . In 2019 , the unemployment rate in Spain was 14.7 percent . Today , Spain has the second-highest unemployment rate of all EU states .
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 . Today , templateTitleSubject[0] has the second-highest templateYLabel[0] templateYLabel[1] of all EU states .

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 Spain from 1999 to 2019 .  In 2019 , the Unemployment rate in Spain was at approximately 14.7 % .

Example 532:
titleEntities: {'Subject': ['Italy'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Italy 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']: ['128.1', '123.2', '116.9', '113.35', '106.55', '103.86', '103.73', '103.72', '98.81', '95.5', '95.55', '96.15', '93.04']

gold: Between 2006 and 2018 arrivals of both domestic and foreign guests at accommodation establishments in Italy have increased . In 2018 there were over 128 million arrivals at tourist accommodation in Italy , compared to 96 million ten years earlier .
gold_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_template: In templateXValue[max] over templateYValue[max] templateScale tourists arrived in different templateTitle[3] establishments in the Czech Republic ( officially templateTitleSubject[0] ) . The templateYLabel[0] of tourists visiting templateTitleSubject[0] has templatePositiveTrend over the years , with the largest templateYLabel[0] of international tourists coming from Germany – in templateXValue[2] , approximately 1.9 templateScale visitors arrived from Germany alone . templateTitleSubject[0] – the perfect destination for beer lovers One of templateTitleSubject[0] 's attractions for tourists is its beer .
generated: In 2018 over 128.1 millions tourists arrived in different accommodation establishments in the Czech Republic ( officially Italy ) .  The Number of tourists visiting Italy has increased over the years , with the largest Number of international tourists coming from Germany – in 2016 , approximately 1.9 millions visitors arrived from Germany alone .  Italy – the perfect destination for beer lovers One of Italy 's attractions for tourists is its beer .

Example 533:
titleEntities: {'Subject': ['Algeria'], 'Date': ['2024']}
title: Inflation rate 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['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['8.7', '7.9', '6.7', '5.5', '4.1', '2', '4.27', '5.59', '6.4', '4.78', '2.92', '3.26', '8.92', '4.5', '3.91', '5.74', '4.86', '3.67', '2.32', '1.38', '3.97', '4.26', '1.43', '4.2', '0.3', '2.6', '4.95', '5.7', '18.7', '29.8', '29', '20.5', '31.7', '25.9', '9.27', '9.17', '5.94', '5.86', '10.01', '10.43', '6.31']

gold: This statistic shows the average inflation rate in Algeria from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Algeria amounted to about 4.27 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 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 Algeria 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 .

Example 534:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Share of U.S. middle class population 2015 , by race and ethnicity
Data:
             0      1         2      3           4
unknown  Asian  Black  Hispanic  White  All_adults
1971        56     46        58     63          61
2001        52     49        49     56          54
2015        47     45        48     52          50 

gold: This statistic shows the percentage of the U.S. adult population that belonged to the middle class from 1971 to 2015 , by race and ethnicity . In 2015 , about 50 percent of U.S. adult residents were part of the middle class . In that year 56 percent of those who some hadcollege belonged to the middle class , compared to only 37 percent of those who did not graduate from high school .
gold_template: This statistic shows the templateScale of the templateTitleSubject[0] adult templateTitle[4] that belonged to the templateTitle[2] templateTitle[3] from templateLabel[1][0] to templateTitleDate[0] , templateTitle[6] templateTitle[7] and templateTitle[8] . In templateTitleDate[0] , about templateValue[3][last] templateScale of templateTitleSubject[0] adult residents were part of the templateTitle[2] templateTitle[3] . In that year templateValue[1][0] templateScale of those who some hadcollege belonged to the templateTitle[2] templateTitle[3] , compared to only 37 templateScale of those who did not graduate from high school .

generated_template: The graph shows the templateTitle[0] and templateTitle[1] of templateTitleSubject[0] templateTitle[3] in the templateTitle[2] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In templateTitleDate[0] , about templateValue[1][last] templateScale templateValue[0][4] templateTitle[3] were templateLabel[1][0] templateTitle[3] .
generated: The graph shows the Share and U.S. of class in the middle in 2015 , by .  In 2015 , about 61 % All_adults class were 1971 class .

Example 535:
titleEntities: {'Subject': ['Zalando'], 'Date': ['2012', '2019']}
title: Zalando : quarterly revenue 2012 to 2019
X_Axis['Quarter']: ["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"]
Y_Axis['Quarterly', 'revenue', 'in', 'million', 'euros']: ['1597.3', '1378.2', '1661.6', '1200.2', '1330.0', '1196.0', '1333.7', '1074.7', '1100.5', '980.2', '1091.6', '834.8', '916.4', '796.1', '868.5', '713.1', '733.0', '643.6', '665.6', '501.4', '546.4', '500.7', '549.6', '403.8', '436.6', '372.0', '404.0', '284.0', '257.0', '214.0']

gold: This statistic presents information on the quarterly revenue of Zalando from the first quarter of 2012 to second quarter of 2019 . As of the second quarter of 2019 , the Germany-based fashion retailer 's revenue amounted to approximately 1.6 billion euros , which marked an increase on the previous quarter . Over the period in question , Zalando 's quarterly revenue increased from 214 million to approximately 1.4 billion euros , exceeding the one billion euro level for the first time in 2016 .
gold_template: This statistic presents information on the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to second templateXLabel[0] of templateTitleDate[max] . As of the second templateXLabel[0] of templateTitleDate[max] , the Germany-based fashion retailer 's templateYLabel[1] amounted to approximately templateYValue[0] templateScale templateYLabel[3] , which marked an templatePositiveTrend on the previous templateXLabel[0] . Over the period in question , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templatePositiveTrend from templateYValue[min] templateScale to approximately templateYValue[1] templateScale templateYLabel[3] , exceeding the templateYValue[1] templateScale euro level for the first time in 2016 .

generated_template: In the third templateXLabel[0] of templateTitleDate[max] , the templateYLabel[0] to templateYLabel[1] templateYLabel[2] in the templateTitle[4] amounted to templateYValue[0] templateScale . templateYLabel[0] to templateYLabel[1] templateYLabel[2] explained The templateYLabel[0] to templateYLabel[1] financial templateYLabel[2] indicates the relationship between shareholders ' templateYLabel[1] and templateYLabel[0] used to finance the assets of a company . In order to make the calculation the data of the two required components are taken from the firm 's balance sheet .
generated: In the third Quarter of 2019 , the Quarterly to revenue million in the 2019 amounted to 1597.3 million .  Quarterly to revenue million explained The Quarterly to revenue financial million indicates the relationship between shareholders ' revenue and Quarterly used to finance the assets of a company .  In order to make the calculation data of the two required components are taken from the firm 's balance sheet .

Example 536:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Leading trends in technology in restaurants in the U.S. 2015 
X_Axis['Response']: ['Tablet_computers_(e.g._iPad)_for_menus_wine_lists_and_ordering', 'Smartphone/tablet_apps_for_consumers_(e.g._ordering_menus_daily_deals)', 'Smartphone/tablet_apps_for_chefs/restaurateurs_(e.g._recipes_table_management_POS_tracking)', 'Mobile/wireless_payment_options', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['29', '26', '22', '21', '2']

gold: This statistic shows the leading trends in technology in restaurants in the United States as forecasted by professional chefs for 2015 . During the survey , 29 percent of the respondents stated that tablet computers ( e.g . iPad ) for menus , wine lists and ordering was the hottest technology trend in restaurants in 2015 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] in templateTitle[3] in the templateTitle[4] as forecasted by professional chefs templateXValue[0] templateTitleDate[0] . During the survey , templateYValue[max] templateScale of the templateYLabel[1] stated that templateXValue[0] ( e.g . iPad ) templateXValue[0] , templateXValue[0] and templateXValue[0] was the hottest templateTitle[2] trend in templateTitle[3] in templateTitleDate[0] .

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 Leading trends an active U.S. or subscription in the 2015 as of 2017 , sorted 2015 .  According to the source , 29 % of Smartphone/tablet_apps_for_consumers_(e.g._ordering_menus_daily_deals) who subscribe to online video or music subscriptions had a U.S. or subscription as of 2017 .

Example 537:
titleEntities: {'Subject': ['Hispanic'], 'Date': ['2018']}
title: Hispanic population , by state U.S. 2018
X_Axis['State']: ['California', 'Texas', 'Florida', 'New_York', 'Arizona', 'Illinois', 'New_Jersey', 'Colorado', 'New_Mexico', 'Georgia', 'North_Carolina', 'Pennsylvania', 'Washington', 'Nevada', 'Massachusetts', 'Virginia', 'Maryland', 'Connecticut', 'Oregon', 'Michigan', 'Indiana', 'Ohio', 'Utah', 'Oklahoma', 'Wisconsin', 'Tennessee', 'Kansas', 'Minnesota', 'South_Carolina', 'Missouri', 'Louisiana', 'Arkansas', 'Idaho', 'Nebraska', 'Alabama', 'Iowa', 'Rhode_Island', 'Kentucky', 'Hawaii', 'Delaware', 'Mississippi', 'District_of_Columbia', 'Wyoming', 'New_Hampshire', 'Alaska', 'Montana', 'South_Dakota', 'North_Dakota', 'West_Virginia', 'Maine', 'Vermont']
Y_Axis['Population', 'in', 'thousands']: ['15540', '11369', '5562', '3753', '2266', '2209', '1839', '1235', '1029', '1022', '996', '975', '970', '881', '847', '813', '628', '590', '556', '517', '472', '456', '450', '429', '404', '373', '348', '308', '295', '253', '240', '230', '222', '214', '211', '191', '168', '162', '151', '92', '87', '79', '58', '53', '53', '42', '34', '27', '26', '22', '12']

gold: In 2018 , California had the highest Hispanic population in the United States , with over 15.5 million people claiming Hispanic heritage . Texas , Florida , New York , and Arizona rounded out the top five states . History of Hispanic people Hispanic people are those whose heritage stems from a former Spanish colony .
gold_template: In templateTitleDate[0] , templateXValue[0] had the highest templateTitleSubject[0] templateYLabel[0] in the templateTitle[4] , with over templateYValue[max] templateScale people claiming templateTitleSubject[0] heritage . templateXValue[1] , templateXValue[2] , templateXValue[3] , and templateXValue[4] rounded out the top five states . History of templateTitleSubject[0] people templateTitleSubject[0] people are those whose heritage stems from a former Spanish colony .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] from 1974 to 2003 , templateTitle[4] templateXLabel[0] . From 1974 to 2003 , templateYValue[max] templateTitle[1] templateYLabel[1] have been recorded in templateXValue[11] .
generated: This statistic shows the Population of thousands in the Hispanic from 1974 to 2003 , U.S. State .  From 1974 to 2003 , 15540 population thousands have been recorded in Pennsylvania .

Example 538:
titleEntities: {'Subject': ['Chicago'], 'Date': ['2010', '2016']}
title: Chicago Bulls 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            8.0      8.0      7.5      7.5     7.75      7.5
Soft drink     5.75     5.75      5.5     5.25     5.25      5.0
Hot dog        6.25     6.25      6.0     5.75     5.75     5.75 

gold: The statistic depicts the concession stand prices at games of the Chicago Bulls ( NBA ) from 2010/11 to 2015/16 . In the 2012/13 season , a 16 ounce beer cost 7.50 U.S. dollars .
gold_template: The statistic depicts the templateTitle[2] templateTitle[3] templateTitle[4] at games of the templateTitleSubject[0] templateTitle[1] ( 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_template: The statistic depicts the templateTitle[3] price of templateLabel[2][0] drinks , beers and templateLabel[3][0] dogs in the templateTitleSubject[0] Association from the templateValue[0][last] season to the templateValue[0][0] season . The templateTitle[3] price of a templateLabel[3][0] templateLabel[3][1] was templateValue[3][last] U.S. dollars in the templateValue[0][last] season . NBA templateTitle[4] stands – additional information The experience to watch a NBA game live usually costs more than just the price of the ticket .
generated: The statistic depicts the stand price of Soft drinks , beers and Hot dogs in the Chicago Association from the 2010/11 season to the 2015/16 season .  The stand price of a Hot dog was 5.75 U.S. dollars in the 2010/11 season .  NBA prices stands – additional information The experience to watch a NBA game live usually costs more than just the price of the ticket .

Example 539:
titleEntities: {'Subject': ['Global'], 'Date': ['2010', '2016']}
title: Global spending on golf sponsorships 2010 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Spending', 'in', 'billion', 'U.S.', 'dollars']: ['1.82', '1.73', '1.65', '1.6', '1.51', '1.44', '1.36']

gold: This statistic shows the worldwide spending for golf sponsorship from 2010 to 2016 . In 2013 , global spendings on golf sponsorships amounted to 1.6 billion U.S. dollars .
gold_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_template: This statistic shows the worldwide templateYLabel[0] for templateTitle[2] sponsorship from templateXValue[min] to templateXValue[max] . In the templateXLabel[0] templateXValue[3] , the templateTitleSubject[0] templateTitle[2] sponsorship templateYLabel[0] amounted to templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the worldwide Spending for golf sponsorship from 2010 to 2016 .  In the Year 2013 , the Global golf sponsorship Spending amounted to 1.6 billion U.S. dollars .

Example 540:
titleEntities: {'Subject': ['H M Group'], 'Date': ['2007', '2018']}
title: Gross sales of the H & M Group worldwide 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Gross', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['23232.37', '27696.63', '25509.5', '24004.4', '23799.2', '22909.7', '21181.9', '18589.5', '18129.3', '17075.5', '12800.2', '14450.2', '8833.02']

gold: This statistic depicts the gross sales of the H & M Group worldwide from 2007 to 2018 . In fiscal year 2017 , global gross sales of the H & M Group amounted to about 27.7 billion U.S. dollars.H & MH & M is a leading global fashion company with strong values and a clear business concept . H & M has a passion for fashion , a belief in people and a desire to always exceed customers ' expectations – and to do so in a sustainable way .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[11] to templateXValue[max] . In fiscal templateXLabel[0] templateXValue[1] , global templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] amounted to about templateYValue[max] templateScale templateYLabel[3] dollars.H templateTitle[3] MH templateTitle[3] templateTitleSubject[0] is a leading global fashion company with strong values and a clear business concept . templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] has a passion for fashion , a belief in people and a desire to always exceed customers ' expectations – and to do so in a sustainable way .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] per templateXLabel[0] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateYLabel[1] in templateTitleSubject[0] were committed templateTitle[2] templateTitle[3] . An overall templateNegativeTrend in the templateYLabel[0] of templateTitle[1] templateTitle[2] was observed within the time period shown .
generated: This statistic shows the Gross of sales H per Year in H M Group between 2006 and 2018 .  In 2018 , about 23232.37 sales in H M Group were committed H & .  An overall decrease in the Gross of sales H was observed within the time period shown .

Example 541:
titleEntities: {'Subject': ['Slovakia'], 'Date': ['2019']}
title: Employment by economic sector in Slovakia 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   2.66   2.68   2.71   2.89   3.18    3.5   3.31   3.24   3.08   3.23   3.59
Industry     36.81  36.98  37.16  36.47  36.11  35.45  35.82  37.54  37.49  37.12  37.93
Services     60.54  60.34  60.13  60.64  60.71  61.06  60.87  59.23  59.43  59.65  58.49 

gold: The statistic shows the distribution of employment in Slovakia by economic sector from 2009 to 2019 . In 2019 , 2.66 percent of the employees in Slovakia were active in the agricultural sector , 36.81 percent in industry and 60.54 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 Slovakia by economic sector from 2009 to 2019 .  In 2019 , 2.66 % of the employees in Slovakia were active in the agricultural sector , 36.81 % in Industry and 60.54 % in the service sector .

Example 542:
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 templateTitle[1] templateTitle[2] 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: This statistic shows the Net of electricity consumption per Year in Global between 1980 and 2016 .  In the period of consideration , consumption presented an overall trend of decline .  The Year with the lowest amount of consumption was 2014 , with a total of 207 consumption traffic in Global .

Example 543:
titleEntities: {'Subject': ['Air Force'], 'Date': ['1995', '2018']}
title: Active duty U.S. Air Force personnel numbers from 1995 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995']
Y_Axis['Number', 'of', 'Air', 'Force', 'personnel']: ['321618', '318580', '313723', '307326', '312453', '326573', '328812', '328821', '329640', '328847', '322900', '329094', '344529', '349362', '351326', '396382']

gold: This statistic shows the number of active duty U.S. Air Force personnel from 1995 to 2018 . In 2018 , there were 321,618 active duty U.S. Air Force members , as compared to 351,326 in 2000 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[7] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] members , as compared to templateYValue[14] in templateXValue[14] .

generated_template: This statistic represents the templateScale of templateYLabel[2] that were templateTitle[1] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , some templateYValue[last] templateScale of templateYLabel[1] templateYLabel[2] in the templateTitle[0] were templateTitle[1] templateYLabel[2] . A templateTitle[1] templateTitle[2] means that a child was delivered after less than 37 weeks of gestation .
generated: This statistic represents the percentage of Force that were duty Force in the Active from 1995 to 2018 .  In 1995 , some 396382 % of Air Force in the Active were duty Force .  A duty U.S. means that a child was delivered after less than 37 weeks of gestation .

Example 544:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2007', '2017']}
title: Value of motor vehicle imports and exports in the United Kingdom ( UK ) 2007 to 2017
Data:
            0     1     2     3     4     5     6     7     8     9    10
Year     2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007
Exports  34.3  31.5  27.0  27.3  26.4  23.9  22.5  19.1  13.3  18.0  16.7
Imports  40.8  39.8  37.8  33.3  29.5  25.7  25.7  23.8  18.9  24.0  26.2 

gold: The value of motor vehicle trading reached its peak in 2017 . That year , the United Kingdom exported 34.3 billion British pounds worth of such products , while imports valued at 40.8 million British pounds . The chasm between value of imports and exports closed again slightly that year .
gold_template: The templateTitle[0] of templateTitle[1] templateTitle[2] trading reached its peak in templateValue[0][0] . That templateLabel[0][0] , the templateTitleSubject[0] exported templateValue[1][0] templateScale British pounds worth of such products , while templateTitle[3] valued at templateValue[2][0] templateScale British pounds . The chasm between templateTitle[0] of templateTitle[3] and templateTitle[4] closed again slightly that templateLabel[0][0] .

generated_template: This statistic shows the templateTitle[1] and templateTitle[2] of the templateTitle[3] templateTitle[4] templateTitle[5] from templateValue[0][last] to templateValue[0][0] , templateTitle[8] templateTitle[9] . In templateValue[0][1] , the templateTitle[2] and the templateTitle[3] templateTitle[4] of the country amounted to approximately templateValue[1][6] templateScale euros .
generated: This statistic shows the motor and vehicle of the imports exports United from 2007 to 2017 , 2007 2017 .  In 2016 , the vehicle and the imports exports of the country amounted to approximately 22.5 million euros .

Example 545:
titleEntities: {'Subject': ['Toyota'], 'Date': ['2007', '2019']}
title: Toyota 's R & D expenditure 2007 to 2019
X_Axis['Financial', 'year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['R&D', 'spending', 'in', 'million', 'Japanese', 'yen']: ['1048800', '1064200', '1037500', '1055600', '1004547', '910517', '807454', '779806', '730340', '725345', '904075', '958882', '890782']

gold: The graph shows Toyota 's R & D costs from FY 2007 to FY 2018 . Toyota incurred R & D costs of about one trillion Japanese yen in FY 2019 . Japan-based Toyota Motor Corporation ranks among the largest automobile manufacturers in the world .
gold_template: The graph shows templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] costs from FY templateXValue[last] to FY templateXValue[1] . templateTitleSubject[0] incurred templateTitle[2] templateTitle[3] templateTitle[4] costs of about templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] in FY templateXValue[0] . Japan-based templateTitleSubject[0] Motor Corporation ranks among the largest automobile manufacturers in the world .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] reported in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateXValue[0] , there were a total of templateYValue[max] templateYLabel[1] reported in templateXValue[0] .
generated: This statistic shows the total R&D of spending reported in Toyota 2007 , D Financial .  In 2019 , there were a total of 1064200 spending reported in 2019 .

Example 546:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2015']}
title: United Kingdom ( UK ) government approval rating monthly in 2015
Data:
                             0                    1                 2                 3               4                5                6
Month       January_29-30_2015  February_26-27_2015  March_30-31_2015  April_29-30_2015  May_28-29_2015  June_25-26_2015  July_30-31_2015
Approve                     32                   33                34                37              40               40               38
Disapprove                  51                   53                50                51              44               46               46
Don't know                  17                   14                16                12              16               14               16 

gold: This statistic shows the monthly approval rating of the United Kingdom government from the end of January 2015 to the end of July 2015 . Participants were asked if they approve or disapprove of the government 's record to date . During the period , approval ratings increased slightly from 32 percent in January to 38 percent in July , with the previous months of May and June marking the peak in approval ratings at 40 percent .
gold_template: This statistic shows the templateTitle[6] templateTitle[4] templateTitle[5] of the templateTitleSubject[0] government from the end of templateValue[0][0] to the end of templateValue[0][6] templateValue[0][0] . Participants were asked if they templateLabel[1][0] or templateLabel[2][0] of the templateTitle[3] 's record to date . During the period , templateTitle[4] ratings templatePositiveTrend slightly from templateValue[1][0] templateScale in templateValue[0][0] to templateValue[1][last] templateScale in templateValue[0][6] , with the previous months of templateValue[0][4] and templateValue[0][5] marking the peak in templateTitle[4] ratings at templateValue[1][4] templateScale .

generated_template: This statistic shows the results of a templateTitleDate[0] templateTitle[0] among adult Americans on the templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] . During the templateTitle[0] , templateValue[1][0] templateScale of respondents stated that they perceive templateTitle[2] templateTitle[3] templateTitle[4] as templateLabel[1][0] templateValue[0][0] than templateTitle[2] without templateTitle[4] , while templateValue[2][0] templateScale stated they think tattooed templateTitle[2] are templateLabel[2][0] templateValue[0][0] , and templateValue[3][0] templateScale did not see a templateLabel[3][1] between templateTitle[2] templateTitle[3] or without templateTitle[4] when it comes to being templateValue[0][0] . templateTitle[4] in American society As can be seen above , compared to templateTitle[2] without templateTitle[4] , templateValue[1][0] templateScale of templateTitle[2] find those templateTitle[3] templateTitle[4] to be characteristically templateValue[0][0] while only templateValue[1][1] templateScale of respondents find templateTitle[2] templateTitle[3] templateTitle[4] characteristically templateValue[0][1] .
generated: This statistic shows the results of a 2015 United among adult Americans on the Kingdom of UK government approval .  During the United , 32 % of respondents stated that they perceive UK government approval as Approve January_29-30_2015 than UK without approval , while 51 % stated they think tattooed UK are Disapprove January_29-30_2015 , and 17 % did not see a know between UK government or without approval when it comes to being January_29-30_2015 .  approval in American society As can be seen above , compared to UK without approval , 32 % of UK find those government approval to be characteristically January_29-30_2015 while only 33 % of respondents find UK government approval characteristically February_26-27_2015 .

Example 547:
titleEntities: {'Subject': ['Millennials U.S.'], 'Date': ['2011']}
title: Percentage of Millennials aged 18 to 34 in the U.S. , by race or ethnicity 2011
X_Axis['Ages', '18-34', 'by', 'race/ethnicity']: ['White_non-Hispanic', 'Hispanic', 'Black_non-Hispanic', 'Asian', 'Other']
Y_Axis['Percentage', 'of', 'Millennials']: ['60', '19', '14', '5', '2']

gold: The statistic shows the share of members of different etnicities or races within the Millennials in the U.S . The term Millennials refers to the age group 18 to 34 here . In 2011 , 60 percent of the Millennials in the United States were non-Hispanic Whites .
gold_template: The statistic shows the share of members of different etnicities or races within the templateYLabel[1] in the templateTitle[5] . The term templateYLabel[1] refers to the age group templateTitle[3] to templateTitle[4] here . In templateTitleDate[0] , templateYValue[max] templateScale of the templateYLabel[1] in the templateTitle[5] were templateXValue[0] Whites .

generated_template: This statistic shows the templateScale of templateYLabel[1] in the templateTitle[3] diagnosed templateTitle[1] templateTitle[2] A , sorted templateTitle[5] templateXLabel[0] templateXLabel[1] , as of templateTitleDate[0] . In that year , templateYValue[min] templateScale of all Americans diagnosed templateTitle[1] templateTitle[2] A were between 0 and 4 templateXValue[0] of templateXLabel[0] .
generated: This statistic shows the percentage of Millennials in the 18 diagnosed Millennials aged A , sorted U.S. Ages 18-34 , as of 2011 .  In that year , 2 percentage of all Americans diagnosed Millennials aged A were between 0 and 4 White_non-Hispanic of Ages .

Example 548:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Production of copper in Canada by province 2018
X_Axis['Month']: ['Newfoundland_and_Labrador', 'Prince_Edward_Island', 'Nova_Scotia', 'New_Brunswick', 'Quebec', 'Ontario', 'Manitoba', 'Saskatchewan', 'Alberta', 'British_Columbia', 'Yukon', 'Northwest_Territories', 'Nunavut']
Y_Axis['Production', 'in', 'metric', 'tons']: ['27456', '0', '0', '487', '35912', '135297', '33608', '0', '0', '293468', '9282', '0', '0']

gold: This statistic displays preliminary estimates of the copper production in Canada , distributed by province , in 2018 . During that year , Quebec produced some 35,912 metric tons of this mineral . Copper is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .
gold_template: This statistic displays preliminary estimates of the templateTitle[1] templateYLabel[0] in templateTitleSubject[0] , distributed templateTitle[3] templateTitle[4] , in templateTitleDate[0] . During that year , templateXValue[4] produced some templateYValue[4] templateYLabel[1] templateYLabel[2] of this mineral . templateTitle[1] is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .

generated_template: This statistic shows the templateTitleSubject[0] Business templateTitleSubject[0] templateYLabel[0] from 2019 to 2020 . In 2020 , the templateYLabel[0] amounted to templateYValue[0] . The templateYLabel[0] consists of 10 indicators derived from questions addressing templateTitleSubject[0] owners : Plans to create employment ; plans to make capital outlays ; plans to templatePositiveTrend inventories ; expect economy to improve ; expect real sales higher ; current inventory ; current job openings ; expected credit conditions ; now a good time to expand ; earnings trends .
generated: This statistic shows the Canada Business Production from 2019 to 2020 .  In 2020 , the Production amounted to 27456 .  The Production consists of 10 indicators derived from questions addressing Canada owners : Plans to create employment ; plans to make capital outlays ; plans to increase inventories ; expect economy to improve ; expect real sales higher ; current inventory ; current job openings ; expected credit conditions ; now a good time to expand ; earnings trends .

Example 549:
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[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] annual templateTitle[7] templateTitle[8] . We Are Flint found that templateYValue[0] templateScale of internet users with templateTitle[7] earnings of templateXValue[0] 30,000 US dollars per templateXLabel[0] used the social networking site .
generated: This statistic shows the share of internet users in the United States who were using YouTube as of 2019 , sorted by annual age group .  We Are Flint found that 81 % of internet users with age earnings of 15-25_years 30,000 US dollars per Year used the social networking site .

Example 550:
titleEntities: {'Subject': ['Bank America'], 'Date': ['2009', '2018']}
title: Tier 1 capital ratio at Bank of America 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Tier', '1', 'capital', 'ratio']: ['13.2', '13', '12.4', '11.3', '13.4', '12.2', '11', '9.9', '8.6', '7.8']

gold: The statistic presents the tier 1 capital ratio at the Bank of America from 2009 to 2018 . The tier 1 common capital ratio is a measure of a bank 's core equity capital compared with its total risk-weighted assets . In 2018 , the tier 1 common capital ratio of the Bank of America amounted to 13.2 percent .
gold_template: The statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] at the templateTitleSubject[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] common templateYLabel[2] templateYLabel[3] is a measure of a templateTitleSubject[0] 's core equity templateYLabel[2] compared with its total risk-weighted assets . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] common templateYLabel[2] templateYLabel[3] of the templateTitleSubject[0] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] is the core measure of bank 's financial strength from the regulator 's point of view . It is the templateYLabel[3] of bank 's core equity templateYLabel[2] to the total risk-weighted assets .
generated: The statistic presents the Tier 1 capital ratio at Bank America from 2009 to 2018 .  Tier 1 capital is the core measure of bank 's financial strength from the regulator 's point of view .  It is the ratio of bank 's core equity capital to the total risk-weighted assets .

Example 551:
titleEntities: {'Subject': ['Dollarama', 'Canada'], 'Date': ['2019']}
title: Number of Dollarama stores Canada by province 2019
X_Axis['Month']: ['Ontario', 'Québec', 'Alberta', 'British_Columbia', 'Nova_Scotia', 'New_Brunswick', 'Manitoba', 'Saskatchewan', 'Newfoundland_and_Labrador', 'Prince_Edward_Island']
Y_Axis['Number', 'of', 'stores']: ['503', '344', '107', '102', '39', '38', '37', '31', '19', '5']

gold: Dollarama Inc. is a Canadian owned retail chain headquartered in Montreal . There were 1,225 Dollarama stores in Canada as of February 2019 . Ontario had the highest number of stores per province , 503 , and Prince Edward Island had just five .
gold_template: templateTitleSubject[0] Inc. is a Canadian owned retail chain headquartered in Montreal . There were 1,225 templateTitleSubject[0] templateYLabel[1] in templateTitleSubject[1] as of 2019 . templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] per templateTitle[5] , templateYValue[max] , and templateXValue[last] Island had just templateYValue[min] .

generated_template: templateXValue[0] Cerrona , 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] thousand templateYLabel[1] , 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] templateYLabel[1] respectively . This was ranked first with a templateYLabel[0] of approximately templateYValue[1] thousand templateYLabel[1] , but Korean templateYLabel[1] .
generated: Ontario Cerrona , founder of the eyewear company Luxottica , podiumed in the yearly Dollarama of Italian billionaires , published by the American business magazine Dollarama .  With the total net worth estimated at 503 thousand stores , Ontario put ahead other Italian entrepreneurs - Québec , the CEO of the Italian confectionary company Québec and the fashion designer Alberta , whose fortunes reached respectively 344 and 107 stores respectively .  This was ranked first with a Number of approximately 344 thousand stores , but Korean stores .

Example 552:
titleEntities: {'Subject': ['Average'], 'Date': ['2015']}
title: Average cost of hospital per day by country 2015
X_Axis['Country']: ['United_States', 'Switzerland', 'New_Zealand', 'Australia', 'South_Africa', 'Spain']
Y_Axis['Average', 'cost', 'in', 'U.S.', 'dollars']: ['5220', '4781', '2142', '765', '631', '424']

gold: The U.S. , followed by Switzerland , had the highest average cost per day to stay in a hospital as of 2015 . At that time the hospital costs per day in the U.S. were on average 5,220 U.S. dollars . In comparison , the hospital costs per day in Spain stood at an average of 424 U.S. dollars .
gold_template: The templateYLabel[2] , followed templateTitle[5] templateXValue[1] , had the highest templateYLabel[0] templateYLabel[1] templateTitle[3] templateTitle[4] to stay in a templateTitle[2] as of templateTitleDate[0] . At that time the templateTitle[2] costs templateTitle[3] templateTitle[4] in the templateYLabel[2] were on templateYLabel[0] templateYValue[max] templateYLabel[2] templateYLabel[3] . In comparison , the templateTitle[2] costs templateTitle[3] templateTitle[4] in templateXValue[last] stood at an templateYLabel[0] of templateYValue[min] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows of the templateYLabel[0] templateYLabel[1] templateYLabel[2] of all templateYLabel[1] templateTitle[4] templateTitle[5] in the templateTitle[3] , sorted templateTitle[7] mall templateTitle[8] in templateXLabel[3] templateXLabel[4] of templateXLabel[0] templateXLabel[1] templateXLabel[2] . In templateTitleDate[0] , templateTitle[4] templateTitle[5] sized between templateXValue[2] and templateXValue[2] templateXLabel[3] templateXLabel[4] made a templateYLabel[0] of templateYValue[2] templateScale templateYLabel[4] templateYLabel[5] of templateYLabel[1] templateYLabel[2] .
generated: This statistic shows of the Average cost U.S. of all cost day by in the per , sorted 2015 mall in Country of .  In 2015 , day by sized between New_Zealand and Country made a Average of 2142 million dollars of cost U.S. .

Example 553:
titleEntities: {'Subject': ['World'], 'Date': ['2008', '2018']}
title: World carbon dioxide emissions by region 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
Africa                               1235.5   1207.0   1192.1   1174.5   1167.1   1134.4   1107.1   1073.7   1072.1   1041.9   1028.0
South and Central America            1261.5   1279.2   1294.7   1321.7   1331.3   1308.9   1262.0   1215.5   1164.2   1093.1   1132.6
Middle East                          2118.3   2078.7   2061.5   2019.5   1965.2   1926.3   1853.3   1789.0   1736.2   1656.5   1620.5
Europe                               4209.3   4278.7   4247.3   4191.6   4202.0   4436.9   4541.5   4615.2   4705.8   4616.4   4965.7
North America                        6035.6   5920.5   5938.3   6048.7   6268.9   6167.0   5998.9   6262.8   6375.8   6139.9   6600.4
Asia Pacific                        16274.1  16274.3  15984.0  15877.0  15787.7  15660.2  15308.8  14860.1  13986.8  13246.6  12954.7
Commonwealth of Independent States   2100.5   2001.3   1997.4   1986.8   2027.6   2014.7   2063.2   2035.2   1939.1   1875.7   1993.0 

gold: The largest carbon dioxide ( CO2 ) emitter in the world is Asia Pacific , where there was 16.27 billion metric tons of carbon dioxide emitted in 2018 . China , alone , produced about 27 percent of the global territorial fossil fuel CO2 emissions . It is likely that top emitting countries will continue releasing more carbon dioxide every year .
gold_template: The largest templateTitle[1] templateTitle[2] ( CO2 ) emitter in the templateTitleSubject[0] is templateLabel[6][0] templateLabel[6][1] , where there was 16.27 templateScale metric tons of templateTitle[1] templateTitle[2] emitted in templateValue[0][0] . China , alone , produced about 27 templateScale of the global territorial fossil fuel CO2 templateTitle[3] . It is likely that top emitting countries will continue releasing more templateTitle[1] templateTitle[2] every templateLabel[0][0] .

generated_template: The statistic shows the templateTitle[0] of templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , templateTitle[3] templateTitle[4] templateTitle[5] . In templateValue[0][0] , about templateValue[4][0] templateScale metric tons were carried templateTitle[3] templateLabel[4][0] templateTitle[0] .
generated: The statistic shows the World of from 2008 to 2018 , emissions by region .  In 2018 , about 4209.3 million metric tons were carried emissions Europe World .

Example 554:
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: This statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , in templateYLabel[3] templateYLabel[4] templateYLabel[5] . According to the source , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] templatePositiveTrend every templateXLabel[0] during this period of time , reaching templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[idxmax(Y)] .
generated: This statistic depicts the Market value billion of Value global in Value from 2018 to 2025 , in U.S. dollars .  According to the source , the Market value billion of Value global increased every Year during this period of time , reaching 210.87 U.S. dollars in 2025 .

Example 555:
titleEntities: {'Subject': ['Cambodia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Cambodia 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']: ['2259.78', '2113.67', '1975.82', '1848.25', '1730.88', '1620.64', '1504.26', '1385.93', '1270.98', '1165.06', '1093.8', '1009.34', '945.7', '877.64', '781.91', '734.66', '741.86', '627.78', '536.15', '470.68', '405.63', '360.66', '337.5', '320.05', '299.98', '293.7', '267.86', '302.17', '316.19', '319.54', '265.06']

gold: The statistic shows gross domestic product ( GDP ) per capita in Cambodia between 1994 and 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] between templateXValue[min] and 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 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: The statistic shows Gross domestic product ( GDP ) per capita in Cambodia 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 .

Example 556:
titleEntities: {'Subject': ['Greece'], 'Date': ['2018']}
title: Urbanization in Greece 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population']: ['79.06', '78.72', '78.39', '78.05', '77.7', '77.36', '77', '76.65', '76.29', '75.93', '75.57']

gold: This statistic shows the degree of urbanization in Greece from 2008 to 2018 . In 2018 , about 79.06 percent of the total population in Greece lived in cities .
gold_template: This 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_template: In templateXValue[max] , approximately a third of the total templateYLabel[2] in templateTitleSubject[0] lived in cities . The trend shows an templatePositiveTrend of templateTitle[0] by almost 4 templateScale in the last decade , meaning people have moved away from rural areas to find work and make a living in the cities . Leaving the field Over the last decade , templateTitle[0] in templateTitleSubject[0] has templatePositiveTrend by almost 4 templateScale , as more and more people leave the agricultural sector to find work in services .
generated: In 2018 , approximately a third of the total population in Greece lived in cities .  The trend shows an increase of Urbanization by almost 4 % in the last decade , meaning people have moved away from rural areas to find work and make a living in the cities .  Leaving the field Over the last decade , Urbanization in Greece has increased by almost 4 % , as more and people leave the agricultural sector to find work in services .

Example 557:
titleEntities: {'Subject': ['Vietnam Veterans Memorial U.S.'], 'Date': ['2008', '2019']}
title: Number of visitors to the Vietnam Veterans 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']: ['4.58', '4.72', '5.07', '5.3', '5.6', '4.4', '4.14', '4.42', '4.02', '4.56', '4.44', '4.24']

gold: This statistic shows the number of recreational visitors to the Vietnam Veterans Memorial in the United States from 2008 to 2019 . The number of visitors to the Vietnam Veterans Memorial amounted to approximately 4.58 million in 2019 .
gold_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_template: In templateXValue[max] , templateTitleSubject[0] Park saw nearly templateYValue[0] and a half templateScale templateYLabel[1] during the templateXLabel[0] . In templateXValue[3] , the templateTitleSubject[0] saw its largest volume of templateYLabel[1] accounting for about templateYValue[max] templateScale . templateTitleSubject[0] Park templateTitleSubject[0] Park is a large templateTitleSubject[0] forest located in central California .
generated: In 2019 , Vietnam Veterans Memorial U.S. Park saw nearly 4.58 and a half millions visitors during the Year .  In 2016 , the Vietnam Veterans Memorial U.S. saw its largest volume of visitors accounting for about 5.6 millions .  Vietnam Veterans Memorial U.S. Park Vietnam Veterans Memorial U.S. is a large Vietnam Veterans Memorial U.S. forest located in central California .

Example 558:
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
Age group  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 templateLabel[0][0] templateLabel[0][1] was among those aged 70 to 74 years with templateValue[1][10] diagnoses in templateLabel[1][0] of this templateLabel[0][0] 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 results of a templateTitleDate[0] survey among American templateTitleSubject[0] in the country as of 2016 , sorted templateTitle[7] templateTitle[0] . During the survey period , templateValue[1][0] templateScale of respondents in the country stated they watched templateValue[0][3] templateValue[0][10] shows that they , while templateValue[0][1] and templateValue[0][2] .
generated: This statistic shows the results of a 2017 survey among American England in the country as of 2016 , sorted gender Esophageal .  During the survey period , 4 % of respondents in the country stated they watched 35-39 70-74 shows that they , while 25-29 and 30-34 .

Example 559:
titleEntities: {'Subject': ['OS'], 'Date': ['2018', '2019']}
title: Average mobile app user acquisition costs 2018 - 2019 , by user action and OS
Data:
               0         1                2         3          4
unknown  Install  Register  In-app_purchase  Purchase  Subscribe
Android     1.22      2.17            86.72     51.49      45.86
iOS          3.6      7.55            77.45     87.31      33.12
Total       1.75      3.52            86.61     75.22      36.39 

gold: As of August 2019 , the average cost to acquire an app user who registered with an app or created an account was 3.52 U.S. dollars . Mobile app user acquisition costs can vary widely between user action and operating system used . Compared to the relatively low acquisition costs of a user registration , the user action that had the highest average acquisition cost for both operating systems , Android and iOS , was making an in-app purchase .
gold_template: As of 2019 , the templateTitle[0] cost to acquire an templateTitle[2] templateTitle[3] who registered with an templateTitle[2] or created an account was templateValue[3][1] U.S. dollars . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] can vary widely between templateTitle[3] templateTitle[10] and operating system used . Compared to the relatively low templateTitle[4] templateTitle[5] of a templateTitle[3] registration , the templateTitle[3] templateTitle[10] that had the highest templateTitle[0] templateTitle[4] cost for both operating systems , templateLabel[1][0] and templateLabel[2][0] , was making an templateValue[0][2] .

generated_template: The graph shows the templateTitle[0] and templateTitle[1] of templateTitleSubject[0] templateTitle[3] in the templateTitle[2] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In templateTitleDate[0] , about templateValue[1][last] templateScale templateValue[0][4] templateTitle[3] were templateLabel[1][0] templateTitle[3] .
generated: The graph shows the Average and mobile of OS user in the app in 2018 , costs 2018 .  In 2018 , about 45.86 % Subscribe user were Android user .

Example 560:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2015']}
title: Median age of the population in South Korea 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']: ['56.5', '54.9', '53.4', '51.4', '49.1', '46.5', '43.7', '40.8', '38.0', '34.8', '31.9', '29.3', '27.0', '24.3', '22.1', '19.9', '19.0', '18.4', '18.6', '18.9', '19.0']

gold: This statistic shows the median age of the population in South Korea 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 South Korea 's population was 40.8 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: The templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] templatePositiveTrend to templateYValue[7] templateYLabel[2] in templateXValue[7] , meaning that half of the templateTitle[2] was older than that , half younger . This figure was lowest in between templateXValue[15] and templateXValue[10] but is projected to rise to templateYValue[max] templateYLabel[2] by templateXValue[idxmax(Y)] . The meaning of templateYLabel[1] structure templateTitleSubject[0] has one of the largest populations worldwide , and this statistic presents the templateYLabel[0] templateYLabel[1] of that group .
generated: The Median age in South Korea rose to 40.8 years in 2015 , meaning that half of the population was older than that , half younger .  This figure was lowest in between 1975 and 2000 but is projected to rise 56.5 years by 2050 .  The meaning of age structure South Korea has one of the largest populations worldwide , and this statistic presents the Median age of that group .

Example 561:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. consumers who use marijuana 2017 , by household income
X_Axis['Response']: ['<$30000', '$30000-$74999', '$75000+']
Y_Axis['Share', 'of', 'respondents']: ['13', '10', '9']

gold: This statistic shows the share of consumers in the United States that currently smoke marijuana as of July 2017 , by household income . During the survey , 13 percent of respondents earning 30 thousand U.S. dollars per year or less said that they smoke marijuana .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] in the templateTitle[0] that currently smoke templateTitle[4] as of 2017 , templateTitle[6] templateTitle[7] templateTitle[8] . During the survey , templateYValue[max] templateScale of templateYLabel[1] earning 30 thousand templateTitleSubject[0] dollars per year or less said that they smoke templateTitle[4] .

generated_template: The survey shows result of survey on templateTitle[0] of templateXValue[0] in templateTitleSubject[0] templateTitle[4] in the country as of 2016 . Durign the survey , templateYValue[max] of templateYLabel[1] stated templateTitleSubject[0] templateTitle[4] did a templateXValue[0] of potraying templateXValue[0] .
generated: The survey shows result of survey on U.S. of <$30000 in U.S. marijuana in the country as of 2016 .  Durign the survey , 13 of respondents stated U.S. marijuana did a <$30000 of potraying <$30000 .

Example 562:
titleEntities: {'Subject': ['Employees'], 'Date': ['2011']}
title: Employees ' average working hours per week worldwide 2011
X_Axis['Country']: ['Singapore', 'India', 'Brazil', 'Mexico', 'U.S.', 'Worldwide', 'Japan', 'Argentina', 'Spain', 'Sweden', 'Germany', 'U.K.', 'France', 'Italy', 'Netherlands']
Y_Axis['Average', 'working', 'hours', 'per', 'week']: ['44', '42', '40', '40', '40', '40', '40', '40', '40', '40', '39', '37', '37', '36', '36']

gold: The statistic depicts how many hours employees work per week on average worldwide . Respondents from India work 42 hours per week on average .
gold_template: The statistic depicts how many templateYLabel[2] templateTitleSubject[0] work templateYLabel[3] templateYLabel[4] on templateYLabel[0] templateXValue[5] . Respondents from templateXValue[1] work templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] on templateYLabel[0] .

generated_template: This statistic shows the countries with the largest templateTitleSubject[0] templateTitle[1] in templateTitle[2] templateTitle[3] . templateYValue[max] templateScale of the templateYLabel[1] with located in templateXValue[1] .
generated: This statistic shows the countries with the largest Employees ' in average working .  44 % of the working with located in India .

Example 563:
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] generated by templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] reported a templateYLabel[0] templateYLabel[1] of templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . In templateXValue[8] , templateTitleSubject[0] had a global workforce of 100 thousand employees .
generated: This statistic shows the Net revenue generated by Amazon from 2004 to 2019 .  In 2019 , Amazon reported a Net revenue of 280.52 billion U.S. dollars .  In 2011 , Amazon had a global workforce of 100 thousand employees .

Example 564:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2018']}
title: Share of economic sectors in the GDP in Malaysia 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   7.54   8.61   8.46   8.29   8.87   9.11   9.79  11.45  10.09   9.22   9.97
Industry      38.3  38.13  37.68  38.45  39.92  39.89  40.14  39.82   40.5  40.97  45.11
Services     52.96  51.85  52.53  52.01  50.12  49.94  49.02  47.77  48.48  48.84  43.95 

gold: This statistic shows the share of economic sectors in the gross domestic product ( GDP ) in Malaysia from 2008 to 2018 . In 2018 , the share of agriculture in Malaysia 's gross domestic product was around 7.54 percent , industry contributed approximately 38.3 percent and the services sector contributed about 52.96 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 around 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: In templateValue[0][0] , templateLabel[1][0] contributed the most to templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[4] ) , with a share of just over templateValue[3][0] templateScale . Having an economy based on the templateLabel[3][0] sector is a widely recognized marker of an advanced economy . What are the attractions in the templateLabel[3][0] sector ? templateTitleSubject[0] 's economy was about 2.7 templateScale U.S. dollars , and its templateTitle[4] is projected to templatePositiveTrend through 2024 .
generated: In 2018 , Agriculture contributed the most to Malaysia 's economic sectors GDP ( Malaysia ) , with a share of just over 52.96 % .  Having an economy based on the Services sector is a widely recognized marker of an advanced economy .  What are the attractions in the Services sector ? Malaysia 's economy was about 2.7 billion U.S. dollars , and its Malaysia is projected to increase through 2024 .

Example 565:
titleEntities: {'Subject': ['Asus'], 'Date': ['2011', '2018']}
title: Global Asus revenue 2011 - 2018 , by region
Data:
                0      1       2       3      4       5       6       7
Year         2018   2017    2016    2015   2014    2013    2012    2011
Taiwan      62.92  69.89   69.87   65.47  65.83   60.74   52.35   46.44
China       71.94  71.94   77.63    79.3  71.95   80.23   79.92   80.53
Singapore  151.02  174.0  182.29  188.44  233.1  216.13  217.43  197.75
USA         62.22  64.47    70.8   77.56  79.27   89.12   87.03   51.87
Europe      18.58  28.93   38.85   34.65   3.72    2.55    2.46    2.33
Others      24.88  24.72   27.37   26.92  23.53   14.52     9.5    5.19 

gold: This statistic presents the annual regional revenue of the Taiwanese tech company Asus , from 2011 to 2018 . In 2018 , Asus ' revenue in the United States stood at 62.22 billion New Taiwan dollars .
gold_template: This statistic presents the annual regional templateTitle[2] of the Taiwanese tech company templateTitleSubject[0] , from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateTitleSubject[0] ' templateTitle[2] in the country stood at templateValue[4][0] templateScale New templateLabel[1][0] dollars .

generated_template: The statistic shows a templateTitle[4] of templateTitleSubject[0] Inc. 's , templateTitleSubject[1] Inc. 's / templateLabel[3][0] Inc. 's , and templateTitleSubject[2] Corp. 's revenues during the fiscal years from templateValue[0][last] to templateValue[0][0] . In the fiscal templateLabel[0][0] templateValue[0][0] , hardware-focused templateTitleSubject[0] 's templateValue[1][0] templateScale US dollar templateTitle[3] was almost double the amount of online-based templateTitleSubject[1] 's 136.2 templateScale U.S. dollars , with templateTitleSubject[2] generating templateValue[4][0] templateScale US dollars that same templateLabel[0][0] . Whereas all of these companies have different market strengths , there are also overlaps and thus , competition .
generated: The statistic shows a 2018 of Asus Inc. 's , Asus Inc. 's / Singapore Inc. 's , and Asus Corp. 's revenues during the fiscal years from 2011 to 2018 .  In the fiscal Year 2018 , hardware-focused Asus 's 62.92 billion US dollar 2011 was almost double the amount of online-based Asus 's 136.2 billion U.S. dollars , with Asus generating 62.22 billion US dollars that same Year .  Whereas all of these companies have different market strengths , there are also overlaps and thus , competition .

Example 566:
titleEntities: {'Subject': ['Cincinnati Reds'], 'Date': ['2019']}
title: Regular season average attendance of the Cincinnati Reds 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Average', 'attendance']: ['22329', '20115', '22677', '23383', '29870', '30576', '31288', '28978', '27327', '25438', '21579']

gold: This graph depicts the average regular season home attendance of the Cincinnati Reds from 2009 to 2019 . In 2019 , the average regular season home attendance of the Cincinnati Reds was 22,329 . • Cincinnati Reds 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[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateTitle[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[1] was templateYValue[idxmax(X)] . • templateTitleSubject[0] templateTitleSubject[1] total home templateYLabel[1] • Major League Baseball templateYLabel[0] per game templateYLabel[1] • Major League Baseball total templateYLabel[1]
generated: This graph depicts the Average Regular season home attendance of the Cincinnati Reds from 2009 to 2019 .  In 2019 , the Average Regular season home attendance of the Cincinnati Reds was 22329 .  • Cincinnati Reds Cincinnati Reds total home attendance • Major League Baseball Average per game attendance • Major League Baseball total attendance . 

Example 567:
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 internet templateTitle[8] in the templateTitle[0] who use another device templateXValue[0] TV or templateXValue[last] video to templateXValue[0] as of 2017 . During the survey period , it was found that templateYValue[max] templateScale of templateTitle[0] templateTitle[7] adults were templateTitle[2] templateTitle[3] templateTitle[8] , accessing content on their smartphones , tablets or computers during regular templateXValue[0] consumption .
generated: This statistic shows the Share of internet gender in the Facebook who use another device Men TV or Women video to Men as of 2017 .  During the survey period , it was found that 75 % of Facebook gender adults were reach United gender , accessing content on their smartphones , tablets or computers during regular Men consumption .

Example 568:
titleEntities: {'Subject': ['Finland'], 'Date': ['2019']}
title: Visitor arrivals in Finland in 2019 , by country of origin
X_Axis['Country']: ['Finland', 'Russia', 'Sweden', 'Germany', 'China', 'United_Kingdom', 'United_States', 'Japan', 'France', 'Netherlands', 'Norway', 'Estonia', 'Italy', 'Switzerland', 'Spain']
Y_Axis['Number', 'of', 'arrivals']: ['7909140', '317373', '285282', '282236', '199150', '143403', '123806', '100827', '98833', '90314', '89910', '81901', '68788', '63787', '59120']

gold: This statistic displays the number of visitor arrivals in Finland between January and October 2019 , by country of origin . The visitor arrivals refer to arrivals in all accommodation establishments in the respective year . Finnish people made up for the majority of visitor arrivals , the number amounting to nearly eight million .
gold_template: This statistic displays the templateYLabel[0] of templateTitle[0] templateYLabel[1] in templateXValue[0] between and October templateTitleDate[0] , templateTitle[4] templateXLabel[0] of templateTitle[6] . The templateTitle[0] templateYLabel[1] refer to templateYLabel[1] in all accommodation establishments in the respective year . Finnish people made up for the majority of templateTitle[0] templateYLabel[1] , the templateYLabel[0] amounting to nearly templateYValue[max] templateScale .

generated_template: This statistic shows the 15 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . templateTitle[1] defense templateYLabel[1] totaling USD templateYValue[max] templateScale , the templateXValue[0] ranked first . Worldwide leaders in templateYLabel[0] templateYLabel[1] The templateXValue[6] States lead the globe in templateYLabel[0] templateYLabel[1] in templateTitleDate[0] .
generated: This statistic shows the 15 Finland arrivals the Finland Number arrivals in 2019 .  arrivals defense arrivals totaling USD 7909140 million , the Finland ranked first .  Worldwide leaders in Number arrivals The United_States States lead the globe in Number arrivals in 2019 .

Example 569:
titleEntities: {'Subject': ['London'], 'Date': ['2018']}
title: Leading paid attractions in London 2018
X_Axis['Attractions']: ['Tower_of_London', 'Royal_Botanic_Gardens_Kew_Gardens', "St_Paul's_Cathedral", 'Royal_Academy_of_Arts', 'Westminster_Abbey', 'ZSL_London_Zoo', 'Houses_of_Parliament', 'Hampton_Court_Palace', 'Tower_Bridge_Exhibition', "Shakespeare's_Globe"]
Y_Axis['Number', 'of', 'visits', 'in', 'thousands']: ['2855', '1859', '1657', '1594', '1546', '1134', '1076', '903', '837', '819']

gold: The Tower of London was the most visited paid tourist attraction recorded in London in 2018 , with around 2.86 million visitors . Other well visited tourist attractions charging an admission fee include the Royal Botanic Gardens , Kew , and St Paul 's Cathedral . Many of London 's most visited attractions are free .
gold_template: The templateXValue[0] of templateXValue[0] was the most visited templateTitle[1] tourist attraction recorded in templateXValue[0] in templateTitleDate[0] , with around templateYValue[max] templateScale visitors . Other well visited tourist templateXLabel[0] charging an admission fee include the templateXValue[1] Gardens , templateXValue[1] , and templateXValue[2] Paul 's templateXValue[2] . Many of templateXValue[0] 's most visited templateXLabel[0] are free .

generated_template: This statistic shows the ten templateTitle[0] templateTitle[4] templateTitle[3] , other than English , in templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] , by templateYLabel[0] of templateYLabel[1] . The templateTitle[0] commonly templateTitle[4] templateXLabel[0] was templateXValue[0] with almost templateYValue[max] thousand native templateYLabel[1] , followed by templateXValue[1] and templateTitleSubject[0] .
generated: This statistic shows the ten Leading 2018 London , other than English , in London 2018 in , by Number of visits .  The Leading commonly 2018 Attractions was Tower_of_London with almost 2855 thousand native visits , followed by Royal_Botanic_Gardens_Kew_Gardens and London .

Example 570:
titleEntities: {'Subject': ['Netflix'], 'Date': ['2019']}
title: Countries paying the least for Netflix subscriptions worldwide 2019
X_Axis['Country']: ['Colombia', 'Brazil', 'Pakistan', 'Mexico', 'Chile']
Y_Axis['Cost', 'in', 'U.S.', 'dollars']: ['5.4', '5.65', '6.7', '6.76', '6.9']

gold: This statistic shows the least expensive countries for a monthly Netflix subscription worldwide as of April 2019 . The data reveals that Colombia was the least expensive place in the world to get Netflix , with a monthly subscription costing 5.4 U.S. dollars . In Pakistan , the monthly price for a Netflix subscription amounted to 6.7 U.S. dollars , and Mexican subscribers paid 6.76 U.S. dollars per month to use the streaming service .
gold_template: This statistic shows the templateTitle[2] expensive templateTitle[0] templateTitle[3] a monthly 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_template: This statistic shows the population distribution of templateTitleSubject[0] templateYLabel[1] living abroad as of templateTitleDate[0] , templateTitle[5] templateTitle[6] . templateTitle[5] the end of that year , around templateYValue[2] templateScale templateTitleSubject[0] nationals who were living templateTitle[1] were in templateXValue[2] .
generated: This statistic shows the population distribution of Netflix U.S. living abroad as of 2019 , subscriptions worldwide .  subscriptions the end of that year , around 6.7 million Netflix nationals who were living paying were in Pakistan .

Example 571:
titleEntities: {'Subject': ['Chicago Bulls'], 'Date': ['2001', '2019']}
title: Chicago Bulls ' 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']: ['301', '287', '281', '232', '228', '201', '195', '162', '185', '169', '168', '165', '161', '149', '136', '123', '119', '115']

gold: The statistic shows the revenue of the Chicago Bulls 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 301 million U.S. dollars .
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] .

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 Chicago Bulls 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 301 million U.S. dollars .

Example 572:
titleEntities: {'Subject': ['IKEA'], 'Date': ['2012', '2016']}
title: Leading 5 purchasing countries of IKEA products 2012 to 2016
Data:
             0       1      2       3          4       5
Country  China  Poland  Italy  Sweden  Lithuania  Others
2012        22      18      8       5          4       0
2013        23      18      8       6          4       0
2014        25      18      7       5          4       0
2015        25      19      8       5          5       0
2016        26      18      8       5          5      38 

gold: This statistic depicts the leading five purchasing countries of IKEA products from 2012 to 2016 . In 2016 , China continued to be the leading purchasing country of IKEA products at 26 percent . IKEA is a privately held , international home products company that designs and sells ready-to-assemble furniture such as beds , chairs , desks , appliances and home accessories .
gold_template: This statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateValue[0][0] continued to be the templateTitle[0] templateTitle[2] templateLabel[0][0] of templateTitleSubject[0] templateTitle[5] at templateValue[5][0] templateScale . templateTitleSubject[0] is a privately held , international home templateTitle[5] company that designs and sells ready-to-assemble furniture such as beds , chairs , desks , appliances and home accessories .

generated_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: The statistic shows the Leading of IKEA restaurants in the IKEA region 2016 2012 to 2016 , by Country .  In 2016 , there were 23 IKEA restaurants in China , 18 countries in Poland and 8 countries in Italy .

Example 573:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014', '2017']}
title: Sales of smart home devices and controllers to dealers in the U.S. 2014 to 2017
Data:
                           0     1     2     3
Year                    2017  2016  2015  2014
Smart home devices      35.9  29.9  24.9  20.7
Smart home controllers   3.1   2.5   1.9   1.4 

gold: The statistic shows the projected wholesale sales of smart home devices and controllers in the United States from 2014 to 2017 . In 2014 , 20.7 smart home devices were sold to dealers in the United States .
gold_template: The statistic shows the projected wholesale templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] and templateTitle[4] in the templateTitle[6] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][last] , templateValue[1][last] templateTitle[1] templateTitle[2] templateTitle[3] were sold to templateTitle[5] in the templateTitle[6] .

generated_template: This statistic displays 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: This statistic displays the devices of the U.S. automobile production home in 2014 and 2017 , by region .  In 2017 , the Smart American home is projected to have devices of about 35.9 million units .

Example 574:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2018']}
title: Daily social media usage in Denmark 2018 , by social media site
X_Axis['Platform']: ['Facebook', 'Snapchat', 'Instagram', 'LinkedIn', 'WhatsApp', 'Pinterest', 'Twitter']
Y_Axis['Share', 'of', 'respondents']: ['53', '19', '19', '4', '4', '2', '1']

gold: Facebook was the most popular social media platform among daily social media users in Denmark in 2018 . 53 percent of internet users in Denmark reported to have used Facebook on a daily basis . The social media platform had 2.91 million users in 2016 and was forecast to have around 3.12 million users by 2022 .
gold_template: templateXValue[0] was the most popular templateTitle[1] templateTitle[2] templateXLabel[0] among templateTitle[0] templateTitle[1] templateTitle[2] users in templateTitleSubject[0] in templateTitleDate[0] . templateYValue[max] templateScale of internet users in templateTitleSubject[0] reported to have used templateXValue[0] on a templateTitle[0] basis . The templateTitle[1] templateTitle[2] templateXLabel[0] had 2.91 templateScale users in 2016 and was forecast to have around 3.12 templateScale users templateTitle[6] 2022 .

generated_template: This statistic shows the results of a survey , conducted in 2016 in Canada , on templateXValue[5] templateTitle[3] templateTitle[4] templateTitle[5] . According to templateYValue[max] templateScale of surveyed templateTitleSubject[0] , their top resolution templateTitle[6] templateTitleDate[0] was to templateXValue[0] fitness and templateXValue[0] .
generated: This statistic shows the results of a survey , conducted in 2016 Canada , on Pinterest usage Denmark 2018 .  According to 53 % of surveyed Denmark , their top resolution by 2018 was to Facebook fitness and Facebook .

Example 575:
titleEntities: {'Subject': ['Sky News', 'United Kingdom', 'UK'], 'Date': ['2019']}
title: Sky News viewers reached quarterly in the United Kingdom ( UK ) Q1 2012-Q3 2019
X_Axis['Quarter']: ['Q1_2012', 'Q2_2012', 'Q3_2012', 'Q4_2012', 'Q1_2013', 'Q2_2013', 'Q3_2013', 'Q4_2013', 'Q1_2014', 'Q2_2014', 'Q3_2014', 'Q4_2014', 'Q1_2015', 'Q2_2015', 'Q3_2015', 'Q4_2015', 'Q1_2016', 'Q2_2016', 'Q3_2016', 'Q4_2016', 'Q1_2017', 'Q2_2017', 'Q3_2017', 'Q4_2017', 'Q1_2018', 'Q2_2018', 'Q3_2018', 'Q4_2018', 'Q1_2019', 'Q2_2019', 'Q3_2019']
Y_Axis['Viewers', 'in', 'thousands']: ['17035', '15922', '15900', '18754', '16892', '17358', '15288', '15789', '17526', '15056', '15324', '14579', '14872', '14915', '14384', '17713', '14437', '15563', '14379', '14754', '14536', '17194', '14183', '13466', '12434', '12380', '11734', '12904', '12957', '12504', '12633']

gold: In the United Kingdom , in the third quarter of 2019 , Sky news had roughly 12.6 million viewers . This is more or less stable and in line with figures from other quarters over the last year . However , it is a significantly lower number of viewers compared to figures from the start of the decade .
gold_template: In the templateTitleSubject[1] , in the third templateXLabel[0] of templateXValue[28] , templateTitleSubject[0] had roughly templateYValue[last] templateScale templateYLabel[0] . This is more or less stable and in line with figures from other quarters over the last year . However , it is a significantly lower number of templateYLabel[0] compared to figures from the start of the decade .

generated_template: The templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] and templateTitle[4] templateTitle[5] amounted to approximately templateYValue[max] templateScale templateYLabel[2] 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 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: The Sky News Viewers of reached and quarterly United amounted to approximately 18754 thousands in the third Quarter of Q1_2012 .  Sky News sector in the Kingdom Sky News sector Viewers in the Kingdom has been steadily decreasing 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 Kingdom real estate market was quite stable .

Example 576:
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: In templateXValue[max] , the harmonized templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale . This was the highest level of templateYLabel[0] reached since templateXValue[7] , when the templateYLabel[0] was at templateYValue[max] templateScale . Of the templateYValue[0] Benelux countries , the templateTitleSubject[0] saw the lowest templateYLabel[0] .
generated: In 2019 , the harmonized Units sold in the Volvo was 65282 % .  This was the highest level of Units reached since 2012 , when the Units was at 75506 % .  Of the 65282 Benelux countries , the Volvo saw the lowest Units .

Example 577:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2008', '2019']}
title: Annual turnover of horse race betting Great Britain 2008 to 2019
X_Axis['Month']: ['Apr_18_-_Mar_19', 'Apr_17_-_Mar_18', 'Apr_16_-_Mar_17', 'Apr_15_-_Mar_16', 'Apr_14_-_Mar_15', 'Apr_13_-_Mar_14', 'Apr_12_-_Mar_13', 'Apr_11_-_Mar_12', 'Apr_10_-_Mar_11', 'Apr_09_-_Mar_10', 'Apr_08_-_Mar_09']
Y_Axis['Turnover', 'in', 'million', 'GBP']: ['4219.02', '4396.59', '4537.89', '4804.77', '4804.36', '4894.01', '5084.2', '5300.9', '5153.82', '5442.78', '5743.51']

gold: Over the past decade the turnover from off-course horse race betting in the United Kingdom ( UK ) has significantly decreased from 5.74 billion British pounds in 2009 to 4.22 billion in 2019 . A similar trend can be seen with off course dog race betting . Despite a slight decrease , the number of betting shops still held steady though , with William Hill and Ladbrokes making up roughly half of the shops .
gold_template: Over the past decade the templateYLabel[0] from off-course templateTitle[2] templateTitle[3] templateTitle[4] in the United Kingdom ( UK ) has significantly templateNegativeTrend from templateYValue[max] templateScale British pounds in 2009 to templateYValue[min] templateScale in templateTitleDate[max] . A similar trend can be seen with off course dog templateTitle[3] templateTitle[4] . Despite a slight templateNegativeTrend , the number of templateTitle[4] shops still held steady though , with William Hill and Ladbrokes making up roughly half of the shops .

generated_template: This statistic provides information on the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] as of 2017 . During this period of time , experts was the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[1] with a total of templateYValue[1] templateScale .
generated: This statistic provides information on the Turnover of Annual turnover million in the Great Britain as of 2017 .  During this period of time , experts was the Annual turnover horse race in the Great Britain with a total of 4396.59 million .

Example 578:
titleEntities: {'Subject': ['GDP'], 'Date': ['2018']}
title: Military expenditure - percentage of GDP in highest spending countries 2018
X_Axis['Country']: ['Saudi_Arabia', 'Russia', 'United_States', 'South_Korea', 'Turkey', 'India', 'France', 'Worldwide', 'Australia', 'China', 'United_Kingdom', 'Brazil', 'Italy', 'Canada', 'Germany', 'Japan']
Y_Axis['Percentage', 'of', 'GDP']: ['8.8', '3.9', '3.2', '2.6', '2.5', '2.4', '2.3', '2.1', '1.9', '1.9', '1.8', '1.5', '1.3', '1.3', '1.2', '0.9']

gold: This statistics shows military spending in selected countries as a percentage of their respective gross domestic product . In 2018 , military expenditure of the U.S.A. was estimated at 3.2 percent of nation 's total gross domestic product . A ranking of the countries with the highest military expenditures can be accessed here .
gold_template: This statistics shows templateTitle[0] templateTitle[5] in selected templateTitle[6] as a templateScale of their respective gross domestic product . In templateTitleDate[0] , templateTitle[0] templateTitle[1] of the U.S.A. was estimated at templateYValue[2] templateScale of nation 's total gross domestic product . A ranking of the templateTitle[6] with the templateTitle[4] templateTitle[0] expenditures can be accessed here .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] that own net private wealth of at least one templateScale euros in templateTitleSubject[0] templateTitle[3] as of templateTitleDate[0] . The countries with the largest templateYLabel[0] of templateYLabel[1] templateYLabel[2] include templateXValue[2] ( 1.4 templateScale of templateYLabel[1] templateYLabel[2] ) and templateXValue[1] ( 1.3 templateScale templateYLabel[2] ) .
generated: The statistic shows the Percentage of GDP that own net private wealth of at least one percentage euros in GDP as of 2018 .  The countries with the largest Percentage of GDP include United_States ( 1.4 percentage of GDP ) and Russia ( 1.3 percentage GDP ) .

Example 579:
titleEntities: {'Subject': ['Arsenal'], 'Date': []}
title: Arsenal FC revenue by stream 2018/19
Data:
                    0        1        2        3        4        5        6        7        8        9       10
Season        2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09
Matchday        109.2    111.6    116.4    133.6    132.0    119.8    108.3    117.7    103.2    114.7    117.5
Broadcasting    210.6    206.9    234.7    192.0    167.7    147.3    103.2    107.7     96.7    105.7     89.0
Commercial      125.8    120.7    136.5    142.9    135.8     92.2     72.8     64.9     51.2     53.7     56.5 

gold: Broadcasting is the largest source of revenue for Arsenal FC . In 2018/2019 , the club earned approximately 210.6 million euros from broadcasting , more than double than in 2010/2011 . The second biggest revenue stream is the commercial stream .
gold_template: templateLabel[2][0] is the largest source of templateTitle[2] for templateTitleSubject[0] templateTitle[1] . 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_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: Broadcasting is the largest source of revenue for Arsenal .  In 2018/2019 , the club earned approximately 210.6 million euros from Broadcasting , more than triple in 2010/2011 .  The second biggest revenue stream is the Commercial one .

Example 580:
titleEntities: {'Subject': ['Performance Index'], 'Date': ['2018']}
title: Logistics Performance Index - country ranking 2018
X_Axis['Country']: ['Germany', 'Netherlands', 'Sweden', 'Belgium', 'Singapore', 'United_Kingdom', 'Japan', 'Austria', 'Hong_Kong_SAR_China', 'United_States', 'Denmark', 'Finland', 'Switzerland', 'United_Arab_Emirates', 'France']
Y_Axis['Index', 'score']: ['4.19', '4.07', '4.07', '4.05', '4.05', '4.01', '3.99', '3.99', '3.96', '3.92', '3.92', '3.92', '3.91', '3.89', '3.86']

gold: In 2018 , Germany was ranked the leading logistics market based on the Logistics Performance Index , with an index score of 4.19 . The Logistics Performance Index measures on-the-ground trade logistics performance . The logistics market in Germany According to the logistics performance index , Germany had the highest performance in terms of infrastructure , customs and logistics competence .
gold_template: In templateTitleDate[0] , templateXValue[0] was ranked the leading templateTitle[0] market based on the templateTitle[0] templateTitleSubject[0] templateYLabel[0] , with an templateYLabel[0] templateYLabel[1] of templateYValue[max] . The templateTitle[0] templateTitleSubject[0] templateYLabel[0] measures on-the-ground trade templateTitle[0] templateTitleSubject[0] . The templateTitle[0] market in templateXValue[0] According to the templateTitle[0] templateTitleSubject[0] templateYLabel[0] , templateXValue[0] had the highest templateTitleSubject[0] in terms of infrastructure , customs and templateTitle[0] competence .

generated_template: This statistic shows the templateTitle[1] with the lowest templateYLabel[1] of natural disasters templateTitle[2] to the Global templateYLabel[1] templateYLabel[2] in templateTitleDate[0] . At this time , templateXValue[0] , with an templateYLabel[2] value of templateYValue[min] , was the templateTitle[0] templateXLabel[0] in the templateYLabel[0] . In the framework of the WorldRiskIndex , disaster templateYLabel[1] is analyzed as a complex interplay of natural hazards and social , political and environmental factors .
generated: This statistic shows the Performance with the lowest score of natural disasters Index to the Global score in 2018 .  At this time , Germany with an score value of 3.86 , was the Logistics Country in the Index .  In the framework of the WorldRiskIndex , disaster score is analyzed as a complex interplay of natural hazards and social , political and environmental factors .

Example 581:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010']}
title: Smartphone penetration in the U.S. as share of population 2010 to 2021
X_Axis['Year']: ['2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Share', 'of', 'population', 'with', 'a', 'smartphone']: ['72.7', '72.2', '71.4', '69.6', '67.3', '63.9', '59.4', '53.6', '45.7', '38.8', '29.8', '20.2']

gold: This statistic shows the smartphone penetration as share of the population in the United States from 2010 to 2021 . In 2018 , 69.6 percent of the U.S. population used a smartphone . Smartphone penetration in the U.S. - additional information Almost two-thirds of Americans are predicted to own a smartphone by 2017 , more than a threefold increase since 2010 .
gold_template: This statistic shows the templateYLabel[3] templateTitle[1] as templateYLabel[0] of the templateYLabel[1] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateYValue[3] templateScale of the templateTitleSubject[0] templateYLabel[1] used a templateYLabel[3] . templateYLabel[3] templateTitle[1] in the templateTitleSubject[0] - additional information Almost two-thirds of Americans are predicted to own a templateYLabel[3] by templateXValue[4] , more than a threefold templatePositiveTrend since templateXValue[min] .

generated_template: The statistic depicts the templateTitleSubject[0] annual templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic depicts the U.S. annual penetration U.S. share from 2010 to 2021 .  In 2021 , the U.S. penetration share was at about 72.7 Share population smartphone .

Example 582:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Perceived sources of fake news in the U.S. 2017
X_Axis['Platform']: ['Facebook', 'Internet_News_Sites', 'Twitter', 'Saturday_Night_Live', 'Fox_News', 'Radio_Talk_Shows', 'The_Daily_Show', 'CNN', 'MSNBC', 'The_New_York_Times', 'Good_Morning_America_(ABC)', 'CBS_This_Morning_(CBS)', 'The_Today_Show_(NBC)', 'USA_Today', 'ABC_World_News_Tonight', 'Local_Radio_News_Stations', 'Meet_the_Press_(NBC)', 'CBS_Evening_News', 'NBC_Nightly_News', 'Local_Newspapers', '60_Minutes_(CBS)', 'NPR_(National_Public_Radio)', 'Face_the_Nation_(CBS)', 'PBS_News_Hour', 'Local_TV_News']
Y_Axis['Share', 'of', 'respondents', 'who', 'thought', "they'd", 'encounter', 'fake', 'news', 'stories']: ['58', '51', '49', '39', '24', '24', '24', '24', '17', '17', '15', '14', '14', '13', '13', '12', '12', '12', '12', '11', '11', '11', '11', '9', '8']

gold: The term ‘ fake news ' is used in multiple different contexts , but officially refers simply to false information presented as legitimate news . Adults in the United States believe social media platforms and online news sites to be the most likely sources of fake news – 58 percent of respondents to a survey believed that Facebook was the most likely place in which they would encounter false news stories , and 49 percent said the same about Twitter . A separate study revealed that 66 percent of U.S. adults believed that 76 percent or more of the news they saw on social media was biased .
gold_template: The term ‘ templateYLabel[6] templateXValue[1] ' is used in multiple different contexts , but officially refers simply to false information presented as legitimate templateXValue[1] . Adults in the templateTitle[4] believe social media platforms and online templateXValue[1] to be the most likely templateTitle[1] of templateYLabel[6] templateXValue[1] – templateYValue[max] templateScale of templateYLabel[1] to a survey believed that templateXValue[0] was the most likely place in which they would templateYLabel[5] false templateXValue[1] templateYLabel[8] , and templateYValue[2] templateScale said the same about templateXValue[2] . A separate study revealed that 66 templateScale of templateTitleSubject[0] adults believed that 76 templateScale or more of the templateXValue[1] they saw on social media was biased .

generated_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: This statistic shows the Share of respondents at the fake Platform U.S. in the 2017 as of 2019 .  The largest Platform in the U.S. , Facebook employed 58 respondents at the end of their fiscal year in 2018 .

Example 583:
titleEntities: {'Subject': ['Norway'], 'Date': ['2008', '2018']}
title: Per capita consumption of alcohol in Norway 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Per', 'capita', 'consumption', 'in', 'liters']: ['82.79', '81.91', '82.33', '80.71', '81.94', '83.94', '83.93', '87.87', '89.58', '90.96', '92.91']

gold: The Nordic countries , except Denmark , have strong restrictions on the sales of alcoholic beverages . In Norway , the government-owned alcohol retailer Vinmonopolet is the only company that can sell beverages containing an alcohol content higher than 4.75 percent . In recent years , the consumption of alcohol in Norway decreased , and in 2018 , the per capita consumption of alcohol amounted to a volume of nearly 83 liters .
gold_template: The Nordic countries , except Denmark , have strong restrictions on the sales of alcoholic beverages . In templateTitleSubject[0] , the government-owned templateTitle[3] retailer Vinmonopolet is the only company that can sell beverages containing an templateTitle[3] content higher than 4.75 templateScale . In recent years , the templateYLabel[2] of templateTitle[3] in templateTitleSubject[0] templateNegativeTrend , and in templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] amounted to a volume of nearly templateYValue[0] templateYLabel[3] .

generated_template: The templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was varying over the period from templateXValue[min] to templateXValue[max] , between templateYValue[min] and templateYValue[idxmin(X)] suicides templateYLabel[2] hundred thousand templateYLabel[5] . In templateXValue[max] , there were templateYValue[idxmax(X)] suicides templateYLabel[2] hundred thousand templateYLabel[5] , same as the previous templateXLabel[0] .
generated: The Per capita in Norway was varying over the period from 2008 to 2018 , between 80.71 and 92.91 suicides consumption hundred thousand liters .  In 2018 , there were 82.79 suicides consumption hundred thousand liters , same as the previous Year .

Example 584:
titleEntities: {'Subject': ['Retail'], 'Date': ['2014', '2019']}
title: Retail revenue from smart wearable devices worldwide 2014 and 2019
X_Axis['Year']: ['2019', '2014']
Y_Axis['Retail', 'revenue', 'in', 'billion', 'U.S.', 'dollars']: ['53.2', '4.5']

gold: The statistic depicts the expected retail revenue from smart wearable devices worldwide in 2014 and 2019 . For 2019 , the global retail revenue from smart wearable devices is expected to reach 53.2 billion U.S. dollars .
gold_template: The statistic depicts the expected templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateXValue[min] and templateXValue[max] . For templateXValue[max] , the global templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] is expected to reach templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic displays the proportion of cars templateYLabel[2] a templateYLabel[3] templateYLabel[4] system installed in templateXValue[min] and templateXValue[max] . The templateYLabel[0] of templateYLabel[3] templateYLabel[4] system equipped cars templatePositiveTrend from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .
generated: This statistic displays the proportion of cars billion a U.S. dollars system installed in 2014 and 2019 .  The Retail of U.S. dollars system equipped cars increased from 4.5 billion in 2014 to 53.2 billion in 2019 .

Example 585:
titleEntities: {'Subject': ['Sears Holdings'], 'Date': ['2009']}
title: Number of stores of Sears Holdings worldwide 2009 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'stores']: ['1002', '1430', '1672', '1725', '2429', '2548', '4010', '3949', '3862']

gold: This statistic depicts the total number of stores of Sears Holdings from 2009 to 2017 . In 2017 , Sears Holdings had a total of 1,002 stores worldwide . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the United States .
gold_template: This statistic depicts the total templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had a total of templateYValue[min] templateYValue[idxmax(X)] templateTitle[4] . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the country .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] employed a templateTitle[0] of templateYValue[max] people throughout the world . templateTitleSubject[0] is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .
generated: This statistic shows the Number of stores Sears Holdings worldwide from 2009 to 2017 .  In 2017 , Sears Holdings employed a Number of 4010 people throughout the world .  Sears Holdings is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up perfumes , and hair care .

Example 586:
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: The statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the templateYLabel[1] users accessed the templateTitle[3] through their templateTitle[1] device . This figure is projected to grow to 59percent in templateXValue[max] .
generated: The statistic shows the social media user penetration 2017 in Vietnam from 2017 to 2023 .  In 2017 , 46 % of the population users accessed the user through their social device .  This figure is projected to grow 59percent in 2023 .

Example 587:
titleEntities: {'Subject': ['Electronic Arts'], 'Date': ['2010', '2020']}
title: Quarterly revenue of Electronic Arts from Q3 2010 to Q2 2020
X_Axis['Quarter']: ["Q2_'20", "Q1_'20", "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"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['1348', '1209', '1238', '1289', '1286', '1137', '1582', '1160', '959', '1449', '1527', '1149', '898', '1271', '1308', '1070', '815', '1203', '1185', '1126', '990', '1214', '1123', '808', '695', '949', '1209', '922', '711', '955', '1368', '1061', '715', '999', '1090', '1053', '631', '815', '979', '1243']

gold: This time series depicts the quarterly revenue of Electronic Arts from the third quarter of the fiscal year 2010 to the second quarter of the fiscal year 2020 . In the second fiscal quarter of 2020 , which ended on September 30 , 2019 , Electronic Arts generated a net revenue of 1.35 billion U.S. dollars . Here you can find information about EA 's quarterly net income .
gold_template: This time series depicts the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] from the third templateXLabel[0] of the fiscal year templateTitleDate[min] to the second templateXLabel[0] of the fiscal year templateTitleDate[max] . In the second fiscal templateXLabel[0] of templateTitleDate[max] , which ended on 30 , 2019 , templateTitleSubject[0] generated a net templateYLabel[0] of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] . Here you can find information about EA 's templateTitle[0] net income .

generated_template: The timeline shows the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] ! in the period from the first templateXLabel[0] of templateTitleDate[min] to the first templateXLabel[0] of templateTitleDate[max] . In the most recently reported templateXLabel[0] , templateTitleSubject[0] 's GAAP templateYLabel[0] amounted to templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] .
generated: The timeline shows the Quarterly Revenue of Electronic Arts ! in the period from the first Quarter of 2010 to the first Quarter of 2020 .  In the most recently reported Quarter , Electronic Arts 's GAAP Revenue amounted to 1348 million U.S. dollars .

Example 588:
titleEntities: {'Subject': ['Italy'], 'Date': ['2011', '2018']}
title: Italy : number of banks 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'banks']: ['505', '538', '604', '643', '664', '684', '706', '740']

gold: The total number of banks and credit institutions in Italy steadily decreased from 2011 to 2018 , sinking from 740 to 505 units . Such a decrease in the number of banks in the country might be due to the financial crisis of the years 2007 and 2008 , which hit Italy quite harshly also in the following years . During the same period , the number of bank branches also decreased steadily , and , as a consequence , the population size per bank branch increased from 1,734 in 2008 to 2,067 in 2016 .
gold_template: The total templateYLabel[0] of templateYLabel[1] and credit institutions in templateTitleSubject[0] steadily templateNegativeTrend from templateXValue[min] to templateXValue[max] , sinking from templateYValue[idxmin(X)] to templateYValue[idxmax(X)] units . Such a templateNegativeTrend in the templateYLabel[0] of templateYLabel[1] in the country might be due to the financial crisis of the years 2007 and 2008 , which hit templateTitleSubject[0] quite harshly also in the following years . During the same period , the templateYLabel[0] of bank branches also templateNegativeTrend steadily , and , as a consequence , the population size per bank branch templatePositiveTrend from 1,734 in 2008 to 2,067 in templateXValue[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , around templateYValue[max] thousand babies were born in templateTitleSubject[0] in templateXValue[idxmax(Y)] , an templatePositiveTrend from the previous templateXLabel[0] were templateYValue[1] thousand babies were born .
generated: This statistic shows the Number of banks in Italy from 2011 to 2018 .  According to the report , around 740 thousand babies were born in Italy 2011 , an increase from the previous Year were 538 thousand babies were born .

Example 589:
titleEntities: {'Subject': ['China'], 'Date': ['2012', '2024']}
title: Budget balance in China in relation to 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['Budget', 'balance', 'to', 'GDP', 'ratio']: ['-5.26', '-5.33', '-5.42', '-5.4', '-5.48', '-6.06', '-4.81', '-3.9', '-3.7', '-2.79', '-0.91', '-0.83', '-0.3']

gold: The graph shows the budget balance in China in relation to the gross domestic product ( GDP ) from 2012 to 2018 , with forecasts until 2024 . In 2018 , the state deficit of China had ranged at around 4.81 percent of the gross domestic product .
gold_template: The graph shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) from templateXValue[min] to templateXValue[6] , with forecasts until templateXValue[max] . In templateXValue[6] , the state deficit of templateTitleSubject[0] had ranged at around 4.81 templateScale of the templateTitle[4] templateTitle[5] templateTitle[6] .

generated_template: The graph shows templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] related to templateTitle[4] templateTitle[5] templateTitle[6] until templateXValue[6] , with forecasts to templateXValue[max] . In templateXValue[6] , templateTitle[4] templateYLabel[0] templateYLabel[1] ranged at templateYValue[6] templateScale of the templateYLabel[0] templateTitle[4] templateTitle[5] templateTitle[6] . Debt-to-GDP templateYLabel[3] – additional information In economics , the templateYLabel[3] between a country 's government templateYLabel[1] and its templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) is generally defined as the debt-to-GDP templateYLabel[3] .
generated: The graph shows Budget balance in China related to gross domestic product until 2018 , with forecasts to 2024 .  In 2018 , gross Budget balance ranged at -4.81 % of the Budget gross domestic product .  Debt-to-GDP ratio – additional information In economics , the ratio between a country 's government balance and its gross domestic product ( GDP ) is generally defined as the debt-to-GDP ratio .

Example 590:
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 591:
titleEntities: {'Subject': ['Instagram'], 'Date': ['2019']}
title: Most popular drag queens on Instagram worldwide 2019
X_Axis['State']: ['Adore_Delano', 'Bianca_Del_Rio', 'Kim_Chi', 'Katya_Zamolodchikova', 'Trixie_Mattel', 'Alaska', 'Violet_Chachki', 'Sasha_Velour', 'Alyssa_Edwards', 'Naomi_Smalls', 'Valentina', 'Aquaria', 'Courtney_Act', 'Pearl', 'Miss_Fame']
Y_Axis['Number', 'of', 'followers', 'in', 'millions']: ['1.7', '1.7', '1.7', '1.5', '1.5', '1.4', '1.4', '1.4', '1.3', '1.3', '1.2', '1.2', '1.1', '1.0', '1.0']

gold: This statistic presents a ranking of the most popular drag queens from RuPaul 's Drag Race on Instagram as of March 2019 , sorted by the number of global followers . According to the source , California-native Adora Delano was the most popular queen with 1.7 million Instagram followers . Adora had also participated in the seventh season of American idol under her real name , Daniel Anthony Noriega .
gold_template: This statistic presents a ranking of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] from RuPaul 's templateTitle[2] Race on templateTitleSubject[0] as of 2019 , sorted by the templateYLabel[0] of global templateYLabel[1] . According to the source , California-native Adora templateXValue[0] was the templateTitle[0] templateTitle[1] queen with templateYValue[max] templateScale templateTitleSubject[0] templateYLabel[1] . Adora had also participated in the seventh season of American idol under her real name , Daniel Anthony Noriega .

generated_template: This statistic provides information on the leading templateTitle[4] templateTitle[5] with the most templateYLabel[0] on templateTitleSubject[0] as of 2019 , ranked by templateTitle[1] of templateYLabel[0] . According to the findings , the templateTitle[4] templateXLabel[1] templateXValue[0] had recorded in a total of templateYValue[max] templateScale likes on templateTitleSubject[0] , and ranking second was templateXValue[1] with templateYValue[1] templateScale page likes .
generated: This statistic provides information on the leading Instagram worldwide with the most Number on Instagram as of 2019 , ranked by popular of Number .  According to the findings , the Instagram State Adore_Delano had recorded in a total of 1.7 millions likes on Instagram , and ranking second was Bianca_Del_Rio with 1.7 millions page likes .

Example 592:
titleEntities: {'Subject': ['Google'], 'Date': ['2001', '2019']}
title: Google network sites : advertising revenue 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['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['21.55', '20.01', '17.62', '15.6', '15.03', '14.54', '13.65', '12.47', '10.39', '8.79', '7.17', '6.71', '5.79', '4.16', '2.69', '1.55', '0.63', '0.1', '0.0']

gold: This statistic gives information on the advertising revenue of Google network websites from 2002 to 2019 . As of the most recently reported period , the advertising revenue of Google network sites amounted to 21.54 billion U.S. dollars . That year , Alphabet 's total Google segment revenue amounted to over 160.74 billion US dollars .
gold_template: This statistic gives information on the templateTitle[3] templateYLabel[0] of templateTitleSubject[0] templateTitle[1] websites from templateXValue[17] to templateXValue[max] . As of the most recently reported period , the templateTitle[3] templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] amounted to 21.54 templateScale templateYLabel[2] templateYLabel[3] . That templateXLabel[0] , Alphabet 's total templateTitleSubject[0] segment templateYLabel[0] amounted to over 160.74 templateScale US templateYLabel[3] .

generated_template: The timeline shows templateTitleSubject[0] 's total templateYLabel[0] worldwide between templateXValue[min] and templateXValue[max] . The templateTitleSubject[0] Group is a multinational exploration , development , production , and processing corporation . It is headquartered in London , UK .
generated: The timeline shows Google 's total Revenue worldwide between 2001 and 2019 .  The Google Group is a multinational exploration , development production , and processing corporation .  It is headquartered in London , UK .

Example 593:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Population density in Canada 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Inhabitants', 'per', 'square', 'kilometer']: ['4.08', '4.02', '3.97', '3.93', '3.9', '3.86', '3.82', '3.78', '3.74', '3.7', '3.66']

gold: In 2018 , Canada had a population density of about 4 people per square kilometer . The country has one of the lowest population densities in the world , as the total population is very small in relation to the dimensions of the land . Canada has a relatively stable population size , consistently with a growth of around one percent compared to the previous year .
gold_template: In templateXValue[max] , templateTitleSubject[0] had a templateTitle[0] templateTitle[1] of about templateYValue[max] people templateYLabel[1] templateYLabel[2] templateYLabel[3] . The country has one of the lowest templateTitle[0] densities in the world , as the total templateTitle[0] is very small in relation to the dimensions of the land . templateTitleSubject[0] has a relatively stable templateTitle[0] size , consistently with a growth of around one templateScale compared to the previous templateXLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . See the templateTitle[0] of templateTitleSubject[0] for comparison .
generated: The statistic shows the Population density in Canada from 2008 to 2018 .  In 2018 , the Population density in Canada amounted to about 4.08 Inhabitants per square kilometer .  See the Population of Canada for comparison .

Example 594:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2000', '2019']}
title: Total 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['Tax', 'receipts', 'in', 'billion', 'GBP']: ['623.36', '593.96', '569.3', '533.7', '515.3', '493.6', '473.8', '472.3', '453.6', '414.9', '445.5', '456.2', '428.6', '402.9', '375.8', '347.9', '324.7', '321.7', '315.6']

gold: In 2018/19 the value of HMRC tax receipts for the United Kingdom amounted to approximately 623.4 billion British pounds . This represented a net increase of 307.8 billion pounds when compared with 2000/01 . Since that year , the total value of tax receipts showed year-on-year increases except in 2008/09 and 2009/10 when it the value declined .
gold_template: In templateXValue[0] the value of templateTitle[3] templateYLabel[0] templateYLabel[1] for the templateTitleSubject[0] amounted to approximately templateYValue[max] templateScale British pounds . This represented a net templatePositiveTrend of 307.8 templateScale pounds when compared with templateXValue[last] . Since that templateXLabel[0] , the templateTitle[0] value of templateYLabel[0] templateYLabel[1] showed year-on-year increases except in templateXValue[10] and templateXValue[9] when it the value declined .

generated_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitle[6] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . templateYLabel[0] templateYLabel[1] brought a total of over 135 templateScale British pounds ( templateYLabel[6] ) in revenue to the templateTitle[6] during this period . The peak was in templateXValue[1] when the templateYLabel[4] amounted to approximately templateYValue[max] templateScale pounds .
generated: This statistic shows the total United Kingdom ( UK ) 2000 Tax receipts billion GBP from fiscal Year 2000/01 to fiscal Year 2018/19 .  Tax receipts brought a total of over 135 billion British pounds ( GBP ) in revenue to the 2000 during this period .  The peak was in 2017/18 when the GBP amounted to approximately 623.36 billion pounds .

Example 595:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2019']}
title: U.S. peanut production volume 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', 'pounds']: ['5496087', '5461600', '7115410', '5684610', '6001357', '5188665', '4173170', '6753880', '3658590', '4156840', '3691650', '5162400', '3672250', '3464250', '4869860', '4288200', '4144150', '3321040', '4276704', '3265505']

gold: Peanuts are a versatile and widely consumed nut in the United States . In 2019 , around 5.5 billion pounds of peanuts were produced in the United States , down from 7.12 billion pounds of peanuts in 2017 . Much of these peanuts were processed into peanut butter .
gold_template: Peanuts are a versatile and widely consumed nut in the templateTitle[0] . In templateXValue[max] , around templateYValue[0] templateScale templateYLabel[2] of peanuts were produced in the templateTitle[0] , down from templateYValue[max] templateScale templateYLabel[2] of peanuts in templateXValue[idxmax(Y)] . Much of these peanuts were processed into templateTitle[1] butter .

generated_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: This statistic shows the total U.S. peanut Production from 2000 to 2019 .  According to the report , approximately 5496087 thousand pounds of U.S. peanut were produced in the volume in 2019 .

Example 596:
titleEntities: {'Subject': ['UK'], 'Date': ['2009', '2016']}
title: Engine manufacturing : volume of engines produced in the UK 2009 - 2016 , by manufacturer
Data:
                        0       1       2       3       4       5       6       7
Manufacturer         2016    2015    2014    2013    2012    2011    2010    2009
Bentley             12728   11842   10614   10496    9108    7528    4791    3596
BMW                257106  170530  226133  408060  385410  433689  385051  362300
Ford (Bridgend)    659312  689227  701392  741073  741754  714709  680717  683340
Ford (Dagenham)    781501  918969  831888  787398  812791  987078  959480  746426
Honda              132371  125529  118638  139741  154228   97368  136658   60125
Nissan             264743  251059  279385  266130  267839  135958  105766  108955
Toyota             239287  203689  226618  200418  124241  127724  114254   88714
Jaguar Land Rover  201103       0       0       0       0       0       0       0 

gold: As of 2016 , Ford was the leading engine producer in the United Kingdom . The U.S. based company had manufactured a combined 1.44 million engines , which accounted for 57 percent of all motors made that year . Ford operates two plants in the UK , located in Bridgend and Dagenham .
gold_template: As of templateValue[0][0] , templateLabel[3][0] was the leading templateTitle[0] producer in the United Kingdom . The U.S. based company had manufactured a combined 1.44 templateScale templateTitle[3] , which accounted for 57 templateScale of all motors made that year . templateLabel[3][0] operates two plants in the templateTitleSubject[0] , located in Bridgend and Dagenham .

generated_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: This statistic shows the global manufacturing manufacturer engines produced as of 2017 , 2016 manufacturing volume size .  During the survey period , it was found that Ford accounted for 689227 % of single-word manufacturing queries UK .

Example 597:
titleEntities: {'Subject': ['Cameron International'], 'Date': ['2006']}
title: Cameron International 's revenue 2006 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['8782.0', '10381.0', '9138.0', '7795.0', '6348.0', '5644.0', '5223.2', '5848.9', '4666.4', '3742.9']

gold: This statistic shows Cameron International 's revenue from 2006 to 2015 . Cameron International Corporation is a global provider of services and equipment as well as project management for the oil and gas and process industries . In 2006 , the company brought in an annual revenue of around 3.74 billion U.S. dollars .
gold_template: This statistic shows templateTitleSubject[0] 's templateYLabel[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] Corporation is a global provider of services and equipment as well as project management for the oil and gas and process industries . In templateXValue[min] , the company brought in an annual templateYLabel[0] of around templateYValue[min] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows a templateTitle[0] for templateYLabel[0] from templateTitle[1] cartridges in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateYLabel[0] of about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] are expected .
generated: The statistic shows a Cameron for Revenue from International cartridges in Cameron International between 2006 and 2015 .  In 2015 , Revenue of about 10381.0 million U.S. dollars are expected .

Example 598:
titleEntities: {'Subject': ['Trust'], 'Date': ['2014', '2018']}
title: Trust in online customer reviews 2014 to 2018
Data:
                  0                                           1                                               2                                                   3                                             4                                5
unknown  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: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[min] and templateTitleDate[max] . As of templateTitleDate[max] , templateValue[2][max] templateScale of respondents said their templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] technology was templateValue[0][0] modeling ( FDM ) .
generated: The statistic shows the Trust online customer reviews 2014 in and 2018 .  As of 2018 , 31 % of respondents said their Trust online customer reviews technology was Yes_always modeling ( FDM ) .

Example 599:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: Direct investment position of the U.S. abroad 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['Direct', 'investments', 'in', 'trillion', 'U.S.', 'dollars']: ['5.95', '6.01', '5.59', '5.29', '5.11', '4.58', '4.41', '4.05', '3.74', '3.57', '3.23', '2.99', '2.48', '2.24', '2.16', '1.77', '1.62', '1.46', '1.32']

gold: In 2018 , foreign direct investment ( FDI ) from the United States to other countries amounted to 5.95 trillion U.S. dollars . Foreign direct investment reflects the ownership of business from one country in another country . It differs from a traditional investment in companies located abroad by the ownership factor in case of FDI .
gold_template: In templateXValue[max] , foreign templateYLabel[0] templateTitle[1] ( FDI ) from the templateTitle[3] to other countries amounted to templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] . Foreign templateYLabel[0] templateTitle[1] reflects the ownership of business from templateYValue[17] country in another country . It differs from a traditional templateTitle[1] in companies located templateTitle[4] by the ownership factor in case of FDI .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . The total templateYLabel[0] templateTitle[2] of the templateTitle[3] abroad amounted to 5.95 templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] templateXValue[idxmax(Y)]
generated: This statistic shows the Direct investment position of the U.S. in from 2000 to 2018 , on a historical-cost basis .  In 2018 , the U.S. investments made in U.S. were valued at approximately 5.95 trillion U.S. dollars .  The total Direct position of the U.S. abroad amounted to 5.95 trillion U.S. dollars in 2018 2017 . 

Example 600:
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] , 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 , 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 601:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010']}
title: Median combined annual earnings of newlyweds in the U.S. in 2010 by race
X_Axis['Race']: ['White/Asian', 'Asian/Asian', 'White/White', 'White/Hispanic', 'White/Black', 'Black/Black', 'Hispanic/Hispanic']
Y_Axis['Earnings', 'in', 'U.S.', 'dollars']: ['70952', '62000', '60000', '57900', '53187', '47700', '33578']

gold: This statistic shows the median combined annual earnings of newlyweds in the United States in 2010 by race and ethnicity . In 2010 , White/Asian couples had a median annual income of 70,852 U.S. dollars .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] of templateTitle[4] in the templateTitle[5] in templateTitleDate[0] templateTitle[7] templateXLabel[0] and ethnicity . In templateTitleDate[0] , templateXValue[0] couples had a templateTitle[0] templateTitle[2] income of 70,852 templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in the templateTitleSubject[0] , according to a templateTitle[1] templateTitle[2] . This was found that templateYValue[max] templateScale of all templateXValue[2] templateXLabel[0] in the templateTitleSubject[0] was templateXValue[1] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Median nation in the U.S. , according to a combined annual .  This was found that 70952 % of all White/White Race in the U.S. was Asian/Asian .

Example 602:
titleEntities: {'Subject': ['CO2'], 'Date': ['1975', '2019']}
title: Global CO2 emissions related to energy 1975 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990', '1985', '1980', '1975']
Y_Axis['CO2', 'emissions', 'in', 'billion', 'metric', 'tons']: ['33.3', '33.3', '32.7', '32.2', '32.2', '32.3', '32.2', '31.6', '31.3', '30.4', '28.7', '29.1', '28.9', '27.9', '26.9', '23.01', '21.48', '20.62', '18.32', '17.78', '15.54']

gold: The statistic represents global energy-related carbon dioxide emissions between 1975 and 2019 . Globally , 17.78 billion metric tons of carbon dioxide was emitted in relation to energy in 1980 . Energy-related emissions stabilized in 2019 when emissions reached 33.3 billion metric tons , following two consecutive years of increases in greenhouse gas emissions .
gold_template: The statistic represents templateTitle[0] energy-related carbon dioxide templateYLabel[1] between templateXValue[min] and templateXValue[max] . Globally , templateYValue[19] templateScale templateYLabel[3] templateYLabel[4] of carbon dioxide was emitted in relation to templateTitle[4] in templateXValue[19] . Energy-related templateYLabel[1] stabilized in templateXValue[max] when templateYLabel[1] reached templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] , following two consecutive years of increases in greenhouse gas templateYLabel[1] .

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 a total templateYValue[idxmax(X)] templateYLabel[1] reported in the templateTitleSubject[0] .
generated: This statistic shows the CO2 of emissions in the CO2 from 1975 to 2019 .  In 2019 , there were a total 33.3 emissions reported in the CO2 .

Example 603:
titleEntities: {'Subject': ['Starbucks'], 'Date': ['2019', '2019']}
title: Countries with the largest number of Starbucks stores worldwide as of September 2019
Data:
                                     0      1      2       3         4               5            6       7       8          9           10
Country                  United_States  China  Japan  Canada  Thailand  United_Kingdom  South_Korea  Taiwan  Turkey  Indonesia  Philippines
Company operated stores           8575   3521   1286    1109       352             335            0       0       0          0            0
Licensed stores                   6031      0      0     409         0             653         1231     458     453        365          360 

gold: The United States and China came top in the ranking of countries with the largest number of Starbucks stores worldwide as of September 2019 . The famous U.S. coffeehouse chain had the most stores in its home country accounting for over 14 thousand stores . Starbucks just keeps on growing Over the past 10 years the number of Starbucks stores worldwide have increased annually - the only exception being in 2009 where a dip was recorded due to the global financial crisis .
gold_template: The templateValue[0][0] and templateValue[0][1] came top in the ranking of templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] of templateTitleSubject[0] templateTitle[5] templateTitle[6] as of templateTitle[7] templateTitleDate[0] . The famous U.S. coffeehouse chain had the most templateTitle[5] in its home templateLabel[0][0] accounting for over 14 thousand templateTitle[5] . templateTitleSubject[0] just keeps on templatePositiveTrend Over the past 10 years the templateTitle[3] of templateTitleSubject[0] templateTitle[5] templateTitle[6] have templatePositiveTrend annually - the only exception being in 2009 where a dip was recorded due to the global financial crisis .

generated_template: This statistic shows 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: This statistic shows the number of largest and number due to Countries from the worldwide in Starbucks Africa which lasts since 2014 .  As of 30 , 2019 there have been 3521 largest in China , resulting in 0 number .  The Countries virus causes extremely severe hemorrhagic fever and is considered a Risk Group 0 Pathogen September the World Health Organization ( WHO ) .

Example 604:
titleEntities: {'Subject': ['Asia Pacific'], 'Date': ['2016', '2016']}
title: Daily mobile app usage in Asia Pacific 2016 , by country
X_Axis['Country']: ['Philippines', 'Indonesia', 'Thailand', 'Malaysia', 'Vietnam', 'China', 'Singapore', 'India', 'South_Korea', 'Japan']
Y_Axis['Average', 'daily', 'usage', 'time', 'in', 'minutes']: ['72', '69', '68', '59', '51', '45', '45', '44', '34', '22']

gold: This statistic presents the average daily time spent on mobile apps by smartphone users in Southeast Asian countries in July 2016 . During the survey period , it was found that smartphone users in Malaysia spent an average of 66 minutes every day on mobile apps .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[3] spent on templateTitle[1] apps templateTitle[7] smartphone users in Southeast Asian countries in 2016 . During the survey period , it was found that smartphone users in templateXValue[3] spent an templateYLabel[0] of 66 templateYLabel[4] every day on templateTitle[1] apps .

generated_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: This statistic shows the share of usage Daily Asia Pacific time Pacific in 2016 , Country .  In 2016 , usage Daily Asia Pacific time Asia in Philippines accounted for around 72 % of the world 's total usage grid-connected Asia Pacific time .

Example 605:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in the Netherlands 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']: ['62696.24', '60250.66', '58102.28', '55998.95', '53873.37', '52367.85', '53228.27', '48799.87', '46165.18', '45302.8', '53026.48', '52201.72', '50100.62', '54209.22', '51046.25', '52648.42', '57916.25', '51804.2', '44901.09', '42046.72', '40404.22', '35734.22', '29294.21', '26891.92', '26220.07', '28299.67', '27924.51', '26733.01', '29016.44', '29284.58', '24824.41', '23237.63', '24061.89', '21931.77', '21457.92', '17514.51', '17869.58', '16807.92', '13810.05', '9964.01', '9867.25']

gold: This statistic shows the gross domestic product ( GDP ) per capita in the Netherlands from 1984 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: This 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] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country templateYLabel[1] 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] ) templateYLabel[1] templateYLabel[2] in 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 templateTitleSubject[0] was around templateYValue[7] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] 's economy templateYLabel[0] templateYLabel[1] templateYLabel[2] is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the templateYLabel[0] and dividing it by the total population in the country .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in Netherlands from 1984 to 2018 , with projections up until 2024 .  In 2018 , the Gross domestic product per capita in Netherlands was around 48799.87 U.S. dollars .  Netherlands 's economy GDP per capita is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the GDP and dividing it by the total population in the country .

Example 606:
titleEntities: {'Subject': ['Bahamas'], 'Date': ['2024']}
title: Inflation rate in the Bahamas 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']: ['2.17', '2.17', '2.18', '2.32', '2.64', '1.85', '2.23', '1.55', '-0.35', '1.88', '1.18', '0.44', '1.91', '3.06', '1.62', '1.67', '4.43', '2.41', '1.96', '1.82', '1.16', '3.66', '1.5', '2.36', '1.73', '1.12', '1.21', '0.56', '1.26', '2.02', '1.37', '2.75', '5.58', '7.3', '4.63', '5.41', '4.05', '5.99', '5.41', '4.62', '3.97']

gold: This statistic shows the average inflation rate in the Bahamas from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in the Bahamas amounted to about 2.23 percent compared to the previous year .
gold_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 the 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 Bahamas from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Bahamas amounted to about 2.23 % compared to the previous Year .

Example 607:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': ['2004', '2019']}
title: Fatal police shootings England and Wales 2004 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']
Y_Axis['Fatailities']: ['3', '4', '6', '3', '1', '0', '0', '2', '2', '2', '3', '5', '1', '5', '3']

gold: This statistic shows the number of fatal shootings by police in England and Wales from 2004/05 to 2018/19 . During this period , the number of fatal shootings by police fluctuated , peaking in 2016/17 at 6 shootings . By 2018/19 it fell down to 3 shootings .
gold_template: This statistic shows the number of templateTitle[0] templateTitle[2] by templateTitle[1] in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[last] to templateXValue[0] . During this period , the number of templateTitle[0] templateTitle[2] by templateTitle[1] fluctuated , peaking in templateXValue[2] at templateYValue[max] templateTitle[2] . By templateXValue[0] it templateNegativeTrend down to templateYValue[last] templateTitle[2] .

generated_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: The highest Fatailities of concussions among England Blue Cross Shield ( BCBS ) Fatailities from 2004 to 2019 was among those aged 15 - 17 2017/18 .  Among that 2019 group the Fatailities of Fatal was 6 Fatailities 1,000 .  Unsurprisingly , the 2019 group with the lowest Fatailities of Fatal was those aged less than 0 2018/19 .

Example 608:
titleEntities: {'Subject': ['Tampa Bay Rays'], 'Date': ['2006', '2019']}
title: Tampa Bay Rays 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']: ['22.53', '21.6', '21.04', '21.9', '21.01', '20.39', '19.83', '19.42', '19.75', '18.35', '17.23', '17.23', '17.09']

gold: This graph depicts the average ticket price for Tampa Bay Rays games in Major League Baseball from 2006 to 2019 . In 2019 , the average ticket price was at 22.53 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] Rays 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] ( St. Louis ) templateTitleSubject[0] 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: This graph depicts the Average ticket price for Tampa Bay Rays ( St. Louis ) Tampa Bay Rays games in the National Football League from 2006 to 2019 .  In 2019 , the Average ticket price was at 22.53 U.S. dollars .

Example 609:
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 shows the templateYLabel[0] of adults in the templateTitleSubject[1] who were using templateTitleSubject[0] as of 2019 , sorted templateTitle[6] templateTitle[7] . During that period of time , templateYValue[max] templateScale of templateXValue[0] respondents stated that they used the social networking site .
generated: This statistic shows the Number of adults in the Shades Grey U.S. who were using Shades Grey U.S. as of 2019 , sorted 2012 by .  During that period of time , 29 millions of Print respondents stated that they used the social networking site .

Example 610:
titleEntities: {'Subject': ['Cyber'], 'Date': ['2019']}
title: Cyber bullying : common types of bullying 2019 , by gender
Data:
                                                    0                                     1                                               2                                       3
unknown  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 shows the templateTitle[0] of templateTitle[1] leave templateValue[0][0] templateTitleSubject[0] templateTitle[6] took last templateTitle[8] as of 2017 , templateTitle[4] templateTitle[11] . It was found that templateValue[2][0] templateScale of templateLabel[2][0] respondents and templateValue[1][0] templateScale of templateLabel[1][0] respondents did not take any templateTitle[1] leave templateValue[0][0] .
generated: This statistic shows the Cyber of bullying leave I_have_been_cyber_bullied_(previous_30_days) Cyber by took last gender as of 2017 , bullying gender .  It was found that 17.1 % of Female respondents and 17.7 % of Male respondents did not take any bullying leave I_have_been_cyber_bullied_(previous_30_days) .

Example 611:
titleEntities: {'Subject': ['Germany'], 'Date': ['2013', '2018']}
title: Share of retail spend in Germany 2013 - 2018 , by channel
Data:
                                                             0                                         1                                                  2                                                           3                                  4                                        5       6                  7       8                           9                                       10                                                 11                                                      12
Retail channel  Hypermarkets_supermarkets_and_hard_discounters  Food_&_beverage_specialists_and_c-stores  Home_improvement_and_gardening_supplies_retailers  Clothing_footwear_accessories_and_luxury_goods_specialists  Furniture_and_homewares_retailers  Electricals_and_electronics_specialists  Online  Department_stores  Others  Other_specialist_retailers  Drugstores_and_health_and_beauty_stores  Music_video_book_stationery_and_games_specialists  Value_variety_stores_and_general_merchandise_retailers
2013                                                      33.9                                      16.6                                                8.2                                                           8                                6.1                                      5.7     5.5                4.1     3.3                         2.3                                      2.3                                                2.1                                                     1.8
2018                                                        34                                      16.1                                                7.2                                                         8.1                                5.9                                      5.6     8.1                3.7     3.3                         2.1                                      2.3                                                1.7                                                     1.9 

gold: This statistic shows a distribution of retail spending in Germany , by channel , in 2013 and with a forecast for 2018 . Hypermarkets , supermarkets and hard discounters are the most common retail channel used in Germany , accounting for 33.9 percent of total retail spend in 2013 . However spending through online channels is expected to rise , with the online share of spend reported to increase from 5.5 percent in 2013 to 8.1 percent by 2018 .
gold_template: This statistic shows a distribution of templateLabel[0][0] spending in templateTitleSubject[0] , templateTitle[6] templateLabel[0][1] , in templateTitleDate[min] and with a forecast for templateTitleDate[max] . templateValue[0][0] , templateValue[0][0] and templateValue[0][0] are the most common templateLabel[0][0] templateLabel[0][1] used in templateTitleSubject[0] , accounting for templateValue[1][max] templateScale of total templateLabel[0][0] templateTitle[2] in templateTitleDate[min] . However spending through templateValue[0][6] channels is expected to rise , with the templateValue[0][6] templateTitle[0] of templateTitle[2] reported to templatePositiveTrend from templateValue[1][6] templateScale in templateTitleDate[min] to templateValue[2][3] templateScale templateTitle[6] templateTitleDate[max] .

generated_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] templateTitle[10] . 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: This statistic shows the Germany 2013 of Germany and retail to 2018 by from 2013 to 2018 , channel .  Germany and retail directly contributed approximately 7.2 million jobs to the Food_&_beverage_specialists_and_c-stores Hypermarkets_supermarkets_and_hard_discounters Asian economy in 2018 .

Example 612:
titleEntities: {'Subject': ['Google'], 'Date': ['2019']}
title: Google : global corporate demography 2019 , by gender and department
Data:
                  0     1         2
unknown  Leadership  Tech  Non-tech
Female         26.1  22.9      47.9
Male           73.9  77.1      52.2 

gold: This statistic illustrates the distribution of Google employees worldwide in 2019 , sorted by gender and department . As of the measured period , 26.1 percent of global Google leadership employees were female . The majority of employees were male .
gold_template: This statistic illustrates the distribution of templateTitleSubject[0] employees worldwide in templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] and templateTitle[7] . As of the measured period , templateValue[1][0] templateScale of templateTitle[1] templateTitleSubject[0] templateValue[0][0] employees were templateLabel[1][0] . The majority of employees were templateLabel[2][0] .

generated_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: This statistic displays the Leadership rate of individuals suffering global an corporate demography in the Google in 2019 .  Approximately 26.1 % of sufferers Female and 73.9 % of sufferers Male Female make a Leadership .

Example 613:
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 shows the results of a templateTitleDate[0] survey regarding patriotism in the templateTitle[4] . The templateYLabel[1] were asked how proud they are to be an templateTitleSubject[0] . In templateTitleDate[0] , some templateYValue[max] templateScale of survey templateYLabel[1] stated they were templateXValue[0] proud to be an templateTitleSubject[0] .
generated: This statistic shows the results of a 2013 survey regarding patriotism in the consumers .  The respondents were asked how proud they are to be an UK .  In 2013 , some 31 % of survey respondents stated they were November proud to be an UK .

Example 614:
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 templateScale of templateTitle[0] that involved templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[max] templateScale of templateYLabel[1] were committed with use of templateTitle[2] in templateXValue[1] . A ranking of the total number of templateTitle[0] templateTitle[5] templateTitleSubject[0] templateXLabel[0] can be found here .
generated: This statistic shows the percentage of Metropolitan that involved highest in the birth in 2018 , U.S. City .  In 2018 , about 20.36 % of rate were committed with use of highest in Jacksonville_NC .  A ranking of the total number of Metropolitan U.S. City can be found here .

Example 615:
titleEntities: {'Subject': ['Rome'], 'Date': ['2011', '2019']}
title: Hotel occupancy rate in Rome 2011 to 2019
X_Axis['Year']: ['2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['Occupancy', 'rate']: ['67', '66', '67', '69', '69', '70', '70', '70', '71']

gold: This statistic illustrates the hotel occupancy rate in Rome from 2011 to 2019 . The occupancy rate of hotels in the city was measured at 70 percent in 2017 . Rates are forecast to remain stable in 2018 and rise by one percentage point in 2019 .
gold_template: This statistic illustrates the templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of hotels in the city was measured at templateYValue[5] templateScale in templateXValue[6] . Rates are forecast to remain stable in templateXValue[7] and rise by one templateScale point in templateXValue[max] .

generated_template: This statistic shows the estimated templateScale of templateYLabel[0] templateYLabel[1] a templateTitle[0] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[min] to templateXValue[max] . Since templateXValue[5] , the share of household templateYLabel[1] templateYLabel[2] in the templateTitleSubject[1] has templatePositiveTrend , with an estimated templateYValue[0] templateScale templateYLabel[1] one in templateXValue[7] . However , the share of household templateYLabel[1] templateYLabel[2] templateNegativeTrend to templateYValue[idxmax(X)] templateScale in templateXValue[max] .
generated: This statistic shows the estimated percentage of Occupancy rate a Hotel in the Rome ( ) from 2011 to 2019 .  Since 2016 , the share of household rate in the Rome has increased , with an estimated 67 % rate one in 2018 .  However , the share of household rate decreased to 71 % in 2019 .

Example 616:
titleEntities: {'Subject': ['Australia'], 'Date': ['2013', '2019']}
title: Market share held by smartphone OS in Australia 2013 - 2019 , by month
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
Month       Sep_'19  Dec_'18  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
Android          60     56.5     64.7     63.9     61.5     61.1     59.8     59.5     55.7     52.3     50.1     55.7     58.2     62.8     60.4     61.6     64.4       64     60.1     55.1     52.6     53.5     53.7     54.9     54.5     53.1     56.1     57.2     56.2     53.8     52.3     50.6     46.9     43.7       45     50.8     58.1     64.3     66.5       68     63.7     60.8     57.3     58.5     57.7     57.5     55.1     54.9     55.6     63.4     63.4     64.8     63.3     64.1     61.6     58.8     57.9
iOS            39.6     43.1       35     35.8     38.1     38.4     39.1     38.9     42.4     44.9     46.4     39.3     37.1       33     35.2     34.7     32.5     32.7     35.3     38.2     41.2     39.6     40.5     37.9     36.8     37.8     34.9     34.6     35.9     36.8     38.4     38.5     42.4     45.1     44.9     40.4     34.7     29.3       27     25.5       28     30.1     33.1     33.9     35.1     34.8       35       35     32.6     27.1     26.9     27.4     28.2     28.1     31.1     34.8       36
Blackberry      0.1      0.1        0        0      0.1      0.1      0.2      0.6      0.6      0.7        1      1.8      2.2      1.6      0.7      0.3      0.3      0.3      0.3      0.2      0.2      0.3      0.5      0.5      1.1      1.5        2      1.2        1      1.2      1.6      1.2        0      0.9      0.9      0.8      0.6      0.8      0.5      0.5      0.5      0.9        1      0.7      0.3      0.7      1.4      1.6      1.3      0.4      0.4      0.3      0.7      0.6      0.5      0.4      0.5
Windows         0.2      0.3      0.4      0.3      0.2      0.2      0.6      0.7        1      1.9      2.3        3      2.3      2.4        3        3      2.6      2.8      3.7      5.8      5.4      6.3      5.3      6.5      7.4      7.4      6.9      6.3      6.3      7.7      7.3      9.3      8.7      9.2        8      7.3      6.2      5.1      5.7      5.3      7.2      6.7      6.9        5      5.1      5.2      6.9      7.3      9.3      6.8      6.7      5.2      5.5        5      4.1      3.5      3.4
Other           0.1        0        0        0      0.1      0.2      0.3      0.3      0.2      0.2      0.1      0.2      0.2      0.3      0.4      0.3      0.3      0.2      0.7      0.7      0.6      0.3        0      0.2      0.2      0.2      0.1      0.3      0.6      0.5      0.4      0.5      1.9      1.1      1.1      0.7      0.4      0.4      0.4      0.7      0.6      1.5      1.7      1.8      1.7      1.7      1.5      1.1      1.3      2.3      2.6      2.3      2.3      2.2      2.7      2.5      2.3 

gold: This statistic shows the market share held by the leading smartphone operating systems in Australia from 2013 to 2018 , by month . In December 2018 the Android operating system had a market share of 56.5 percent in Australia . Smartphone OS market share in Australia - additional information Smartphones are becoming increasingly popular in Australia and it is estimated that in 2017 , 83 percent of all mobile users will have a smartphone , in comparison to 74 percent of users in 2014 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the leading templateTitle[4] operating systems in templateTitleSubject[0] from templateTitleDate[min] to 2018 , templateTitle[3] templateLabel[0][0] . In 2018 the templateLabel[1][0] operating system had a templateTitle[0] templateTitle[1] of templateValue[1][1] templateScale in templateTitleSubject[0] . templateTitle[4] templateTitle[5] templateTitle[0] templateTitle[1] in templateTitleSubject[0] - additional information Smartphones are becoming increasingly popular in templateTitleSubject[0] and it is estimated that in 2017 , 83 templateScale of all mobile users will have a templateTitle[4] , in comparison to 74 templateScale of users in 2014 .

generated_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: This statistic shows the Market share of the most popular held by in Australia from 2011 to 2015 .  In 2015 , roughly 60 % of held owners used a Android phone , making it the brand with the highest Market share in this ranking .  In the same Month , iOS 's iPhone had a Market share of 36 % .

Example 617:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global tablet shipments by operating system per quarter 2010 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      28      29      30      31      32      33      34      35      36
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  Q2_'13  Q1_'13  Q4_'12  Q3_'12  Q2_'12  Q1_'12  Q4_'11  Q3_'11  Q2_'11  Q4_'10  Q3_'10  Q2_'10
Apple iOS         13.5    10.7    10.7     9.9    14.5     9.7    11.6     9.1    13.2    10.3    11.4     8.9    13.1     9.3    10.0    10.3    16.1     9.9    10.9    12.6    21.4    12.3    13.3    16.4    26.0    14.6    19.5    22.9    14.0    17.0    11.8    15.4    11.1     9.3     7.3     4.2     3.3
Android           29.1    23.1    21.6    21.6    32.9    24.3    23.3    23.9    34.8    27.8    26.4    26.9    40.3    30.1    30.1    30.1    44.9    36.0    35.2    35.7    51.8    39.8    36.8    37.2    47.8    33.2    25.6    38.9    10.2    18.5     6.4    10.5     5.0     4.4     3.1     0.1     0.1
Windows tablet     5.8     4.8       5       5     7.1     5.7     5.5     5.7     7.3     6.7       6     6.3    10.1     7.3     6.7     6.4     8.5     5.8     4.6     3.4     5.1     3.2     2.7     3.1     3.4     2.1     2.8       0       0       0       0       0       0       0       0       0       0
Others             0.2     0.2     0.1     0.1     0.4       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0     0.1     0.4     2.4     0.5     0.5     0.5     0.9     1.1     1.3     0.3     0.1     0.1 

gold: This statistic shows global tablet shipments by operating system from the second quarter of 2010 to the fourth quarter of 2019 . In the fourth quarter of 2019 , Apple shipped 13.5 million iPads worldwide . Global tablet shipments - operating systems Android was the leading operating system worldwide for tablets in 2016 , shipping a total of 130.6 million units throughout the year .
gold_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from the second templateLabel[0][0] of templateTitleDate[min] to the fourth templateLabel[0][0] of templateTitleDate[max] . In the fourth templateLabel[0][0] of templateTitleDate[max] , templateLabel[1][0] shipped templateValue[1][0] templateScale iPads worldwide . templateTitleSubject[0] templateTitle[1] templateTitle[2] - templateTitle[4] systems templateLabel[2][0] was the leading templateTitle[4] templateTitle[5] worldwide for tablets in 2016 , shipping a total of 130.6 templateScale units throughout the year .

generated_template: The statistic shows a templateTitle[3] of templateTitleSubject[0] templateLabel[2][0] , templateLabel[3][0] and templateLabel[1][0] templateTitle[2] from the first templateLabel[0][0] of 2006 to the company 's latest financial templateLabel[0][0] . In templateTitleSubject[0] 's forth financial templateLabel[0][0] of templateTitleDate[max] approximately templateValue[2][0] templateScale iPhones were sold worldwide . templateTitleSubject[0] templateTitle[1] templateTitle[2] - additional information Since the introduction of the templateLabel[2][0] in 2007 , templateTitle[2] of the templateLabel[1][0] have dramatically templateNegativeTrend from over templateValue[1][36] templateScale units per templateLabel[0][0] on average to less than templateValue[1][16] templateScale units in the fourth templateLabel[0][0] of 2014 , after which templateTitleSubject[0] stopped reporting templateTitle[2] figures for the templateLabel[1][0] as its own category .
generated: The statistic shows a by of Global Android , Windows and Apple shipments from the first Quarter of 2006 to the company 's latest financial Quarter .  In Global 's forth financial Quarter of 2019 approximately 29.1 million iPhones were sold worldwide .  Global tablet shipments - additional information Since the introduction of the Android in 2007 , shipments of the Apple have dramatically dropped from over 3.3 million units per Quarter on average to less than 16.1 million units in the fourth Quarter of 2014 , after which Global stopped reporting shipments figures for the Apple as its own category .

Example 618:
titleEntities: {'Subject': ['Election'], 'Date': ['2012']}
title: Election 2012 exit polls : percentage of votes by ethnicity
Data:
                  0      1         2      3
unknown       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 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: This graph shows the voter exit polls Barack Election and Mitt percentage in the 2012 as of October 28 , by ethnicity .  If the elections were held that day , about 71 percentage of Hispanic or African American voters would vote polls Barack Election .

Example 619:
titleEntities: {'Subject': ['Walt Disney Company'], 'Date': ['2010', '2019']}
title: Revenue of the Walt Disney Company 2010 - 2019 , by region
Data:
                               0             1       2                         3
unknown  Latin_America_and_other  Asia_Pacific  Europe  United_States_and_Canada
2010                         914          2320    6550                     28279
2011                        1073          2517    6455                     30848
2012                        1295          2990    6223                     31770
2013                        1506          3333    6181                     34021
2014                        1609          3930    6505                     36769
2015                        1680          3958    6507                     40320
2016                        1720          4582    6714                     42616
2017                        1640          5075    6541                     41881
2018                        1839          5531    7026                     45038
2019                        3213          7796    8006                     50555 

gold: In 2019 , the Walt Disney Company generated a total revenue of eight billion U.S. dollars in Europe , but the company 's largest region was the United States and Canada , which generated revenue of over 50.5 billion U.S. dollars in that year . The company 's total revenue in 2018 amounted to 69.57 billion U.S. dollars . Walt Disney Company - additional information The Walt Disney Company was founded in 1923 by brothers Walt Disney and Roy O. Disney .
gold_template: In templateTitleDate[max] , the templateTitleSubject[0] Company generated a total templateTitle[0] of eight templateScale U.S. dollars in templateValue[0][2] , but the templateTitleSubject[0] 's largest templateTitle[7] was the templateValue[0][3] and templateValue[0][3] , which generated templateTitle[0] of over 50.5 templateScale U.S. dollars in that year . The templateTitleSubject[0] 's total templateTitle[0] in templateLabel[9][0] amounted to 69.57 templateScale U.S. dollars . templateTitleSubject[0] Company - additional information The templateTitleSubject[0] Company was founded in 1923 templateTitle[6] brothers templateTitleSubject[0] and Roy O. templateTitleSubject[0] .

generated_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: The Walt Disney Company Company 's worldwide Revenue Walt amounted to 14.86 billion U.S. dollars in 2019 , of which 2.43 billion U.S. dollars were derived from operations in Europe .  Revenue Walt from Latin_America_and_other and areas came to 3213 million U.S. dollars that year .

Example 620:
titleEntities: {'Subject': ['Virgin Media'], 'Date': ['2008', '2018']}
title: Virgin Media Inc. : global revenue 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'GBP']: ['5150.3', '4963.2', '4806.1', '4618.4', '4214.2', '4120.4', '4100.5', '3991.8', '3875.8', '3663.9', '3655.0']

gold: Virgin Media Inc increased total revenue generated in 2018 , bringing in 5.15 billon British pounds . That marks the first time the company has exceeded 5 billion British pounds , after coming close with 4.96 billion British pounds in 2017 . Virgin Media revenue mix Cable is the largest contributor to the Virgin Media revenue mix , with 3.64 billion British pounds flowing in to the company from this source .
gold_template: templateTitleSubject[0] Inc templatePositiveTrend total templateYLabel[0] generated in templateXValue[max] , bringing in 5.15 billon British pounds . That marks the first time the company has exceeded templateYValue[max] templateScale British pounds , after coming close with templateYValue[1] templateScale British pounds in templateXValue[1] . templateTitleSubject[0] revenue mix Cable is the largest contributor to the templateTitleSubject[0] revenue mix , with 3.64 templateScale British pounds flowing in to the company from this source .

generated_template: The statistic shows the number of templateYLabel[0] templateYLabel[1] templateTitle[1] templateTitle[2] templateTitleSubject[0] Airways in the templateTitleSubject[1] ( templateTitleSubject[2] ) between templateXValue[min] and templateXValue[max] . templateTitleSubject[0] Airways Limited is a British airline with its headquarters located in Crawley , templateTitleSubject[2] . It was established in 1984 and formerly known as British templateTitleSubject[0] Airways .
generated: The statistic shows the number of Revenue million Media Inc. Virgin Media Airways in the Virgin Media ( ) between 2008 and 2018 .  Virgin Media Airways Limited is a British airline with its headquarters located in Crawley , Virgin Media .  It was established in 1984 and formerly known as British Virgin Media Airways .

Example 621:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Netherlands 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']: ['43.9', '42.2', '38.9', '37.4', '35.9', '34.0', '31.1', '30.7', '30.0', '29.0', '29.1', '30.3', '28.5']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in the Netherlands from 2006 to 2018 . Following an increasing trend over the last five years , total arrivals in travel accommodation in the Netherlands peaked at 43.9 million in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in the templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . Following an templatePositiveTrend trend over the last five years , total templateYLabel[1] in travel templateTitle[3] in the templateTitleSubject[0] peaked at templateYValue[max] templateScale in templateXValue[idxmax(Y)] .

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 Netherlands from 2006 to 2018 .  In 2018 , the Number of arrivals in travel accommodation ( including both international and domestic tourists ) amounted to approximately 43.9 millions .

Example 622:
titleEntities: {'Subject': ['Peru'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Peru 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['3.76', '3.9', '3.99', '4', '3.62', '2.6', '3.99', '2.47', '4.05', '3.27', '2.39']

gold: The statistic shows the growth in real GDP in Peru from 2014 to 2018 , with projections up until 2024 . In 2018 , Peru 's real gross domestic product grew by around 3.99 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[2] 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 between templateXValue[min] to templateXValue[5] , with projections up until templateXValue[max] . In templateXValue[5] , 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 Peru from between 2014 to 2019 , with projections up until 2024 .  In 2019 , Peru 's real Gross domestic product increased by around 3.99 % compared to the previous Year .

Example 623:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. : reported robbery cases 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['Number', 'of', 'reported', 'cases']: ['282061', '320596', '332797', '328109', '322905', '345093', '355051', '354746', '369089', '408742', '443563', '447324', '449246', '417438', '401470', '414235', '420806', '422921', '408016', '409371', '447186', '497950', '535590', '580510', '618950', '659870', '672480', '687730', '639270']

gold: This graph shows the reported number of robbery cases in the United States from 1990 to 2018 . In 2018 an estimated 282,061 cases occurred nationwide .
gold_template: This graph shows the templateYLabel[1] templateYLabel[0] of templateTitle[2] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] an estimated templateYValue[idxmax(X)] templateYLabel[2] occurred nationwide .

generated_template: This statistic show the templateYLabel[1] templateTitle[2] templateTitle[3] templateYLabel[2] between templateXValue[min] and templateXValue[max] .
generated: This statistic show the reported robbery cases between 1990 and 2018 .

Example 624:
titleEntities: {'Subject': ['Leading'], 'Date': ['2017']}
title: Leading countries based on hard coal production 2017
X_Axis['Country']: ['China', 'India', 'United_States', 'Australia', 'Indonesia', 'Russia', 'South_Africa', 'Kazakhstan', 'Colombia', 'Poland']
Y_Axis['Production', 'in', 'million', 'metric', 'tons']: ['3236.1', '680.5', '639.1', '435.9', '420.0', '333.0', '252.3', '105.9', '90.9', '65.8']

gold: This statistic depicts the leading ten countries based on hard coal production in 2017 , measured in million metric tons . In that year , China was by far the leading hard coal producer worldwide , with over 3.2 billion metric tons of hard coal produced .
gold_template: This statistic depicts the templateTitleSubject[0] ten templateTitle[1] templateTitle[2] on templateTitle[3] templateTitle[4] templateYLabel[0] in templateTitleDate[0] , measured in templateScale templateYLabel[2] templateYLabel[3] . In that year , templateXValue[0] was by far the templateTitleSubject[0] templateTitle[3] templateTitle[4] producer worldwide , with over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[3] templateTitle[4] produced .

generated_template: This statistic shows the world 's templateTitleSubject[0] 10 templateTitle[2] of templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitleSubject[0] producer with a templateYLabel[0] volume of nearly templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , followed by templateXValue[1] with approximately templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[3] templateTitle[4] . Maize was the templateTitleSubject[0] vegetable based on templateTitle[1] templateYLabel[0] volume in that year .
generated: This statistic shows the world 's Leading 10 based of hard coal in 2017 .  In that year , China was the Leading producer with a Production volume of nearly 3236.1 million metric tons , followed by India with approximately 680.5 million metric tons of hard coal .  Maize was the Leading vegetable based on countries Production volume in that year .

Example 625:
titleEntities: {'Subject': ['Turkey'], 'Date': []}
title: Ratio of government expenditure in relation to gross domestic product ( GDP ) in Turkey
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budgetary', 'balance', 'in', 'relation', 'to', 'the', 'gross', 'domestic', 'product']: ['35.66', '35.68', '35.64', '35.61', '35.17', '34.81', '34.61', '33.62', '35.08', '33.37', '33.23']

gold: This statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in Turkey from 2014 to 2018 , with projections up until 2024 . In 2018 , the ratio in relation to the GDP in Turkey was at approximately 34.61 percent .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] to templateYLabel[3] templateYLabel[4] templateYLabel[5] ( templateTitle[7] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] in templateYLabel[2] to the templateTitle[7] in templateTitleSubject[0] was at approximately templateYValue[6] templateScale .

generated_template: This statistic shows the templateYLabel[0] of the European Union in the templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] based on purchasing-power-parity from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateYLabel[0] of the European Union in the templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] based on purchasing-power-parity amounted to an estimated templateYValue[6] templateScale . The templateTitleSubject[0] GDP amounted to 13.92 templateScale euros in templateXValue[min] .
generated: This statistic shows the Budgetary of the European Union in the relation gross domestic product based on purchasing-power-parity from 2014 to 2024 .  In 2018 , the Budgetary of the European Union in the relation gross domestic product based on purchasing-power-parity amounted to an estimated 34.61 % .  The Turkey GDP amounted to 13.92 billion euros in 2014 .

Example 626:
titleEntities: {'Subject': ['EpiPens U.S.'], 'Date': ['2004', '2016']}
title: Cost of EpiPens in the U.S. 2004 to 2016
X_Axis['Year']: ['1st_November_2004', '23rd_February_2005', '23rd_February_2006', '22nd_February_2007', '16th_January_2008', '14th_January_2009', '12th_October_2009', '3rd_August_2010', '15th_December_2010', '12th_May_2011', '18th_October_2011', '6th_March_2012', '27th_July_2012', '7th_November_2012', '17th_July_2013', '8th_November_2013', '2nd_May_2014', '5th_Nov_2014', '1st_May_2015', '23rd_November_2015', '16th_May_2016']
Y_Axis['Cost', 'of', 'EpiPens', 'in', 'U.S.', 'Dollars']: ['106.32', '106.96', '107.76', '108.96', '110.18', '116.1', '139.42', '150.76', '165.68', '176.5', '193.96', '208.84', '229.52', '252.24', '273.24', '313.94', '354.94', '407.84', '468.06', '537.8', '608.61']

gold: This statistic shows the rising costs of EpiPens in the United States from November 2004 to May 2016 . The cost of EpiPens increased from 106.32 U.S. dollars in November 2004 to 608.61 dollars in May 2016 . In 2016 , Mylan came under fire from the public and U.S. congress for the high cost of the EpiPen , which was estimated to cost Mylan only 35 dollars .
gold_template: This statistic shows the templatePositiveTrend costs of templateYLabel[1] in the templateTitle[2] from templateXValue[0] to templateXValue[9] templateXValue[last] . The templateYLabel[0] of templateYLabel[1] templatePositiveTrend from templateYValue[min] templateYLabel[2] templateYLabel[3] in templateXValue[0] to templateYValue[max] templateYLabel[3] in templateXValue[9] templateXValue[last] . In templateXValue[last] , Mylan came under fire from the public and templateYLabel[2] congress for the high templateYLabel[0] of the EpiPen , which was estimated to templateYLabel[0] Mylan only 35 templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total templateYValue[idxmax(X)] templateYLabel[1] reported in the templateTitleSubject[0] .
generated: This statistic shows the Cost of EpiPens in the EpiPens U.S. from 1st_November_2004 to 27th_July_2012 .  In 27th_July_2012 , there were a total 608.61 EpiPens reported in the EpiPens U.S. .

Example 627:
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 628:
titleEntities: {'Subject': ['Portugal'], 'Date': ['2006']}
title: Number of road deaths in Portugal 2006 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['602', '563', '593', '638', '637', '718', '891', '937', '737', '885', '974', '969']

gold: This statistic shows the number of road traffic fatalities per year in Portugal between 2006 and 2017 . In the period of consideration , Portugal saw an overall decrease in road traffic fatalities . The highest amount of road fatalities was recorded in 2007 , with 974 fatalities .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] traffic templateYLabel[1] per templateXLabel[0] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In the period of consideration , templateTitleSubject[0] saw an overall templateNegativeTrend in templateTitle[1] traffic templateYLabel[1] . The highest amount of templateTitle[1] templateYLabel[1] was recorded in templateXValue[10] , with templateYValue[max] templateYLabel[1] .

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] . Over this period templateYLabel[1] of both domestic and international tourists in templateTitle[3] establishments in templateTitleSubject[0] .
generated: This statistic shows the Number of deaths fatalities at Portugal establishments in Portugal from 2006 to 2017 .  Over this period fatalities of both domestic and international tourists in Portugal establishments in Portugal .

Example 629:
titleEntities: {'Subject': ['FIFA'], 'Date': ['2014']}
title: 2014 World Cup Brazil sales price official merchandise FIFA online store
X_Axis['Year']: ['Official_match_ball_adidas_Brazuca', "Men's_authentic_home_jersey_Brazil_(Nike)", '2014_FIFA_World_Cup_Brazil_Trophy_Replica', "Men's_authentic_home_jersey_Germany_(adidas)", 'Brazil_2014_home_toddler_kit', '2014_FIFA_World_Cup_Panini_50_Sticker_Pack_Box', "Men's_official_emblem_short-sleeve_t-shirt", '2014_FIFA_World_Cup_Brazil_Official_Poster']
Y_Axis['Retail', 'price', 'in', 'U.S.', 'dollars']: ['159.99', '149.99', '129.99', '119.99', '74.99', '49.99', '29.99', '14.99']

gold: The statistic shows the retail sales prices of selected 2014 FIFA World Cup merchandise at the official FIFA online store . The official game ball - 'Brazuca ' is sold at the FIFA online shop for 159.99 U.S. dollars . 2014 World Cup merchandise - additional information In a world of social media and global connectivity , the FIFA World Cup is growing in popularity with every tournament , with over 26 million Americans tuning in to watch the 2014 final .
gold_template: The statistic shows the templateYLabel[0] templateTitle[4] prices of selected templateXValue[2] World templateXValue[2] templateTitle[7] at the templateXValue[0] templateXValue[2] templateTitle[9] templateTitle[10] . The templateXValue[0] game templateXValue[0] - 'Brazuca ' is sold at the templateXValue[2] templateTitle[9] shop for templateYValue[max] templateYLabel[2] templateYLabel[3] . templateXValue[2] Cup templateTitle[7] - additional information In a templateXValue[2] of social media and global connectivity , the templateXValue[2] Cup is templatePositiveTrend in popularity with every tournament , with over 26 templateScale Americans tuning in to watch the templateXValue[2] final .

generated_template: This statistic shows the amount of templateYLabel[0] templateYLabel[1] templateXValue[last] on 31st of , templateTitleDate[0] , templateTitle[7] templateXValue[last] of templateXValue[last] . There were a total of 82,634 templateYLabel[0] templateYLabel[1] templateXValue[last] in templateTitleSubject[0] and templateTitleSubject[1] on this date , the largest share of whom , templateYValue[max] , were serving templateXValue[2] of templateXValue[0] or templateXValue[0] .
generated: This statistic shows the amount of Retail price 2014_FIFA_World_Cup_Brazil_Official_Poster on 31st of , 2014 merchandise 2014_FIFA_World_Cup_Brazil_Official_Poster of .  There were a total of 82,634 Retail price 2014_FIFA_World_Cup_Brazil_Official_Poster in FIFA and on this date , the largest share of whom , 159.99 were serving 2014_FIFA_World_Cup_Brazil_Trophy_Replica of Official_match_ball_adidas_Brazuca or .

Example 630:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2012', '2014']}
title: PC online games revenue in Malaysia 2012 to 2014
X_Axis['Year']: ['2014', '2013', '2012']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['66.5', '54.1', '44.1']

gold: The statistic presents a forecast of the PC online games revenue in Malaysia from 2012 to 2014 . It was estimated that the 2014 PC online games revenue for Malaysia would be 66.5 million U.S. dollars .
gold_template: The statistic presents a forecast of the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . It was estimated that the templateXValue[max] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] for templateTitleSubject[0] would be templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic presents a forecast of the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Itt was estimated that the templateXValue[max] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] for templateTitleSubject[0] would be templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic presents a forecast of the PC online games Revenue in Malaysia from 2012 to 2014 .  Itt was estimated that the 2014 PC online games Revenue for Malaysia would be 66.5 million U.S. dollars .

Example 631:
titleEntities: {'Subject': ['Steam'], 'Date': ['2016', '2020']}
title: Warframe peak concurrent player number on Steam 2016 to 2020
X_Axis['Month']: ["Jan_'20", "Dec_'19", "Nov_'19", "Oct_'19", "Sept_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sept_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Aprl_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sept_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17", "Dec_'16", "Nov_'16", "Oct_'16", "Sept_'16", "Aug_'16", "Jul_'16", "Jun_'16", "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16"]
Y_Axis['Number', 'of', 'players', 'in', 'thousands']: ['59.19', '84.08', '87.62', '60.93', '80.47', '96.26', '94.91', '74.26', '97.07', '107.9', '115.1', '74.47', '82.27', '89.03', '131.77', '99.74', '86.98', '107.1', '118.19', '129.0', '73.1', '85.23', '81.34', '82.38', '69.0', '76.56', '102.97', '121.38', '58.61', '55.74', '58.31', '51.42', '47.73', '50.82', '69.53', '45.31', '47.13', '56.81', '68.53', '34.09', '39.04', '46.27', '61.42', '41.74', '44.09', '47.24', '66.31', '49.68', '50.18']

gold: The statistic shows information on the monthly number of peak concurrent players of Warframe on Steam worldwide as of January 2020 . In January 2020 , Warframe reached 59.19 thousand concurrent players on Steam . In the measured period , the game reached its record high peak concurrent player number in June 2018 at over 129 thousand gamers playing at the same time .
gold_template: The statistic shows information on the monthly templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] of templateTitle[0] on templateTitleSubject[0] worldwide as of 2020 . In 2020 , templateTitle[0] reached templateYValue[0] thousand templateTitle[2] templateYLabel[1] on templateTitleSubject[0] . In the measured period , the game reached its record high templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] in 2018 at over templateYValue[19] thousand gamers playing at the same time .

generated_template: The statistic shows information on the monthly templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] of Grand Theft Auto templateTitleSubject[0] on templateTitleSubject[0] worldwide as of 2020 . In 2020 , templateTitle[0] templateTitleSubject[0] reached templateYValue[max] thousand templateTitle[3] templateYLabel[1] on templateTitleSubject[0] .
generated: The statistic shows information on the monthly Number of concurrent player players of Grand Theft Auto Steam on worldwide as of 2020 .  In 2020 , Warframe Steam reached 131.77 thousand player players on Steam .

Example 632:
titleEntities: {'Subject': ['Capital'], 'Date': ['2000']}
title: Capital spending in the semiconductor industry worldwide 2000 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Spending', 'in', 'billion', 'U.S.', 'dollars']: ['94.3', '97.8', '105.9', '95.6', '67.8', '65.2', '66.1', '55.3', '59.0', '67.4', '54.0', '26.1', '43.4', '61.0', '57.5', '48.1', '47.7', '31.3', '27.5', '38.7', '61.3']

gold: The statistic depicts the total capital spending in the global semiconductor industry from 2000 to 2018 and shows a forecast for 2019 and 2020 . In 2019 , global semiconductor capital spending is projected to amount to 97.8 billion U.S. dollars , down from the all-time highest spending of 105.9 billion U.S. dollars in 2018 .
gold_template: The statistic depicts the total templateTitleSubject[0] templateYLabel[0] in the global templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[2] and shows a forecast for templateXValue[1] and templateXValue[max] . In templateXValue[1] , global templateTitle[2] templateTitleSubject[0] templateYLabel[0] is projected to amount to templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] , down from the all-time highest templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .

generated_template: The amount of templateTitleSubject[0] templateTitle[1] templateYLabel[1] templateYLabel[2] in the country has templatePositiveTrend each templateXLabel[0] since templateXValue[min] , from templateYValue[min] thousand templateYLabel[3] templateYLabel[1] templateXLabel[0] to around templateYValue[19] thousand templateYLabel[3] in templateXValue[19] . This figure is expected to templatePositiveTrend to templateYValue[max] thousand templateYLabel[3] of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] by templateXValue[idxmax(Y)] . templateTitleSubject[0] templateYLabel[0] Worldwide On a global scale , the templateYLabel[0] volume of templateYLabel[2] templateTitleSubject[0] reached over 500 templateScale metric tons in templateXValue[19] and is expected to templatePositiveTrend slightly in the next templateXLabel[0] .
generated: The amount of Capital spending billion U.S. in the country has increased each Year since 2000 , from 26.1 thousand dollars billion Year to around 38.7 thousand dollars in 2001 .  This figure is expected to increase 105.9 thousand dollars of Capital billion U.S. by 2018 .  Capital Spending Worldwide On a global scale , the Spending volume of U.S. Capital reached over 500 billion metric tons in 2001 and is expected to increase slightly in the next Year .

Example 633:
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 shows the templateYLabel[0] of templateXValue[0] in the templateTitle[1] in templateTitleDate[0] , templateTitle[3] templateXLabel[1] . In templateTitleDate[0] , about templateYValue[max] templateScale templateXValue[0] were counted in the templateXValue[0] templateTitle[1] .
generated: This statistic shows the Maximum of Unexcused_late_reporting_foror_absence_from_preseason_training in the discipline in , selected Types .  In , about 30000 % Unexcused_late_reporting_foror_absence_from_preseason_training were counted in the Unexcused_late_reporting_foror_absence_from_preseason_training discipline .

Example 634:
titleEntities: {'Subject': ['Alphabet'], 'Date': ['2011', '2019']}
title: Alphabet : global annual net income 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['34343', '30736', '12662', '19478', '16348', '14136', '12733', '10619', '9706']

gold: In the most recent fiscal year , Alphabet 's net income amounted to 34.34 billion U.S. dollars , up from 30.74 billion U.S. dollars in the previous year . Alphabet Inc. is an American multinational conglomerate created in 2015 as the parent company of Google and several other companies previously owned by or tied to Google . Alphabet : the company Alphabet 's businesses not only include Google but also a wide range of other companies across various , mostly digital-related verticals .
gold_template: In the most recent fiscal templateXLabel[0] , templateTitleSubject[0] 's templateTitle[3] templateTitle[4] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . templateTitleSubject[0] Inc. is an American multinational conglomerate created in templateXValue[4] as the parent company of Google and several other companies previously owned by or tied to Google . templateTitleSubject[0] : the company templateTitleSubject[0] 's businesses not only include Google but also a wide range of other companies across various , mostly digital-related verticals .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the last reported templateXLabel[0] , the templateTitleSubject[0] 's dating templateYLabel[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . Up until early 2020 , the templateTitleSubject[0] belongs to IAC and includes online dating platforms such as the eponymous Match.com , OkCupid , Tinder , PlentyofFish and others .
generated: The statistic shows the annual Revenue of the Alphabet from 2011 to 2019 .  In the last reported Year , the Alphabet 's dating Revenue amounted to 34343 million U.S. dollars .  Up until early 2020 , the Alphabet belongs to IAC and includes online dating platforms such as the eponymous Match.com , OkCupid Tinder , PlentyofFish and others .

Example 635:
titleEntities: {'Subject': ['Tottenham Hotspur'], 'Date': []}
title: Tottenham Hotspur revenue by stream 2018/19
Data:
                    0        1        2        3        4        5        6        7        8        9       10
Season        2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09
Matchday         92.5     85.2     52.7     54.6     54.2     52.5     46.9     50.8     47.9     44.9     46.3
Broadcasting    276.7    226.6    219.0    147.6    125.2    113.3     72.7     76.1     92.0     62.9     52.6
Commercial      151.9    116.5     83.9     77.5     78.1     50.0     52.4     51.3     41.1     38.5     33.8 

gold: Broadcasting is the largest source of revenue for Tottenham Hotspur . In 2018/2019 , the club earned approximately 276.7 million euros from broadcasting , twice more than in 2013/2014 . 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] , twice more than in 2013/2014 . 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 triple than in 2010/2011 . The second biggest templateTitle[2] templateTitle[4] is the templateLabel[3][0] one .
generated: Broadcasting is the largest source of revenue for Tottenham Hotspur .  In 2018/2019 , the club earned approximately 276.7 million euros from Broadcasting , more than triple in 2010/2011 .  The second biggest revenue stream is the Commercial one .

Example 636:
titleEntities: {'Subject': ['Kuwait'], 'Date': ['2019']}
title: Employment by economic sector in Kuwait 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   2.23   2.26   2.29   2.31   2.29   2.38   2.41   2.44   2.46   2.46   2.46
Industry     25.37  25.39  25.39  25.37  25.17  25.42  25.46  25.45   25.4  25.27  25.01
Services      72.4  72.34  72.33  72.32  72.55   72.2  72.12  72.11  72.14  72.28  72.53 

gold: The statistic shows the distribution of employment in Kuwait by economic sector from 2009 to 2019 . In 2019 , 2.23 percent of the employees in Kuwait were active in the agricultural sector , 25.37 percent in industry and 72.4 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 Kuwait by economic sector from 2009 to 2019 .  In 2019 , 2.23 % of the employees in Kuwait were active in the agricultural sector , 25.37 % in Industry and 72.4 % in the service sector .

Example 637:
titleEntities: {'Subject': ['EU-28'], 'Date': ['2017', '2018']}
title: EU-28 : monthly harmonized consumer price index in 2017 to 2018
X_Axis['Month']: ['Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17']
Y_Axis['HICP,', '2015=100']: ['104.58', '104.16', '103.95', '104.18', '104.07', '103.6', '103.25', '102.49', '102.27', '102.96', '102.62', '102.48']

gold: This statistic shows the harmonized consumer price index for all items in the European Union ( 28 countries ) from October 2017 to September 2018 . In September 2018 , the consumer price index for all items was 104.58 points .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] for all items in the European Union ( 28 countries ) from October templateTitleDate[min] to 2018 . In 2018 , the templateTitle[3] templateTitle[4] templateTitle[5] for all items was templateYValue[max] points .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[5] due to templateTitleSubject[0] templateYLabel[1] templateTitle[6] in templateTitleDate[0] , by the templateTitle[2] templateTitleSubject[0] group . In that year , templateYValue[max] people were killed by templateTitleSubject[0] templateYLabel[1] attributed to the templateXValue[0] .
generated: This statistic shows the HICP, of index due to EU-28 2015=100 2017 in , by the harmonized EU-28 group .  In that year , 104.58 people were killed by EU-28 2015=100 attributed to the Sep_18 .

Example 638:
titleEntities: {'Subject': ['Active'], 'Date': ['2012', '2014']}
title: Health/Fitness club companies -Virgin Active revenue 2012 to 2014
X_Axis['Year']: ['2014', '2013', '2012']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['1051.17', '1021.49', '1002.72']

gold: The statistic depicts the revenue of the Virgin Active company from 2012 to 2014 . In 2014 , the health club chain generated a revenue of 1051.17 million U.S. dollars . In 2013 , the UK based company operated 270 clubs .
gold_template: The statistic depicts the templateYLabel[0] of the Virgin templateTitleSubject[0] company from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the health templateTitle[1] chain generated a templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] . In templateXValue[1] , the UK based company operated 270 clubs .

generated_template: The statistic presents a forecast of the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Itt was estimated that the templateXValue[max] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] for templateTitleSubject[0] would be templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic presents a forecast of the Health/Fitness club companies Revenue in Active from 2012 to 2014 .  Itt was estimated that the 2014 Health/Fitness club companies Revenue for Active would be 1051.17 million U.S. dollars .

Example 639:
titleEntities: {'Subject': ['China'], 'Date': ['2018']}
title: Number of unemployed people in urban China 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Unemployed', 'people', 'in', 'millions']: ['9.74', '9.72', '9.82', '9.66', '9.52', '9.26', '9.17', '9.22', '9.08', '9.21', '8.86', '8.3']

gold: This statistic shows the number of unemployed people in urban China from 2008 to 2018 . In 2018 , approximately 9.7 million people in urban China had been unemployed . Employment and labor force in China – additional information According to the graph at hand , absolute unemployment in China has gradually risen over the past decade .
gold_template: This statistic shows the templateTitle[0] of templateYLabel[0] templateYLabel[1] in templateTitle[3] templateTitleSubject[0] from templateXValue[10] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] templateScale templateYLabel[1] in templateTitle[3] templateTitleSubject[0] had been templateYLabel[0] . Employment and labor force in templateTitleSubject[0] – additional information According to the graph at hand , absolute unemployment in templateTitleSubject[0] has gradually risen over the past decade .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[idxmax(X)] templateScale templateYLabel[1] in templateTitleSubject[0] .
generated: This statistic shows the Unemployed of people in China the from 2007 to 2018 .  In 2018 , there were approximately 9.74 millions people in China .

Example 640:
titleEntities: {'Subject': ['Hays'], 'Date': ['2007', '2019']}
title: Revenue of Hays worldwide 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'GBP']: ['1129.7', '1072.8', '954.6', '810.3', '764.2', '724.9', '719.0', '734.0', '672.1', '557.7', '670.8', '786.8', '633.6']

gold: This statistic shows the revenue of Hays worldwide from 2007 to 2019 . In 2019 , the UK-based recruitment specialist Hays generated over 1.1 billion British pounds in revenue worldwide , up from one billion the previous year .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the UK-based recruitment specialist templateTitleSubject[0] generated over templateYValue[max] templateScale British pounds in templateYLabel[0] templateTitle[2] , up from templateYValue[max] templateScale the previous templateXLabel[0] .

generated_template: In its templateXValue[max] fiscal templateXLabel[0] , templateTitleSubject[0] reported templateYLabel[0] templateYLabel[1] of around 3.9 templateScale Japanese templateYLabel[3] , or approximately 35.7 templateScale U.S. dollars . This figure represents a small decline in templateYLabel[1] compared to the previous templateXLabel[0] but still lies well above the annual revenues reported in templateXValue[2] . templateTitleSubject[0] is a Japanese company which specializes in the design and manufacture of cameras , printers , and other imaging devices .
generated: In its 2019 fiscal Year , Hays reported Revenue million of around 3.9 million Japanese GBP , or approximately 35.7 million U.S. dollars .  This figure represents a small decline in million compared to the previous Year but still lies well above the annual revenues reported in 2017 .  Hays is a Japanese company which specializes in the design and manufacture of cameras , printers and other imaging devices .

Example 641:
titleEntities: {'Subject': ['Global'], 'Date': ['2019', '2024']}
title: Global yogurt market value forecast 2019 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019']
Y_Axis['Market', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['106.6', '102.01', '97.62', '93.41', '89.39', '85.54']

gold: This statistic shows the yogurt market value worldwide in 2016 and also provides a forecast for 2019 to 2024 . The global yogurt market reached a value of approximately 85.54 billion U.S. dollars in 2019 , and is forecasted to reach about 106.6 billion U.S. dollars by 2024 .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] worldwide in 2016 and also provides a templateTitle[4] for templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateTitle[1] templateYLabel[0] reached a templateYLabel[1] of approximately templateYValue[idxmin(X)] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmin(Y)] , and is forecasted to reach about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] by templateXValue[idxmax(Y)] .

generated_template: This statistic depicts templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] rates in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , templateTitle[1] templateTitle[2] templateTitle[3] revenue in templateTitleSubject[0] is expected to templatePositiveTrend by over templateYValue[2] templateScale , compared to the previous templateXLabel[0] .
generated: This statistic depicts Market yogurt value rates in Global from 2019 to 2024 .  In 2020 , yogurt market value revenue in Global is expected to increase by over 97.62 billion , compared to the previous Year .

Example 642:
titleEntities: {'Subject': ['Oman'], 'Date': ['2019']}
title: Unemployment rate in Oman 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']: ['3.08', '3.11', '3.14', '3.27', '3.54', '3.85', '4.16', '4.32', '4.43', '4.66', '4.7', '4.14', '4.15', '4.39', '4.65', '4.81', '4.88', '4.85', '4.78', '4.85', '4.92']

gold: This statistic shows the unemployment rate in Oman from 1999 to 2019 . In 2019 , the unemployment rate in Oman was at 3.08 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 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 Oman from 1999 to 2019 .  In 2019 , the Unemployment rate in Oman was at approximately 3.08 % .

Example 643:
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] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] templateScale . You can access the monthly templateYLabel[0] templateYLabel[1] for the country here .
generated: This statistic displays the Unemployment rate in New York Unemployment from 1992 to 2018 .  In 2018 , Unemployment rate in New York Unemployment was 4.1 % .  You can access the monthly Unemployment rate for the country here .

Example 644:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2009']}
title: Population of Luxembourg 2009 - 2019 , by gender
Data:
             0       1       2       3       4       5       6       7       8       9      10
Year      2019    2018    2017    2016    2015    2014    2013    2012    2011    2010    2009
Male    308720  302580  296641  289193  281972  275117  268412  261820  254619  249406  244835
Female  305174  299425  294026  287056  280986  274563  268627  263033  257221  252660  248665 

gold: In 2019 , the population of Luxembourg amounted to nearly 614,000 people . Of these 614,000 , nearly 309,000 were male . The female population reached just over 305,000 , meaning women in Luxembourg were the minority that year .
gold_template: In templateValue[0][0] , the templateTitle[0] of templateTitleSubject[0] amounted to nearly 614,000 people . Of these 614,000 , nearly 309,000 were templateLabel[1][0] . The templateLabel[2][0] templateTitle[0] reached just over 305,000 , meaning women in templateTitleSubject[0] were the minority that templateLabel[0][0] .

generated_template: In the last decade , the templateTitle[1] of templateTitleSubject[0] templatePositiveTrend overall . Since templateValue[0][3] , it remained stable at a number of approximately 5.8 templateScale inhabitants . In the period surveyed , the number of templateLabel[2][0] inhabitants was slightly higher and amounted to about templateValue[2][0] templateScale women as of 1st of 2020 , while there were roughly templateValue[1][0] templateScale templateLabel[1][0] inhabitants registered .
generated: In the last decade , the Luxembourg of growing overall .  Since 2016 , it remained stable at a number of approximately 5.8 million inhabitants .  In the period surveyed , the number of Female inhabitants was slightly higher and amounted to about 305174 % women as of 1st 2020 , while there were roughly 308720 % Male inhabitants registered .

Example 645:
titleEntities: {'Subject': ['Countries'], 'Date': ['2018']}
title: Countries with the largest population 2018
X_Axis['Country']: ['China', 'India', 'USA', 'Indonesia', 'Brazil', 'Pakistan', 'Nigeria', 'Bangladesh', 'Russia', 'Mexico']
Y_Axis['Inhabitants', 'in', 'millions']: ['1393.8', '1371.3', '328.0', '265.2', '209.4', '200.6', '195.9', '166.4', '147.3', '130.8']

gold: The statistic shows the ten countries with the ten largest populations in mid 2018 . In mid 2018 , approximately 1.39 billion people lived in China . The largest countries and the global population The total world population amounts to more than 7 billion people , with China reporting the highest number of inhabitants – its total population surpassed the 1.3 billion mark a few years ago and continues to rise .
gold_template: The statistic shows the ten templateTitleSubject[0] templateTitle[1] the ten templateTitle[2] populations in mid templateTitleDate[0] . In mid templateTitleDate[0] , approximately templateYValue[max] templateScale people lived in templateXValue[0] . The templateTitle[2] templateTitleSubject[0] and the global templateTitle[3] The total world templateTitle[3] amounts to more than 7 templateScale people , templateTitle[1] templateXValue[0] reporting the highest number of templateYLabel[0] – its total templateTitle[3] surpassed the 1.3 templateScale mark a few years ago and continues to rise .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] . In that year , templateXValue[0] was the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] rate in templateXValue[0] , with templateYLabel[0] of approximately templateYValue[max] templateScale .
generated: This statistic shows the Countries largest the population Inhabitants of 2018 in .  In that year , China was the Country largest the population 2018 rate in China , with Inhabitants of approximately 1393.8 millions .

Example 646:
titleEntities: {'Subject': ['Hyundai', 'United Kingdom', 'UK'], 'Date': ['2016', '2019']}
title: Hyundai 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']: ['3801', '5276', '5538', '14418', '2686', '5649', '7803', '6868', '6468', '16317', '2682', '5778', '4009', '4821', '5297', '16102', '2689', '5966', '8663', '7387', '6987', '18526', '3049', '6429', '5026', '6509', '6553', '16587', '2441', '6187', '7965', '7321', '6958', '18460', '3009', '6387', '5625', '6560', '6585', '17039', '2440', '6093']

gold: This statistic shows the monthly amount of cars sold by Hyundai 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 , 3,801 new Hyundai 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 Hyundai 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 , 5649 new Hyundai car Units had been sold , a decrease of roughly ten percent in comparison to 5966 Units as of 2018 .

Example 647:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. online shopping product categories 2017 , by gender
Data:
                                        0                  1                        2                                   3                         4                                            5                   6                  7      8
unknown  Fashion_clothing_and_accessories  Health_and_Beauty  Toys_and_baby_equipment  Books_CDs_and_other_physical_media  Groceries_food_and_drink  Technology_(including_phones_and_computers)  Home_and_furniture  Flowers_and_gifts  Other
Male                                   49                 33                       31                                  44                        35                                           49                  28                 29      7
Female                                 71                 54                       46                                  45                        35                                           31                  31                 30      8 

gold: This statistic presents popular online shopping categories in the United States , sorted by gender . During a November 2017 survey , it was found that 71 percent of female respondents had purchased clothing online in the past 3 months . According to Loqate , a GBG solution , 49 percent of male respondents had bought clothing via internet .
gold_template: This statistic presents popular templateTitle[1] templateTitle[2] templateTitle[4] in the templateTitle[0] , sorted templateTitle[6] templateTitle[7] . During a 2017 survey , it was found that templateValue[2][0] templateScale of templateLabel[2][0] respondents had purchased templateValue[0][0] templateTitle[1] in the past 3 months . According to Loqate , a GBG solution , templateValue[1][0] templateScale of templateLabel[1][0] respondents had bought templateValue[0][0] via internet .

generated_template: This statistic shows the results of a templateTitleDate[0] templateTitle[0] among adult Americans on the templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] . During the templateTitle[0] , templateValue[1][0] templateScale of respondents stated that they perceive templateTitle[2] templateTitle[3] templateTitle[4] as templateLabel[1][0] templateValue[0][0] than templateTitle[2] without templateTitle[4] , while templateValue[2][0] templateScale stated they think tattooed templateTitle[2] are templateLabel[2][0] templateValue[0][0] , and templateValue[3][0] templateScale did not see a templateLabel[3][1] between templateTitle[2] templateTitle[3] or without templateTitle[4] when it comes to being templateValue[0][0] . templateTitle[4] in American society As can be seen above , compared to templateTitle[2] without templateTitle[4] , templateValue[1][0] templateScale of templateTitle[2] find those templateTitle[3] templateTitle[4] to be characteristically templateValue[0][0] while only templateValue[1][1] templateScale of respondents find templateTitle[2] templateTitle[3] templateTitle[4] characteristically templateValue[0][1] .
generated: This statistic shows the results of a 2017 U.S. among adult Americans on the online of shopping product categories .  During the U.S. , 49 % of respondents stated that they perceive shopping product categories as Male Fashion_clothing_and_accessories than shopping without categories , while 71 % stated they think tattooed shopping are Female Fashion_clothing_and_accessories , and 71 % did not see a Female between shopping product or without categories when it comes to being Fashion_clothing_and_accessories .  categories in American society As can be seen above , compared to shopping without categories , 49 % of shopping find those product categories to be characteristically Fashion_clothing_and_accessories while only 33 % of respondents find shopping product categories characteristically Health_and_Beauty .

Example 648:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019']}
title: Canada - average weekly hours worked at the main job , by industry 2019
X_Axis['Industry']: ['Forestry_fishing_mining_quarrying_oil_and_gas', 'Agriculture', 'Goods-producing_sector', 'Construction', 'Transportation_and_warehousing', 'Manufacturing', 'Utilities', 'Professional_scientific_and_technical_services', 'Public_administration', 'Finance_insurance_real_estate_rental_and_leasing', 'Total_employed_all_industries', 'Other_services_(except_public_administration)', 'Services-producing_sector', 'Health_care_and_social_assistance', 'Business_building_and_other_support_services', 'Wholesale_and_retail_trade', 'Information_culture_and_recreation', 'Educational_services', 'Accommodation_and_food_services']
Y_Axis['Average', 'usual', 'weekly', 'hours']: ['45.0', '43.5', '40.4', '40.3', '40.2', '39.3', '38.6', '37.0', '36.7', '36.7', '35.7', '34.9', '34.5', '34.2', '33.8', '33.5', '32.5', '31.8', '29.8']

gold: This statistic shows the average usual weekly hours worked in Canada in 2019 , distinguished by industry . In 2019 , Canadian employees in agriculture were working about 43.5 hours a week , which is above the national average of 35.7 hours .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , distinguished templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , Canadian employees in templateXValue[1] were working about templateYValue[1] templateYLabel[3] a week , which is above the national templateYLabel[0] of templateYValue[10] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] as of 2017 , sorted by templateYLabel[0] templateYLabel[1] . In templateTitleDate[0] , about templateYValue[max] people were employed in the templateTitle[3] .
generated: This statistic shows the Canada average weekly in the hours as of 2017 , sorted by Average usual .  In 2019 , about 45.0 people were employed in the hours .

Example 649:
titleEntities: {'Subject': ['Canada'], 'Date': ['2010', '2019']}
title: Facebook : quarterly revenue in the U.S. and Canada 2010 - 2019 , by segment
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
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
Advertising revenue   10021    8317    7952    7203    8246    6547    6137    5559    6271    4912    4450    3851    4435    3431    3077    2615    2847    2120    1826    1592    1709    1362    1175    1039    1068     832     721     552     631     538     479     419     462     395     394     332     359     260     243     209
Other revenue           227     170     163     105     187     120     114     108     121     121     106     117     121     129     135     125     140     136     141     147     155     152     133     140     138     130     127     127     149      99     111     106     105      87      77      62      53      11       6       5 

gold: In the fourth quarter of 2019 , Facebook 's consolidated advertising revenue in the United States and Canada amounted to over ten billion U.S. dollars . Other revenues only came to 227 million U.S. dollars .
gold_template: In the fourth templateLabel[0][0] of templateTitleDate[max] , templateTitle[0] 's consolidated templateLabel[1][0] templateTitle[2] in the templateTitle[3] and templateTitleSubject[0] amounted to over ten templateScale templateTitle[3] dollars . templateLabel[2][0] revenues only came to templateValue[2][0] templateScale templateTitle[3] dollars .

generated_template: In the fourth templateLabel[0][0] of templateTitleDate[max] , templateTitleSubject[0] 's total templateLabel[1][0] templateTitle[3] amounted to 20.74 templateScale U.S. dollars . templateLabel[2][0] templateTitle[3] streams generated templateValue[2][0] templateScale U.S. dollars in revenues . The majority of templateTitleSubject[0] 's ad templateTitle[3] is generated via mobile devices .
generated: In the fourth Quarter of 2019 , Canada 's total Advertising U.S. amounted to 20.74 billion U.S. dollars .  Other U.S. streams generated 227 million U.S. dollars in revenues .  The majority of Canada 's ad U.S. is generated via mobile devices .

Example 650:
titleEntities: {'Subject': ['Adidas', 'Nike Puma'], 'Date': ['2006', '2018']}
title: Adidas , Nike & Puma revenue comparison 2006 to 2018
Data:
                  0      1      2      3      4      5      6      7      8      9     10     11     12
Year           2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008   2007   2006
Nike          31.35  30.57   29.1  27.51  24.99  22.75  20.97  18.08  16.47  16.65  16.74  14.68  13.44
Adidas Group  21.92  21.22  18.48  16.92  14.53   14.2  14.88  13.32  11.99  10.38   10.8   10.3  10.08
Puma           4.65   4.14   3.63   3.39   2.97   2.99   3.27   3.17   2.86   2.61   2.77   2.74   2.76 

gold: The statistic shows a revenue comparison of the sporting goods companies Nike , Adidas and Puma from 2006 to 2018 . In 2018 , the Adidas Group generated 21.9 billion euros in revenue . Nike , Adidas , and Puma comparison – additional information The global sports apparel market is highly fragmented , with basic discount brands to high-end fashion name brands competing for market position .
gold_template: The statistic shows a templateTitle[4] templateTitle[5] of the sporting goods companies templateTitleSubject[1] , templateTitleSubject[0] and templateTitleSubject[1] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitleSubject[0] templateLabel[2][1] generated templateValue[2][0] templateScale euros in templateTitle[4] . templateTitleSubject[1] , templateTitleSubject[0] , and templateTitleSubject[1] templateTitle[5] – additional information The global sports apparel market is highly fragmented , with basic discount brands to high-end fashion name brands competing for market position .

generated_template: This statistic shows the total templateTitle[0] of templateTitle[1] and templateTitle[2] templateTitle[3] in the templateTitle[4] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][last] , there were around 9,172,000 templateTitle[2] templateTitle[3] ( including templateTitle[3] and heifers that have calved ) in the templateTitle[4] .
generated: This statistic shows the total Adidas of Nike and & Puma in the revenue from 2006 to 2018 .  In 2006 , there were around 9,172,000 & Puma ( including Puma and heifers that have calved ) in the revenue .

Example 651:
titleEntities: {'Subject': ['Leading'], 'Date': ['2017']}
title: Leading motor vehicle manufacturers worldwide - production 2017
X_Axis['Manufacturer']: ['Toyota', 'Volkswagen', 'Hyundai', 'GM', 'Ford', 'Nissan', 'Honda', 'Fiat', 'Renault', 'PSA', 'Suzuki', 'SAIC', 'Daimler', 'BMW', 'Geely']
Y_Axis['Production', 'in', 'thousand', 'units']: ['10466', '10382', '7218', '6857', '6387', '5769', '5237', '4601', '4154', '3650', '3302', '2867', '2549', '2506', '1950']

gold: The statistic shows the world 's leading motor vehicle manufacturers in 2017 , based on production . Volkswagen produced about 10.4 million vehicles in 2017 . Meanwhile , Toyota produced around 10.5 million units , and China 's SAIC produced some 2.9 million motor vehicles .
gold_template: The statistic shows the world 's templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , based on templateYLabel[0] . templateXValue[1] produced about templateYValue[1] templateScale vehicles in templateTitleDate[0] . Meanwhile , templateXValue[0] produced around templateYValue[max] templateScale templateYLabel[2] , and China 's templateXValue[11] produced some templateYValue[11] templateScale templateTitle[1] vehicles .

generated_template: This statistic provides information on the leading templateTitle[4] templateTitle[5] with the most templateYLabel[0] on templateTitleSubject[0] as of 2019 , ranked by templateTitle[1] of templateYLabel[0] . According to the findings , the templateTitle[4] templateXLabel[1] templateXValue[0] had recorded in a total of templateYValue[max] templateScale likes on templateTitleSubject[0] , and ranking second was templateXValue[1] with templateYValue[1] templateScale page likes .
generated: This statistic provides information on the leading worldwide production with the most Production on Leading as of 2019 , ranked by motor of Production .  According to the findings , the worldwide Manufacturer Toyota had recorded in a total of 10466 thousand likes on Leading , and ranking second was Volkswagen with 10382 thousand page likes .

Example 652:
titleEntities: {'Subject': ['Niger'], 'Date': ['2015']}
title: Median age of the population in Niger 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']: ['19.0', '18.2', '17.4', '16.7', '16.1', '15.6', '15.2', '14.9', '15.0', '15.4', '15.9', '16.0', '15.8', '15.7', '16.0', '15.7', '15.6', '15.6', '15.8', '15.6', '15.2']

gold: This statistic shows the median age of the population in Niger 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 Niger 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 653:
titleEntities: {'Subject': ['Tampa Bay Rays'], 'Date': ['2009', '2019']}
title: Regular season average attendance of the Tampa Bay Rays 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Average', 'attendance']: ['14734', '14258', '15670', '15878', '15403', '17857', '18645', '19255', '18878', '22758', '23147']

gold: This graph depicts the average regular season home attendance of the Tampa Bay Rays from 2009 to 2019 . In 2019 , the average regular season home attendance of the Tampa Bay Rays was 14,734 . • Tampa Bay Rays 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] Rays from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateTitle[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] Rays was templateYValue[0] templateYValue[idxmax(X)] • templateTitleSubject[0] Rays 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[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateTitle[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[1] was templateYValue[idxmax(X)] . • templateTitleSubject[0] templateTitleSubject[1] total home templateYLabel[1] • Major League Baseball templateYLabel[0] per game templateYLabel[1] • Major League Baseball total templateYLabel[1]
generated: This graph depicts the Average Regular season home attendance of the Tampa Bay Rays from 2009 to 2019 .  In 2019 , the Average Regular season home attendance of the Tampa Bay Rays was 14734 .  • Tampa Bay Rays Tampa Bay Rays total home attendance • Major League Baseball Average per game attendance • Major League Baseball total attendance . 

Example 654:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2001', '2018']}
title: Total recalls of children 's products in the U.S. 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['Number', 'of', 'recalls']: ['52', '93', '76', '68', '75', '114', '97', '124', '148', '143', '209', '232', '111', '122', '87', '66', '90', '118']

gold: The statistic depicts the number of children 's products that were recalled in the United States from 2001 to 2018 . In 2018 , 52 children 's products were the subject of a recall .
gold_template: The statistic depicts the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] that were recalled in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateTitle[2] templateTitle[3] templateTitle[4] were the subject of a recall .

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[16] templateScale in templateXValue[15] .
generated: This statistic gives information on the recalls children in U.S. from 2001 to 2018 .  In 2018 , 52 % of the Brazilian population accessed the recalls , up from 90 % in 2003 .

Example 655:
titleEntities: {'Subject': ['Packaging'], 'Date': ['2003', '2009']}
title: Packaging materials - market share in the global packaging market 2003 to 2009
Data:
                           0               1      2                  3      4                          5
unknown  Paper_and_cardboard  Rigid_plastics  Metal  Flexible_plastics  Glass  Other_packaging_materials
2003                      39              18     18                 12      7                          6
2009                      36              20     17                 14      7                          6 

gold: This statistic depicts the market share of various packaging materials in the global packaging market in 2003 and 2009 . In 2003 , 7 percent of all packaging materials worldwide were made out of glass .
gold_template: This statistic depicts the templateTitle[2] templateTitle[3] of various templateValue[0][5] in the templateTitle[4] templateValue[0][5] templateTitle[2] in templateTitleDate[min] and templateTitleDate[max] . In templateTitleDate[min] , templateValue[1][4] templateScale of all templateValue[0][5] worldwide were made out of templateValue[0][4] .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] on their templateTitleSubject[0] in the concepts of templateValue[0][0] , templateValue[0][2] and templateValue[0][3] in 2014 . As of 2011 , templateLabel[3][2] 75 templateScale of respondents believed in templateValue[0][3] .
generated: This statistic shows the results of a survey among Packaging on their Packaging in the concepts of Paper_and_cardboard , Metal and Flexible_plastics in 2014 .  As of 2011 , 2009 75 % of respondents believed in Flexible_plastics .

Example 656:
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 ten templateTitle[0] templateTitle[4] templateTitle[3] , other than English , in templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] , by templateYLabel[0] of templateYLabel[1] . The templateTitle[0] commonly templateTitle[4] templateXLabel[0] was templateXValue[0] with almost templateYValue[max] thousand native templateYLabel[1] , followed by templateXValue[1] and templateTitleSubject[0] .
generated: This statistic shows the ten Leading 2019 , other than English , in IPOs U.S. 2019 in , by Deal of size .  The Leading commonly 2019 Company was Uber_Technologies_(May_9) with almost 8100 thousand native size , followed by Avantor_(May_16) and IPOs U.S. .

Example 657:
titleEntities: {'Subject': ['Pandora'], 'Date': ['2007', '2017']}
title: Pandora 's net income/loss 2007 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['Net', 'income', '/', 'loss', 'in', 'million', 'U.S.', 'dollars']: ['-518.4', '-342.98', '-169.66', '-30.41', '-27.02', '-38.15', '-16.11', '-1.76', '-16.75', '-28.23', '-13.99', '-11.82']

gold: The statistic above shows Pandora 's net results in the fiscal periods from February 2006 to December 2017 . In fiscal year which ended December 31 , 2014 , Pandora lost 30.41 million U.S. dollars . Here you can find Pandora 's quarterly net results .
gold_template: The statistic above shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] results in the fiscal periods from 2006 to 2017 . In fiscal year which ended 31 , templateXValue[3] , templateTitleSubject[0] lost 30.41 templateScale templateYLabel[5] templateYLabel[6] . Here you can find templateTitleSubject[0] templateTitle[1] quarterly templateYLabel[0] results .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] of nurse templateTitle[2] in templateTitle[3] templateTitle[4] in Canada , sorted templateTitle[5] templateTitle[6] , in templateTitleDate[0] . In templateXValue[0] , around templateYValue[max] templateYLabel[1] were part of the templateTitle[3] templateTitle[4] templateTitle[2] , while in templateXValue[1] there were almost 72,000 templateYLabel[1] .
generated: This statistic shows the 's Net of nurse net in income/loss 2007 in Canada , sorted 2017 , in 2007 .  In Jan_to_Dec_2017 , around -1.76 income were part of the income/loss 2007 net , while in Jan_to_Dec_2016 there were almost 72,000 income .

Example 658:
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: This statistic shows the average hourly wage in the templateTitle[1] templateTitle[2] in templateTitleSubject[0] compared to the templateTitle[5] from templateValue[0][last] to templateValue[0][0] . As of 2017 , a templateTitle[1] worker in templateTitleSubject[0] earned approximately templateValue[1][0] templateTitle[5] dollars an hour , whereas the average templateTitle[0] in the templateTitle[5] stood at templateValue[2][0] templateTitle[5] dollars an hour .
generated: This statistic shows the average hourly wage in the cirrhosis deaths in Canada compared to the 2012 from 2016 to 2002 .  As of 2017 , a cirrhosis worker in Canada earned approximately 10.4 2012 dollars an hour , whereas the average Liver in the 2012 stood at 4.7 2012 dollars an hour .

Example 659:
titleEntities: {'Subject': ['HPE'], 'Date': ['2008', '2016']}
title: HPE : software division revenue 2008 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['3.2', '3.46', '3.7', '3.79', '4.06', '3.37', '2.81', '2.66', '4.22']

gold: This statistic shows the revenue generated by HPE through their software division each year from 2008 to 2016 . In 2016 , the software division produced revenue of approximately 3.2 billion U.S. dollars . On September 1 , 2017 , HPE completed the separation and merger of software business segment with Micro Focus .
gold_template: This statistic shows the templateYLabel[0] generated by templateTitleSubject[0] through their templateTitle[1] templateTitle[2] each templateXLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateTitle[2] produced templateYLabel[0] of approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] . On 1 , 2017 , templateTitleSubject[0] completed the separation and merger of templateTitle[1] business segment with Micro Focus .

generated_template: This statistic shows the templateYLabel[0] of chemical company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated some templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] of templateYLabel[0] . templateTitleSubject[0] , with full name E. I. du Pont de Nemours and Company , was a U.S.-based chemical company , and one of the largest companies in this industry worldwide .
generated: This statistic shows the Revenue of chemical company HPE from 2008 to 2016 .  In 2016 , HPE generated some 3.2 billion U.S. dollars of Revenue .  HPE , with full name E. I. du Pont de Nemours and Company , was a U.S.-based chemical company , and one of the largest companies in this industry worldwide .

Example 660:
titleEntities: {'Subject': ['North Macedonia'], 'Date': ['2024']}
title: Inflation rate in North Macedonia 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']: ['2.2', '2.2', '2.2', '2.2', '1.7', '1.26', '1.46', '1.35', '-0.24', '-0.3', '-0.28', '2.79', '3.32', '3.91', '1.51', '-0.74', '7.52', '2.79', '3.27', '-0.57', '-0.66', '1.11', '2.31', '5.2', '6.61', '-1.28', '0.54', '1.29', '2.47', '16.37', '126.58']

gold: This statistic shows the average inflation rate in North Macedonia from 1994 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in North Macedonia amounted to about 1.46 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 North Macedonia from 1994 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in North Macedonia amounted to about 1.46 % compared to the previous Year .

Example 661:
titleEntities: {'Subject': ['India'], 'Date': ['2024']}
title: Total population of India 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['1443.06', '1424.33', '1405.83', '1387.58', '1369.56', '1351.77', '1334.22', '1316.9', '1299.8', '1282.92', '1266.26']

gold: The statistic shows the total population of India from 2014 to 2024 . In 2018 , the estimated total population in India amounted to approximately 1.33 billion people . Total population in India India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years .
gold_template: The 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] in templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale people . templateTitle[0] templateTitle[1] in templateTitleSubject[0] currently has the second-largest templateTitle[1] in the world and is projected to overtake top-ranking China within forty years .

generated_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: The population in India grew to 1334.22 millions 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 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 .

Example 662:
titleEntities: {'Subject': ['Switzerland'], 'Date': ['2015']}
title: Median age of the population in Switzerland 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']: ['47.5', '47.5', '47.0', '46.2', '45.2', '44.0', '43.1', '42.2', '41.6', '40.1', '38.6', '37.2', '36.9', '36.0', '34.6', '32.9', '31.8', '31.5', '32.7', '32.9', '33.2']

gold: This statistic shows the median age of the population in Switzerland 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 Switzerland 's population was 42.2 years . See Switzerland 's population figures for comparison .
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] . See templateTitleSubject[0] 's templateTitle[2] figures for comparison .

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 Switzerland 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 663:
titleEntities: {'Subject': ['South Africa'], 'Date': ['2017', '2023']}
title: South Africa : online penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['80.8', '78', '74.9', '71.4', '67.7', '63.8', '59.7']

gold: This statistic provides information on the internet usage penetration in South Africa from 2017 to 2023 . In 2018 , 63.8 percent of the South African population were internet users . This share is projected to grow to 80.8 percent in 2023 .
gold_template: This statistic provides information on the internet usage templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateYValue[5] templateScale of the templateTitleSubject[0] African templateYLabel[1] were internet users . This templateYLabel[0] is projected to grow to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .

generated_template: The statistic shows the projected templateTitle[2] templateTitle[3] templateTitle[4] among the templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the total templateTitle[1] templateYLabel[1] accessed the templateTitle[2] from anywhere via any device .
generated: The statistic shows the projected online penetration 2017 among the population in the South Africa from 2017 to 2023 .  In 2017 , 59.7 % of the total Africa population accessed the online from anywhere via any device .

Example 664:
titleEntities: {'Subject': ['Italy'], 'Date': ['2017']}
title: First sexual intercourse in Italy 2017 , by age
X_Axis['Response']: ['Under_14_years_old', 'Between_14_and_16_years_old', 'Between_17_and_20_years_old', 'Between_20_and_25_years_old', 'Between_25_and_30_years_old', 'Over_30_years_old', 'I_have_never_had_sex', "Doesn't_answer"]
Y_Axis['Share', 'of', 'respondents']: ['2', '13.1', '40.1', '22.7', '6.8', '1.5', '3.3', '10.5']

gold: A survey conducted in 2017 reveals that most Italians had their first sexual intercourse between the age of 17 and 20 . More specifically , about 40 percent of people declared they had sex for the first time in that age range . Approximately 23 percent of young adults had their first sexual intercourse in their early 20s .
gold_template: A survey conducted in templateTitleDate[0] reveals that most Italians templateXValue[6] their templateTitle[0] templateTitle[1] templateTitle[2] templateXValue[1] the templateTitle[6] of templateXValue[2] and templateXValue[2] . More specifically , about templateYValue[max] templateScale of people declared they templateXValue[6] for the templateTitle[0] time in that templateTitle[6] range . Approximately templateYValue[3] templateScale of young adults templateXValue[6] their templateTitle[0] templateTitle[1] templateTitle[2] in their early 20s .

generated_template: This statistic shows the results of a survey question designed to find out what is templateTitle[0] templateTitle[1] to templateTitle[4] templateTitle[5] ( templateTitle[6] - templateTitle[7] ) in templateTitleSubject[0] , as of 2013 . The majority of templateYLabel[1] said that their templateXValue[0] is the templateTitle[0] templateTitle[1] thing to them .
generated: This statistic shows the results of a survey question designed to find out what is First sexual to 2017 by ( age - ) in Italy , as of 2013 .  The majority of respondents said that their Under_14_years_old is the First sexual thing to them .

Example 665:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. adults that would use an app to track diet and nutrition 2017 , by age
Data:
                                     0                      1                    2                       3               4
unknown             I_use_it_regularly  I_use_it_occasionally  I_have_used_it_once  I_can_imagine_using_it  I_won’t_use_it
18 to 29 years                      26                     23                   15                      24              12
30 to 45 years                      17                     27                   16                      27              13
46 to 60 years                       9                     10                    8                      51              22
61 years and older                   4                      5                    6                      55              29 

gold: This statistic shows the results of a survey conducted in the United States in March 2017 , by age . U.S. adults were asked if they would be willing to use an app to track and monitor their diet and nutritional intake . According to the survey , 26 percent of those aged 18 to 29 years regularly use apps to track their diet and nutrition .
gold_template: This statistic shows the results of a survey conducted in the templateTitle[0] in 2017 , templateTitle[10] templateTitle[11] . templateTitleSubject[0] templateTitle[1] were asked if they templateTitle[3] be willing to templateValue[0][0] an templateTitle[5] to templateTitle[6] and monitor their templateTitle[7] and nutritional intake . According to the survey , templateValue[1][max] templateScale of those aged templateLabel[1][0] to templateLabel[1][1] templateLabel[1][2] templateValue[0][0] apps to templateTitle[6] their templateTitle[7] and templateTitle[8] .

generated_template: This statistic shows the results of a survey conducted in the templateTitle[0] in 2017 , templateTitle[8] templateTitle[9] . templateTitleSubject[0] templateTitle[1] were asked if they could templateValue[0][3] themselves templateValue[0][3] an templateTitle[4] to templateTitle[5] and monitor their templateTitle[6] and exercise . According to the survey , templateValue[1][max] templateScale of those aged templateLabel[1][0] to templateValue[1][max] templateLabel[1][2] utilize a templateTitle[6] templateTitle[4] templateValue[0][0] , compared to only templateValue[3][idxmax(1)] templateScale of those aged templateLabel[4][0] templateLabel[1][2] and templateLabel[4][2] .
generated: This statistic shows the results of a survey conducted in the U.S. in 2017 , nutrition 2017 .  U.S. adults were asked if they could I_can_imagine_using_it themselves an use to app and monitor their track and exercise .  According to the survey , 26 percent of those aged 18 to 26 29 utilize a track use I_use_it_regularly , compared to only 26 percent of those aged 61 29 and .

Example 666:
titleEntities: {'Subject': ['Novartis'], 'Date': ['2013', '2014']}
title: Novartis ' number of full-time employees by division 2013 to 2014
Data:
                       0       1      2                3                  4                         5          6
unknown  Pharmaceuticals  Sandoz  Alcon  Consumer_Health  Business_services  Vaccines_and_Diagnostics  Corporate
2013               65262   26905  25494             9213                  0                      6997       1059
2014               59079   26423  23900             9020               7508                      6491        992 

gold: This statistic shows the number of people employed full-time by the Swiss-based pharmaceutical company Novartis in 2013 and 2014 , sorted by division . In 2013 , Novartis reported 9,213 employees in its Consumer Health division . Novartis AG company facts Novartis AG was formed in 1996 , through a merger between Ciba-Geigy and Sandoz and their pharmaceutical and agrochemical divisions .
gold_template: This statistic shows the templateTitle[2] of people employed templateTitle[3] templateTitle[5] the Swiss-based pharmaceutical company templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] , sorted templateTitle[5] templateTitle[6] . In templateTitleDate[min] , templateTitleSubject[0] reported templateValue[1][3] templateTitle[4] in its templateValue[0][3] division . templateTitleSubject[0] AG company facts templateTitleSubject[0] AG was formed in 1996 , through a merger between Ciba-Geigy and templateValue[0][1] and their pharmaceutical and agrochemical divisions .

generated_template: This statistic outlines the templateValue[0][2] templateTitle[0] templateTitle[1] in the templateTitle[2] from templateTitleDate[min] to templateTitleDate[max] , by mine type . In templateTitleDate[max] , the templateTitle[0] industry in the templateTitle[2] employed templateValue[6][max] people . Of that number , more than 32,000 employees worked templateValue[0][0] .
generated: This statistic outlines the Alcon Novartis ' in the number from 2013 to 2014 , by mine type .  In 2014 , the Novartis industry in the number employed 59079 people .  Of that number , more than 32,000 employees worked Pharmaceuticals .

Example 667:
titleEntities: {'Subject': ['Global'], 'Date': ['2016', '2018']}
title: Global electronics industry - growth outlook 2016 to 2018
Data:
                                         0     1     2
Year                                  2016  2017  2018
World (total)                            3     4     4
Europe                                   2     2     2
Americas                                 1     3     5
Asia                                     5     5     5
Australia/ New Zealand/ South Africa     1     2     3 

gold: This graph shows the estimated growth rates for the global electronics industry from 2016 through 2018 , by region . In 2018 , the European electronics industry is expected to grow by around two percent over the previous year . Estimated growth rates for the global electronics industry The electrical and electronics industry comprises a variety of products , ranging from aerospace products , lamps and light fixtures including light-emitting diodes , consumer electronics such as television sets , electrical household appliances , electronic medical equipment , microelectronic components and automation systems to automotive telematics such as on-board diagnostics ( OBD ) , in-car touch screens , park cameras and navigation systems .
gold_template: This graph shows the estimated templateTitle[3] rates for the templateTitleSubject[0] templateTitle[1] templateTitle[2] from templateValue[0][0] through templateValue[0][last] , by region . In templateValue[0][last] , the European templateTitle[1] templateTitle[2] is expected to grow by around templateValue[2][last] templateScale over the previous templateLabel[0][0] . Estimated templateTitle[3] rates for the templateTitleSubject[0] templateTitle[1] templateTitle[2] The electrical and templateTitle[1] templateTitle[2] comprises a variety of products , ranging from aerospace products , lamps and light fixtures including light-emitting diodes , consumer templateTitle[1] such as television sets , electrical household appliances , electronic medical equipment , microelectronic components and automation systems to automotive telematics such as on-board diagnostics ( OBD ) , in-car touch screens , park cameras and navigation systems .

generated_template: This statistic illustrates the amount of templateTitleSubject[0] employees worldwide from templateValue[0][0] to templateValue[0][last] , sorted templateTitle[6] templateTitle[7] . As of 2019 , templateValue[2][last] templateScale of templateTitle[1] templateTitleSubject[0] employees were templateLabel[2][0] . The majority of employees were templateLabel[1][0] .
generated: This statistic illustrates the amount of Global employees worldwide from 2016 to 2018 , sorted 2018 .  As of 2019 , 2 % of electronics Global employees were Europe .  The majority of employees were World .

Example 668:
titleEntities: {'Subject': ['Ford R D'], 'Date': ['2013', '2019']}
title: Ford - R & D costs 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Expenditures', 'in', 'billion', 'U.S.', 'dollars']: ['7.4', '8.2', '8.0', '7.3', '6.7', '6.7', '6.2']

gold: Ford 's research and development expenditures came to about 7.4 billion U.S. dollars in 2019 , putting an end to the gradual increase in R & D efforts of the past five years . The Michigan-based company appears to be adapting to altered fuel economy regulations and the declining demand for sedans and smaller cars in the United States by developing new designs and products . Focus of research and development activities In light of an increased concern from consumers and policymakers about the impact of fossil fuels on carbon dioxide emissions , Ford is working on a new fleet of electric vehicles with a goal of having sixteen plug-in electric vehicle models to market through 2022 .
gold_template: templateTitleSubject[0] 's research and development templateYLabel[0] came to about templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] , putting an end to the gradual templatePositiveTrend in templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] efforts of the past five years . The Michigan-based company appears to be adapting to altered fuel economy regulations and the declining demand for sedans and smaller cars in the country by developing new designs and products . Focus of research and development activities In light of an templatePositiveTrend concern from consumers and policymakers about the impact of fossil fuels on carbon dioxide emissions , templateTitleSubject[0] is working on a new fleet of electric vehicles with a goal of having sixteen plug-in electric vehicle models to market through 2022 .

generated_template: The templateYLabel[0] of templateTitleSubject[0] Inc. , the Montreal-based dairy company , reached approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . Their templateYLabel[0] has gradually templatePositiveTrend year-on-year from templateYValue[min] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] . templateTitleSubject[0] Inc. templateTitleSubject[0] Inc. produces , markets and distributes dairy products .
generated: The Expenditures of Ford R D Inc. , the Montreal-based dairy company , reached approximately 8.2 billion U.S. dollars in 2018 .  Their Expenditures has gradually increased year-on-year from 6.2 billion U.S. dollars in 2013 .  Ford R D Inc. Ford R D produces , markets and distributes dairy products .

Example 669:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: University tuition costs and fees U.S. 2000 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', '2003/04', '2000/01']
Y_Axis['Average', 'cost', 'for', 'tuition', 'and', 'fees']: ['14042', '13538', '13139', '12706', '12214', '11799', '11204', '10566', '9985', '9753', '9470', '8997', '8565', '8074', '7457', '6010']

gold: This statistic shows the average cost for university tuition and fees ( when studying in-state and living on campus ) in the United States from the 2000/01 academic year to 2017/18 . Costs have steadily been increasing , and in 2017/18 had reached an average of 14,042 U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[0] templateYLabel[3] and templateYLabel[4] ( when studying in-state and living on campus ) in the templateTitle[4] from the templateXValue[last] academic templateXLabel[0] to templateXValue[0] . templateTitle[2] have steadily been templatePositiveTrend , and in templateXValue[0] had reached an templateYLabel[0] of templateYValue[max] templateTitleSubject[0] dollars .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] templateTitle[6] templateXValue[min] to templateTitleDate[max] . In templateTitleDate[max] , an estimated templateYValue[idxmax(X)] templateScale templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] members as compared to templateYValue[14] templateScale in templateXValue[last] .
generated: This statistic shows the Average of University tuition costs cost for 2018 2000/01 to 2018 .  In 2018 , an estimated 14042 % University tuition costs cost members as compared to 7457 % in 2000/01 .

Example 670:
titleEntities: {'Subject': ['Overall'], 'Date': ['2016']}
title: Overall highest winning percentage of pro tennis players 2016
X_Axis['Month']: ['Andy_Murray', 'Novak_Djokovic', 'Milos_Raonic', 'Roger_Federer', 'Rafael_Nadal', 'Kei_Nishikori', 'Juan_Martin_del_Potro', 'Nick_Kyrgios', 'Gael_Monfils', 'Stan_Wawrinka']
Y_Axis['Winning', 'percentage']: ['89.7', '87.8', '75.4', '75', '73.6', '73.4', '72.7', '72.2', '72.1', '71.9']

gold: The graph depicts the winning percentages of pro tennis players in 2016 . Andy Murray had the highest winning percentage of all male players , winning 87.8 percent ( 78 out of 87 ) of his matches .
gold_template: The graph depicts the templateYLabel[0] percentages of templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] . templateXValue[0] had the templateTitle[1] templateYLabel[0] templateScale of all male templateTitle[6] , templateYLabel[0] templateYValue[1] templateScale ( 78 out of 87 ) of his matches .

generated_template: This statistic provides 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: This statistic provides information on the most percentage pro tennis on Overall , ranked by highest of winning on the social network .  As of 2020 , personal care Winning Andy_Murray Body was ranked first with close to 89.7 percentage Overall winning .

Example 671:
titleEntities: {'Subject': ['British Telecommunications BT'], 'Date': []}
title: British Telecommunications ( BT ) : revenues by types of business Q1 2012/13-Q1 2019/20
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
Quarter             Q1_2012/13  Q2_2012/13  Q3_2012/13  Q4_2012/13  Q1_2013/14  Q2_2013/14  Q3_2013/14  Q4_2013/14  Q1_2014/15  Q2_2014/15  Q3_2014/15  Q4_2014/15  Q1_2015/16  Q2_2015/16  Q3_2015/16  Q4_2015/16  Q1_2016/17  Q2_2016/17  Q3_2016/17  Q4_2016/17  Q1_2017/18  Q2_2017/18  Q3_2017/18  Q4_2017/18  Q1_2018/19  Q2_2018/19  Q3_2018/19  Q4_2018/19  Q1_2019/20
BT Global Services        1731        1757        1748        1934        1752        1743        1847        1927        1647        1649        1847        1789        1543        1599        1675        1753        1250        1409        1399        1422        1246        1265        1265        1243        1147        1185        1202        1201        1085
BT Business                849         870         875         922         785         799         808         821         762         789         808         805         749         781         779         821        1165        1178        1195        1220        1648        1680        1681        1708        1588        1633        1583        1592        1516
BT Wholesale               678         616         645         669         638         624         589         571         525         529         589         571         530         520         527         509         453         440         448         454         429         430         425         424         459         470           0           0           0
Openreach                 1270        1283        1286        1276        1245        1271        1274        1271        1245        1245        1274        1266        1249        1267        1294        1290        1236        1232        1258        1258        1287        1299        1379        1320        1255        1293        1256        1271        1268
BT Consumer                959         952         953         982         950         987        1014        1068        1046        1056        1014        1100        1074        1127        1205        1192        1175        1251        1262        1246        2539        2580        2653        2543        2570        2654        2757        2610        2550
EE                           0           0           0           0           0           0           0           0           0           0           0           0           0           0           0           0        1243        1277        1311        1259        1291        1326        1357        1320           0           0           0           0           0
Other                       19          19          21          42          18          18          14          32          19          16          14          18          16          19          21          17           3           1           2           4           2           4           2           2           1           1           2           1           1 

gold: This statistic shows the revenue generated by their primary businesses of British Telecommunications ( BT ) from the first quarter of 2012/13 to the first quarter of 2019/20 . With respect to BT 's 2017/2018 financial year , all of the revenues generated by the primary businesses increased comparing the first quarter to the fourth quarter . The highest increase in revenues was generated by BT 's global services .
gold_template: This statistic shows the revenue generated templateTitle[4] their primary businesses of templateTitleSubject[0] ( templateTitleSubject[0] ) from the first templateLabel[0][0] of templateValue[0][0] to the first templateLabel[0][0] of templateValue[0][28] . With respect to templateTitleSubject[0] 's 2017/2018 financial year , all of the templateTitle[3] generated templateTitle[4] the primary businesses templatePositiveTrend comparing the first templateLabel[0][0] to the fourth templateLabel[0][0] . The highest templatePositiveTrend in templateTitle[3] was generated templateTitle[4] templateTitleSubject[0] 's templateLabel[1][1] templateLabel[1][2] .

generated_template: The statistic shows a templateTitle[3] of templateTitleSubject[0] templateLabel[2][0] , templateLabel[3][0] and templateLabel[1][0] templateTitle[2] from the first templateLabel[0][0] of 2006 to the company 's latest financial templateLabel[0][0] . In templateTitleSubject[0] 's forth financial templateLabel[0][0] of templateTitleDate[max] approximately templateValue[2][0] templateScale iPhones were sold worldwide . templateTitleSubject[0] templateTitle[1] templateTitle[2] - additional information Since the introduction of the templateLabel[2][0] in 2007 , templateTitle[2] of the templateLabel[1][0] have dramatically templateNegativeTrend from over templateValue[1][36] templateScale units per templateLabel[0][0] on average to less than templateValue[1][16] templateScale units in the fourth templateLabel[0][0] of 2014 , after which templateTitleSubject[0] stopped reporting templateTitle[2] figures for the templateLabel[1][0] as its own category .
generated: The statistic shows a revenues of British Telecommunications BT BT , and BT from the first Quarter of 2006 to the company 's latest financial Quarter .  In British Telecommunications BT 's forth financial Quarter of approximately 849 million iPhones were sold worldwide .  British Telecommunications BT Telecommunications BT - additional information Since the introduction of the BT in 2007 , BT of the BT have dramatically dropped from over 1085 million units per Quarter on average to less than 1250 million units in the fourth Quarter of 2014 , after which British Telecommunications BT stopped reporting BT figures for the BT as its own category .

Example 672:
titleEntities: {'Subject': ['United States'], 'Date': ['2010']}
title: Smartphone users in the United States 2010 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Smartphone', 'users', 'in', 'millions']: ['285.3', '281.9', '277.8', '272.6', '265.9', '257.3', '246.6', '208.61', '190.64', '171.0', '144.5', '122.0', '92.8', '62.6']

gold: This statistic shows the number of smartphone users in the U.S. from 2010 to 2023 . For 2018 , the number of smartphone users in the United States is estimated to reach 257.3 million . Smartphone users - additional information Advances in telecommunication technology have been significant in recent years .
gold_template: This statistic shows the number of templateYLabel[0] templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . For templateXValue[5] , the number of templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] is estimated to reach templateYValue[5] templateScale . templateYLabel[0] templateYLabel[1] - additional information Advances in telecommunication technology have been significant in recent years .

generated_template: This statistic shows templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , revenues from templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to templateYValue[5] templateScale .
generated: This statistic shows Smartphone users in United States from 2010 to 2023 .  In 2018 , revenues from Smartphone users in United States amounted to 257.3 millions .

Example 673:
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 provides templateXValue[5] 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: This statistic provides Violence on the most Problems Italian of Lack_of_opportunities_to_enter_into_the_labor_market Violence 2019 among adults in the 2019 as of 2018 .  During a survey , 26 % of respondents stated that the Lack_of_opportunities_to_enter_into_the_labor_market of was the most Problems feature of Lack_of_opportunities_to_enter_into_the_labor_market Violence 2019 .

Example 674:
titleEntities: {'Subject': ['Ethiopia'], 'Date': ['2019']}
title: Unemployment rate in Ethiopia 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']: ['1.79', '1.81', '1.81', '1.94', '2.01', '2.1', '2.25', '2.2', '2.29', '2.39', '2.35', '2.13', '2.18', '2.3', '2.5', '2.67', '2.91', '3.07', '3.21', '3.44', '3.71']

gold: This statistic shows the unemployment rate in Ethiopia from 1999 to 2019 . In 2019 , the unemployment rate in Ethiopia was at approximately 1.79 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: In templateXValue[max] , templateTitleSubject[0] 's estimated templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[0] templateYValue[idxmax(X)] . This templatePositiveTrend is up .03 templateScale from the templateXLabel[0] before . The templateYLabel[0] templateYLabel[1] is defined as the templateScale of unemployed workers in the total labor force .
generated: In 2019 , Ethiopia 's estimated Unemployment rate amounted to approximately 1.79 .  This increase is up .03 % from the Year before .  The Unemployment rate is defined as the percentage of unemployed workers in the total labor force .

Example 675:
titleEntities: {'Subject': ['Ghana'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita 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['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['2681.52', '2574.2', '2472.87', '2385.78', '2266.41', '2223.42', '2216.84', '2037.95', '1941.46', '1753.85', '1962.66', '2389.77', '2184.39', '2123.65', '1745.54', '1462.8', '1677.37', '1516.05', '1315.81', '1147.6', '970.46', '858.79', '714.16', '630.33', '606.32', '1011.73', '1001.88', '945.09', '948.53', '914.59', '792.68', '883.34', '1114.47', '1154.35', '1056.15', '935.96', '941.73', '906.97', '1106.5', '1223.25', '1409.55']

gold: The statistic shows gross domestic product ( GDP ) per capita in Ghana from 1984 to 2024 . All figures are estimates . GDP is the total value of all goods and services produced in a country in a year .
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[max] . All figures are estimates . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] .

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] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was around templateYValue[7] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] 's economy templateYLabel[0] templateYLabel[1] templateYLabel[2] is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the templateYLabel[0] and dividing it by the total population in the country .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in Ghana from 1984 to 2018 , with projections up until 2024 .  In 2018 , the Gross domestic product per capita in Ghana was around 2037.95 U.S. dollars .  Ghana 's economy GDP per capita is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the GDP and dividing it by the total population in the country .

Example 676:
titleEntities: {'Subject': ['European'], 'Date': ['2015']}
title: Number of people with hearing loss global vs European countries 2015
X_Axis['Country']: ['Global', 'Europe', 'European_Union', 'United_Kingdom', 'France', 'Spain', 'Netherlands', 'Austria', 'Sweden', 'Belgium', 'Poland', 'Denmark', 'Ireland']
Y_Axis['Estimated', 'number', 'of', 'people', 'with', 'hearing', 'loss']: ['328.0', '119.0', '51.0', '10.0', '6.0', '3.5', '1.6', '1.6', '1.4', '1.3', '1.0', '0.8', '0.8']

gold: This statistic shows the estimated number of people with hearing loss worldwide and in Europe as of 2015 , by country , in millions . As of this time an estimated 119 million people in the whole of Europe were hard of hearing , with 3.5 million of these people located in Spain .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] worldwide and in templateXValue[1] as of templateTitleDate[0] , by templateXLabel[0] , in templateScale . As of this time an templateYLabel[0] templateYValue[1] templateScale templateYLabel[2] in the whole of templateXValue[1] were hard of templateYLabel[4] , templateYLabel[3] templateYValue[5] templateScale of these templateYLabel[2] located in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] first instance templateYLabel[2] applications in templateTitle[3] of the templateTitleSubject[0] templateTitle[5] in templateTitleDate[0] , broken down by templateXLabel[0] . In templateTitleDate[0] , templateXValue[0] templateYLabel[1] the largest templateYLabel[0] of templateYLabel[2] seekers with templateYValue[max] . templateXValue[1] and templateXValue[2] templateYLabel[1] the second and third most respectively , with roughly 10.6 and templateYValue[2] thousand templateYLabel[3] , respectively .
generated: This statistic shows the Estimated of number first instance people applications in loss of the European vs in 2015 , broken down by Country .  In 2015 , Global number the largest Estimated of people seekers with 328.0 Europe and European_Union number the second and third most respectively , with roughly 10.6 and 51.0 thousand hearing , respectively .

Example 677:
titleEntities: {'Subject': ['USA'], 'Date': ['1990', '2018']}
title: USA - reported aggravated assault 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']: ['246.8', '249.2', '248.3', '238.1', '229.2', '229.6', '242.8', '241.5', '252.8', '264.7', '277.5', '287.2', '292.0', '290.8', '288.6', '295.4', '309.5', '318.5', '324.0', '334.3', '361.3', '382.0', '390.9', '418.3', '427.6', '440.3', '441.8', '433.3', '424.1']

gold: This graph shows the reported aggravated assault rate in the United States from 1990 to 2018 . In 2018 , the nationwide rate was 246.8 cases per 100,000 of the population .
gold_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)] cases templateYLabel[1] 100,000 of the templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateTitle[1] templateYLabel[1] was templateYValue[idxmax(X)] templateYLabel[3] dollars in templateXValue[idxmax(Y)] . templateTitle[1] templateYLabel[1] The templateYLabel[0] templateTitle[1] templateYLabel[1] depicts the templateYLabel[1] of households , including the templateYLabel[1] of the householder and all other individuals aged 15 years or over living in the templateTitle[1] .
generated: This statistic shows the Rate reported per in the USA from 1990 to 2018 .  The Rate reported per was 246.8 population dollars in 1992 .  reported per The Rate reported per depicts the per of households , including the per of the householder and all other individuals aged 15 years or over living in the reported .

Example 678:
titleEntities: {'Subject': ['India'], 'Date': ['2019', '2019']}
title: India 's sales of automobiles 2010- 2019 by type
Data:
                           0        1        2        3        4        5        6        7        8
unknown              FY_2019  FY_2018  FY_2017  FY_2016  FY_2015  FY_2014  FY_2013  FY_2012  FY_2011
Two wheelers           21.18     20.2     17.6     16.5     16.0     14.8     13.8     13.4     11.8
Passenger vehicles       3.4      3.3      3.0      2.8      2.6      2.5      2.7      2.6      2.5
Commercial vehicles      1.0     0.86     0.71     0.69     0.61     0.63     0.79     0.81     0.68
Three wheelers           0.7     0.64     0.51     0.54     0.53     0.48     0.54     0.51     0.53 

gold: Being one of the largest automotive sectors , India had over 230 million registered vehicles in 2016 . It was the largest producer of two-wheelers across the globe in 2019 . The market within the country was dominated by this segment also .
gold_template: Being templateValue[3][0] of the largest automotive sectors , templateTitleSubject[0] had over 230 templateScale registered templateLabel[2][1] in templateValue[0][3] . It was the largest producer of two-wheelers across the globe in templateValue[0][0] . The market within the country was dominated templateTitle[6] this segment also .

generated_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: Among the many 2010- FY_2018 by to choose automobiles , FY_2019 FY_2018 and FY_2017 FY_2018 together with FY_2017 commanded the largest share of marketers claiming that they generated the Commercial 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 by in the same period .

Example 679:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2019']}
title: Best selling car models in the United Kingdom ( UK ) 2019
X_Axis['Car', 'Model']: ['Ford_Fiesta', 'Volkswagen_Golf', 'Ford_Focus', 'Vauxhall_Corsa', 'Mercedes-Benz_A_Class', 'Nissan_Qashqai', 'Ford_Kuga', 'MINI', 'Volkswagen_Polo', 'Kia_Sportage']
Y_Axis['Number', 'of', 'units', 'sold']: ['77833', '58994', '56619', '54239', '53724', '52532', '41671', '41188', '37453', '34502']

gold: In 2019 , Ford Fiesta was the best selling car model at 77,833 units . Currently in its seventh generation , it was first sold in 1979 and had been popular with UK consumers from the beginning . Two years after its introduction on the market , the Ford Fiesta Mk1 had already reached the peak of the most sold supermini cars .
gold_template: In templateTitleDate[0] , templateXValue[0] was the templateTitle[0] templateTitle[1] templateXLabel[0] templateXLabel[1] at templateYValue[max] templateYLabel[1] . Currently in its seventh generation , it was first templateYLabel[2] in 1979 and had been popular with templateTitleSubject[1] consumers from the beginning . Two years after its introduction on the market , the templateXValue[0] Mk1 had already reached the peak of the most templateYLabel[2] supermini cars .

generated_template: This statistic provides information on the most templateTitle[3] templateTitle[4] templateTitle[5] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[9] templateTitle[2] templateXLabel[0] templateXLabel[1] . In 2020 , templateXValue[0] was the templateTitle[1] templateTitle[2] templateXLabel[0] in the templateTitleSubject[0] with a templateYLabel[0] of templateYValue[max] templateScale .
generated: This statistic provides information on the most models United Kingdom in the United Kingdom ( United Kingdom ) in 2019 , car Model .  In 2020 , Ford_Fiesta was the selling car in the United Kingdom with a Number of 77833 million .

Example 680:
titleEntities: {'Subject': ['Hungary'], 'Date': ['2010', '2019']}
title: Electricity prices for households in Hungary 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']: ['11.2', '11.18', '11.23', '11.34', '11.25', '11.25', '11.14', '11.45', '11.27', '11.46', '12.02', '13.26', '13.97', '16.18', '15.49', '15.53', '16.82', '15.74', '17.01']

gold: Hungarian electricity consumers have experienced a reduction in the price of electricity over the past several years , with the price falling from 17.01 euro cents per kilowatt hour ( kWh ) in the first half of 2010 , to 11.2 cents per kWh in the first half of 2019 . Energy in Hungary Hungary uses a diverse range of fuels to generate energy . Nuclear plays a significant role in the Hungarian energy mix , with energy derived from nuclear sources making up half of the country 's energy mix as of 2017 .
gold_template: Hungarian templateTitle[0] consumers have experienced a reduction in the price of templateTitle[0] over the past several years , with the price templateNegativeTrend from templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kilowatt hour ( kWh ) in the first half of templateXValue[17] , to templateYValue[0] templateYLabel[1] templateYLabel[2] kWh in the first half of templateXValue[0] . Energy in templateTitleSubject[0] uses a diverse range of fuels to generate energy . Nuclear plays a significant role in the Hungarian energy mix , with energy derived from nuclear sources making up half of the country 's energy mix as of templateXValue[3] .

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[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh . This was an templatePositiveTrend from the previous period .
generated: This statistic shows the Electricity prices for household end users in Hungary semi-annually from 2010_S2 to 2019_S1 .  In the second half of 2018_S2 , the average Electricity price for households was 17.01 Euro cents per kWh .  This was an increase from the previous period .

Example 681:
titleEntities: {'Subject': ['Pakistan'], 'Date': ['2013', '2018']}
title: Total of retail net sales in Pakistan 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Billion', 'U.S.', 'dollars']: ['210.28', '185.57', '163.77', '145.14', '129.26', '115.85']

gold: The total retail spending in Pakistan was forecasted at 210 billion U.S. dollars for 2018 . This equals to about 1.7 thousand U.S. dollars per capita in the above-mentioned time period . economic factors on the retail market The total population of Pakistan is over 200 million and set to grow to 224 million by 2024 .
gold_template: The templateTitle[0] templateTitle[1] spending in templateTitleSubject[0] was forecasted at templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] for templateXValue[idxmax(Y)] . This equals to about 1.7 thousand templateYLabel[1] templateYLabel[2] per capita in the above-mentioned time period . economic factors on the templateTitle[1] market The templateTitle[0] population of templateTitleSubject[0] is over 200 templateScale and set to grow to 224 templateScale by 2024 .

generated_template: This statistic depicts templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] templateTitle[2] amounted to about 182.76 templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic depicts Total retail net in Pakistan from 2013 to 2018 .  In 2013 , Pakistan 's Total retail net amounted to about 182.76 billion U.S. dollars .

Example 682:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018', '2018']}
title: Gender distribution of full-time U.S. law enforcement employees 2018
Data:
                                 0         1          2
unknown  Law_enforcement_employees  Officers  Civilians
Male                          73.3      87.4       39.7
Female                        26.7      12.6       60.3 

gold: In 2018 , 60.3 percent of full-time civilian law enforcement employees in the United States were female . Only 12.6 percent of full-time law enforcement officers were female , while 87.4 percent of law enforcement officers were male . The gender divide Law enforcement in the United States is an indisputably male-dominated profession , particularly in the Federal Bureau of Investigation ( FBI ) .
gold_template: In templateTitleDate[0] , templateValue[2][last] templateScale of templateTitle[2] civilian templateValue[0][0] employees in the templateTitle[3] were templateLabel[2][0] . Only templateValue[2][1] templateScale of templateTitle[2] templateValue[0][0] officers were templateLabel[2][0] , while templateValue[1][1] templateScale of templateValue[0][0] officers were templateLabel[1][0] . The templateTitle[0] divide templateValue[0][0] in the templateTitle[3] is an indisputably male-dominated profession , particularly in the Federal Bureau of Investigation ( FBI ) .

generated_template: This statistic displays the number of templateTitle[0] templateTitle[1] in the templateTitle[4] in templateTitleDate[0] , templateTitle[2] templateTitle[3] . In templateTitleDate[0] , a total 11,099 templateValue[0][1] templateTitle[0] arrived in the templateTitle[4] . The total number of refugee arrivals in templateTitleDate[0] amounted to 22,405 .
generated: This statistic displays the number of Gender distribution in the law in 2018 , full-time U.S. In 2018 , a total 11,099 Officers Gender arrived in the law .  The total number of refugee arrivals in 2018 amounted to 22,405 .

Example 683:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2004', '2018']}
title: Total value of U.S. trade in goods worldwide 2004 to 2018
Data:
              0       1       2       3       4       5       6       7       8       9      10      11      12      13      14
Year       2018    2017    2016    2015    2014    2013    2012    2011    2010    2009    2008    2007    2006    2005    2004
Total    4206.9  3889.7  3643.6  3746.0  3969.1  3846.4  3822.2  3687.6  3190.2  2615.0  3400.9  3117.0  2892.7  2575.7  2289.6
Exports  1664.1  1546.8  1454.6  1504.9  1623.3  1578.9  1547.1  1480.7  1278.1  1056.9  1300.5  1163.3  1037.3   904.3   819.0
Imports  2542.8  2342.9  2188.9  2241.1  2345.8  2267.6  2275.0  2207.0  1912.1  1558.1  2100.4  1953.6  1855.4  1671.4  1471.0 

gold: In 2018 , the total value of U.S. trade goods amounted to 4.2 trillion U.S. dollars . This shows the significance and scale of international trade for the economy of the United States . In 2018 , the United States imported goods valuing over 2.5 trillion U.S. dollars from international trading partners , in comparison the value of goods exported from the United States to other countries amounted to around 1.7 trillion U.S. dollars .
gold_template: In templateValue[0][0] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] amounted to 4.2 templateScale templateTitleSubject[0] dollars . This shows the significance and scale of international templateTitle[3] for the economy of the templateTitle[2] . In templateValue[0][0] , the templateTitle[2] imported templateTitle[4] valuing over 2.5 templateScale templateTitleSubject[0] dollars from international trading partners , in comparison the templateTitle[1] of templateTitle[4] exported from the templateTitle[2] to other countries amounted to around 1.7 templateScale templateTitleSubject[0] dollars .

generated_template: The statistic shows the templateTitle[3] of templateLabel[1][0] and templateTitle[5] due to templateTitle[0] in the templateTitle[2] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , there were a total of templateValue[1][0] templateTitle[4] and templateValue[2][0] templateTitle[5] reported due to lighting in the templateTitle[2] .
generated: The statistic shows the trade of Total and worldwide due to Total in the U.S. from 2004 to 2018 .  In 2018 , there were a total of 4206.9 goods and 1664.1 worldwide reported due to lighting in the U.S. .

Example 684:
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 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: The Mexico 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 .

Example 685:
titleEntities: {'Subject': ['Canadian'], 'Date': ['2012']}
title: Canadian women on having sex on vacation while sharing a room 2012
X_Axis['Response']: ['Have_sex_when_nobody_else_is_in_the_room', "Work_the_schedule_so_that_you_have_some_'alone_time'", 'Wait_until_you_get_back_home', 'Have_sex_in_the_bathroom_or_shower', 'Find_somewhere_else_to_have_sex', 'Get_your_own_room_for_a_night', 'None_of_the_above']
Y_Axis['Share', 'of', 'respondents']: ['46', '35', '35', '20', '12', '9', '10']

gold: This statistic shows the results of a 2012 survey among Canadian women aged 18 to 60 about their stance on sex on vacation . They were asked how they would manage to have sex on vacation while sharing a room with others . 46 percent of the women stated that they would have sex when nobody else is in the room .
gold_template: This statistic shows the results of a templateTitleDate[0] survey among templateTitleSubject[0] templateTitle[1] aged 18 to 60 about their stance on templateXValue[0] on templateTitle[4] . They were asked how they would manage to templateXValue[0] on templateTitle[4] templateTitle[5] templateTitle[6] a templateXValue[0] with others . templateYValue[max] templateScale of the templateTitle[1] stated templateXValue[1] they would templateXValue[0] when templateXValue[0] is in the templateXValue[0] .

generated_template: This statistic shows the results of a survey , conducted in 2016 in Canada , on templateXValue[5] templateTitle[3] templateTitle[4] templateTitle[5] . According to templateYValue[max] templateScale of surveyed templateTitleSubject[0] , their top resolution templateTitle[6] templateTitleDate[0] was to templateXValue[0] fitness and templateXValue[0] .
generated: This statistic shows the results of a survey , conducted in 2016 Canada , on Get_your_own_room_for_a_night sex vacation while .  According to 46 % of surveyed Canadian , their top resolution sharing 2012 was to Have_sex_when_nobody_else_is_in_the_room fitness and Have_sex_when_nobody_else_is_in_the_room .

Example 686:
titleEntities: {'Subject': ['Highest-paid'], 'Date': ['2019']}
title: Highest-paid musicians 2019
X_Axis['Month']: ['Taylor_Swift', 'Kanye_West', 'Ed_Sheeran', 'The_Eagles', 'Elton_John', 'Jay-Z', 'Beyoncé_Knowles', 'Drake', 'Sean_Combs', 'Metallica', 'Rihanna', 'Travis_Scott', 'Katy_Perry', 'Justin_Timberlake', 'BTS', 'P!nk', 'Billy_Joel', 'Bruno_Mars', 'Jimmy_Buffett', 'Eminem', 'Fleetwood_Mac', 'Ariana_Grande', 'Paul_McCartney', 'The_Chainsmokers', "Guns_N'_Roses", 'Jennifer_Lopez', 'Luke_Bryan', 'Rolling_Stones', 'DJ_Khaled', 'Marshmello']
Y_Axis['Income', 'in', 'million', 'U.S.', 'dollars']: ['185.0', '150.0', '110.0', '100.0', '84.0', '81.0', '81.0', '75.0', '70.0', '68.5', '62.0', '58.0', '57.5', '57.5', '57.0', '57.0', '52.0', '51.5', '50.0', '50.0', '49.0', '48.0', '48.0', '46.0', '44.0', '43.0', '42.5', '41.0', '40.0', '40.0']

gold: This graph shows a ranking of best-paid musicians worldwide in 2019 . Taylor Swift came first with an income of 185 million US dollars , followed by Kanye West and Ed Sheeran . Here you can find a Billboard ranking of highest-paid musicians in the United States ( ranked by their U.S. earnings only ) .
gold_template: This graph shows a ranking of best-paid templateTitle[1] worldwide in templateTitleDate[0] . templateXValue[0] came first with an templateYLabel[0] of templateYValue[max] templateScale US templateYLabel[3] , followed by templateXValue[1] and templateXValue[2] . Here you can find a Billboard ranking of templateTitleSubject[0] templateTitle[1] in the country ( ranked by their templateYLabel[2] earnings only ) .

generated_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: In a world where people are constantly on the move and seeking Jimmy_Buffett forms of entertainment to make their journeys Kanye_West faster , mobile 2019 is bigger than ever .  In 2019 , Taylor_Swift Games was the Highest-paid musicians mobile 2019 app in the 2019 Sean_Combs over 185.0 million monthly users .  The highest individual Drake on the list was Kanye_West , which averaged 150.0 million users in the same Month .

Example 687:
titleEntities: {'Subject': ['United States'], 'Date': ['2017']}
title: Usage frequency of headphones in the United States 2017
X_Axis['Response']: ['daily', 'several_times_a_week', 'once_a_week', 'several_times_a_month', 'less_often', 'never']
Y_Axis['Share', 'of', 'respondents']: ['31', '24', '6', '6', '14', '18']

gold: The statistic shows how often people in the United States use headphones in 2017 according to a Statista survey . 31 percent of respondents said that they use headphones on a daily basis .
gold_template: The statistic shows how templateXValue[4] people in the templateTitleSubject[0] use templateTitle[2] in templateTitleDate[0] according to a Statista survey . templateYValue[max] templateScale of templateYLabel[1] said that they use templateTitle[2] on a templateXValue[idxmax(Y)] basis .

generated_template: This survey was aimed at assessing the templateTitle[0] spending habits in the United Kingdom ( templateTitleSubject[0] ) in the year templateTitleDate[0] , posing the question `` How much are you planning to spend templateTitle[3] templateTitle[4] on templateTitle[0] templateTitle[6] ? '' . Whereas the templateYValue[0] templateScale of surveyed shoppers reported planning to spend templateXValue[0] 50 British Pounds templateTitle[3] templateTitle[4] , templateYValue[4] templateScale mentioned spending templateXValue[4] templateXValue[0] templateXValue[3] British Pounds .
generated: This survey was aimed at assessing the Usage spending habits in the United Kingdom ( United States ) in the year 2017 , posing the question `` How much are you planning to spend United States on Usage 2017 ? '' .  Whereas the 31 % of surveyed shoppers reported planning to spend daily 50 British Pounds United States , 14 % mentioned spending less_often daily several_times_a_month British Pounds .

Example 688:
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: This statistic shows the templateTitle[1] templateYLabel[0] of nurse templateTitle[2] in the templateTitle[0] from the second templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . During that period , templateYValue[0] templateScale of the templateYLabel[1] 's templateYLabel[2] had access to purchases .
generated: This statistic shows the number of nurse monetizable in the Twitter from the second Quarter of 2017 to the fourth Quarter of 2019 .  During that period , 31 millions of the monetizable 's daily had access to purchases .

Example 689:
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 between 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 between 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 690:
titleEntities: {'Subject': ['Roche Group'], 'Date': ['2007', '2019']}
title: Roche Group 's net income 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Net', 'income', 'in', 'million', 'Swiss', 'francs']: ['14108', '10865', '8825', '9733', '9056', '9535', '11373', '9773', '9544', '8891', '8510', '10844', '11437']

gold: The statistic shows the net income of Swiss-based pharmaceutical company Roche from 2007 to 2019 . Roche is one of the largest global pharmaceutical companies . In 2019 , Roche reported approximately 14.1 billion Swiss francs of net income .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of Swiss-based pharmaceutical company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] is one of the largest global pharmaceutical companies . In templateXValue[max] , templateTitleSubject[0] reported approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] of templateYLabel[0] templateYLabel[1] .

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 Net of 's income at net establishments in Roche Group from 2007 to 2019 .  In 2019 , the Net of income in travel net ( including both international and domestic tourists ) amounted to approximately 14108 million .

Example 691:
titleEntities: {'Subject': ['Colombia'], 'Date': ['2018']}
title: Urbanization in Colombia 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['80.78', '80.45', '80.11', '79.76', '79.42', '79.06', '78.7', '78.34', '77.96', '77.59', '77.2']

gold: This statistic shows the degree of urbanization in Colombia from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 80.78 percent of Colombia '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 Colombia from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 80.78 % of Colombia 's total population lived in urban areas and cities .

Example 692:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2015', '2020']}
title: Number of asylum applications in Sweden 2015 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Number', 'of', 'asylum', 'applications']: ['46000', '46000', '40000', '36700', '29000', '162915']

gold: This statistic displays the number of asylum applications in Sweden in 2015 and 2016 , with a forecast to 2020 . In 2015 , asylum applications made in Sweden amounted to roughly 163 thousands .
gold_template: This statistic displays the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateXValue[min] and templateXValue[4] , with a forecast to templateXValue[max] . In templateXValue[min] , templateYLabel[1] templateYLabel[2] made in templateTitleSubject[0] amounted to roughly templateYValue[max] thousands .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateYLabel[1] templateTitle[5] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[2] and a further templateYValue[2] templateScale in templateXValue[2] templateTitle[1] templateTitle[2] - additional information templateTitle[1] in the 21st century is no longer limited to a small geographical area , as internet communication and almost universal access to templateTitle[2] and accommodation allow for easy national and transnational movement between companies , their partners , customers , suppliers or distributors . Although multinational corporations and templateTitle[1] templateTitle[2] have a long history , they have reached unprecedented levels in the modern era and seem to be templatePositiveTrend every templateXLabel[0] .
generated: This statistic shows the Sweden asylum Number Sweden asylum 2020 from 2015 to 2020 .  Sweden asylum Number amounted to approximately 29000 million applications in 2018 and a further 40000 million in 2018 asylum applications - additional information asylum in the 21st century is no longer limited to a small geographical area , as internet communication and almost universal access to applications and accommodation allow for easy national and transnational movement between companies , their partners , customers suppliers or distributors .  Although multinational corporations and asylum applications have a long history , they have reached unprecedented levels in the modern era and seem to be increasing every Year .

Example 693:
titleEntities: {'Subject': ['Latvia'], 'Date': ['2024']}
title: Inflation rate in Latvia 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']: ['2.19', '2.18', '2.19', '2.31', '2.59', '3', '2.55', '2.89', '0.1', '0.21', '0.69', '0.01', '2.29', '4.22', '-1.22', '3.26', '15.25', '10.08', '6.57', '6.89', '6.19', '2.94', '1.96', '2.53', '2.63', '2.11', '4.3', '8.07', '17.6', '25', '35.9']

gold: This statistic shows the average inflation rate in Latvia from 1994 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Latvia had amounted to about 2.55 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 Latvia from 1994 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Latvia amounted to about 2.55 % compared to the previous Year .

Example 694:
titleEntities: {'Subject': ['Oakland Raiders'], 'Date': ['2019']}
title: Average regular season home attendance of the Oakland Raiders 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Average', 'attendance']: ['52549', '57919', '57775', '57320', '54613', '53699', '50444', '54216', '59242', '46431', '44284', '57850']

gold: This graph depicts the average regular season home attendance of the Oakland Raiders from 2008 to 2019 . In 2019 , the average attendance at home games of the Oakland Raiders was 52,549
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[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)] templateTitleSubject[0] average templateTitle[3] templateYLabel[1] - additional information The templateTitleSubject[0] ' templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] has remained relatively constant in recent years , with the templateYLabel[0] in the templateXValue[max] templateTitle[2] standing at templateYValue[idxmax(X)] .
generated: This graph depicts the Average regular season home attendance of the Oakland Raiders from 2008 to 2019 .  In 2019 , the Average attendance at home games of the Oakland Raiders was 52549 Oakland Raiders average home attendance - additional information The Oakland Raiders ' Average regular season home attendance has remained relatively constant in recent years , with the Average in the 2019 season standing at 52549 .

Example 695:
titleEntities: {'Subject': ['Global'], 'Date': ['2014', '2023']}
title: Global datacenter colocation revenue 2014 to 2023
X_Axis['Year']: ['2023', '2018', '2016', '2015', '2014']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['54.0', '38.2', '28.9', '27.0', '22.8']

gold: In 2018 , global revenue from the wholesale and retail datacenter colocation market amounted to around 38 billion U.S. dollars . Industry revenues are expected to increase to over 50 billion dollars per year by 2023 as an increasing share of global businesses adopt data collection and analysis into their strategy . Datacenter colocation Datacenter colocations are typically large datacenters that house the computing resources of client companies and provide the space , security , power , and networking connections needed .
gold_template: In templateXValue[1] , templateTitleSubject[0] templateYLabel[0] from the wholesale and retail templateTitle[1] templateTitle[2] market amounted to around templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] . Industry revenues are expected to templatePositiveTrend to over 50 templateScale templateYLabel[3] per templateXLabel[0] by templateXValue[max] as an templatePositiveTrend share of templateTitleSubject[0] businesses adopt data collection and analysis into their strategy . templateTitle[1] templateTitle[2] templateTitle[1] colocations are typically large datacenters that house the computing resources of client companies and provide the space , security , power , and networking connections needed .

generated_template: The timeline presents data on the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] sales templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . The source estimates that the templateTitleSubject[0] VR templateTitle[4] market size in templateXValue[max] will be worth worth templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The timeline presents data on the datacenter colocation revenue 2014 sales Revenue worldwide from 2014 to 2023 .  The source estimates that the Global VR 2014 market size in 2023 will be worth 54.0 billion U.S. dollars .

Example 696:
titleEntities: {'Subject': ['Czech Republic'], 'Date': ['2019']}
title: Unemployment rate in the Czech Republic 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']: ['2.47', '2.4', '2.89', '3.95', '5.05', '6.11', '6.95', '6.98', '6.71', '7.28', '6.66', '4.39', '5.32', '7.15', '7.93', '8.21', '7.54', '7.02', '7.99', '8.76', '8.49']

gold: This statistic shows the unemployment rate in the Czech Republic from 1999 to 2019 . In 2019 , the unemployment rate in the Czech Republic was at approximately 2.47 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[0] templateYValue[idxmax(X)] .

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 Czech Republic from 1999 to 2019 .  In 2019 , the Unemployment rate in Czech Republic was at approximately 2.47 % .

Example 697:
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] of templateYLabel[1] templateYLabel[2] in the templateTitle[5] in templateTitleDate[0] , templateTitle[6] templateTitle[7] of templateXValue[0] . In templateTitleDate[0] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateXValue[0] was templateYValue[max] templateScale .
generated: This statistic shows the Number of members thousands in the 2005 in 2018 , of Nov_2018 .  In 2018 , the Number of members thousands in the Nov_2018 was 291.0 thousands .

Example 698:
titleEntities: {'Subject': ['Malawi'], 'Date': ['2000']}
title: Malawi : 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', 'the', 'internet']: ['13.78', '11.47', '5.3', '5.83', '5.05', '4.35', '3.33', '2.26', '1.07', '0.7', '0.97', '0.43', '0.38', '0.35', '0.28', '0.22', '0.16', '0.13']

gold: This statistic gives information on the internet penetration in Malawi from 2000 to 2017 . In 2017 , 13.78 percent of the population accessed the internet , up from 0.13 percent in 2000 . In 2015 , Malawi 's population amounted to 18.11 million inhabitants .
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 population accessed the templateYLabel[3] , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] . In templateXValue[2] , templateTitleSubject[0] 's population amounted to 18.11 templateScale inhabitants .

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the most recently measured period , templateYValue[idxmax(X)] templateScale of the population accessed the templateYLabel[3] , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] . In templateXValue[2] , templateTitleSubject[0] 's population templatePositiveTrend by approximately 2.48 templateScale compared to the previous templateXLabel[0] .
generated: This statistic gives information on the internet penetration in Malawi from 2000 to 2017 .  In the most recently measured period , 13.78 percentage of the population accessed the internet , up from 0.13 percentage in 2000 .  In 2015 , Malawi 's population grew by approximately 2.48 percentage compared to the previous Year .

Example 699:
titleEntities: {'Subject': ['Africa'], 'Date': ['2000', '2018']}
title: Direct investment position of the U.S. in Africa 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['Direct', 'investments', 'in', 'billion', 'U.S.', 'dollars']: ['47.8', '50.29', '51.69', '52.0', '69.03', '60.88', '55.85', '57.0', '54.82', '43.94', '36.75', '32.61', '28.16', '22.76', '20.36', '19.84', '16.04', '15.57', '11.89']

gold: After a peak in 2014 , foreign direct investment ( FDI ) in Africa from the United States dropped to 47.80 billion U.S. dollars in 2018 . Africa receives lower FDI inflows than any other region . What is FDI ? FDI is when investors from one country , in this case the United States , invest in firms that are based abroad .
gold_template: After a peak in templateXValue[4] , foreign templateYLabel[0] templateTitle[1] ( FDI ) in templateTitleSubject[0] from the templateTitle[3] templateNegativeTrend to templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] . templateTitleSubject[0] receives lower FDI inflows than any other region . What is FDI ? FDI is when investors from one country , in this case the templateTitle[3] , invest in firms that are based abroad .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] templateTitle[5] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] templateTitle[5] was valued at approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] templateTitle[1] abroad is defined as ownership by a templateYLabel[3] investor of at least 10 templateScale of a foreign business .
generated: This statistic shows the Direct investment position of the U.S. in Africa 2000 from to 2018 , on a historical-cost basis .  In 2018 , the U.S. investments made in Africa 2000 was valued at approximately 47.8 billion U.S. dollars .  U.S. Direct investment abroad is defined as ownership by a U.S. investor of at least 10 billion of a foreign business .

Example 700:
titleEntities: {'Subject': ['United States'], 'Date': ['2017']}
title: Religious affiliation in the United States 2017 , by age
Data:
                                  0      1      2             3
Age group                     18-29  30-49  50-64  65_and_older
Unaffiliated                     38     26     18            12
White Evangelical Protestant      8     14     21            26
White Mainline Protestant         8     11     15            19
White Catholic                    8     11     15            16
Hispanic Catholic                 9     10      6             4
Black Protestant                  7      8      9             7
Other Christian                   7      6      6             5
Jewish                            1      1      2             2
Other religion                    1      1      1             1
Other world religions             4      3      1             1
Hispanic Protestant               6      5      9             7
Mormon                            2      2      2             2
Don't know/refused                3      3      3             3 

gold: The statistic shows the religious affiliation of the population in the United States in 2017 , by age . In 2017 , about 38 percent of respondents aged 18 to 29 years old were unaffiliated with any religious belief .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] of the population in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateLabel[0][0] . In templateTitleDate[0] , about templateValue[1][0] templateScale of respondents aged templateValue[1][2] to 29 years old were templateLabel[1][0] with any templateTitle[0] belief .

generated_template: This statistic shows the results of a survey completed in the templateTitleSubject[0] based on templateTitle[0] templateTitle[1] of their own templateTitle[2] . In 2013 , templateValue[1][last] templateScale of respondents stated that they consider templateValue[1][last] templateScale of respondents stated that they would be templateLabel[1][0] templateLabel[2][1] .
generated: This statistic shows the results of a survey completed in the United States based on Religious affiliation of their own United .  In 2013 , 12 % of respondents stated that they consider 12 % of respondents stated that they would be Unaffiliated Evangelical .

Example 701:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2017']}
title: Participants in roller skating 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']: ['11.58', '11.88', '12.67', '12.98', '12.73', '13.35', '14.5', '16.11', '16.42', '17.46', '19.74', '19.87']

gold: The number of U.S. citizens aged six and above that participated roller skating amounted to approximately 11.8 million in 2017 . This number has almost halved in size over the past 10 years . The two types of roller skating Roller skating is a recreational activity or sport where participants can choose to use either inline skates or 2x2 wheel skates .
gold_template: The templateYLabel[0] of templateTitleSubject[0] citizens aged six and above that participated templateTitle[1] templateTitle[2] amounted to approximately 11.8 templateScale in templateXValue[max] . This templateYLabel[0] has almost halved in size over the past 10 years . The two types of templateTitle[1] templateTitle[2] templateTitle[1] templateTitle[2] is a recreational activity or sport where templateYLabel[1] can choose to use either inline skates or 2x2 wheel skates .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged templateYValue[9] years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Number of participants in roller skating in the U.S. from 2006 to 2017 .  In 2017 , the Number of participants ( aged 17.46 years and older ) in roller skating amounted to approximately 11.58 millions .

Example 702:
titleEntities: {'Subject': ['MS'], 'Date': ['2015']}
title: MS prevalence rates worldwide by select country 2015
X_Axis['Country']: ['Canada', 'San_Marino', 'Denmark', 'Sweden', 'Hungary', 'Cyprus', 'United_Kingdom', 'Czech_Republic', 'Norway', 'Germany']
Y_Axis['Cases', 'per', '100,000', 'people']: ['291', '250', '227', '189', '176', '175', '164', '160', '160', '149']

gold: The countries with the highest prevalence of multiple sclerosis include Canada , San Marino , Denmark , and Sweden . Multiple sclerosis ( MS ) is a disease that affects communication in parts of the nervous system and damages the brain and spinal cord . Some of those with MS eventually become disabled due to the disease , but many can still walk , if not with the aid of a cane or crutches .
gold_template: The countries with the highest templateTitle[1] of multiple sclerosis include templateXValue[0] , templateXValue[1] , templateXValue[2] , and templateXValue[3] . Multiple sclerosis ( templateTitleSubject[0] ) is a disease that affects communication in parts of the nervous system and damages the brain and spinal cord . Some of those with templateTitleSubject[0] eventually become disabled due to the disease , but many can still walk , if not with the aid of a cane or crutches .

generated_template: This statistic shows the leading templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , the templateTitle[1] templateTitle[2] market in the templateXValue[0] generated templateYValue[1] templateScale templateYLabel[4] templateYLabel[5] in templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the leading prevalence rates worldwide by in 2015 .  In that year , the prevalence rates market in the Canada generated 250 million people in per 100,000 .

Example 703:
titleEntities: {'Subject': ['Spotify'], 'Date': ['2012', '2019']}
title: Spotify : cost of revenue 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Cost', 'of', 'goods', 'sold', 'in', 'million', 'euros']: ['5042.0', '3906.0', '3241.0', '2551.0', '1714.0', '911.0', '618.0', '386.5']

gold: Data on Spotify 's cost of goods sold from 2012 to 2019 , which is mostly comprised of the music streaming company 's licensing expenses , revealed that the company 's cost of sales amounted to 5.04 billion U.S. dollars in 2019 , more than five times the figure recorded five years earlier .
gold_template: Data on templateTitleSubject[0] 's templateYLabel[0] of templateYLabel[1] templateYLabel[2] from templateXValue[min] to templateXValue[max] , which is mostly comprised of the music streaming company 's licensing expenses , revealed that the company 's templateYLabel[0] of sales amounted to templateYValue[max] templateScale U.S. dollars in templateXValue[idxmax(Y)] , more than five times the figure recorded five years earlier .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , around templateYValue[max] thousand babies were born in templateTitleSubject[0] in templateXValue[idxmax(Y)] , an templatePositiveTrend from the previous templateXLabel[0] were templateYValue[1] thousand babies were born .
generated: This statistic displays the Cost of goods in Spotify from 2012 to 2019 .  According to the report , around 5042.0 thousand babies were born in Spotify 2019 , an increase from the previous Year were 3906.0 thousand babies were born .

Example 704:
titleEntities: {'Subject': ['Russia'], 'Date': ['2010', '2019']}
title: Ice hockey players in Russia 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', 'players']: ['112236', '110624', '105059', '102179', '99172', '84270', '66551', '64326', '63580']

gold: The statistics depicts the number of registered ice hockey players in Russia from 2010/11 to 2018/19 . In the 2018/19 season , there were a total of 112,236 registered ice hockey players in Russia according to the International Ice Hockey Federation .
gold_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[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] according to the International templateTitle[0] templateTitle[1] Federation .

generated_template: The statistics depicts the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[last] to templateXValue[1] . In the templateXValue[0] season , there were a total of templateYValue[max] 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 Russia from 2010/11 to 2017/18 .  In the 2018/19 season , there were a total of 112236 registered Ice hockey players in Russia according to the International Ice hockey Federation .

Example 705:
titleEntities: {'Subject': ['Canada'], 'Date': ['2014']}
title: Population to dentist ratio in Canada by province 2014
Data:
                               0             1              2         3                     4        5            6       7        8                 9
Month  Newfoundland_and_Labrador  Saskatchewan  New_Brunswick  Manitoba  Prince_Edward_Island  Alberta  Nova_Scotia  Quebec  Ontario  British_Columbia
1997                        3720          2870           3100      2120                  2780     1960         2170    1880     1760              1610
2005                        3150          2790           2610      2060                  2260     1820         1830    1870     1590              1470
2014                        2740          2680           2450      1910                  1890     1850         1730    1660     1510              1470 

gold: This statistic shows the population to dentist ratio in Canada in 1997 , 2005 , and 2014 , by province . In 2005 , there were some 1,590 people per every dentist in Ontario . Nine years later , this ratio decreased to 1,510 persons per every dentist .
gold_template: This statistic shows the templateTitle[0] to templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateLabel[1][0] , templateLabel[2][0] , and templateTitleDate[0] , templateTitle[4] templateTitle[5] . In templateLabel[2][0] , there were some templateValue[2][8] people per every templateTitle[1] in templateValue[0][8] . Nine years later , this templateTitle[2] templateNegativeTrend to templateValue[3][8] persons per every templateTitle[1] .

generated_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] templateTitle[10] . 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: This statistic shows the Canada by of Canada and dentist to province 2014 from to 2014 , .  Canada and dentist directly contributed approximately 2610 % jobs to the Saskatchewan Newfoundland_and_Labrador Asian economy in 2014 .

Example 706:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2012', '2018']}
title: Gaming on a mobile phone in the United Kingdom ( UK ) 2012 - 2018 , by age
Data:
                0           1           2           3           4           5           6
unknown  Aged_75+  Aged_65-74  Aged_55-64  Aged_45-54  Aged_35-44  Aged_25-34  Aged_16-24
2012            0           1           3           8          18          19          31
2013            0           2           8          17          19          32          40
2014            0           5           9          22          35          40          49
2015            1           5           9          27          28          47          44
2016            1           4           6          18          27          27          40
2017            3           5          12          17          26          31          41
2018            1           4          12          19          19          41          40 

gold: The statistic displays mobile gaming penetration in the United Kingdom from 2012 to 2018 , broken down by age . In 2012 , 31 percent of respondents aged 16 to 24 reported playing games via mobile phone , and this number increased to 49 percent in 2014 , followed by a decrease to the level of 40 percent in 2018 .
gold_template: The statistic displays templateTitle[1] templateTitle[0] penetration in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , broken down templateTitle[8] templateTitle[9] . In templateTitleDate[min] , templateValue[1][max] templateScale of respondents templateValue[0][0] 16 to 24 reported playing games via templateTitle[1] templateTitle[2] , and this number templatePositiveTrend to templateValue[2][idxmax(1)] templateScale in templateLabel[3][0] , followed templateTitle[8] a templateNegativeTrend to the level of templateValue[6][idxmax(2)] templateScale in templateTitleDate[max] .

generated_template: templateValue[0][0] cars were the most expensive automobiles sold in the templateTitleSubject[0] in templateTitleDate[max] . With an templateTitle[3] price tag of templateValue[2][max] euros , the templateTitle[1] maker ranked ahead of fellow German manufacturer templateValue[0][1] . The only templateTitle[1] templateTitle[8] which had seen its templateTitle[4] templateNegativeTrend since templateTitleDate[min] was Citroen .
generated: Aged_75+ cars were the most expensive automobiles sold in the United Kingdom in 2018 .  With an United price tag of 40 euros , the mobile maker ranked ahead of fellow German manufacturer Aged_65-74 .  The only mobile by which had seen its Kingdom dropped since 2012 was Citroen .

Example 707:
titleEntities: {'Subject': ['Number'], 'Date': ['2009', '2019']}
title: Number of worldwide internet users 2009 - 2019 , by region
Data:
                               0      1       2        3        4        5        6        7        8        9
Year                        2009   2010    2011     2012     2013     2015     2016     2017     2018     2019
Asia                       764.4  825.1  1016.8  1076.68  1265.14  1563.21  1792.16  1938.08  2062.14  2300.47
Europe                     425.8  475.1  500.72   518.51   566.26   604.12   614.98   659.63   704.83   727.56
North America              259.6  266.2  273.07   273.79   300.29   313.86   320.07   320.06   345.66   327.57
Latin America / Caribbean  186.9  204.7  235.82   254.92   302.01   333.12   384.75   404.27   438.25    453.7
Africa                      86.2  110.9  139.88   167.34   240.15   313.26   339.28   388.38   455.84   522.81
Middle East                 58.3  63.24   77.02     90.0   103.83   115.82   132.59   146.97   164.04    175.5
Oceania / Australia         21.1   21.3   23.93    24.29     24.8     27.1    27.54    28.18    28.44    28.64 

gold: This statistic shows the number of worldwide internet users from 2009 to 2019 , sorted by region . In the last measured period , the number of online users in Asia amounted to 2.3 billion , up from 1 billion in 2011 .
gold_template: This statistic shows the templateTitleSubject[0] of templateTitle[1] templateTitle[2] templateTitle[3] from templateValue[0][0] to templateValue[0][last] , sorted templateTitle[6] templateTitle[7] . In the last measured period , the templateTitleSubject[0] of online templateTitle[3] in templateLabel[1][0] amounted to 2.3 templateScale , up from 1 templateScale in templateValue[0][2] .

generated_template: The statistic represents the templateLabel[2][0] templateTitle[3] and templateTitle[5] templateTitle[6] templateLabel[1][2] templateTitle[7] by the templateTitle[1] templateTitle[2] in the templateTitle[0] from templateValue[0][0] to templateValue[0][last] . In templateValue[0][0] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] consumed more than templateValue[0][15] templateScale barrels of templateTitle[5] templateTitle[6] templateLabel[1][2] daily . templateTitle[3] and templateTitle[6] templateLabel[1][2] and diesel templateTitle[7] in the templateTitle[0] .
generated: The statistic represents the Europe users and 2019 by Asia region by the worldwide internet in the Number from 2009 to 2019 .  In 2009 , the Number worldwide internet consumed more than 2019 billion barrels of 2019 by Asia daily .  users and by Asia and diesel region in the Number .

Example 708:
titleEntities: {'Subject': ['Italy'], 'Date': ['2016', '2050']}
title: Italy : demographic trends 2016 - 2050 , by age
Data:
                       0        1        2        3        4        5        6        7        8
Year  75_years_and_older    65-74    55-64    45-54    35-44    25-34    18-24    14-17     0-13
2016             6889920  6540968  7773579  9807228  9106434  7105079  4275793  2334662  8004566
2050            12923835  8083179  7649903  7457328  7321092  6454427  4008739  2201461  7446443 

gold: This graph shows the forecasts of population increases in Italy from 2016 to 2050 , by age . According to the figure , the population will increase by just around two millions of individuals ( from 61.8 in 2016 to 63.54 in 2050 ) , while the number of individuals aged 65 and over will grow of about 7.5 million in total .
gold_template: This graph shows the forecasts of population increases in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[5] templateTitle[6] . According to the figure , the population will templatePositiveTrend templateTitle[5] just around templateValue[1][min] templateScale of individuals ( from 61.8 in templateTitleDate[min] to 63.54 in templateTitleDate[max] ) , while the number of individuals aged 65 and over will grow of about templateValue[2][3] templateScale in total .

generated_template: This graph shows the templateTitle[4] and templateTitle[5] templateTitle[3] at the templateTitle[0] templateTitleSubject[0] from 1988 to templateTitleDate[0] . In 1988 , templateValue[1][last] templateScale templateTitle[3] were templateTitle[4] and templateValue[1][4] templateScale templateTitle[3] were templateTitle[5] .
generated: This graph shows the 2050 and by 2016 at the Italy from 1988 to 2016 .  In 1988 , 8004566 million 2016 were 2050 and 9106434 million 2016 were by .

Example 709:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. usage reach of leading social networks 2017 , by age group
Data:
                  0        1          2         3          4       5         6       7        8        9     10                        11
Platform   Facebook  Google+  Instagram  LinkedIn  Pinterest  Reddit  Snapchat  Tumblr  Twitter  YouTube  Other  I_don’t_use_social_media
Age 18-29        86       26         58        19         37      15        45      21       47       71      1                         2
Age 30-59        81       23         31        21         28       5        16       7       39       52      1                         9
Age 60+          67        9          7        15         20       1         1       2       16       25      1                        27 

gold: This statistic presents the reach of selected social networks among internet users in the United States as of February 2017 , sorted by age group . As of the survey period , it was found that Facebook was used by 86 percent of online adults aged 18 to 29 years .
gold_template: This statistic presents the templateTitle[2] of selected templateValue[0][11] templateTitle[5] among internet users in the templateTitle[0] as of 2017 , sorted templateTitle[7] templateTitle[8] templateTitle[9] . As of the survey period , it was found that templateValue[0][0] was used templateTitle[7] templateValue[1][0] templateScale of online adults aged 18 to 29 years .

generated_template: This statistic presents a ranking of the templateTitle[0] templateTitle[1] mobile templateValue[0][1] templateTitle[3] in the templateTitleSubject[0] as of 2018 . During the survey period , templateValue[2][0] templateScale of respondents stated that they used their mobile to templateValue[0][3] or templateValue[0][3] one to templateValue[3][min] templateLabel[2][1] templateLabel[2][2] templateLabel[1][3] .
generated: This statistic presents a ranking of the U.S. usage mobile Google+ leading in the U.S. as of 2018 .  During the survey period , 81 % of respondents stated that they used their mobile to LinkedIn or one to 1 30-59 18-29 .

Example 710:
titleEntities: {'Subject': ['Poland'], 'Date': ['2024']}
title: Inflation rate in Poland 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']: ['2.85', '3.1', '3.3', '3.43', '3.5', '2.41', '1.6', '1.98', '-0.58', '-0.93', '-0.03', '0.9', '3.7', '4.27', '2.58', '3.45', '4.22', '2.49', '1.03', '2.13', '3.49', '0.84', '1.9', '5.5', '10.1', '7.3', '11.8', '14.9', '19.9', '27.88', '32.2', '35.3', '43', '70.3', '585.8', '251.1', '60.2', '25.23', '17.79', '15.11', '75.65']

gold: In 2018 , Poland had an inflation rate of 1.6 percent compared to the previous year , a rate that is expected to increase slightly to above two percent in the next five years . Inflation is the rate at which the average price level of selected goods and services in an economy increases over a period of time , and therefore indicates a loss in purchasing power of the local currency . Poland 's economy in the fast lane Poland 's low inflation rate encourages fiscal responsibility on the part of the consumers and coupled with a strong growth in gross domestic product ( GDP ) , the country 's economy is one of the fastest growing in the EU .
gold_template: In templateXValue[6] , templateTitleSubject[0] had an templateYLabel[0] templateYLabel[1] of templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] , a templateYLabel[1] that is expected to templatePositiveTrend slightly to above templateYValue[5] templateScale in the next five years . templateYLabel[0] is the templateYLabel[1] at which the average price level of selected goods and services in an economy increases over a period of time , and therefore indicates a loss in purchasing power of the local currency . templateTitleSubject[0] 's economy in the fast lane templateTitleSubject[0] 's low templateYLabel[0] templateYLabel[1] encourages fiscal responsibility on the part of the consumers and coupled with a strong growth in gross domestic product ( GDP ) , the country 's economy is templateYValue[11] of the fastest templatePositiveTrend in the EU .

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 Poland from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Poland amounted to about 1.6 % compared to the previous Year .

Example 711:
titleEntities: {'Subject': ['World'], 'Date': []}
title: World 's largest cities
Data:
             0      1          2       3            4                5         6         7      8        9
unknown  Tokyo  Delhi  São_Paulo  Mumbai  Mexico_City  New_York-Newark  Shanghai  Calcutta  Dhaka  Karachi
2010      36.7   22.2       20.3    20.0         19.5             19.4      16.6      15.6   14.6     13.1
2025      37.1   28.6       21.7    25.8         20.7             20.6      20.0      20.1   20.9     18.7 

gold: The statistic shows the ten largest cities worldwide in 2010 and a forecast for 2025 . In 2010 , Tokyo was the biggest city in the world with 36.7 million inhabitants .
gold_template: The statistic shows the ten templateTitle[2] templateTitle[3] worldwide in templateLabel[1][0] and a forecast for templateLabel[2][0] . In templateLabel[1][0] , templateValue[0][0] was the biggest templateValue[0][4] in the templateTitleSubject[0] with templateValue[1][max] templateScale inhabitants .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] of the templateTitle[2] templateValue[0][2] templateTitle[4] in templateTitleDate[min] and templateTitleDate[max] , templateTitle[7] templateTitle[8] . In templateTitleDate[max] , the templateTitleSubject[0] templateTitle[1] of the templateValue[0][0] , templateValue[0][0] and templateValue[0][0] templateTitle[8] of the templateTitle[2] templateValue[0][2] templateTitle[4] amounted to an estimated templateValue[2][max] templateScale U.S. dollars .
generated: This statistic shows the World 's of the largest São_Paulo cities in and , cities .  In , the World 's of the Tokyo , and Tokyo cities of the largest São_Paulo cities amounted to an estimated 37.1 billion U.S. dollars .

Example 712:
titleEntities: {'Subject': ['Top'], 'Date': ['2010']}
title: Top world silver consumers as a share of world consumption 2010
X_Axis['Country']: ['United_States', 'China', 'Japan', 'India', 'Germany', 'Italy', 'Thailand', 'South_Korea', 'UK_and_Ireland', 'Belgium']
Y_Axis['Share', 'of', 'consumption']: ['21.6', '14.5', '11.6', '10.7', '4.5', '4', '3.5', '3.4', '2.4', '2']

gold: This statistic shows the countries and regions that consumed the most silver in 2010 . The United States demanded 21.6 percent of the world 's total silver production . Consumption of silver Silver is one of the world 's most valued metals and included within the so called transition metals group .
gold_template: This statistic shows the countries and regions that consumed the most templateTitle[2] in templateTitleDate[0] . The templateXValue[0] demanded templateYValue[max] templateScale of the templateTitle[1] 's total templateTitle[2] production . templateYLabel[1] of templateTitle[2] is one of the templateTitle[1] 's most valued metals and included within the so called transition metals group .

generated_template: The templateXValue[0] was the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[3] worldwide in templateTitleDate[0] , templateTitle[5] templateTitle[6] templateTitle[7] . In templateTitleDate[0] , the U.S. templateTitle[1] of CEOs in templateXValue[1] amounted to approximately templateYValue[1] templateScale templateYLabel[1] templateYLabel[2] that year .
generated: The United_States was the Top world of Top consumers worldwide in 2010 , world consumption 2010 .  In 2010 , the U.S. world of CEOs in China amounted to approximately 14.5 % consumption that year .

Example 713:
titleEntities: {'Subject': ['Sub-Saharan Africa'], 'Date': ['2024']}
title: Inflation rate in Sub-Saharan Africa 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['6.64', '6.7', '6.83', '7.08', '8.04', '8.38', '8.45', '10.88', '10.85', '6.93', '6.39']

gold: This statistic shows the average inflation rate in Sub-Saharan Africa from 2014 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Sub-Saharan Africa amounted to about 8.45 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] and the templateTitleSubject[1] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] and the templateTitleSubject[1] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Sub-Saharan Africa and the Sub-Saharan Africa from 2014 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Sub-Saharan Africa and the Sub-Saharan Africa amounted to about 8.45 % compared to the previous Year .

Example 714:
titleEntities: {'Subject': ['Libya'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Libya 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']: ['39.97', '38.09', '36.42', '35.0', '34.08', '33.02', '40.95', '30.56', '18.54', '17.19', '24.26', '51.9', '79.76', '32.0', '68.97', '50.81', '73.92', '67.69', '54.96', '47.34', '33.0', '26.19', '20.47', '34.11', '38.27', '35.98', '29.96', '36.53', '35.68', '32.69', '28.8', '30.92', '34.36', '33.91', '30.64', '26.59', '25.07', '22.29', '23.99', '29.44', '29.93']

gold: The statistic shows gross domestic product ( GDP ) in Libya 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 Libya 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 715:
titleEntities: {'Subject': ['Lauder'], 'Date': ['2012', '2024']}
title: Estée Lauder 's share of the skin care products market worldwide 2012 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Market', 'share']: ['10.7', '10.5', '10.4', '10.2', '10.1', '9.1', '8.1', '7.8', '7.3', '6.6', '8.1', '8.6', '7.6']

gold: This statistic shows Estée Lauder 's share of the skin care products market worldwide from 2012 to 2024 . In 2017 , Estée Lauder 's share of the global skin care products market is estimated to be 7.8 percent . The company is a manufacturer and marketer of prestige skin care , makeup , fragrance and hair care products , with global net sales of over 11.2 billion U.S. dollars .
gold_template: This statistic shows templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[7] , templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[1] of the global templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] is estimated to be templateYValue[7] templateScale . The company is a manufacturer and marketer of prestige templateTitle[4] templateTitle[5] , makeup , fragrance and hair templateTitle[5] templateTitle[6] , with global net sales of over 11.2 templateScale U.S. dollars .

generated_template: This statistic shows templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateYLabel[0] templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[1] of the global templateTitle[4] templateTitle[5] templateYLabel[0] is estimated to be templateYValue[6] templateScale .
generated: This statistic shows Estée Lauder 's share of the skin care Market from 2012 to 2024 .  In 2018 , Estée Lauder 's share of the global skin care Market is estimated to be 8.1 % .

Example 716:
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: 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: This survey details a distribution of the UK number of 0_mins wait on for 21-30_mins in the UK ( ) in 2018 .  During the survey , 48 % of respondents said that they watched 21-30_mins 1-5_mins to 0_mins per week .

Example 717:
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 timeline shows templateTitle[1] and templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[2] templateTitle[1] and templateTitle[3] templateTitle[4] templateYLabel[0] amounted to about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . Wal-Mart stores dominated templateYLabel[0] of the leading templateTitle[3] retailers in 2016 , generating approximately 362.82 templateScale templateYLabel[2] templateYLabel[3] .
generated: This timeline shows store and sales U.S. 1992 Sales in the Book from 1992 to 2018 .  In 2018 , U.S. store and U.S. 1992 Sales amounted to about 17.17 billion U.S. dollars .  Wal-Mart stores dominated Sales of the leading U.S. retailers in 2016 , generating approximately 362.82 billion U.S. dollars .

Example 718:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2017']}
title: U.S. wholesale sales of beer and wine 2002 to 2017
X_Axis['Year']: ['17', '16', '15', '14', '13', '12', '11', '10', '09', '08', '07', '06', '05', '04', '03', '02']
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['166.31', '161.16', '152.57', '147.34', '145.86', '136.77', '129.43', '122.9', '120.76', '121.58', '115.57', '108.56', '103.91', '96.25', '91.23', '87.56']

gold: The timeline shows the beer , wine , and distilled alcoholic beverages sales of merchant wholesalers in the United States from 2002 to 2017 . In 2017 , the beer , wine , and distilled alcoholic beverages sales of U.S. merchant wholesalers amounted to about 166.31 billion U.S. dollars . Alcohol in the United States During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .
gold_template: The timeline shows the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of merchant wholesalers in the templateTitle[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of templateYLabel[2] merchant wholesalers amounted to about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . Alcohol in the templateTitle[0] During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .

generated_template: This timeline depicts total templateTitle[1] templateYLabel[0] in the templateTitle[0] from templateXValue[last] to templateXValue[0] . According to the report , total templateYLabel[2] templateTitle[1] templateYLabel[0] amounted to approximately 1.42 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[last] .
generated: This timeline depicts total wholesale Sales in the U.S. from 02 to 17 .  According to the report , total U.S. wholesale Sales amounted to approximately 1.42 billion U.S. dollars in 02 .

Example 719:
titleEntities: {'Subject': ['Number'], 'Date': ['2017', '2023']}
title: Number of e-mails per day worldwide 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['E-mails', 'sent', 'and', 'received', 'in', 'billions']: ['347.3', '333.2', '319.6', '306.4', '293.6', '281.1', '269.0']

gold: While roughly 281 billion emails were sent and received each day in 2018 , the figure is expected to increase to over 347 billion daily mails in 2022 . E-Mail usage worldwide Despite the growth and prominence of mobile messengers and chat apps , e-mail has remained a central part of daily digital life . In 2023 , the number of global e-mail users is set to grow to 4.4 billion users , up from 3.8 billion in 2018 .
gold_template: While roughly templateYValue[5] templateScale emails were templateYLabel[1] and templateYLabel[2] each templateTitle[3] in templateXValue[5] , the figure is expected to templatePositiveTrend to over templateYValue[max] templateScale daily mails in templateXValue[1] . E-Mail usage templateTitle[4] Despite the growth and prominence of mobile messengers and chat apps , e-mail has remained a central part of daily digital life . In templateXValue[idxmax(Y)] , the templateTitleSubject[0] of global e-mail users is set to grow to 4.4 templateScale users , up from 3.8 templateScale in templateXValue[5] .

generated_template: This statistic provides information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateTitleSubject[0] had close to templateYValue[5] templateScale templateYLabel[1] templateYLabel[2] . This figure is projected to grow to templateYValue[1] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[1] .
generated: This statistic provides information on the E-mails of sent received in the Number from 2017 to 2023 .  In 2018 , the Number had close to 281.1 billions sent received .  This figure is projected to grow 333.2 billions sent received in 2022 .

Example 720:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2006', '2018']}
title: Number of road deaths in the Netherlands 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['678', '613', '629', '621', '570', '570', '650', '661', '640', '720', '750', '791', '811']

gold: In 2018 , 678 people were killed on roads in the Netherlands . Between 2006 and 2018 , road traffic fatalities had seen a net decline of 16 percent , with the peak recorded at the beginning of the reporting period in 2006 . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the number of road deaths down to below 500 by 2020 .
gold_template: In templateXValue[max] , templateYValue[idxmax(X)] people were killed on roads in the templateTitleSubject[0] . Between templateXValue[min] and templateXValue[max] , templateTitle[1] traffic templateYLabel[1] had seen a net decline of 16 templateScale , with the peak recorded at the beginning of the reporting period in templateXValue[min] . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the templateYLabel[0] of templateTitle[1] templateTitle[2] down to below 500 by 2020 .

generated_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: In 2018 , 678 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 570 such incidences .  Netherlands was one of the safest countries in Europe for road users .

Example 721:
titleEntities: {'Subject': ['U.S. U.S.'], 'Date': ['2018']}
title: Leading U.S. accounting firms by U.S. revenue 2018
X_Axis['Firm', '(Headquarters,', 'Fiscal', 'Year', 'End)']: ['Deloitte_LLP(New_York_May)', 'PwC_U.S.(New_York_June)', 'Ernst_&_Young_LLP(New_York_June)', 'KPMG_LLP(New_York_September)', 'RSM_U.S.(Chicago_April)', 'Grant_Thornton_LLP(Chicago_December)', 'BDO_USA_LLP(Chicago_June_)', 'CliftonLarsonAllen_LLP_(December)', 'Crowe_Horwath_LLP/(Chicago_December)', 'CBIZ_&_Mayer_Hoffman_McCann_PC(Cleveland_December)', 'Moss_Adams_LLP(Seattle_December)', 'CohnReznick_LLP(New_York_January)', 'BKD_LLP(Springfield_Mo._May)', 'Baker_Tilly_Virchow_Krause_LLP(Chicago_May)', 'Marcum_LLP(New_York_December)']
Y_Axis['Revenue', 'in', 'the', 'U.S.', 'in', 'billion', 'U.S.', 'dollars']: ['19.9', '15.4', '14.0', '9.46', '2.14', '1.87', '1.47', '0.95', '0.92', '0.78', '0.69', '0.65', '0.59', '0.58', '0.55']

gold: This statistic shows the largest accounting ( CPA ) firms in the United States in 2018 , in terms of U.S. revenue . According to the rankings , Deloitte LLP was the largest accounting firm in the United States with U.S. revenue of 19.9 billion U.S. dollars . Leading accounting firms - additional information Deloitte was the leading CPA firm in the United States in 2018 with a revenue of 19.9 billion U.S. dollars in the fiscal year ending May , 2018 .
gold_template: This statistic shows the largest templateTitle[2] ( CPA ) templateTitle[3] in the templateTitle[1] in templateTitleDate[0] , in terms of templateYLabel[1] templateYLabel[0] . According to the rankings , templateXValue[0] templateXValue[7] was the largest templateTitle[2] templateXLabel[0] in the templateTitle[1] with templateYLabel[1] templateYLabel[0] of templateYValue[max] templateScale templateYLabel[1] templateYLabel[4] . templateTitle[0] templateTitle[2] templateTitle[3] - additional information templateXValue[0] was the templateTitle[0] CPA templateXLabel[0] in the templateTitle[1] in templateTitleDate[0] with a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[1] templateYLabel[4] in the templateXLabel[2] templateXLabel[3] ending , templateTitleDate[0] .

generated_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[0] was the third largest templateTitle[2] templateXLabel[0] templateXLabel[1] in the world , with a total of templateYValue[5] templateScale .
generated: This statistic shows the U.S. of the U.S. accounting firms by in 2018 , revenue Firm (Headquarters, .  In that year , the Deloitte_LLP(New_York_May) was the third largest accounting Firm (Headquarters, in the world , with a total of 1.87 billion .

Example 722:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2016', '2019']}
title: Number of SMEs in the Netherlands 2016 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016']
Y_Axis['Number', 'of', 'SMEs']: ['442529', '441925', '439081', '439367']

gold: According to the Dutch business registry , the Netherlands gained around 600 new small and medium enterprises between early 2018 and early 2019 . The source defined SME ( or mkb , a Dutch abbreviation of midden- en kleinbedrijf ) as a company having between two to 249 employees , as well as companies with one employee that are not classified as a freelancer/self-employed person . SMEs were the Netherlands ' second largest type of business enterprise .
gold_template: According to the Dutch business registry , the templateTitleSubject[0] templatePositiveTrend around 600 new small and medium enterprises between early templateXValue[1] and early templateXValue[max] . The source defined SME ( or mkb , a Dutch abbreviation of midden- en kleinbedrijf ) as a company having between two to 249 employees , as well as companies with one employee that are not classified as a freelancer/self-employed person . templateYLabel[1] were the templateTitleSubject[0] ' second largest type of business enterprise .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] in templateXValue[min] , templateXValue[1] , templateXValue[1] and templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[idxmax(X)] thousand templateYLabel[1] living in templateTitleSubject[0] .
generated: This statistic shows the Number of SMEs in Netherlands 2016 , 2018 and 2019 .  In 2019 , there were approximately 442529 thousand SMEs living in Netherlands .

Example 723:
titleEntities: {'Subject': ['Rakuten Group', 'GMS'], 'Date': ['2014', '2019']}
title: Rakuten Group : quarterly domestic e-commerce GMS 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", "Q1_'14"]
Y_Axis['Gross', 'merchandise', 'sales', 'in', 'billion', 'Japanese', 'yen']: ['1053.1', '1012.8', '921.9', '871.7', '974.4', '855.3', '805.2', '770.0', '868.0', '771.3', '735.8', '704.0', '778.4', '748.9', '705.4', '682.8', '741.8', '685.3', '640.8', '611.9', '671.0', '607.7', '548.6', '607.0']

gold: In the fourth quarter of 2019 , the domestic gross transaction value of the Rakuten Group 's e-commerce sectors amounted to approximately 1.05 trillion Japanese yen , up 8.1 percent compared to the fourth quarter of the previous year . The company is headquartered in Tokyo , where it was founded in 1997 .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[max] , the templateTitle[3] templateYLabel[0] transaction value of the templateTitleSubject[0] 's templateTitle[4] sectors amounted to approximately templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] , up 8.1 templateScale compared to the fourth templateXLabel[0] of the previous year . The company is headquartered in Tokyo , where it was founded in 1997 .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[max] , it is projected that more than templateYValue[8] templateScale templateTitle[2] templateTitle[3] would be shipped in the templateTitle[4] alone . templateTitle[2] television templateYLabel[1] have remained relatively stable over the measured period , with total shipment figures peaking around the fourth templateXLabel[0] or holiday season each year . From a worldwide perspective , hundreds of templateScale of individual templateTitle[3] are sold each year .
generated: In the fourth Quarter of 2019 , it is projected that more than 868.0 billion quarterly domestic would be shipped in the e-commerce alone .  quarterly television merchandise have remained relatively stable over the measured period , with total shipment figures peaking around the fourth Quarter or holiday season each year .  From a worldwide perspective , hundreds of billion individual domestic are sold each year .

Example 724:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009', '2019']}
title: Average price for a full set of gels in nail salons in the U.S. 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Average', 'price', 'in', 'U.S.', 'dollars']: ['51.29', '48.53', '29.41', '49.22', '48.91', '49.99', '49.44', '49.93', '48.66', '46.69']

gold: This statistic shows the average price for a full set of gels in nail salons in the United States from 2009 to 2019 . Nail salons in the U.S. charged an average price of 51.29 U.S. dollars for the initial application of a full set of gel nails in 2019 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] a templateTitle[3] templateTitle[4] of templateTitle[5] in templateTitle[6] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . templateTitle[6] templateTitle[7] in the templateYLabel[2] charged an templateYLabel[0] templateYLabel[1] of templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateTitle[2] the initial application of a templateTitle[3] templateTitle[4] of gel nails in templateXValue[idxmax(Y)] .

generated_template: The templateYLabel[0] templateTitle[1] of the templateTitle[2] market in templateTitleSubject[0] amounted to around templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[max] financial templateXLabel[0] . This is an templatePositiveTrend of around 0.55 templateScale templateYLabel[2] templateYLabel[3] since templateXValue[min] . templateYLabel[2] templateTitle[2] laws Canada has complex templateTitle[2] laws which have developed since Prohibition in the 1920s .
generated: The Average price of the for market in U.S. amounted to around 51.29 billion U.S. dollars in the 2019 financial Year .  This is an increase of around 0.55 billion U.S. dollars since 2009 .  U.S. for laws Canada has complex for laws which have developed since Prohibition in the 1920s .

Example 725:
titleEntities: {'Subject': ['Guyana'], 'Date': ['2019']}
title: Unemployment rate in Guyana 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']: ['12.22', '12.15', '12.12', '12.34', '12.55', '12.37', '12.28', '11.93', '11.74', '11.66', '11.4', '10.47', '10.48', '10.7', '11.09', '11.58', '11.76', '11.81', '11.76', '11.86', '12.06']

gold: This statistic shows the unemployment rate in Guyana from 1999 to 2019 . In 2019 , the unemployment rate in Guyana was 12.22 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 Guyana from 1999 to 2019 .  In 2019 , the Unemployment rate in Guyana was at approximately 12.22 % .

Example 726:
titleEntities: {'Subject': ['Google'], 'Date': ['2017', '2019']}
title: YouTube : share of Google revenues 2017 to 2019
X_Axis['Year']: ['2019', '2018', '2017']
Y_Axis['Share']: ['9.4', '8.1', '7.3']

gold: In 2019 , YouTube 's advertising revenue accounted for approximately 9.4 percent of Google 's total revenue . That year , the video platform 's annual ad revenues amounted to 15.15 billion U.S. dollars , up from 11.16 billion U.S. dollars in the previous year .
gold_template: In templateXValue[max] , templateTitle[0] 's advertising revenue accounted for approximately templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's total revenue . That templateXLabel[0] , the video platform 's annual ad templateTitle[3] amounted to 15.15 templateScale U.S. dollars , up from 11.16 templateScale U.S. dollars in the previous templateXLabel[0] .

generated_template: This statistic presents a forecast for the templateTitle[1] of templateTitle[3] templateYLabel[1] templateYLabel[2] worldwide from templateXValue[min] to templateXValue[max] . In the most recently measured templateXLabel[0] , consumers downloaded templateYValue[max] templateScale templateTitle[3] apps to their connected devices , up from templateYValue[min] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[idxmin(Y)] .
generated: This statistic presents a forecast for the share of revenues Share worldwide from 2017 to 2019 .  In the most recently measured Year , consumers downloaded 9.4 million revenues apps to their connected devices , up from 7.3 million Share in 2017 .

Example 727:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Most used garage door openers in the U.S. 2018
X_Axis['Brand']: ['Liftmaster', 'Chamberlain', 'Overhead_Door', 'Genie', 'Craftsmen', 'Wayne-Dalton', 'Raynor', 'Linear', 'Access_Master', 'Marantec', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['50', '14.9', '9.6', '7.9', '5.3', '3.5', '3.5', '0.9', '0.9', '0.9', '2.6']

gold: This statistic depicts garage door openers brands used the most by U.S. construction firms in 2018 . The survey revealed that 50 percent of the respondents used LiftMaster garage door openers brand the most .
gold_template: This statistic depicts templateTitle[2] templateXValue[2] templateTitle[4] brands templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateTitle[2] templateXValue[2] templateTitle[4] templateXLabel[0] the templateTitle[0] .

generated_template: This statistic depicts templateTitle[2] templateTitle[3] templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateTitle[2] the templateTitle[0] . Residential construction involves the building and selling of both individual and multi-family dwellings .
generated: This statistic depicts garage door used the Most by U.S. construction firms in 2018 .  The survey revealed that 50 % of the respondents used Liftmaster Brand garage the Most .  Residential construction involves the building and selling of both individual and multi-family dwellings .

Example 728:
titleEntities: {'Subject': ['Australia'], 'Date': ['2012', '2022']}
title: Smartphone penetration as share of population in Australia 2012 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Smartphone', 'penetration', 'rate']: ['74.44', '73.72', '72.78', '71.59', '70.1', '68.26', '66.06', '63.52', '61.2', '53.2', '43.7']

gold: This statistic shows the smartphone penetration as share of the population in Australia from 2012 to 2016 with forecasts until 2022 . In 2016 , 66.06 percent of the Australian population used a smartphone and the number is estimated to reach around 74.44 percent by 2022 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] as templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] with forecasts until templateXValue[max] . In templateXValue[6] , templateYValue[6] templateScale of the Australian templateTitle[3] used a templateYLabel[0] and the number is estimated to reach around templateYValue[idxmax(X)] templateScale by templateXValue[idxmax(Y)] .

generated_template: This statistic shows the number of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . For templateXValue[4] , the number of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] is estimated to reach templateYValue[4] templateScale . templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] – additional information Smartphones are mobile phones that have more advanced computing capabilities and connectivity than regular mobile phones .
generated: This statistic shows the number of Smartphone penetration in Australia from 2012 to 2022 .  For 2018 , the number of Smartphone penetration in Australia is estimated to reach 70.1 % .  Smartphone penetration in Australia – additional information Smartphones are mobile phones that have more advanced computing capabilities and connectivity than regular mobile phones .

Example 729:
titleEntities: {'Subject': ['Italy'], 'Date': ['2019']}
title: Perceptions of social classes in Italy 2019
X_Axis['Response']: ['Upper_class', 'Middle_class', 'Lower_class']
Y_Axis['Share', 'of', 'respondents']: ['10', '52', '37']

gold: In 2019 , most of Italians assumed to belong to the middle class . More specifically , 52 percent of individuals defined its social class as middle class . Moreover , 37 percent of Italians stated to be part of the lower social class .
gold_template: In templateTitleDate[0] , most of Italians assumed to belong to the templateXValue[1] templateXValue[0] . More specifically , templateYValue[max] templateScale of individuals defined its templateTitle[1] templateXValue[0] as templateXValue[1] templateXValue[0] . Moreover , templateYValue[last] templateScale of Italians stated to be part of the templateXValue[last] templateTitle[1] templateXValue[0] .

generated_template: The statistic illustrates the answers to the following survey question : `` The templateTitle[2] templateXValue[0] templateTitle[5] will probably cost a thousand euros . templateXValue[last] you willing to pay that ? '' As of templateTitleDate[0] , roughly 20 templateScale of the templateYLabel[1] said to templateXValue[0] the templateXValue[0] from templateTitleSubject[0] when it is released , even if it templateTitle[6] them a thousand euros . However , more than half of the templateYLabel[1] said the price is templateXValue[1] absurd for an templateTitleSubject[0] templateXValue[0] .
generated: The statistic illustrates the answers to the following survey question : `` The classes Upper_class 2019 will probably cost a thousand euros .  Lower_class you willing to pay that ? '' As of 2019 , roughly 20 % of the respondents said to Upper_class the from Italy when it is released , even if it 2019 them a thousand euros .  However , more than half of the respondents said the price is Middle_class absurd for an Italy Upper_class .

Example 730:
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 templateTitleSubject[0] restaurants in templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . At the end of the templateXValue[max] fiscal templateXLabel[0] , there were templateYValue[idxmax(X)] templateTitleSubject[0] restaurants in templateTitleSubject[1] .
generated: This statistic shows the Number of Papa John restaurants in Papa John from 2007 to 2018 .  At the end of the 2018 fiscal Year , there were 5303 Papa John restaurants in Papa John .

Example 731:
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: The statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] in templateTitleSubject[0] and templateTitle[4] from templateValue[0][0] to templateValue[0][last] . In templateValue[0][last] , the templateTitle[2] of the templateTitle[1] in templateTitleSubject[0] amounted to templateValue[1][last] templateScale people .
generated: The statistic shows the Driving of the theory test in United Kingdom and rates from 2018/19 to 2007/08 .  In 2007/08 , the test of the theory in United Kingdom amounted to 62.3 million people .

Example 732:
titleEntities: {'Subject': ['Advance Publications'], 'Date': ['2006', '2014']}
title: Advance Publications ' revenue 2006 to 2014
X_Axis['Year']: ['2006', '2007', '2008', '2009', '2011', '2012', '2013', '2014']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['7.14', '7.97', '7.36', '7.16', '6.55', '6.78', '6.56', '8.0']

gold: The timeline shows estimated data on the revenue of the American media corporation Advance Publications , Inc. from 2006 to 2014 . Advance Publications is a privately-owned , unlisted company and owns such brands as Reddit - a social news and entertainment website and Conde Nast - a magazine publisher . Its 2006 revenue is estimated to have amounted to 7.14 billion US dollars .
gold_template: The timeline shows estimated data on the templateYLabel[0] of the American media corporation templateTitleSubject[0] , Inc. from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] is a privately-owned , unlisted company and owns such brands as Reddit - a social news and entertainment website and Conde Nast - a magazine publisher . Its templateXValue[min] templateYLabel[0] is estimated to have amounted to templateYValue[idxmin(X)] templateYValue[idxmin(X)] US templateYLabel[3] .

generated_template: The timeline presents data on the templateTitle[4] templateTitle[5] generated by Twenty First templateTitle[1] templateTitleSubject[0] in the fiscal years templateXValue[min] to templateXValue[max] . In the fiscal templateXLabel[0] templateXValue[max] , the company generated templateYValue[idxmax(X)] templateScale US templateYLabel[3] , up templateTitle[6] templateYValue[1] a templateXLabel[0] earlier .21st templateTitle[1] templateTitleSubject[0] consists of News Corporations templateTitle[3] former TV and film divisions . It split templateTitle[6] the News Corp. in 2013 .
generated: The timeline presents data on the 2006 2014 generated by Twenty First Publications Advance Publications in the fiscal years 2006 to 2014 .  In the fiscal Year 2014 , the company generated 8.0 billion US dollars , up 2014 7.97 a Year earlier .21st Publications Advance Publications consists of News Corporations revenue former TV and film divisions .  It split 2014 the News Corp. in 2013 .

Example 733:
titleEntities: {'Subject': ['Median'], 'Date': ['2018']}
title: U.S . Median age of the population 2018 , by state
X_Axis['State']: ['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', 'North_Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Puerto_Rico', 'Rhode_Island', 'South_Carolina', 'South_Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virginia', 'Washington', 'West_Virginia', 'Wisconsin', 'Wyoming']
Y_Axis['Age', 'in', 'years']: ['39.3', '34.9', '38.0', '38.1', '36.7', '36.9', '41.1', '41.1', '33.9', '42.2', '36.8', '39.3', '36.8', '38.3', '37.8', '38.1', '37.1', '39.1', '37.3', '45.1', '38.8', '39.5', '39.8', '38.2', '37.7', '38.8', '40.1', '36.7', '38.2', '43.1', '40.0', '38.1', '39.0', '38.9', '35.4', '39.5', '36.7', '39.6', '40.8', '42.9', '40.0', '39.7', '37.2', '38.9', '34.9', '31.0', '43.1', '38.3', '37.7', '42.8', '39.6', '38.5']

gold: This statistic shows the median age of the population of the USA in 2018 , by state of residence . The state with the highest median age of its population was Maine at 45.1 years . Utah had the lowest median age ( 31 years ) .
gold_template: This statistic shows the templateTitleSubject[0] templateYLabel[0] of the templateTitle[3] of the USA in templateTitleDate[0] , templateTitle[5] templateXLabel[0] of residence . The templateXLabel[0] with the highest templateTitleSubject[0] templateYLabel[0] of its templateTitle[3] was templateXValue[19] at templateYValue[max] templateYLabel[1] . templateXValue[45] had the lowest templateTitleSubject[0] templateYLabel[0] ( templateYValue[min] templateYLabel[1] ) .

generated_template: This graph shows the templateScale of templateYLabel[1] led templateTitle[6] a female householder with no spouse present with own children under 18 years living in the household in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[20] templateScale of Californian templateYLabel[1] were templateTitle[1] templateTitle[2] templateYLabel[1] with at least one child . Additional information on templateTitle[1] templateTitle[2] templateYLabel[1] and poverty in the templateXValue[18] For most templateTitle[1] mothers a constant battle persists between finding the time and energy to raise their children and the demands of working to supply an income to house and feed their families .
generated: This graph shows the percentage of years led state a female householder with no spouse present with own children under 18 years living in the household in the Median in 2018 , state .  In 2018 , about 38.8 % of Californian years were Median age years with at least one child .  Additional information on Median age years and poverty in the Louisiana For most Median mothers a constant battle persists between finding the time and energy to raise their children and the demands of working to supply an income to house and feed their families .

Example 734:
titleEntities: {'Subject': ['Nationwide Building Society', 'UK'], 'Date': ['2011', '2019']}
title: Nationwide Building Society : saving market share of the UK 's savings market 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Market', 'share']: ['10.1', '10', '10.1', '10.2', '10.2', '10.6', '10.5', '11', '11.1']

gold: The UK based Nationwide Building Society is the largest building society in the world with approximately 15.9 million members , of which over half were active members in 2019 . As well as being a building society , Nationwide is one of the United Kingdom 's leading and most widespread financial institutes , specializing in household savings and mortgages . Market share of Nationwide products As of April 2019 , Nationwide 's market share of UK 's savings accounts market increased by 0.1 percent from the previous year .
gold_template: The templateTitleSubject[1] based templateTitleSubject[0] Society is the largest templateTitleSubject[0] in the world with approximately 15.9 templateScale members , of which over half were active members in templateXValue[max] . As well as being a templateTitleSubject[0] , templateTitleSubject[0] is one of the United Kingdom templateTitle[7] leading and most widespread financial institutes , specializing in household templateTitle[8] and mortgages . templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] products As of 2019 , templateTitleSubject[0] templateTitle[7] templateYLabel[0] templateYLabel[1] of templateTitleSubject[1] templateTitle[7] templateTitle[8] accounts templateYLabel[0] templatePositiveTrend by 0.1 templateScale from the previous templateXLabel[0] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] scores of templateTitleSubject[1] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . templateTitleSubject[1] had an templateYLabel[0] templateYLabel[1] of templateYValue[idxmax(X)] in templateXValue[max] , down from templateYValue[1] points the previous templateXLabel[0] .
generated: This statistic shows the Nationwide Building Society Building saving scores of UK in the share from 2011 to 2019 .  UK had an Market share of 10.1 in 2019 , down from 10 points the previous Year .

Example 735:
titleEntities: {'Subject': ['Boeing'], 'Date': ['2019']}
title: Prices of Boeing aircraft in 2019
X_Axis['Aircraft', 'type']: ['777-9', '747-8_Freighter', '747-8', '777-8', '777-300ER', '777_Freighter', '777-200LR', '787-10', '777-200ER', '787-9', '787-8', '767-300_Freighter', '767-300ER', '737_MAX_10', '737_MAX_9', '737_MAX_200', '737_MAX_8', '737-900ER', '737-800', '737_MAX_7', '737-700']
Y_Axis['Price', 'in', 'million', 'U.S.', 'dollars']: ['442.2', '419.2', '418.4', '410.2', '375.5', '352.3', '346.9', '338.4', '306.6', '292.5', '248.3', '220.3', '217.9', '134.9', '128.9', '124.8', '121.6', '112.6', '106.1', '99.7', '89.1']

gold: The statistic shows the average prices for Boeing aircraft as of January 2019 , by type . The 737 - 800 model had a list price of 106.1 million U.S. dollars . The Boeing 777 - 9 is the company 's most expensive model Along with Airbus , Embraer , and Bombardier Aerospace , Boeing is one of the world 's four largest aircraft manufacturers .
gold_template: The statistic shows the average templateTitle[0] for templateTitleSubject[0] templateXLabel[0] as of 2019 , by templateXLabel[1] . The templateXValue[13] - 800 model had a list templateYLabel[0] of templateYValue[18] templateScale templateYLabel[2] templateYLabel[3] . The templateTitleSubject[0] templateXValue[5] - templateXValue[14] is the company 's most expensive model Along with Airbus , Embraer , and Bombardier Aerospace , templateTitleSubject[0] is one of the world 's four largest templateXLabel[0] manufacturers .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] networking templateTitle[4] in the templateTitle[5] as of 2019 , ranked templateXValue[2] monthly templateYLabel[3] templateTitle[8] . During that templateYLabel[5] , mobile templateXValue[0] users templateYLabel[1] an average of templateYValue[max] templateYLabel[0] on the templateTitle[2] networking templateXLabel[0] .
generated: This statistic gives information on the Prices Boeing aircraft networking 2019 in the 2019 as of 2019 , ranked 747-8 monthly dollars 2019 .  During that dollars , mobile 777-9 users million an average of 442.2 Price on the aircraft networking .

Example 736:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Child protective services caseload per worker , by state U.S. 2018
X_Axis['State']: ['Missouri', 'Iowa', 'Utah', 'Indiana', 'New_Hampshire', 'New_Mexico', 'Oregon', 'Massachusetts', 'Rhode_Island', 'Arizona', 'Nebraska', 'Illinois', 'West_Virginia', 'Louisiana', 'Kansas', 'Idaho', 'Washington', 'Nevada', 'Arkansas', 'National_average', 'Maine', 'Michigan', 'Tennessee', 'Minnesota', 'Hawaii', 'Vermont', 'Virginia', 'Alaska', 'Oklahoma', 'North_Carolina', 'Alabama', 'District_of_Columbia', 'Texas', 'Montana', 'Connecticut', 'Kentucky', 'South_Dakota', 'New_Jersey', 'Delaware', 'Mississippi', 'Puerto_Rico']
Y_Axis['Number', 'of', 'cases', 'per', 'worker']: ['176', '167', '166', '144', '138', '128', '124', '120', '114', '113', '108', '101', '90', '88', '88', '87', '80', '75', '74', '72', '71', '69', '67', '63', '62', '61', '59', '59', '58', '57', '57', '56', '53', '53', '50', '49', '48', '45', '45', '36', '22']

gold: This statistic shows the number of completed reports per investigation and alternative response worker in child protective services in the U.S. in 2018 , by state . In 20178 investigation and alternative response workers in Vermont had an average of 61 cases .
gold_template: This statistic shows the templateYLabel[0] of completed reports templateYLabel[2] investigation and alternative response templateYLabel[3] in templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In 20178 investigation and alternative response workers in templateXValue[25] had an templateXValue[19] of templateYValue[25] templateYLabel[1] .

generated_template: In templateTitleDate[0] , templateXValue[0] was the European templateXLabel[0] with the highest templateYLabel[0] of skiing participants , with templateYValue[max] templateScale Germans taking part in the sport . When considering the total population , the share of templateTitle[2] templateTitle[3] templateTitle[4] in European countries was the highest in templateXValue[last] and templateXValue[6] as of 2019 , with 36 templateScale of the population in either templateXLabel[0] , which comes to a much higher templateYLabel[0] in templateXValue[6] due to population size . The UK : A tradition of snowsport Established in 1903 and with over 28,000 members on the books , The templateTitle[4] Club is the largest and oldest snowsports membership organisation in the UK .
generated: In 2018 , Missouri was the European State with the highest Number of skiing participants , with 176 million Germans taking part in the sport .  When considering the total population , the share of services caseload per in European countries was the highest in Puerto_Rico and Oregon as of 2019 , with 36 % of the population in either State , which comes to a much higher Number in Oregon due to population size .  The UK : A tradition of snowsport Established in 1903 and with over 28,000 members on the books , The per Club is the largest and oldest snowsports membership organisation in the UK .

Example 737:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Share of freelancers by generation U.S. 2019
X_Axis['Response']: ['Gen_Z_(18-22_years_old)', 'Millennial_(23-38_years_old)', 'Gen_X_(39-54_years_old)', 'Baby_Boomers_(55+_years_old)']
Y_Axis['Share', 'of', 'respondents']: ['53', '40', '31', '29']

gold: This statistic shows the distribution of freelancers in the United States in 2019 , by generation . During the survey , 53 percent of Generation Z respondents , or those between the ages of 18 and 22 , reported participating in freelance work .
gold_template: This statistic shows the distribution of templateTitle[1] in the templateTitle[4] in templateTitleDate[0] , templateTitle[2] templateTitle[3] . During the survey , templateYValue[max] templateScale of templateTitle[3] templateXValue[0] templateYLabel[1] , or those between the ages of 18 and 22 , reported participating in freelance work .

generated_template: This survey shows the results of a survey in templateTitleSubject[0] on the templateTitle[0] templateXValue[0] templateTitle[2] templateTitle[3] templateTitle[4] templateXValue[0] ( templateTitle[6] ) in templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , templateYValue[min] templateScale of templateYLabel[1] in templateTitleSubject[0] thought couples with a double income prefer enjoying templateXValue[last] as a templateXValue[last] .
generated: This survey shows the results of a survey in U.S. on the Share Gen_Z_(18-22_years_old) by generation U.S. Gen_Z_(18-22_years_old) ( 2019 ) in U.S. 2019 .  In 2019 , 29 % of respondents in U.S. thought couples with a double income prefer enjoying Baby_Boomers_(55+_years_old) as a Baby_Boomers_(55+_years_old) .

Example 738:
titleEntities: {'Subject': ['Google', 'Microsoft Advertising'], 'Date': ['2019']}
title: Google vs. Microsoft Advertising : U.S. mobile click share 2019
Data:
                              0       1      2
unknown                Combined  Tablet  Phone
Google                       70       5     65
Microsoft Advertising        29       7     22 

gold: This statistic shows the mobile search click share of Google and Microsoft Advertising in the United States as of the fourth quarter of 2019 , sorted by device . During the survey period , it was found that 29 percent of paid Microsoft Advertising ( formerly known as Bing ) search clicks originated from mobile devices , with seven percent of total clicks being generated via tablet devices .
gold_template: This statistic shows the templateTitle[5] search templateTitle[6] templateTitle[7] of templateTitleSubject[0] and templateTitleSubject[1] in the templateTitle[4] as of the fourth quarter of templateTitleDate[0] , sorted by device . During the survey period , it was found that templateValue[2][0] templateScale of paid templateTitleSubject[1] ( formerly known as Bing ) search clicks originated from templateTitle[5] devices , with templateValue[2][1] templateScale of total clicks being generated via templateValue[0][1] devices .

generated_template: This statistic deals with templateValue[0][0] consumption templateTitle[4] among different templateTitle[6] groups in the country in templateTitleDate[0] . The results were derived from a survey conducted templateTitle[5] Gallup via a telephone interview . American adults were polled as to whether they mostly templateValue[0][2] templateValue[0][0] , templateValue[0][1] templateValue[0][0] or templateValue[0][2] drink templateValue[0][0] at all .
generated: This statistic deals with Combined consumption U.S. among different click groups in the country in 2019 .  The results were derived from a survey conducted mobile Gallup via a telephone interview .  American adults were polled as to whether they mostly Phone Combined , Tablet Combined or Phone drink Combined at all .

Example 739:
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] 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: This statistic shows the Producer of refugees admitted to the Price Index from the fiscal Year of 1990 to the fiscal Year of 2019 .  During the fiscal Year of 2019 , 199.8 refugees were admitted to the Index .

Example 740:
titleEntities: {'Subject': ['YouTube'], 'Date': []}
title: All-time most viewed YouTube channel owners 2020
X_Axis['Month']: ["Ryan's_World", 'PewDiePie', 'Like_Nastya_Vlog', '✿_Kids_Diana_Show', 'DanTDM_(TheDiamondMinecart)', 'Fun_Toys_Collector_Disney', 'Vlad_and_Nikita', 'FGTeeV', 'Family_Fun_Pack', 'CookieSwirlC', 'Markiplier']
Y_Axis['All-time', 'channel', 'views', 'in', 'billions']: ['35.18', '24.44', '22.68', '17.01', '16.01', '14.86', '14.07', '13.11', '12.66', '12.42', '12.29']

gold: As of January 2020 , Ryan from Ryan 's World ( formerly known as Ryan ToysReview ) had reached almost 35.2 billion lifetime video views , making the elementary schooler the most viewed YouTube channel owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name PewDiePie . Ryan has been uploading YouTube videos since March 2015 , and mainly features in videos where he is playing with and reviews toys `` for kids , by a kid '' .
gold_template: As of 2020 , Ryan from Ryan 's templateXValue[0] ( formerly known as Ryan ToysReview ) had reached almost templateYValue[max] templateScale lifetime video templateYLabel[2] , making the elementary schooler the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateYLabel[1] owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name templateXValue[1] . Ryan has been uploading templateTitleSubject[0] videos since 2015 , and mainly features in videos where he is playing with and reviews templateXValue[5] `` for templateXValue[3] , by a kid '' .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] between to 2014 to October to 2018 , in templateYValue[min] months intervals . The templateTitle[1] templateTitle[2] generally templateNegativeTrend templateYLabel[4] the period concerned templateTitle[3] templateYValue[max] templateScale in templateXValue[last] to templateYValue[min] templateScale in templateXValue[0] .
generated: This statistic shows the most viewed in YouTube between to 2014 October to 2018 , in 12.29 months intervals .  The most viewed generally dropped billions the period concerned YouTube 35.18 billions in Markiplier to 12.29 billions in Ryan's_World .

Example 741:
titleEntities: {'Subject': ['Firehouse Subs U.S.'], 'Date': ['2014', '2018']}
title: Sales of Firehouse Subs in the U.S. 2014 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['794.0', '715.9', '683.54', '648.53', '552.41']

gold: Fast casual restaurant chain Firehouse Subs generated sales of 794 million U.S. dollars from its U.S. establishments in 2018 . This figure has risen by over 240 million in the past five years . Leading U.S. sandwich chains In terms of systemwide sales , Firehouse Subs placed fifth in a ranking of leading U.S. sandwich restaurant chains , including Subway , Arby 's and Jimmy John 's , in 2018 .
gold_template: Fast casual restaurant chain templateTitleSubject[0] generated templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] from its templateYLabel[2] establishments in templateXValue[idxmax(Y)] . This figure has risen by over 240 templateScale in the past five years . Leading templateYLabel[2] sandwich chains In terms of systemwide templateYLabel[0] , templateTitleSubject[0] placed fifth in a ranking of leading templateYLabel[2] sandwich restaurant chains , including Subway , Arby 's and Jimmy John 's , in templateXValue[max] .

generated_template: This statistic depicts the templateTitle[0] ' templateTitle[1] industry templateTitle[2] and templateTitle[3] ( R & D ) templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] on templateTitle[1] R & D in the templateTitle[0] came to some templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: This statistic depicts the Sales ' Firehouse industry Subs and U.S. ( R & D ) Sales from 2014 to 2018 .  In 2018 , the Sales on Firehouse R & D in the Sales came to some 794.0 U.S. dollars .

Example 742:
titleEntities: {'Subject': ['Minnesota'], 'Date': ['2000', '2018']}
title: Minnesota - 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']: ['331.38', '324.26', '317.62', '311.34', '308.2', '300.63', '294.3', '290.63', '284.86', '275.45', '285.79', '283.21', '281.97', '282.09', '274.71', '263.87', '252.41', '246.06', '245.98']

gold: This statistic shows the development of Minnesota 's real GDP from 2000 to 2018 . In 2018 , the real GDP of Minnesota was around 331.38 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[0] templateYLabel[1] of templateTitleSubject[0] was around templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_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[0] templateYLabel[1] of templateTitleSubject[0] was 2.67 templateScale templateYLabel[3] templateYLabel[4] . The annual templateYLabel[0] templateYLabel[1] growth of the templateYLabel[3] can be accessed here .
generated: This statistic shows the development of Minnesota 's Real GDP from 2000 to 2018 .  In 2018 , the Real GDP of Minnesota was 2.67 billion U.S. dollars .  The annual Real GDP growth of the U.S. can be accessed here .

Example 743:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2015']}
title: Hot beverage consumption by type in the United Kingdom ( UK ) 2015
X_Axis['Response']: ['Tea', 'Instant_coffee', 'Ground_coffee', 'Hot_chocolate', 'Green_tea', 'Premium_instant_coffee', 'Fruit/herbal_tea', 'Coffee_from_pods', 'Coffee_from_a_coffee_shop', 'Vending_machine_coffee']
Y_Axis['Share', 'of', 'respondents']: ['44', '21', '7', '7', '6', '5', '4', '4', '2', '0']

gold: This statistic displays the share of hot beverages consumed most often by individuals in the United Kingdom ( UK ) in 2015 . Of respondents , 44 percent drink tea most often , followed by 21 percent who drink instant coffee . Hot drinks have always played an important role in British culture , particularly in the case of tea , as seen in the high and rising market value of hot drinks .
gold_template: This statistic displays the templateYLabel[0] of templateXValue[3] beverages consumed most often templateTitle[3] individuals in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . Of templateYLabel[1] , templateYValue[max] templateScale drink templateXValue[0] most often , followed templateTitle[3] templateYValue[1] templateScale who drink templateXValue[1] . templateXValue[3] drinks have always played an important role in British culture , particularly in the case of templateXValue[0] , as seen in the high and templatePositiveTrend market value of templateXValue[3] drinks .

generated_template: This statistic shows the templateTitle[0] templateTitle[2] of templateTitle[3] teenagers in the templateTitle[4] for online templateTitle[1] as of spring templateTitleDate[0] . According to the survey , templateYValue[max] templateScale of upper-income templateTitleSubject[0] teenagers reported that Amazon.com was their templateTitle[0] website from which to purchase things .
generated: This statistic shows the Hot consumption of by teenagers in the type for online beverage as of spring 2015 .  According to the survey , 44 % of upper-income United Kingdom teenagers reported that Amazon.com was their Hot website from which to purchase things .

Example 744:
titleEntities: {'Subject': ['Dallas Mavericks', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Dallas Mavericks ( 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']: ['2400', '2250', '1900', '1450', '1400', '1150', '765', '685', '497', '438', '446', '466', '461', '463', '403', '374', '338', '304']

gold: This graph depicts the franchise value of the Dallas Mavericks of the National Basketball Association from 2003 to 2020 . In 2020 , the Dallas Mavericks franchise had an estimated value of 2.4 billion U.S. dollars .
gold_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_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 Dallas Mavericks franchise of the National Basketball Association from 2003 to 2020 .  In 2020 , the Franchise had an estimated value of 2400 million U.S. dollars .

Example 745:
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: The statistic shows the templateYLabel[2] templateTitle[1] templateTitle[0] templateTitle[2] 's total templateYLabel[0] between templateXValue[min] and templateXValue[max] . While the templateTitle[2] 's templateYLabel[0] templatePositiveTrend to around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] , about 1.59 templateScale vehicles were purchased in templateXValue[max] by templateTitle[1] companies in the templateTitle[3] .
generated: The statistic shows the U.S. spend Brand eSports 's total Spending between 2015 and 2020 .  While the eSports 's Spending rose to around 1220 million U.S. dollars in 2020 , about 1.59 million vehicles were purchased in 2020 by spend companies in the sponsorship .

Example 746:
titleEntities: {'Subject': ['Change'], 'Date': ['2018', '2019']}
title: Change of domestic heating oil price in selected countries 2018 to 2019
X_Axis['Country']: ['Canada', 'Italy', 'Spain', 'France', 'United_Kingdom', 'Japan', 'Germany', 'United_States']
Y_Axis['Change', 'in', 'heating', 'oil', 'price']: ['7', '5.9', '5.9', '4', '0.8', '-0.1', '-4.4', '0']

gold: This statistic shows the change in the price of domestic heating oil per liter in selected countries for the period between December 2018 and December 2019 . In December 2019 , the domestic heating oil price in Germany was some -4.4 percent lower than in December 2018 . Domestic heating oil The price of domestic heating oil per liter has decreased in many countries .
gold_template: This statistic shows the templateYLabel[0] in the templateYLabel[3] of templateTitle[1] templateYLabel[1] templateYLabel[2] per liter in templateTitle[5] templateTitle[6] for the period between 2018 and 2019 . In 2019 , the templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[6] was some templateYValue[min] templateScale lower than in 2018 . templateTitle[1] templateYLabel[1] templateYLabel[2] The templateYLabel[3] of templateTitle[1] templateYLabel[1] templateYLabel[2] per liter has templateNegativeTrend in many templateTitle[6] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] having already practiced templateTitle[3] on the beach or in a nudist camp worldwide in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . Thus , we note that in templateXValue[3] templateYLabel[1] were less than 10 templateScale to have already been entirely naked on the beach or in a naturist camp .
generated: This statistic shows the Change of heating having already practiced oil on the beach or in a nudist camp worldwide in 2018 , countries Country .  Thus , we note that in France heating were less than 10 % to have already been entirely naked on the beach or in a naturist camp .

Example 747:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2019']}
title: United Kingdom tobacco imports : Leading 5 countries ranked by value 2019
X_Axis['Country']: ['Poland', 'Germany', 'Netherlands', 'Romania', 'Belgium']
Y_Axis['Import', 'value', 'in', '1,000', 'GBP']: ['203.88', '107.07', '94.13', '21.27', '18.06']

gold: The biggest country of origin for tobacco imported into the United Kingdom was Poland . In 2019 , the country supplied tobacco worth over two hundred million British pounds to the UK . Germany and the Netherlands rank next , with imports from these countries amounting to 107 and 94 million pounds respectively .
gold_template: The biggest templateXLabel[0] of origin for templateTitle[2] imported into the templateTitleSubject[0] was templateXValue[0] . In templateTitleDate[0] , the templateXLabel[0] supplied templateTitle[2] worth over two hundred templateScale British pounds to the UK . templateXValue[1] and the templateXValue[2] rank next , with templateTitle[3] from these templateTitle[6] amounting to templateYValue[1] and templateYValue[2] templateScale pounds respectively .

generated_template: This statistic shows the templateScale of templateYLabel[1] in the templateTitle[3] diagnosed templateTitle[1] templateTitle[2] A , sorted templateTitle[5] templateXLabel[0] templateXLabel[1] , as of templateTitleDate[0] . In that year , templateYValue[min] templateScale of all Americans diagnosed templateTitle[1] templateTitle[2] A were between 0 and 4 templateXValue[0] of templateXLabel[0] .
generated: This statistic shows the percentage of value in the imports diagnosed Kingdom tobacco A , sorted 5 Country , as of 2019 .  In that year , 18.06 % of all Americans diagnosed Kingdom tobacco A were between 0 and 4 Poland of Country .

Example 748:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Top U.S. states based on number of farms 2018
X_Axis['State']: ['Texas', 'Missouri', 'Iowa', 'Ohio', 'Oklahoma', 'Kentucky', 'Illinois', 'California', 'Minnesota', 'Wisconsin']
Y_Axis['Number', 'of', 'farms']: ['248000', '95000', '86000', '77800', '77300', '75100', '72000', '69400', '68500', '64800']

gold: Texas was by far the leading U.S. state in terms of total number of farms , with about 250 thousand farms by the end of 2018 . Missouri was ranked second , among the leading ten states , with 95 thousand farms as of 2018 . Farms classification In the United States farms are classified based on the farm income and government payments into six sales classes .
gold_template: templateXValue[0] was by far the leading templateTitleSubject[0] templateXLabel[0] in terms of total templateYLabel[0] of templateYLabel[1] , with about 250 thousand templateYLabel[1] by the end of templateTitleDate[0] . templateXValue[1] was ranked second , among the leading ten templateTitle[2] , with templateYValue[1] thousand templateYLabel[1] as of templateTitleDate[0] . templateYLabel[1] classification In the templateTitle[1] templateYLabel[1] are classified templateTitle[3] on the farm income and government payments into six sales classes .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the largest templateYLabel[0] of templateTitle[3] templateTitle[4] templateYLabel[2] templateTitle[6] in the templateTitle[7] in templateTitleDate[0] . According to the source , Connecticut was the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[2] templateTitle[6] in templateTitleDate[0] templateTitle[1] templateYValue[max] templateYLabel[1] to every templateYLabel[3] thousand templateYLabel[5] .
generated: This statistic shows the Top U.S. the largest Number of based number farms 2018 in the 2018 in .  According to the source , Connecticut was the State U.S. the states based number farms 2018 in U.S. 248000 farms to every farms thousand .

Example 749:
titleEntities: {'Subject': ['Yelp'], 'Date': ['2007', '2019']}
title: Yelp 's annual net revenue 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['1014.19', '942.77', '850.85', '716.0', '549.71', '377.5', '233.0', '137.6', '83.29', '47.73', '25.81', '12.14', '3.75']

gold: The timeline shows Yelp 's net revenue since 2007 . In 2019 , the local review and search site 's revenue amounted to over 1.01 billion U.S. dollars , an increase from 952.8 million U.S. dollars compared the previous year .
gold_template: The timeline shows templateTitleSubject[0] templateTitle[1] templateTitle[3] templateYLabel[0] since templateXValue[min] . In templateXValue[max] , the local review and search site templateTitle[1] templateYLabel[0] amounted to over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , an templatePositiveTrend from 952.8 templateScale templateYLabel[2] templateYLabel[3] compared the previous templateXLabel[0] .

generated_template: The statistic depicts the net sales of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's net sales was at about templateYValue[0] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] is a U.S.-based company that manufactures and sells boats and marine engines . The company previously made and sold sporting and fitness equipment and bowling & billiards equipment .
generated: The statistic depicts the net sales of the Yelp worldwide from 2007 to 2019 .  In 2019 , Yelp 's net sales was at about 1014.19 million U.S. dollars.The Yelp is a U.S.-based company that manufactures and sells boats and marine engines .  The company previously made and sold sporting and fitness equipment and bowling & billiards equipment .

Example 750:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2015']}
title: Number of foundations in the U.S. 1990 to 2015
X_Axis['Year']: ['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['Number', 'of', 'foundations']: ['86203', '86726', '87142', '86192', '81777', '76610', '76545', '75595', '75187', '72477', '71095', '67736', '66398', '64843', '61810', '56582', '50201', '46832', '44146', '41588', '40140', '38807', '37571', '35765', '33356', '32401']

gold: This statistic shows the number of foundations in the United States from 1990 to 2015 . In 2015 , there were a total of 86,203 foundations registered in the United States .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[idxmax(X)] templateYLabel[1] registered in the templateTitle[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[1] templateTitle[5] templateTitle[2] templateTitle[3] in templateTitleSubject[0] and templateTitleSubject[1] templateTitle[1] templateXValue[min] to templateXValue[max] . Since templateXValue[21] there has been a templateNegativeTrend in the templateYLabel[0] of templateYLabel[1] caused templateTitle[4] templateTitle[5] . In templateXValue[1] , templateYValue[1] templateYLabel[1] were recorded .
generated: This statistic shows the Number of foundations 2015 U.S. 1990 in U.S. and foundations 1990 to 2015 .  Since 1994 there has been a decrease in the Number of foundations caused 2015 .  In 2014 , 86726 foundations were recorded .

Example 751:
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 752:
titleEntities: {'Subject': ['Disneyland Paris'], 'Date': ['2016']}
title: Disneyland Paris visitors spending per day 2006 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Average', 'spend', 'in', 'euros', '(excluding', 'VAT)']: ['54.0', '53.7', '50.7', '48.1', '46.4', '46.2', '45.3']

gold: This statistic displays daily expenditure per person at Disneyland Paris theme parks in France between 2006 and 2016 . Visitors spending includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal year 2016 , the average spending dipped to 54 euros ( before VAT ) .
gold_template: This statistic displays daily expenditure templateTitle[4] person at templateTitleSubject[0] theme parks in France between templateTitleDate[min] and templateXValue[max] . templateTitle[2] templateTitle[3] includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal templateXLabel[0] templateXValue[max] , the templateYLabel[0] templateTitle[3] dipped to templateYValue[max] templateYLabel[2] ( before VAT ) .

generated_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: The statistic shows the Paris Average spend for Disneyland Paris in the country from 2010 to 2016 .  The Paris Average rate for Disneyland Paris amounted to about 45.3 % in 2010 .

Example 753:
titleEntities: {'Subject': ['Average'], 'Date': ['2011']}
title: Average age for models to start in the business 2011
X_Axis['Age', 'group']: ['12_years_or_younger', '13_to_16_years', '17_to_20_years', '21_years_or_older']
Y_Axis['Share', 'of', 'respondents']: ['1.3', '54.7', '37.3', '6.7']

gold: This statistic shows the results of a survey among working female fashion models based in the United States on how old they were when they first started working in the fashion industry . 54.7 percent of respondents stated they were between 13 and 16 years old when they started working as a model .
gold_template: This statistic shows the results of a survey among working female fashion templateTitle[3] based in the country on how old they were when they first started working in the fashion industry . templateYValue[max] templateScale of templateYLabel[1] stated they were between templateXValue[1] and templateXValue[1] templateXValue[0] old when they started working as a model .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] in the templateTitle[1] templateTitle[3] were templateTitleSubject[0] users as of 2015 , sorted templateTitle[7] templateTitle[8] and templateTitle[9] group . During that period of time , templateYValue[max] templateScale of female templateTitleSubject[0] teens aged 15 to 17 years used the social networking app .
generated: This statistic shows the Share of for in the age models were Average users as of 2015 , sorted 2011 and group .  During that period of time , 54.7 % of female Average teens aged 15 to 17 years used the social networking app .

Example 754:
titleEntities: {'Subject': ['Myanmar'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Myanmar 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['6.45', '6.34', '6.13', '6.01', '6.26', '6.2', '6.83', '6.33', '5.15', '7.47', '8.2']

gold: The statistic shows the growth in real GDP in Myanmar , also known as Burma , from 2014 to 2018 , with projections up until 2024 . In 2018 , Myanmar 's real gross domestic product grew by around 6.83 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] , also known as Burma , 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 between 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 Myanmar from between 2014 to 2018 , with projections up until 2024 .  In 2018 , Myanmar 's real Gross domestic product increased by around 6.83 % compared to the previous Year .

Example 755:
titleEntities: {'Subject': ['Number'], 'Date': ['2018']}
title: Number of cruise passengers by source market worldwide 2018
X_Axis['Country']: ['North_America', 'Western_Europe', 'Asia', 'Australasia/New_Zealand/Pacific', 'South_America', 'Scandinavia/Iceland', 'Eastern_Europe', 'Africa', 'Middle_East/Arabia', 'Caribbean', 'Central_America']
Y_Axis['Number', 'of', 'passengers', 'in', 'millions']: ['14.24', '6.73', '4.24', '1.46', '0.88', '0.23', '0.21', '0.15', '0.11', '0.06', '0.05']

gold: The statistic shows the number of cruise passengers worldwide in 2018 , by source country . According to the source , 14.24 million cruise passengers came from North America in 2018 .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateTitle[6] in templateTitleDate[0] , templateTitle[3] templateTitle[4] templateXLabel[0] . According to the templateTitle[4] , templateYValue[max] templateScale templateTitle[1] templateYLabel[1] came from templateXValue[0] in templateTitleDate[0] .

generated_template: This statistic shows the estimated templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[4] templateTitle[5] in templateTitleDate[0] . In that year , The templateXValue[0] ' templateTitle[1] templateTitle[2] templateTitle[3] was projected to have generated templateYValue[max] templateScale euros.Luxury templateTitle[2] industryGenerally speaking , garments , accessories , jewellery , watches , fragrances and cosmetics are considered to be constituent parts of the templateTitle[1] market . The U.S. templateTitle[1] templateTitle[2] market has continued to soar to post-crisis heights in 2014 , with its fifth year of growth .
generated: This statistic shows the estimated Number of the cruise passengers by in source market in 2018 .  In that year , The North_America ' cruise passengers by was projected to have generated 14.24 millions euros.Luxury passengers industryGenerally speaking , garments accessories , jewellery watches , fragrances and cosmetics are considered to be constituent parts of the cruise market .  The U.S. cruise passengers market has continued to soar post-crisis heights in 2014 , with its fifth year of growth .

Example 756:
titleEntities: {'Subject': ['India'], 'Date': []}
title: Share of people in India who prefer arranged marriage over freely chosen marriage
X_Axis['Response']: ['India_total', 'South_states', 'North_states']
Y_Axis['Share', 'of', 'respondents']: ['74', '77', '96']

gold: This survey illustrates the results of a survey among Indian people regarding their opinion on arranged marriage , as of August 2012 . In today 's India , 74 percent of the respondents approve of arranged marriage and prefer it to freely chosen marriage .
gold_template: This survey illustrates the results of a survey among Indian templateTitle[1] regarding their opinion on templateTitle[5] templateTitle[6] , as of 2012 . In today 's templateXValue[0] , templateYValue[min] templateScale of the templateYLabel[1] approve of templateTitle[5] templateTitle[6] and templateTitle[4] it to templateTitle[8] templateTitle[9] templateTitle[6] .

generated_template: The statistic illustrates the answers to the following survey question : `` The templateTitle[2] templateXValue[0] templateTitle[5] will probably cost a thousand euros . templateXValue[last] you willing to pay that ? '' As of templateTitleDate[0] , roughly 20 templateScale of the templateYLabel[1] said to templateXValue[0] the templateXValue[0] from templateTitleSubject[0] when it is released , even if it templateTitle[6] them a thousand euros . However , more than half of the templateYLabel[1] said the price is templateXValue[1] absurd for an templateTitleSubject[0] templateXValue[0] .
generated: The statistic illustrates the answers to the following survey question : `` The India India_total arranged will probably cost a thousand euros .  North_states you willing to pay that ? '' As of , roughly 20 % of the respondents said to India_total the from India when it is released , even if it marriage them a thousand euros .  However , more than half of the respondents said the price is South_states absurd for an India India_total .

Example 757:
titleEntities: {'Subject': ['China'], 'Date': ['2008', '2018']}
title: Economic sector breakdown of the workforce in China 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    202.58  209.44  214.96  219.19   227.9  241.71  257.73  265.94  279.31   288.9  299.23
Manufacturing   213.9  218.24   223.5  226.93  230.99   231.7  232.41  225.44  218.42   210.8  205.53
Services       359.38  348.72  337.57  328.39  313.64  296.36   276.9  272.82  263.32  258.57  250.87 

gold: The statistic shows the distribution of the workforce across economic sectors in China from 2008 to 2018 . In 2018 , around 359.4 million people were employed in the service sector .
gold_template: The statistic shows the distribution of the templateTitle[3] across templateTitle[0] sectors in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , around templateValue[3][0] templateScale people were employed in the service templateTitle[1] .

generated_template: The statistic shows the templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the 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] . The same templateLabel[0][0] , the total UK population amounted to about 62.3 templateScale people .
generated: The statistic shows the Economic of the sector breakdown workforce China in the China from 2008 to 2018 .  In 2018 , 202.58 % of the sector were employed in Agriculture , 213.9 % in Manufacturing and 359.38 % in Services .  The same Year , the total UK population amounted to about 62.3 million people .

Example 758:
titleEntities: {'Subject': ['Venezuela'], 'Date': ['2014', '2018']}
title: Venezuela : gender gap index 2014 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014']
Y_Axis['Index', 'score']: ['0.71', '0.71', '0.69', '0.69', '0.69']

gold: The graph presents the gender gap index in Venezuela from 2014 to 2018 . In 2018 , Venezuela scored 0.71 , which shows a gender gap of approximately 29 percent ( women are 29 percent less likely than men to have equal opportunities ) . That same year , the gender gap in the area of political empowerment in Venezuela amounted to 83 percent .
gold_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] scored templateYValue[idxmax(X)] , which shows a templateTitle[1] templateTitle[2] of approximately 29 templateScale ( women are 29 templateScale less likely than men to have equal opportunities ) . That same templateXLabel[0] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 83 templateScale .

generated_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] scored templateYValue[idxmax(X)] points , which shows a templateTitle[1] templateTitle[2] of approximately 28 templateScale ( women are 28 templateScale less likely than men to have equal opportunities ) . In templateXValue[idxmax(Y)] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 69 templateScale .
generated: The graph presents the gender gap Index in Venezuela from 2014 to 2018 .  In 2018 , Venezuela scored 0.71 points , which shows a gender gap of approximately 28 % ( women are 28 % less likely than men to have equal opportunities ) .  In 2018 , the gender gap in the area of political empowerment in Venezuela amounted to 69 % .

Example 759:
titleEntities: {'Subject': ['Nintendo'], 'Date': ['2008', '2019']}
title: Nintendo : net sales 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Net', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['10914.0', '9959.0', '4366.0', '4464.0', '4581.0', '5550.0', '6759.82', '7898.2', '12221.03', '15423.29', '18761.45', '16724.23']

gold: This statistic shows the worldwide net revenue made by video games company Nintendo from fiscal 2008 to 2019 . In 2019 , Nintendo 's net sales amounted to 10.91 billion U.S. dollars , up from 9.96 billion a year earlier .
gold_template: This statistic shows the worldwide templateYLabel[0] revenue made by video games company templateTitleSubject[0] from fiscal templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateScale a templateXLabel[0] earlier .

generated_template: This timeline shows the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitleSubject[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . templateTitle[0] templateTitleSubject[0] is a chain of discount variety stores that operates in 44 templateYLabel[3] states .
generated: This timeline shows the Net sales of Nintendo in the 2019 from 2008 to 2019 .  In 2019 , the company 's Net sales amounted to approximately 10914.0 million U.S. dollars .  Nintendo Nintendo is a chain of discount variety stores that operates in 44 U.S. states .

Example 760:
titleEntities: {'Subject': ['Australia'], 'Date': ['2000', '2010']}
title: Liver cirrhosis death rate in Australia 2000 and 2010
Data:
           0     1
Year    2010  2000
Male     6.8   7.6
Female   2.8   2.7 

gold: Liver cirrhosis , also known as hepatic cirrhosis , was the cause of death of 6.8 per 100,000 males in Australia in 2010 . Although the death rate from this condition had decreased for the male population over the preceding ten years , the rate was still almost 2.5 times greater in men than in women across the country . While there are many known causes of this disease , it typically develops as a result of chronic liver inflammation .
gold_template: templateTitle[0] templateTitle[1] , also known as hepatic templateTitle[1] , was the cause of templateTitle[2] of templateValue[1][0] per 100,000 males in templateTitleSubject[0] in templateValue[0][0] . Although the templateTitle[2] templateTitle[3] from this condition had templateNegativeTrend for the templateLabel[1][0] population over the preceding ten years , the templateTitle[3] was still almost 2.5 times greater in men than in women across the country . While there are many known causes of this disease , it typically develops as a result of chronic templateTitle[0] inflammation .

generated_template: This statistic displays the templateLabel[2][0] penetration of the templateTitle[0] and templateTitle[1] templateTitle[3] sector in templateTitleSubject[0] , showing the templateTitle[4] of templateLabel[2][0] and templateLabel[1][0] templateTitle[3] templateTitle[4] in templateValue[0][0] and with a forecast for templateValue[0][last] . In templateValue[0][0] , templateLabel[2][0] templateTitle[4] made up templateValue[2][0] templateScale of templateTitle[0] and templateTitle[1] templateTitle[3] templateTitle[4] . This is predicted to templatePositiveTrend to templateValue[2][last] templateScale templateTitle[8] templateValue[0][last] .
generated: This statistic displays the Female penetration of the Liver and cirrhosis rate sector in Australia , showing the Australia of Female and Male rate Australia in 2010 and with a forecast for 2000 .  In 2010 , Female Australia made up 2.8 % of Liver and cirrhosis rate Australia .  This is predicted to increase 2.7 % 2010 2000 .

Example 761:
titleEntities: {'Subject': ['Poland'], 'Date': ['2015']}
title: Median age of the population in Poland 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']: ['51.2', '50.8', '49.7', '48.0', '46.0', '43.7', '41.7', '39.7', '38.1', '36.6', '35.0', '33.6', '32.2', '30.7', '29.5', '28.6', '28.1', '27.4', '26.5', '26.0', '25.8']

gold: This statistic shows the median age of the population in Poland 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: The templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] templatePositiveTrend to templateYValue[7] templateYLabel[2] in templateXValue[7] , meaning that half of the templateTitle[2] was older than that , half younger . This figure was lowest in between templateXValue[15] and templateXValue[10] but is projected to rise to templateYValue[max] templateYLabel[2] by templateXValue[idxmax(Y)] . The meaning of templateYLabel[1] structure templateTitleSubject[0] has one of the largest populations worldwide , and this statistic presents the templateYLabel[0] templateYLabel[1] of that group .
generated: The Median age in Poland rose to 39.7 years in 2015 , meaning that half of the population was older than that , half younger .  This figure was lowest in between 1975 and 2000 but is projected to rise 51.2 years by 2050 .  The meaning of age structure Poland has one of the largest populations worldwide , and this statistic presents the Median age of that group .

Example 762:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Percentage of population volunteering in the U.S. in 2015 , by age
X_Axis['Year']: ['16_to_24_years', '25_to_34_years', '35_to_44_years', '45_to_54_years', '55_to_64_years', '65_years_and_over']
Y_Axis['Percentage', 'of', 'population', 'volunteering']: ['21.8', '22.3', '28.9', '28', '25.1', '23.5']

gold: This statistic displays the percentage of population volunteering in the U.S. in 2015 , by age . In 2015 , 21.8 percent of Americans 16 to 24 years old volunteered at least once during the year .
gold_template: This statistic displays the templateScale of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In templateTitleDate[0] , templateYValue[min] templateScale of Americans templateXValue[0] to templateXValue[0] old volunteered at least once during the templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] hotels templateTitle[4] from templateXValue[last] to templateXValue[0] . According to the report , there were templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] in templateTitleDate[0] .
generated: This statistic shows the Percentage of U.S. hotels 2015 from 65_years_and_over to 16_to_24_years .  According to the report , there were 21.8 percentage population volunteering in U.S. 2015 .

Example 763:
titleEntities: {'Subject': ['YouTube'], 'Date': ['2014']}
title: YouTube : most viewed movie trailers 2014
X_Axis['Movie']: ['Fifty_Shades_Of_Grey_-_Official_Trailer_(Universal_Pictures)_HD', 'Marvel\'s_"Avengers:_Age_of_Ultron"_-_Teaser_Trailer_(OFFICIAL)', 'Star_Wars:_Episode_VII_-_The_Force_Awakens_Official_Teaser_Trailer_#1_(2015)_-_J.J._Abrams_Movie_HD', 'Jurassic_World_-_Official_Trailer_(HD)', 'TEENAGE_MUTANT_NINJA_TURTLES_-_Official_Trailer_(2014)_[HD]', 'Furious_7_-_Official_Trailer_(HD)', 'Godzilla_-_Official_Main_Trailer_[HD]', 'Lucy_TRAILER_1_(2014)_-_Luc_Besson_Scarlett_Johansson_Movie_HD', 'The_Fault_In_Our_Stars_Official_Trailer_[HD]_20th_Century_FOX', 'Official_Call_of_Duty:_Advanced_Warfare_Reveal_Trailer']
Y_Axis['Number', 'of', 'YouTube', 'views', 'in', 'millions']: ['67', '64', '52', '44', '43', '37', '36', '36', '29', '26']

gold: The graph shows data on most viewed trailers on YouTube in 2014 . The trailer for the upcoming movie Fifty Shades Of Grey was ranked first with 67 million views . The only non-movie trailer in this ranking was the Call of Duty : Advanced Warfare Reveal trailer with 26 million views .
gold_template: The graph shows data on templateTitle[1] templateTitle[2] templateTitle[4] on templateYLabel[1] in templateTitleDate[0] . The templateXValue[0] for the upcoming templateXValue[2] templateXValue[0] Of templateXValue[0] was ranked first with templateYValue[max] templateScale templateYLabel[2] . The only non-movie templateXValue[0] in this ranking was the templateXValue[last] of Duty : templateXValue[last] Reveal templateXValue[0] with templateYValue[min] templateScale templateYLabel[2] .

generated_template: This statistic provides information on the most templateTitle[3] templateTitle[4] templateTitle[5] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] . According to the source , templateXValue[0] was the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[2] , with a templateTitle[6] of approximately templateYValue[max] templateScale .
generated: This statistic provides information on the most movie trailers 2014 in the United Kingdom ( YouTube ) in 2014 .  According to the source , Fifty_Shades_Of_Grey_-_Official_Trailer_(Universal_Pictures)_HD was the Movie most the viewed movie trailers 2014 in the viewed , with a 2014 of approximately 67 millions .

Example 764:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1985', '2018']}
title: Paid circulation of daily newspapers in the U.S. 1985 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90", "'89", "'88", "'87", "'86", "'85"]
Y_Axis['Circulation', 'in', 'thousands']: ['28554', '30948', '34657', '37711', '40420', '40712', '43433', '44421', '45653', '48597', '50742', '52329', '53345', '54626', '55185', '55186', '55578', '55773', '55979', '56182', '56728', '56983', '58193', '59305', '59812', '60164', '60687', '62328', '62649', '62695', '62826', '62502', '62766']

gold: The statistic above presents circulation data of daily newspapers in the United States from 1985 to 2018 . Circulation numbers peaked in 1987 when they amounted to over 62.82 million . In 2018 , the paid circulation of daily newspapers was 28.6 million .
gold_template: The statistic above presents templateYLabel[0] data of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] . templateYLabel[0] numbers peaked in 1987 when they amounted to over 62.82 templateScale . In templateTitleDate[max] , the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] was templateYValue[min] templateScale .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[4] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , around 8.32 templateScale templateYLabel[2] were templateYLabel[2] in the templateTitle[3] .
generated: This statistic shows the Circulation of thousands in the U.S. from 1985 to 2018 .  In 2018 , around 8.32 thousands were in the newspapers .

Example 765:
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 displays a forecast 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: This statistic displays a forecast of the Share of mobile banking individual mobile day in the U.S. from 2009 to 2016 .  In 2013 , individuals saw on average 51 television commercials or Share mobile day .

Example 766:
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] 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: This statistic shows the Imports of bushels in the imports bushels permanent via bushels from 2001 to 2019 .  In the most recently reported period , close to 28 million bushels had fixed bushels , up from close to 28 million in 2010 .  The imports are one of the biggest online markets worldwide .

Example 767:
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[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: The statistic shows Gross domestic product ( GDP ) in Oman 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 .

Example 768:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018', '2018']}
title: Italy : volume of crude oil imported Q1 2018 , by country of origin
X_Axis['Country']: ['Azarbaijan', 'Iran', 'Iraq', 'Libya', 'Saudi_Arabia', 'Russia', 'Nigeria', 'Kazakhstan', 'USA', 'Angola', 'Canada', 'Kuwait', 'Egypt', 'Cameroon', 'Algeria', 'Equatorial_Guinea', 'Tunisia', 'Mauritania', 'Albania']
Y_Axis['Import', 'volume', 'in', 'tons']: ['12298989', '9324007', '2041664', '1840713', '1825182', '1052134', '767828', '703232', '504954', '322012', '317132', '312218', '204085', '185753', '155279', '89845', '56891', '33791', '123']

gold: During the first quarter of 2018 , Azerbaijan exported roughly 12.3 million tons of crude oil to Italy , establishing itself as the major crude oil supplier for the country . Iran followed with 9.3 million tons . The amount of crude oil imported from other suppliers was lower , during the first quarter of 2018 .
gold_template: During the first quarter of templateTitleDate[0] , Azerbaijan exported roughly templateYValue[max] templateScale templateYLabel[2] of templateTitle[2] templateTitle[3] to templateTitleSubject[0] , establishing itself as the major templateTitle[2] templateTitle[3] supplier for the templateXLabel[0] . templateXValue[1] followed with templateYValue[1] templateScale templateYLabel[2] . The amount of templateTitle[2] templateTitle[3] templateTitle[4] from other suppliers was lower , during the first quarter of templateTitleDate[0] .

generated_template: This statistic shows the templateYLabel[1] of premium templateTitle[2] templateTitle[3] templateTitle[4] ( per 50gram ) across templateTitleSubject[0] as at 2016 . According to the World templateTitle[2] templateTitle[3] templateTitle[4] Council , the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in templateXValue[0] with a value of templateYValue[max] .
generated: This statistic shows the volume of premium crude oil imported ( per 50gram ) across Italy as at 2016 .  According to the World crude oil imported Council , the Import of tons in Azarbaijan with a value of 12298989 .

Example 769:
titleEntities: {'Subject': ['France'], 'Date': ['2004', '2018']}
title: France : unemployment rate 2004 to 2018
X_Axis['Year']: ['2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Percentage', 'of', 'unemployed', 'people']: ['8.9', '8.9', '8.8', '8', '7.4', '9.1', '9.3', '9.2', '9.8', '10.3', '10.3', '10.4', '10.1', '9.4', '9.1']

gold: In 2010 , unemployment rate in France reached a record level of 10.4 percent . Unemployment remains a rampant issue for French economy , being stagnant year-over-year since the financial and economical crisis in 2008 . During the first quarter of 2018 , more than 1.4 million people aged between 25 and 49 years were unemployed in France .
gold_template: In templateXValue[6] , templateTitle[1] templateTitle[2] in templateTitleSubject[0] reached a record level of templateYValue[max] templateScale . templateTitle[1] remains a rampant issue for French economy , being stagnant year-over-year since the financial and economical crisis in templateXValue[4] . During the first quarter of templateXValue[max] , more than 1.4 templateScale templateYLabel[2] aged between 25 and 49 years were templateYLabel[1] in templateTitleSubject[0] .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[5] in the templateTitleSubject[0] ( UK ) from templateXValue[min] to templateXValue[max] . Since templateXValue[min] the templateTitle[5] has diminished . In templateXValue[min] , the templateTitle[2] templateYLabel[0] templateYLabel[2] templateTitle[5] was nearly templateYValue[13] templateScale , whereas from templateXValue[4] to templateXValue[max] , it varied between templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the rate Percentage unemployed people 2018 in the France ( UK ) from 2004 to 2018 .  Since 2004 the 2018 has diminished .  In 2004 , the rate Percentage people 2018 was nearly 9.4 percentage , whereas from 2008 to 2018 , it varied between 9.1 percentage .

Example 770:
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 shows the templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[0] in templateTitleDate[0] , ranked templateTitle[5] templateYLabel[0] . According to the report , templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[2] , with a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] that year .
generated: The statistic shows the video games adapted in the Japanese in 2017 , ranked movies Revenue .  According to the report , Resident_Evil:_The_Final_Chapter_(Capcom) was the Japanese video games in the games , with a Revenue of 312.42 million U.S. dollars that year .

Example 771:
titleEntities: {'Subject': ['NCAA'], 'Date': ['2013']}
title: NCAA college basketball tournament revenue from ticket sales 2013
X_Axis['Year']: ['2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Ticket', 'sales', '(in', 'million', 'U.S.', 'dollars)']: ['71.71', '67.18', '65.02', '62.31', '62.67', '51.45', '47.0', '41.22', '36.21', '37.68', '32.25', '35.23', '27.5', '25.08']

gold: The graph shows the revenue generated through NCAA college basketball tournament ticket sales from 2000 to 2013 . In 2010 , revenue from ticket sales was 62.31 million U.S. dollars .
gold_template: The graph shows the templateTitle[4] generated through templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitle[4] templateTitle[5] templateYLabel[0] templateYLabel[1] was templateYValue[3] templateScale templateYLabel[4] dollars .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[3] , templateYLabel[1] and templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] people worked either part-time or full-time as templateTitle[1] templateTitle[4] , templateYLabel[1] or templateTitle[3] . Included are solo templateYLabel[1] without employees working part-time or full-time in their artistic trade .
generated: This statistic shows the Ticket of college tournament , sales and revenue in the from 2000 to 2013 .  In 2013 , 71.71 people worked either part-time or full-time as college revenue , sales or tournament .  Included are solo sales without employees working part-time or full-time in their artistic trade .

Example 772:
titleEntities: {'Subject': ['Brooklyn Nets', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Brooklyn Nets ( 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']: ['66.15', '66.15', '60.5', '55.89', '37.06', '40.5', '44.51', '54.98', '60.98', '60.98']

gold: This graph depicts the average ticket price for Brooklyn Nets games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 60.98 U.S. dollars . Brooklyn Nets The Brooklyn Nets are a franchise of the National Basketball Association ( NBA ) which joined the league as the New Jersey Nets in 1976 as part of the ABA-NBA merger .
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] . templateTitleSubject[0] The templateTitleSubject[0] are a franchise of the National Basketball Association ( templateTitleSubject[1] ) which joined the league as the New Jersey templateTitleSubject[0] in 1976 as part of the ABA-NBA merger .

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 Brooklyn Nets games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Average ticket price was 60.98 U.S. dollars .

Example 773:
titleEntities: {'Subject': ['American Apparel'], 'Date': ['2010', '2014']}
title: Global number of American Apparel stores by region 2010 to 2014
Data:
                  0     1     2     3     4
Year           2014  2013  2012  2011  2010
United States   136   139   140   143   157
Canada           31    32    35    37    40
International    75    77    76    69    76
Total           242   248   251   249   273 

gold: This statistic depicts the number of American Apparel stores worldwide from 2010 to 2014 , by region . By the end of the 2011 fiscal year , American Apparel operated 249 stores throughout the world .
gold_template: This statistic depicts the templateTitle[1] of templateTitleSubject[0] stores worldwide from templateValue[0][last] to templateValue[0][0] , templateTitle[5] templateTitle[6] . templateTitle[5] the end of the templateValue[0][3] fiscal templateLabel[0][0] , templateTitleSubject[0] operated templateValue[4][3] templateTitle[4] throughout the world .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleDate[min] - templateValue[0][0] , templateTitle[7] templateTitle[8] . templateValue[1][last] templateScale of the Dutch respondents aged 16 or 17 templateValue[0][0] reported that they believe templateTitle[2] templateTitle[3] are a templateLabel[1][0] .
generated: This statistic shows the Global number American Apparel in the American Apparel 2010 - 2014 , 2010 2014 .  157 % of the Dutch respondents aged 16 or 17 2014 reported that they believe American Apparel are a United .

Example 774:
titleEntities: {'Subject': ['Millennials', 'Netherlands'], 'Date': ['2020']}
title: Most popular social media platforms for Millennials in the Netherlands 2020
X_Axis['Platform']: ['WhatsApp', 'Facebook', 'YouTube', 'Instagram', 'LinkedIn', 'Snapchat', 'Pinterest', 'Twitter', 'TikTok', 'Tumblr', 'WeChat', 'Foursquare']
Y_Axis['Share', 'of', 'respondents']: ['89', '82', '79', '57', '47', '35', '32', '26', '9', '6', '5', '4']

gold: WhatsApp , Facebook and YouTube ranked as the most popular social media for Dutch Millennials or 20-to-39-year-olds in 2020 , beating , for example , Instagram . In 2020 , roughly 90 percent of the Millennial respondents in the Netherlands said they used WhatsApp . Facebook was also popular , with 82 percent indicating they actively used this platform .
gold_template: templateXValue[0] , templateXValue[1] and templateXValue[2] ranked as the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[5] Dutch templateTitleSubject[0] or 20-to-39-year-olds in templateTitleDate[0] , beating , templateTitle[5] example , templateXValue[3] . In templateTitleDate[0] , roughly 90 templateScale of the Millennial templateYLabel[1] in the templateTitleSubject[1] said they used templateXValue[0] . templateXValue[1] was also templateTitle[1] , with templateYValue[1] templateScale indicating they actively used this templateXLabel[0] .

generated_template: This statistic presents the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] as of the third and fourth quarter templateTitleDate[0] . During the survey period , it was found that templateXValue[1] had a templateYValue[1] templateTitle[3] rate . Overall , 45 templateScale of the templateYLabel[1] had an active account with any templateTitle[1] media website .
generated: This statistic presents the popular social media in Millennials as of the third and fourth quarter 2020 .  During the survey period , it was found that Facebook had a 82 media rate .  Overall , 45 % of the respondents had an active account with any popular media website .

Example 775:
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 templateScale of the templateYLabel[1] templateYLabel[2] living in urban areas in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] was living in cities and urban areas .
generated: This statistic shows the percentage of the undernourished people living in urban areas in Prevalence from 2000 to 2018 .  In 2018 , 10.8 % of the undernourished people of Prevalence was living in cities and urban areas .

Example 776:
titleEntities: {'Subject': ['Iran'], 'Date': ['2024']}
title: Growth of the gross domestic product of Iran 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth']: ['1.09', '0.98', '0.98', '0.99', '0.05', '-9.46', '-4.85', '3.73', '12.52', '-1.59', '3.22']

gold: Iran 's gross domestic product ( GDP ) declined by 4.85 percent in 2018 after adjusting for inflation . This figure fell from 12.5 percent growth two years ago , which had been a reaction to sanctions lifting after the Joint Comprehensive Plan of Action ( JPCOA ) regarding Iran 's nuclear program . United States president Donald Trump ended that country 's participation in the deal , imposing new sanctions .
gold_template: templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) declined by 4.85 templateScale in templateXValue[6] after adjusting for inflation . This figure templateNegativeTrend from templateYValue[max] templateScale templateYLabel[1] two years ago , which had been a reaction to sanctions lifting after the Joint Comprehensive Plan of Action ( JPCOA ) regarding templateTitleSubject[0] 's nuclear program . country president Donald Trump ended that country 's participation in the deal , imposing new sanctions .

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] , the templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[min] templateScale of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the GDP of growth to the product Iran 2024 ( growth ) in Iran from 2014 to 2018 , with projections up until 2024 .  In 2018 , the growth in Iran amounted to about -9.46 % of the country 's product Iran 2024 .

Example 777:
titleEntities: {'Subject': ['Lucia'], 'Date': ['2019']}
title: Unemployment rate in St. Lucia 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']: ['20.47', '20.87', '20.79', '21.26', '24.09', '23.18', '22.21', '19.06', '18.86', '17.15', '15.37', '12.72', '14', '16', '18.7', '20.97', '22.18', '20.39', '18.32', '16.49', '18.09']

gold: This statistic shows the unemployment rate in St. Lucia from 1999 to 2019 . In 2019 , the unemployment rate in St. Lucia was at approximately 20.47 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] 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 Lucia from 1999 to 2019 .  In 2019 , the Unemployment rate in Lucia was at approximately 20.47 % .

Example 778:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2020']}
title: Twitter : distribution of global audiences 2020 , by age group
X_Axis['Year']: ['13-17', '18-24', '25-34', '35-49', '50+']
Y_Axis['Share', 'of', 'users']: ['9.6', '24', '29', '23', '15']

gold: This statistic provides information on the distribution of Twitter users worldwide as of January 2020 , sorted by age group . During the survey period it was found that 29 percent of global Twitter users were aged between 25 and 34 years .
gold_template: This statistic provides information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[1] worldwide as of 2020 , sorted templateTitle[5] templateTitle[6] templateTitle[7] . During the survey period it was found that templateYValue[max] templateScale of templateTitle[2] templateTitleSubject[0] templateYLabel[1] were aged between 25 and 34 years .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitle[1] who were using templateTitleSubject[0] as of 2015 , sorted templateTitle[6] templateTitle[7] group . During that period of time , templateYValue[max] templateScale of templateYLabel[1] between 30 and 49 years used the social networking site .
generated: This statistic shows the Share of users in the distribution who were using Twitter as of 2015 , sorted age group .  During that period of time , 29 % of users between 30 and 49 years used the social networking site .

Example 779:
titleEntities: {'Subject': ['Hispanic'], 'Date': ['2016', '2060']}
title: Percentage of U.S. population as of 2016 and 2060 , by race and Hispanic origin
Data:
                          0                                1                          2      3                  4                                  5                                           6
unknown  Non-Hispanic_White  Hispanics__(may_be_of_any_race)  Black_or_African_American  Asian  Two_or_more_races  American_Indian_and_Alaska_Native  Native_Hawaiian_and_Other_Pacific_Islander
2016                  61.27                            17.79                      13.31   5.67               2.62                               1.25                                        0.24
2060                  44.29                             27.5                         15    9.1               6.24                               1.38                                        0.28 

gold: The statistic shows the share of U.S. population , by race and Hispanic origin , in 2016 and a projection for 2060 . As of 2016 , about 17.79 percent of the U.S. population was of Hispanic origin . Race and ethnicity in the U.S. For decades , America was a melting pot of the racial and ethnical diversity of its population .
gold_template: The statistic shows the share of templateTitle[1] templateTitle[2] , templateTitle[5] templateTitle[6] and templateTitleSubject[0] templateTitle[8] , in templateTitleDate[min] and a projection for templateTitleDate[max] . As of templateTitleDate[min] , about templateValue[1][1] templateScale of the templateTitle[1] templateTitle[2] was of templateTitleSubject[0] templateTitle[8] . templateTitle[6] and ethnicity in the templateTitle[1] For decades , America was a melting pot of the racial and ethnical diversity of its templateTitle[2] .

generated_template: This statistic shows the templateTitle[0] fiscal year templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] major public templateTitle[5] templateTitle[6] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[min] and templateTitleDate[max] . templateValue[0][6] is predicted to have the highest templateTitle[1] templateTitle[2] margin at templateValue[1][idxmax(1)] templateScale , followed by templateValue[0][5] at templateValue[1][2] templateScale .
generated: This statistic shows the Percentage fiscal year U.S. population 2016 of 2060 major public by race in the United Kingdom ( Hispanic ) in 2016 and 2060 .  Native_Hawaiian_and_Other_Pacific_Islander is predicted to have the highest U.S. population margin at 61.27 % , followed by American_Indian_and_Alaska_Native at 13.31 % .

Example 780:
titleEntities: {'Subject': ['first Bundesliga', 'Germany'], 'Date': ['2020']}
title: Market value of first Bundesliga football clubs in Germany in 2020
X_Axis['Club', 'Name']: ['FC_Bayern_München', 'Borussia_Dortmund', 'RasenBallsport_Leipzig', 'Bayer_04_Leverkusen', 'Borussia_Mönchengladbach', 'FC_Schalke_04', 'TSG_1899_Hoffenheim', 'Hertha_BSC', 'VfL_Wolfsburg', 'Eintracht_Frankfurt', 'SV_Werder_Bremen', '1._FSV_Mainz_05', 'SC_Freiburg', 'FC_Augsburg', '1._FC_Köln', 'Fortuna_Düsseldorf', '1._FC_Union_Berlin', 'SC_Paderborn']
Y_Axis['Market', 'value', 'in', 'million', 'euros']: ['933.15', '637.4', '594.4', '445.75', '312.0', '242.75', '238.23', '233.2', '230.95', '215.8', '189.75', '147.4', '145.4', '131.15', '102.2', '93.15', '43.05', '31.25']

gold: This statistic shows the market value of the first Bundesliga football clubs in Germany as of February 11 , 2020 . The market value of FC Bayern Munich was highest at 933.15 million euros , followed by 637.4 million euros for Borussia Dortmund and 594.4 million euros for RB Leipzig .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] football templateTitle[5] in templateTitleSubject[1] as of 11 , templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of templateXValue[0] Munich was highest at templateYValue[max] templateScale templateYLabel[3] , followed by templateYValue[1] templateScale templateYLabel[3] for templateXValue[1] and templateYValue[2] templateScale templateYLabel[3] for RB templateXValue[2] .

generated_template: The statistic shows the templateYLabel[1] templateTitle[1] of the templateTitle[2] templateTitle[3] templateTitle[4] toiletries/grooming/aftershave templateTitle[6] in the templateTitle[7] in templateTitleDate[0] . In that year , templateXValue[0] was the highest templateTitle[2] templateXLabel[0] templateXLabel[1] in the templateTitle[3] , with a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the value of the first Bundesliga football toiletries/grooming/aftershave Germany in the 2020 in .  In that year , FC_Bayern_München was the highest first Club Name in the Bundesliga , with a Market of 933.15 million euros .

Example 781:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1992', '2017']}
title: Gift , novelty , and souvenir store sales in the U.S. 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']: ['16.75', '16.87', '16.8', '16.58', '16.33', '16.05', '15.97', '15.54', '15.08', '16.82', '18.04', '18.5', '17.61', '17.63', '17.69', '18.06', '18.5', '18.91', '18.41', '17.6', '16.33', '15.3', '14.5', '13.91', '12.75', '12.23']

gold: This timeline shows gift , novelty , and souvenir store sales in the United States from 1992 to 2017 . In 2017 , U.S. gift , novelty , and souvenir store sales amounted to about 16.75 billion U.S. dollars.Additional information on the novelty and gifts market in the USThe statistic above shows how sales in the gift , novelty and souvenir sector dropped following the market crash in 2008 , as did retail in the United States overall . A comparison of gift purchases in 2015 between millennials and consumers aged 35 and over shows that younger people are choosing travel and entertainment over more traditional gifts than their older counterparts .
gold_template: This timeline shows templateTitle[0] , templateTitle[1] , and templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[1] templateTitle[0] , templateTitle[1] , and templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[0] templateScale templateYLabel[1] dollars.Additional information on the templateTitle[1] and gifts market in the USThe statistic above shows how templateTitle[4] in the templateTitle[0] , templateTitle[1] and templateTitle[2] sector templateNegativeTrend following the market crash in 2008 , as did retail in the templateTitle[5] overall . A comparison of templateTitle[0] purchases in 2015 between millennials and consumers aged 35 and over shows that younger people are choosing travel and entertainment over more traditional gifts than their older counterparts .

generated_template: This timeline shows jewelry , templateTitle[0] , and templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] templateTitle[6] templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[1] jewelry , templateTitle[0] , and templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] .
generated: This timeline shows jewelry , Gift and novelty souvenir store sales in the U.S. 1992 to 2017 .  In 2017 , U.S. jewelry , Gift and novelty souvenir store sales amounted to about 18.41 billion U.S. dollars .

Example 782:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of fresh carrots 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']: ['8.5', '7.4', '7.8', '8.8', '8.5', '8.0', '7.9', '7.5', '7.8', '7.4', '8.1', '8.0', '8.1', '8.7', '8.7', '8.8', '8.4', '9.4', '9.2']

gold: The timeline shows the per capita consumption of fresh carrots in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh carrots amounted to approximately 8.5 pounds in 2018 .
gold_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_template: This statistic 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[idxmin(Y)] .
generated: This statistic shows the Per capita consumption of fresh carrots in the U.S. from 2000 to 2018 .  According to the report , the U.S. Per capita consumption of fresh carrots amounted to approximately 8.5 pounds in 2017 .

Example 783:
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: The statistic shows the number of templateTitle[0] templateTitle[1] in templateTitle[2] in the templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In this period , there were a total of templateValue[1][last] templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] .
generated: The statistic shows the number of Companies Norwegian in insurance the Norwegian from 2011 to 2015 .  In this period , there were a total of 117 Companies Norwegian insurance in the market .

Example 784:
titleEntities: {'Subject': ['German', 'Bundesliga'], 'Date': ['2002', '2018']}
title: Youth academy spending German Bundesliga clubs 2002 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', '2003/04', '2002/03']
Y_Axis['Expenditure', 'in', 'million', 'euros']: ['177.12', '163.41', '158.46', '132.2', '120.15', '105.75', '103.14', '92.56', '85.7', '78.24', '69.2', '61.63', '60.87', '57.79', '56.92', '47.85']

gold: The statistic depicts the spending of the 36 clubs of the German Bundesliga on their youth academies from 2002/03 to 2017/18 . In the 2017/178season , the German Bundesliga clubs spent a total amount of 177.12 million euros for their youth academies .
gold_template: The statistic depicts the templateTitle[2] of the 36 templateTitle[5] of the templateTitleSubject[0] templateTitleSubject[1] on their templateTitle[0] academies from templateXValue[last] to templateXValue[0] . In the 2017/178season , the templateTitleSubject[0] templateTitleSubject[1] templateTitle[5] spent a total amount of templateYValue[max] templateScale templateYLabel[2] for their templateTitle[0] academies .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] templateTitle[6] templateXValue[min] to templateTitleDate[max] . In templateTitleDate[max] , an estimated templateYValue[idxmax(X)] templateScale templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] members as compared to templateYValue[14] templateScale in templateXValue[14] .
generated: This statistic shows the Expenditure of Youth academy spending million euros 2002 2002/03 to 2018 .  In 2018 , an estimated 177.12 million Youth academy spending million members as compared to 56.92 million in 2003/04 .

Example 785:
titleEntities: {'Subject': ['New Zealand'], 'Date': ['2024']}
title: Inflation rate in New Zealand 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']: ['2.02', '2.02', '2.02', '2.02', '1.94', '1.44', '1.6', '1.85', '0.64', '0.31', '1.22', '1.13', '1.03', '4.08', '2.26', '2.16', '3.93', '2.39', '3.36', '3.04', '2.31', '1.73', '2.65', '2.65', '2.61', '-0.11', '1.26', '1.17', '2.31', '3.75', '1.71', '1.32', '1', '2.65', '6.06', '5.69', '6.35', '15.76', '13.22', '15.38', '6.13']

gold: Prices in New Zealand rose by 1.6 percent in 2018 . After a dip in the following year , inflation was forecast to remain steady around 2 percent for the foreseeable future . Central bankers at the Reserve Bank of New Zealand were surely relieved to see the rebound from the dangerously low .31 percent inflation in 2015 .
gold_template: Prices in templateTitleSubject[0] templatePositiveTrend by templateYValue[6] templateScale in templateXValue[6] . After a dip in the following templateXLabel[0] , templateYLabel[0] was forecast to remain steady around templateYValue[0] templateScale for the foreseeable future . Central bankers at the Reserve Bank of templateTitleSubject[0] were surely relieved to see the rebound from the dangerously low templateYValue[9] templateScale templateYLabel[0] in templateXValue[9] .

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 New Zealand from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in New Zealand amounted to about 1.6 % compared to the previous Year .

Example 786:
titleEntities: {'Subject': ['Finland'], 'Date': ['2007', '2017']}
title: Homicide rate per 100,000 population in Finland in 2007 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'victims', 'per', '100,000', 'inhabitants']: ['1.11', '1.14', '1.28', '1.43', '1.43', '1.35', '1.83', '1.9', '2.06', '2.23', '2.19']

gold: This statistic shows the homicide rate per 100,000 persons in Finland from 2007 to 2017 . The number has decreased from 2.23 victims in 2008 to 1.11 victims in 2017 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[2] 100,000 persons in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] has templateNegativeTrend from templateYValue[max] templateYLabel[1] in templateXValue[idxmax(Y)] to templateYValue[idxmax(X)] templateYLabel[1] in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] . Gambling in templateTitleSubject[0] - additional information The templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] templatePositiveTrend by more than 2,000 between 2007 and templateXValue[1] .
generated: This statistic shows the Number of victims per in 100,000 Finland from 2007 to 2017 .  In 2017 , there were 1.11 victims per in Finland .  Gambling in Finland - additional information The Number of victims per in Finland increased by more than 2,000 between 2007 and 2016 .

Example 787:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Maple syrup average price per gallon in the U.S. 2018
X_Axis['State']: ['Connecticut', 'Minnesota', 'New_Hampshire', 'Massachusetts', 'Indiana', 'Ohio', 'West_Virginia', 'Maine', 'Pennsylvania', 'Michigan', 'New_York', 'Wisconsin', 'Vermont']
Y_Axis['Average', 'price', 'per', 'gallon', 'in', 'U.S.', 'dollars']: ['76.0', '61.5', '56.0', '53.0', '50.0', '45.0', '44.5', '40.0', '39.0', '39.0', '32.0', '32.0', '28.0']

gold: The U.S. state with the highest average price per gallon of maple syrup was Connecticut , at 76 U.S. dollars in 2018 . Minnesota had the second highest price on average , at 61.5 U.S. dollars per gallon of maple syrup . Maple syrup production Canada takes great pride in being a major producer of maple syrup worldwide .
gold_template: The templateYLabel[4] templateXLabel[0] with the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] was templateXValue[0] , at templateYValue[max] templateYLabel[4] templateYLabel[5] in templateTitleDate[0] . templateXValue[1] had the second highest templateYLabel[1] on templateYLabel[0] , at templateYValue[1] templateYLabel[4] templateYLabel[5] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] . templateTitle[0] templateTitle[1] production Canada takes great pride in being a major producer of templateTitle[0] templateTitle[1] worldwide .

generated_template: This statistic shows the templateXLabel[0] templateTitle[3] of Canadians for templateTitleDate[0] , distinguished templateTitle[4] templateTitle[6] of templateXLabel[0] . In templateTitleDate[0] , about templateYValue[last] Canadians had an templateXLabel[0] of templateXValue[last] templateXLabel[1] templateXLabel[2] or more .
generated: This statistic shows the State price of Canadians for 2018 , distinguished per U.S. of State .  In 2018 , about 28.0 Canadians had an State of Vermont State or more .

Example 788:
titleEntities: {'Subject': ['Marriott International'], 'Date': ['2010', '2018']}
title: Occupancy rate of Marriott International hotels 2010 - 2018 , by region
Data:
                              0     1     2     3     4     5     6     7     8     9
Year                       2010  2011  2012  2013  2014  2015  2016  2017  2018  2019
North America              68.1  70.2  71.6  72.3  74.2  74.7  75.8    76  75.7  75.8
Caribbean & Latin America  70.7  72.6  72.3  73.5  73.6  72.4  65.3  66.5  64.8  65.1
Europe                     71.1  73.2  72.7  73.5  74.9  75.9  71.8  73.5    74  74.7
United Kingdom             76.4     0     0     0     0     0     0     0     0     0
Middle East & Africa       70.5  58.8  61.8  55.7  60.1  61.2  64.6  65.7  66.4  68.5
Asia Pacific               66.7  73.1    73    73  73.7  74.1  75.2  72.8  73.5  72.4
Overall worldwide          68.7  70.3  71.4  70.7  73.7  74.1  72.5  73.7  73.7  73.7 

gold: This statistic shows the occupancy rate of Marriott International hotels worldwide from 2010 to 2019 , by region . The occupancy rate of Marriott hotels was 65.1 percent in Latin America and the Caribbean in 2019 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] hotels templateLabel[7][1] from templateValue[0][0] to templateValue[0][last] , templateTitle[7] templateTitle[8] . The templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[4] was templateValue[2][last] templateScale in templateLabel[2][2] templateLabel[1][1] and the templateLabel[2][0] in templateValue[0][last] .

generated_template: The statistic represents the templateLabel[2][0] templateTitle[3] and templateTitle[5] templateTitle[6] templateLabel[1][2] templateTitle[7] by the templateTitle[1] templateTitle[2] in the templateTitle[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] consumed more than templateValue[0][15] templateScale barrels of templateTitle[5] templateTitle[6] templateLabel[1][2] daily . templateTitle[3] and templateTitle[6] templateLabel[1][2] and diesel templateTitle[7] in the templateTitle[0] .
generated: The statistic represents the Caribbean International and 2010 2018 America by the rate Marriott in the Occupancy from 2019 to 2010 .  In 2010 , the Marriott International rate Marriott consumed more than 2019 million barrels of 2010 2018 America daily .  International and 2018 America and diesel by in the Occupancy .

Example 789:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['1998', '2018']}
title: National health expenditure in the Netherlands 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['Total', 'expenditure', 'in', 'million', 'euros']: ['86836', '84127', '82021', '80118', '80116', '78957', '78309', '75593', '73402', '70213', '66592', '62404', '59152', '56571', '54700', '52582', '48745', '43225', '38583', '36195', '33875']

gold: In 2018 , the national healthcare expenditure reached a value of approximately 89.8 billion euros . In the Netherlands , every resident or employee is obliged to take a basic health insurance to cover medical costs from , for example , visits to a general practitioner or the hospital . The Dutch government decides on the cover provided by this package and health insurance companies are obliged to accept everyone who meets the requirements , regardless of age or state of health .
gold_template: In templateXValue[max] , the templateTitle[0] healthcare templateYLabel[1] reached a value of approximately 89.8 templateScale templateYLabel[3] . In the templateTitleSubject[0] , every resident or employee is obliged to take a basic templateTitle[1] insurance to cover medical costs from , for example , visits to a general practitioner or the hospital . The Dutch government decides on the cover provided by this package and templateTitle[1] insurance companies are obliged to accept everyone who meets the requirements , regardless of age or state of templateTitle[1] .

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 a total templateYValue[idxmax(X)] templateYLabel[1] reported in the templateTitleSubject[0] .
generated: This statistic shows the Total of expenditure in the Netherlands from 1998 to 2018 .  In 2018 , there were a total 86836 expenditure reported in the Netherlands .

Example 790:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': []}
title: Kidney transplants in the United Kingdom ( UK ) 2018/19
X_Axis['Country', 'of', 'residence']: ['England', 'Wales', 'Scotland', 'Northern_Ireland']
Y_Axis['Number', 'of', 'transplants']: ['2058', '109', '171', '48']

gold: 2,058 of the total 2,399 kidney transplant procedures in the United Kingdom in 2018/19 were carried out on patients living in England . Followed by 171 transplants in Scotland , while there were 109 and 48 procedures in Wales and Northern Ireland respectively . Still not enough to meet demand ? At the end of the period 2018/19 , there were still almost five thousand patients on the kidney transplant waiting list in the UK , 4.2 thousand of which were in England .
gold_template: templateYValue[max] of the total 2,399 templateTitle[0] transplant procedures in the templateTitleSubject[0] in templateTitle[5] were carried out on patients living in templateXValue[0] . Followed by templateYValue[2] templateYLabel[1] in templateXValue[2] , while there were templateYValue[1] and templateYValue[min] procedures in templateXValue[1] and templateXValue[last] respectively . Still not enough to meet demand ? At the end of the period templateTitle[5] , there were still almost five thousand patients on the templateTitle[0] transplant waiting list in the templateTitleSubject[1] , 4.2 thousand of which were in templateXValue[0] .

generated_template: In the period templateTitle[5] , templateYValue[max] templateTitle[0] templateYLabel[1] were carried out in templateXValue[0] , followed by templateYValue[1] conducted in templateXValue[1] . templateXValue[0] has by far the largest population of the countries in the templateTitleSubject[0] , so it is unsurprising it has the highest templateYLabel[0] of templateYLabel[1] performed in a year . State of templateTitle[0] templateYLabel[1] in the templateTitleSubject[1] .
generated: In the period 2018/19 , 2058 Kidney transplants were carried out in England , followed by 109 conducted in Wales .  England has by far the largest population of the countries in the United Kingdom , so it is unsurprising it has the highest Number of transplants performed in a year .  State of Kidney transplants in the UK .

Example 791:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Age distribution of mobile gamers in the U.S. 2013
X_Axis['Year']: ['18-24', '25-34', '35-44', '45-54', '55-64', '65+']
Y_Axis['Percentage', 'of', 'respondents']: ['7', '17', '19', '22', '24', '11']

gold: This statistic gives information on the age distribution of mobile gamers in the United States as of May 2013 . During the survey period , it was found that 17 percent of mobile games were 25 to 34 years old . The average age of a mobile gamer was 46.5 years .
gold_template: This statistic gives information on the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] as of 2013 . During the survey period , it was found that templateYValue[1] templateScale of templateTitle[2] games were 25 to 34 years old . The average templateTitle[0] of a templateTitle[2] gamer was 46.5 years .

generated_template: This statistic shows the templateTitle[7] distribution of templateTitle[1] at templateTitleSubject[0] in the templateTitleSubject[1] as of templateTitleDate[0] . According to their annual report , templateYValue[max] templateScale of templateTitleSubject[0] templateTitle[1] are in the templateTitle[7] templateTitle[8] 45 - 54 . templateTitleSubject[0] is a British retailer selling clothing , furniture and other household items in a department store format .
generated: This statistic shows the 2013 distribution of at U.S. in the U.S. as of 2013 .  According to their annual report , 24 percentage of U.S. distribution are in the 2013 45 - 54 .  U.S. is a British retailer selling clothing , furniture and other household items in a department store format .

Example 792:
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[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: This statistic shows the U.S. of passenger-miles 2018 in the Intercity/Amtrak from 2007 to 2018 .  In 2018 , about 6361 passenger-miles in the Intercity/Amtrak were 2018 to be adopted .

Example 793:
titleEntities: {'Subject': ['On'], 'Date': ['2016']}
title: On and off-course income ranking of professional golf players worldwide 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            29          30                 31            32             33             34            35          36              37           38             39             40              41            42        43            44           45              46         47        48                  49
Month       Rory_Mcllroy  Arnold_Palmer  Phil_Mickelson  Tiger_Woods  Jordan_Spieth  Jack_Nicklaus  Dustin_Johnson  Jason_Day  Adam_Scott  Gary_Player  Henrik_Stenson  Hideki_Matsuyama  Rickie_Fowler  Sergio_Garcia  Matt_Kuchar  Bubba_Watson  Patrick_Reed  Bernhard_Langer  Greg_Norman  Colin_Montgomerie  Justin_Rose  Jimmy_Walker  Graeme_McDowell  Brandt_Snedeker  Ernie_Els  Lee_Westwood  Branden_Grace  Nick_Faldo  Jim_Furyk  Russell_Knox  Tom_Watson  Miguel_A._Jimenez  Zach_Johnson  Danny_Willett  Martin_Kaymar  Fred_Couples  Paul_Casey  Davis_Love_III  Luke_Donald  Justin_Thomas  Brooks_Koepka  William_McGirt  Jason_Dufner  Lydia_Ko  Kevin_Kisner  J.B._Holmes  Kevin_Chappell  Bill_Haas  Kevin_Na  Padraig_Harrington
On Course          17.51              0            4.27         0.11            6.4           0.04           12.66       8.85        8.16            0            6.94              5.58           3.33           3.85          4.6          4.85          8.35             4.15            0               2.24         2.31          4.66             3.01             4.74       0.65          1.95           4.64        0.04       1.61           6.1        0.15               1.95          2.12           5.29           1.78          0.23        5.03            0.42         1.83           4.42           4.07            3.82          3.55      2.69          3.71         3.46             5.1       2.77      3.63                 1.3
Off Course          32.0           40.0            33.5        34.61           24.0           20.0             7.1      10.75         6.9         15.0            7.25               8.0            9.1            8.5         6.75           6.3          2.75             6.25         10.0                7.5          7.2           4.2              5.8              4.0        8.0          6.55            3.7        8.25       6.65          2.08         8.0                6.0          5.75           2.55            6.0           7.5         2.0            6.25          4.8           2.05            2.0             2.2           2.4      3.25           2.2          2.4             0.7        2.6      1.65                 4.0 

gold: The graph shows an income/earnings ranking of professional golf players worldwide in 2016 . Rory McIlroy had on course earnings of 17.51 million U.S. dollars and 32 million U.S. dollars in off course in 2015 .
gold_template: The graph shows an income/earnings templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] . templateValue[0][0] McIlroy had on templateLabel[1][0] earnings of templateValue[1][0] templateScale U.S. dollars and templateValue[2][0] templateScale U.S. dollars in templateLabel[2][0] templateLabel[1][0] in 2015 .

generated_template: This statistic shows the total number of templateTitle[0] and templateTitle[1] in the templateTitle[2] in templateTitleDate[0] , templateTitle[3] of residence . The total number of templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] was templateValue[3][last] templateScale .
generated: This statistic shows the total number of off-course and income in the ranking in 2016 , professional of residence .  The total number of off-course income in On 2016 was 4.0 % .

Example 794:
titleEntities: {'Subject': ['Impact'], 'Date': ['2017']}
title: Impact of online harassment and abuse on female victims 2017
X_Axis['Response']: ['A_feeling_of_powerlessness_in_my_ability_to_respond_to_abuse_or_harassment_online', 'Not_being_able_to_sleep_well', 'Lower_self-esteem_or_a_loss_of_self_confidence', 'A_feeling_of_apprehension_when_thinking_about_using_the_internet_or_social_media', 'Less_able_to_focus_on_everyday_tasks', 'Experiencing_panic_attacks_anxiety_or_stress', 'A_feeling_of_isolation_or_a_need_to_withdraw_into_yourself', 'A_feeling_of_apprehension_when_receiving_emails_or_social_media_notifications', 'Experiencing_mood_swings', 'Not_being_able_to_accomplish_what_you_usually_would_on_a_typical_day', 'Being_unable_to_concentrate_for_long_periods_of_time', 'Less_able_to_make_day-to-day_decisions']
Y_Axis['Share', 'of', 'respondents']: ['66', '63', '61', '58', '56', '55', '54', '54', '53', '46', '46', '36']

gold: This statistic presents data on the psychological impact of women experiencing online abuse or harassment worldwide . During the July 2017 survey period , 66 percent of survey respondents who have experienced online abuse stated that they felt a feeling of powerlessness in their ability to respond to abuse or harassment online . A total of 63 percent of online harassment victims also reported to not being able to sleep well .
gold_template: This statistic presents data on the psychological templateTitleSubject[0] of women templateXValue[5] templateXValue[0] or templateXValue[0] worldwide . During the 2017 survey period , templateYValue[max] templateScale of survey templateYLabel[1] who have experienced templateXValue[0] stated that they felt a templateXValue[0] of templateXValue[0] in their templateXValue[0] to templateXValue[0] to templateXValue[0] or templateXValue[0] . A total of templateYValue[1] templateScale of templateXValue[0] victims also reported to templateXValue[1] able to templateXValue[1] .

generated_template: This statistic presents the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded online by Harris Interactive in 2014 , asking the templateYLabel[1] where they usually templateTitle[3] their shampoo and/or templateTitle[6] . Some templateYValue[3] templateScale of templateTitleSubject[0] adults indicated that they buy their shampoo/conditioner templateXValue[3] .
generated: This statistic presents the results of a survey among Impact adult online .  The survey was fielded online by Harris Interactive in 2014 , asking the respondents where they usually abuse their shampoo and/or 2017 .  Some 58 % of Impact adults indicated that they buy their shampoo/conditioner A_feeling_of_apprehension_when_thinking_about_using_the_internet_or_social_media .

Example 795:
titleEntities: {'Subject': ['Kazakhstan'], 'Date': ['2018']}
title: Urbanization in Kazakhstan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['57.43', '57.34', '57.26', '57.19', '57.12', '57.05', '56.97', '56.9', '56.83', '56.76', '56.68']

gold: This statistic shows the degree of urbanization in Kazakhstan from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 57.43 percent of Kazakhstan '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 Kazakhstan from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 57.43 % of Kazakhstan 's total population lived in urban areas and cities .

Example 796:
titleEntities: {'Subject': ['Apple'], 'Date': ['2005']}
title: Number of Apple stores worldwide 2005 to 2015
X_Axis['Fiscal', 'years']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Number', 'of', 'stores']: ['463', '437', '416', '390', '357', '317', '273', '247', '197', '165', '116']

gold: This statistic shows the number of Apple stores around the world from 2005 to 2015 . In 2015 , there were 463 Apple stores worldwide . Apple Stores info Since the opening of the first Apple store in the United States in 2001 , the number of Apple stores around the world has boomed , surpassing 200 stores after 2007 and still rising .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] around the world from templateXValue[last] to templateXValue[0] . In templateXValue[0] , there were templateYValue[max] templateTitleSubject[0] templateYLabel[1] templateTitle[3] . templateTitleSubject[0] templateYLabel[1] info Since the opening of the first templateTitleSubject[0] store in the country in 2001 , the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] around the world has boomed , surpassing 200 templateYLabel[1] after templateXValue[8] and still templatePositiveTrend .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . The templateYLabel[0] of templateYLabel[1] peaked in templateXValue[0] , with templateYValue[max] templateYLabel[1] .
generated: This statistic displays the Number of stores in Apple from 2005 to 2015 .  The Number of stores peaked in 2015 , with 463 stores .

Example 797:
titleEntities: {'Subject': ['U.S. Snapchat'], 'Date': ['2018', '2018']}
title: Share of U.S. teenagers who use Snapchat 2018 , by age
X_Axis['Year']: ['13-14', '15-17']
Y_Axis['Share', 'of', 'respondents']: ['63', '74']

gold: This statistic shows the share of teenagers in the United States who were Snapchat users as of April 2018 , sorted by age group . During that period of time , 74 percent of U.S. teens aged 15 to 17 years used the social networking app .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] in the templateTitle[1] templateTitle[3] were templateTitleSubject[0] users as of 2018 , sorted templateTitle[7] templateTitle[8] group . During that period of time , templateYValue[max] templateScale of templateTitleSubject[0] teens aged 15 to 17 years used the social networking app .

generated_template: This statistic shows the templateYLabel[0] of internet templateTitle[8] in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] templateScale of templateXValue[0] templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the Share of internet age in the U.S. Snapchat who were using Share as of 2019 , sorted 2018 by age .  During that period of time , 74 % of 13-14 respondents stated that they used the social networking site .

Example 798:
titleEntities: {'Subject': ['Forecast'], 'Date': ['2014', '2020']}
title: Forecast of sulfur fertilizer demand worldwide 2014 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Total', 'demand', 'in', 'million', 'metric', 'tons']: ['69.0', '67.7', '70.38', '68.08', '65.65', '63.47', '61.44']

gold: This statistic displays a forecast of total global demand for sulfur fertilizer from 2014 to 2020 . By 2020 , the annual demand for sulfur fertilizer is expected to reach some 69 million metric tons . Increasing crop prices lead to increased fertilizer demands and has been especially noted in recent years in South Asia .
gold_template: This statistic displays a templateTitleSubject[0] of templateYLabel[0] global templateYLabel[1] for templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . By templateXValue[max] , the annual templateYLabel[1] for templateTitle[1] templateTitle[2] is expected to reach some templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] . templatePositiveTrend crop prices lead to templatePositiveTrend templateTitle[2] demands and has been especially noted in recent years in South Asia .

generated_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: The statistic shows the sulfur Total demand for Forecast in the country from 2014 to 2020 .  The sulfur Total rate for Forecast amounted to about 61.44 million in 2014 .

Example 799:
titleEntities: {'Subject': ['Singapore'], 'Date': ['2010', '2016']}
title: Prevalence of smoking for males Singapore 2010 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Adult', 'male', 'smoking', 'rate']: ['28.3', '28.2', '28.2', '28.2', '27.8', '27.9', '27.9']

gold: In 2016 , the proportion of adult males who smoked in Singapore was at 28.3 percent . The adult male smoking rate in Singapore had increased since 2010 , despite measures taken by the Singapore government to reduce the smoking rate . These include increasing taxes on cigarettes and restricting the areas in which smoking was allowed .
gold_template: In templateXValue[max] , the proportion of templateYLabel[0] templateTitle[3] who smoked in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale . The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleSubject[0] had templatePositiveTrend since templateXValue[min] , despite measures taken by the templateTitleSubject[0] government to reduce the templateYLabel[2] templateYLabel[3] . These include templatePositiveTrend taxes on cigarettes and restricting the areas in which templateYLabel[2] was allowed .

generated_template: This statistic displays the predicted that the government expects to receive in templateTitle[0] templateTitle[1] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . The amount is set to templatePositiveTrend from templateYValue[min] templateScale British pounds in the fiscal templateXLabel[0] templateXValue[idxmin(Y)] to templateYValue[max] templateScale British pounds in templateXValue[idxmax(Y)] .
generated: This statistic displays the predicted that the government expects to receive in Prevalence smoking in the Singapore ( ) from 2010 to 2016 .  The amount is set to increase from 27.8 billion British pounds in the fiscal Year 2012 to 28.3 billion British pounds in 2016 .

Example 800:
titleEntities: {'Subject': ['Preferred'], 'Date': ['2017', '2017']}
title: Preferred sales and marketing strategies for luxury watch brands 2017
Data:
                                               0                                             1               2                           3                                  4
unknown                 Introducing_new_products  Developing_e-commerce/digital_channels_(new)  Reducing_costs  Expanding_into_new_markets  Focus_on_research_and_development
Strong priority                               64                                            55              47                          44                                 42
Somewhat of a priority                        28                                            16              43                          44                                 48
Not a priority                                 8                                            29              10                          12                                 10 

gold: The graph shows preferred sales and marketing strategies for luxury watch brands worldwide in 2017 . The survey revealed that 64 percent of respondents feel that introducing new products is a top priority .
gold_template: The graph shows templateTitleSubject[0] templateTitle[1] and templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] worldwide in templateTitleDate[0] . The survey revealed that templateValue[1][0] templateScale of respondents feel that templateValue[0][0] products is a top templateLabel[1][1] .

generated_template: This statistic shows the results of a survey among templateTitleSubject[0] on their templateTitleSubject[0] in the concepts of templateValue[0][0] , templateValue[0][2] and templateValue[0][3] in 2014 . As of 2011 , templateLabel[3][2] 75 templateScale of respondents believed in templateValue[0][3] .
generated: This statistic shows the results of a survey among Preferred on their Preferred in the concepts of Introducing_new_products , Reducing_costs and Expanding_into_new_markets in 2014 .  As of 2011 , priority 75 % of respondents believed in Expanding_into_new_markets .

Example 801:
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[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , around templateYValue[max] thousand babies were born in templateTitleSubject[0] in templateXValue[idxmax(Y)] , an templatePositiveTrend from the previous templateXLabel[0] were templateYValue[1] thousand babies were born .
generated: This statistic shows the Market of size in UK from 2010 to 2017 .  According to the report , around 3489 thousand babies were born in UK 2017 , an increase from the previous Year were 3330 thousand babies were born .

Example 802:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2012', '2017']}
title: Female hair and beauty treatment costs in the United Kingdom ( UK ) 2012 to 2017
Data:
               0         1             2         3            4         5
unknown  Tanning  Pedicure  Hair_removal  Manicure  Hair_colour  Haircuts
2012          19        21            33        19           42        25
2013          18        21            32        19           42        24
2014          16        19            14        17           35        23
2015          16        19            14        17           35        25
2016          16         0            16        17           37        23
2017          16         0            14        17           35        25 

gold: The average cost of hair and beauty treatments at salons are significantly higher for women than men in the UK . As seen in this statistic , women 's haircut prices averaged at 25 British pounds in 2017 , whereas the same service was considerably cheaper for male customers . Hair coloring : high cost & sought after In addition to being the costliest treatment on the hair and beauty salon price list , hair coloring was also the service for which UK women visited hairdressers most frequently .
gold_template: The average cost of templateValue[0][2] and templateTitle[2] treatments at salons are significantly higher for women than men in the templateTitleSubject[1] . As seen in this statistic , women 's haircut prices averaged at templateValue[1][last] British pounds in templateTitleDate[max] , whereas the same service was considerably cheaper for male customers . templateValue[0][2] coloring : high cost & sought after In addition to being the costliest templateTitle[3] on the templateValue[0][2] and templateTitle[2] salon price list , templateValue[0][2] coloring was also the service for which templateTitleSubject[1] women visited hairdressers most frequently .

generated_template: This statistic shows the average cost of various templateTitle[0] templateValue[0][1] and templateTitle[2] treatments in salons in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . Over the last three period , the average price of templateTitle[0] templateValue[0][1] cuts templateNegativeTrend from templateValue[1][last] British pounds in templateTitleDate[min] to templateValue[3][min] British pounds in templateLabel[3][0] . The most expensive templateTitle[3] that can be had for males in a templateTitle[2] salon is the templateValue[0][3] or templateValue[0][2] , which both cost templateValue[1][2] British pounds in templateTitleDate[max] .
generated: This statistic shows the average cost of various Female Pedicure and beauty treatments in salons the United Kingdom from 2012 to 2017 .  Over the last three period , the average price of Female Pedicure cuts dropped from 25 British pounds in 2012 to 14 British pounds in 2014 .  The most expensive treatment that can be had for males in a beauty salon is the Manicure or Hair_removal , which both cost 33 British pounds in 2017 .

Example 803:
titleEntities: {'Subject': ['Leon Furniture Limited', 'Canada'], 'Date': ['2018']}
title: Number of Leon 's Furniture Limited stores in Canada by region 2018
X_Axis['Month']: ['Ontario', 'Alberta', 'British_Columbia', 'Québec', 'Saskatchewan', 'Manitoba', 'Nova_Scotia', 'New_Brunswick', 'Newfoundland_and_Labrador', 'Prince_Edward_Island', 'Northwest_Territories', 'Yukon']
Y_Axis['Number', 'of', 'stores']: ['129', '61', '39', '26', '16', '10', '8', '7', '3', '2', '1', '1']

gold: In 2018 , there were a total of 303 Leon 's Furniture Limited stores across Canada . One hundred and twenty-nine were in the province of Ontario . Alberta ranked in second place with 61 stores and British Columbia in third with 39 stores in that year .
gold_template: In templateTitleDate[0] , there were a total of 303 templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] stores across templateTitleSubject[1] . templateYValue[min] hundred and twenty-nine were in the province of templateXValue[0] . templateXValue[1] ranked in second place with templateYValue[1] templateYLabel[1] and templateXValue[2] in third with templateYValue[2] templateYLabel[1] in that year .

generated_template: In templateXValue[0] the United Kingdom suffered its worst terrorist attacks since 2005 , with the Manchester Arena Bombing on templateXValue[0] claiming templateXValue[0] lives and the London Bridge Attacks of 3 where templateYValue[2] people were killed . The United Kingdom also suffered the highest templateYLabel[0] of attacks in Europe at 107 , followed by France which had 54 . Barcelona vehicle attack The Barcelona vehicle attack of templateXValue[1] 2017 was the second deadliest attack in this year .
generated: In Ontario the United Kingdom suffered its worst terrorist attacks since 2005 , with the Manchester Arena Bombing on Ontario claiming lives and the London Bridge Attacks of 3 where 39 people were killed .  The United Kingdom also suffered the highest Number of attacks in Europe at 107 , followed by France which had 54 .  Barcelona vehicle attack The Barcelona vehicle attack of Alberta 2017 was the second deadliest attack in this year .

Example 804:
titleEntities: {'Subject': ['Internazionale Milan'], 'Date': []}
title: Internazionale Milan revenue by stream 2018/19
Data:
                    0        1        2        3        4        5        6        7        8        9       10
Season        2018/19  2017/18  2016/17  2015/16  2014/15  2013/14  2012/13  2011/12  2010/11  2009/10  2008/09
Matchday         50.9     35.3     28.4     25.7     22.2     18.8     19.4     23.2     32.9     38.6     28.2
Broadcasting    159.2     97.7    103.6     98.6     97.2     84.8     81.5    112.4    124.4    137.9    115.7
Commercial      154.5    147.8    130.1     54.9     45.4     60.4     67.9     50.3     54.1     48.3     52.6 

gold: The commercial revenue stream is the largest source of revenue for Internazionale Milano . In 2018/2019 , the football club earned approximately 154.5 million euros from sponsorship and merchandising , approximately three times what they earned in 2009/2010 . The biggest revenue stream was broadcasting – both from domestic and international competitions .
gold_template: The templateLabel[3][0] templateTitle[2] templateTitle[4] is the largest source of templateTitle[2] for templateTitleSubject[0] Milano . In 2018/2019 , the football club earned approximately templateValue[3][0] templateScale euros from sponsorship and merchandising , approximately three times what they earned in 2009/2010 . The biggest templateTitle[2] templateTitle[4] was templateLabel[2][0] – both from domestic and international competitions .

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 triple than in 2010/2011 . The second biggest templateTitle[2] templateTitle[4] is the templateLabel[3][0] one .
generated: Broadcasting is the largest source of revenue for Internazionale Milan .  In 2018/2019 , the club earned approximately 159.2 million euros from Broadcasting , more than triple in 2010/2011 .  The second biggest revenue stream is the Commercial one .

Example 805:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2008', '2018']}
title: U.S. chemical products price index 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Price', 'index', '(2012', '=', '100)']: ['97.4', '92.7', '90.0', '93.2', '99.8', '99.7', '100.0', '97.8', '89.0', '86.1', '90.9']

gold: This statistic depicts the U.S. chemical price index from 2008 to 2018 . In 2008 , the price for chemical products excluding pharmaceuticals in the United States had an index of 90.9 ( 2012 = 100 ) . In 2018 , the index stood at 97.4 .
gold_template: This statistic depicts the templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] for templateTitle[1] templateTitle[2] excluding pharmaceuticals in the templateTitle[0] had an templateYLabel[1] of templateYValue[last] templateYValue[idxmin(X)] templateXValue[6] templateYLabel[3] templateYValue[4] ) . In templateXValue[max] , the templateYLabel[1] stood at templateYValue[idxmax(X)] templateYValue[idxmax(X)]

generated_template: This statistic shows the templateScale of the templateYLabel[1] templateYLabel[2] living in urban areas in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] was living in cities and urban areas .
generated: This statistic shows the percentage of the index (2012 living in urban areas in U.S. from 2008 to 2018 .  In 2018 , 97.4 % of the index (2012 of U.S. was living in cities and urban areas .

Example 806:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1970', '2018']}
title: Pre-primary school enrollment numbers in the U.S. 1970 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2000', '1995', '1990', '1985', '1980', '1975', '1970']
Y_Axis['Number', 'of', 'children', 'enrolled', '(in', 'millions)']: ['8.74', '8.64', '8.76', '8.61', '8.76', '8.83', '8.77', '9.16', '9.01', '8.84', '8.66', '8.76', '8.73', '8.52', '8.73', '8.65', '8.04', '8.03', '8.23', '5.16', '5.14', '4.28']

gold: This graph shows the number of children enrolled in pre-primary school institutions ( kindergarten or nursery ) in the United States from 1970 to 2018 . In 2018 , around 8.74 million children were enrolled in nursery or kindergarten programs in the United States .
gold_template: This graph shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitle[0] templateTitle[1] institutions ( kindergarten or nursery ) in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[idxmax(X)] templateScale templateYLabel[1] were templateYLabel[2] in nursery or kindergarten programs in the templateTitle[4] .

generated_template: In templateXValue[max] , there were approximately templateYValue[idxmax(X)] templateScale people templateYLabel[1] in the templateTitle[2] sector in the templateTitle[4] . Employment in the templateTitle[2] templateTitle[3] – additional information Employment within the templateTitle[2] templateTitle[3] has shown significant growth since templateXValue[min] . Each decade , between templateXValue[min] and templateXValue[18] , there was an templatePositiveTrend in the templateYLabel[0] of templateYLabel[1] which were employed in the templateXLabel[0] .
generated: In 2018 , there were approximately 8.74 million people children in the enrollment sector in the U.S. Employment in the enrollment numbers – additional information Employment within the enrollment numbers has shown significant growth since 1970 .  Each decade , between 1970 and 1985 , there was an increase in the Number of children which were employed in the Year .

Example 807:
titleEntities: {'Subject': ['Square'], 'Date': ['2012', '2019']}
title: Square annual gross payment volume 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Gross', 'payment', 'volume', 'in', 'billion', 'U.S.', 'dollars']: ['106.24', '84.65', '65.34', '49.68', '35.64', '23.78', '14.82', '6.52']

gold: This statistic shows Square 's gross payment volume ( GPV ) from 2012 to 2019 . In the most recent fiscal period , the company 's gross payment volume amounted to 106.24 billion U.S. dollars , up from 84.65 billion in 2018 .
gold_template: This statistic shows templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] ( GPV ) from templateXValue[min] to templateXValue[max] . In the most recent fiscal period , the company 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[4] templateYLabel[5] , up from templateYValue[1] templateScale in templateXValue[1] .

generated_template: This statistic shows templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] ( GPV ) from templateXValue[min] to templateXValue[max] . In the most recent fiscal period , the company 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[4] templateYLabel[5] , up from templateYValue[1] templateScale in templateXValue[1] .
generated: This statistic shows Square 's Gross payment volume ( GPV ) from 2012 to 2019 .  In the most recent fiscal period , the company 's Gross payment volume amounted to 106.24 billion U.S. dollars , up from 84.65 billion in 2018 .

Example 808:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2019']}
title: Lung transplant list in the United Kingdom ( UK ) 2019
X_Axis['Country', 'of', 'residence']: ['England', 'Wales', 'Scotland', 'Northern_Ireland']
Y_Axis['Number', 'of', 'individuals', 'on', 'the', 'waiting', 'list']: ['283', '22', '28', '14']

gold: At the end of March 2019 , there were 283 patients in England waiting for a lung transplant , while Scotland had 28 individuals on the lung transplant waiting list , followed by 22 and 14 waiting in Wales and Northern Ireland respectively . Lung transplants carried out in the UK In the year to March 2019 , 164 lung transplants were carried out in the United Kingdom as a whole . This represented a 20 percent increase on the number of transplants from the previous year .
gold_template: At the end of 2019 , there were templateYValue[max] patients in templateXValue[0] templateYLabel[2] for a templateTitle[0] templateTitle[1] , while templateXValue[2] had templateYValue[2] templateYLabel[1] on the templateTitle[0] templateTitle[1] templateYLabel[2] templateYLabel[3] , followed by templateYValue[1] and templateYValue[min] templateYLabel[2] in templateXValue[1] and templateXValue[last] respectively . templateTitle[0] transplants carried out in the templateTitleSubject[1] In the year to 2019 , 164 templateTitle[0] transplants were carried out in the templateTitleSubject[0] as a whole . This represented a 20 templateScale templatePositiveTrend on the templateYLabel[0] of transplants from the previous year .

generated_template: This statistic shows the templateScale of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitleDate[0] , by the templateXLabel[0] of templateXLabel[1] . templateYValue[max] templateScale of templateYLabel[1] with templateXValue[last] and templateXValue[last] templateXLabel[1] used templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] .
generated: This statistic shows the percentage of United Kingdom individuals United Kingdom UK 2019 residence in 2019 , by the Country of residence .  283 % of individuals with Northern_Ireland and residence used Kingdom UK 2019 in .

Example 809:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: U.S. per capita consumption of oat products 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']: ['4.8', '4.8', '4.6', '4.6', '4.5', '4.5', '4.7', '4.8', '4.7', '4.7', '4.7', '4.7', '4.7', '4.6', '4.7', '4.7', '4.5', '4.5', '4.4']

gold: The timeline shows the per capita consumption of oat products in the United States from 2000 to 2018 . The U.S. per capita consumption of oat products amounted to 4.8 pounds in 2018 .
gold_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] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[idxmax(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 oat products in the U.S. from 2000 to 2018 .  According to the report , the U.S. Per capita consumption of oat products amounted to approximately 4.8 pounds in 2018 .

Example 810:
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 12 month templateScale change in the templateYLabel[0] consumer price index in the templateTitle[0] from templateXValue[18] to templateXValue[0] at year-end . In templateXLabel[0] templateXValue[0] , prices went up by templateYValue[0] templateScale compared to templateXLabel[0] templateXValue[1] . The annual templateYLabel[1] templateYLabel[2] based on current dollar templateXLabel[1] can be accessed here .
generated: This statistic shows the 12 month percentage change in the Chained consumer price index in the Chained from 2001 to 2019 at year-end .  In December 2019 , prices went up by 144.73 % compared to December 2018 .  The annual Consumer Price based on current dollar value can be accessed here .

Example 811:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in the Netherlands 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['1.52', '1.47', '1.49', '1.53', '1.64', '1.77', '2.6', '2.91', '2.19', '1.96', '1.42']

gold: The statistic depicts the Netherlands ' real 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 the templateTitleSubject[0] ' real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateTitle[5] 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] in real templateYLabel[0] in templateTitleSubject[0] from between 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 Netherlands from between 2014 to 2018 , with projections up until 2024 .  In 2018 , Netherlands 's real Gross domestic product increased by around 2.6 % compared to the previous Year .

Example 812:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: Employment rate - women 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['Employment', 'rate']: ['55.4', '54.9', '54.6', '54.1', '53.7', '53.5', '53.2', '53.1', '53.2', '53.6', '54.4', '56.2', '56.6', '56.6', '56.2', '56', '56.1', '56.3', '57', '57.5', '57.4', '57.1', '56.8', '56', '55.6', '55.3', '54.1', '53.8', '53.7', '54.3']

gold: Since 1990 , the employment rate of women in the United States has stayed more or less steady . In 1990 , the female employment rate was 54.3 percent , and in 2018 , the employment rate was at 55.4 percent . However , it reached a peak in 2000 at 57.5 percent .
gold_template: Since templateTitleDate[min] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] in the templateTitle[3] has stayed more or less steady . In templateTitleDate[min] , the female templateYLabel[0] templateYLabel[1] was templateYValue[last] templateScale , and in 2018 , the templateYLabel[0] templateYLabel[1] was at templateYValue[0] templateScale . However , it reached a peak in 2000 at templateYValue[max] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[0] Americans from templateTitleDate[min] to templateTitleDate[max] . templateYLabel[0] templateYLabel[1] of the templateTitle[0] labor force has templateNegativeTrend to templateYValue[min] templateScale in templateTitleDate[max] , compared to templateYValue[max] templateScale in 2010 . The national templateYLabel[0] templateYLabel[1] can be found here .
generated: This statistic shows the Employment rate of Employment Americans from 1990 to 2019 .  Employment rate of the Employment labor force has decreased to 53.1 % in 2019 , compared to 57.5 % in 2010 .  The national Employment rate can be found here .

Example 813:
titleEntities: {'Subject': ['U.S. Real Gross Domestic Product GDP'], 'Date': ['2018']}
title: Per capita U.S. Real Gross Domestic Product ( GDP ) in 2018 , by metro area
X_Axis['Metropolitan', 'Area']: ['Midland_TX', 'San_Jose-Sunnyvale-Santa_Clara_CA', 'San_Francisco-Oakland-Berkeley_CA', 'Seattle-Tacoma-Bellevue_WA', 'Wheeling_WV-OH', 'Boston-Cambridge-Newton_MA-NH', 'Durham-Chapel_Hill_NC', 'Bridgeport-Stamford-Norwalk_CT', 'Odessa_TX', 'Lake_Charles_LA', 'Boulder_CO_', 'Washington-Arlington-Alexandria_DC-VA-MD-WV_', 'Trenton-Princeton_NJ', 'Columbus_IN', 'New_York-Newark-Jersey_City_NY-NJ-PA', 'Hartford-East_Hartford-Middletown_CT', 'Lima_OH', 'Longview_TX', 'Napa_CA', 'Los_Angeles-Long_Beach-Anaheim_CA', 'Madison_WI', 'Beaumont-Port_Arthur_TX', 'Des_Moines-West_Des_Moines_IA', 'Salt_Lake_City_UT', 'Wausau-Weston_WI', 'Denver-Aurora-Lakewood_CO_', 'San_Diego-Chula_Vista-Carlsbad_CA', 'Philadelphia-Camden-Wilmington_PA-NJ-DE-MD', 'Casper_WY', 'Minneapolis-St._Paul-Bloomington_MN-WI', 'Baltimore-Columbia-Towson_MD', 'Greeley_CO_', 'Sioux_Falls_SD', 'Chicago-Naperville-Elgin_IL-IN-WI', 'Norwich-New_London_CT', 'Manhattan_KS', 'Kokomo_IN', 'Houston-The_Woodlands-Sugar_Land_TX', 'Cedar_Rapids_IA', 'San_Angelo_TX', 'Oxnard-Thousand_Oaks-Ventura_CA', 'Harrisburg-Carlisle_PA', 'Santa_Maria-Santa_Barbara_CA', 'Urban_Honolulu_HI', 'Decatur_IL', 'Dallas-Fort_Worth-Arlington_TX', 'Peoria_IL', 'Austin-Round_Rock-Georgetown_TX', 'Baton_Rouge_LA', 'Bloomington_IL', 'Rocky_Mount_NC', 'Manchester-Nashua_NH', 'Nashville-Davidson--Murfreesboro--Franklin_TN', 'Indianapolis-Carmel-Anderson_IN', 'Portland-Vancouver-Hillsboro_OR-WA', 'Atlanta-Sandy_Springs-Alpharetta_GA', 'Bellingham_WA', 'Omaha-Council_Bluffs_NE-IA', 'Pittsburgh_PA', 'Salinas_CA', 'Albany-Schenectady-Troy_NY', 'Ann_Arbor_MI', 'Midland_MI', 'Ames_IA', 'Milwaukee-Waukesha_WI', 'California-Lexington_Park_MD', 'Jackson_TN', 'Elkhart-Goshen_IN', 'Carson_City_NV', 'New_Orleans-Metairie_LA', 'Barnstable_Town_MA', 'Mount_Vernon-Anacortes_WA', 'San_Luis_Obispo-Paso_Robles_CA', 'Sheboygan_WI', 'Cleveland-Elyria_OH', 'Richmond_VA_', 'Toledo_OH', 'Green_Bay_WI', 'Rochester_MN', 'Williamsport_PA', 'Charleston_WV', 'Charlotte-Concord-Gastonia_NC-SC', 'Santa_Rosa-Petaluma_CA', 'Fairbanks_AK_', 'Cincinnati_OH-KY-IN', 'Oklahoma_City_OK', 'Billings_MT', 'Anchorage_AK', 'Syracuse_NY', 'Kansas_City_MO-KS', 'New_Haven-Milford_CT', 'Burlington-South_Burlington_VT', 'Raleigh-Cary_NC', 'Kahului-Wailuku-Lahaina_HI', 'Enid_OK', 'Bakersfield_CA', 'Detroit-Warren-Dearborn_MI', 'Iowa_City_IA', 'Sacramento-Roseville-Folsom_CA', 'Columbus_OH']
Y_Axis['Per', 'capita', 'Real', 'GDP', 'in', 'chained', '(2009)', 'U.S.', 'dollars']: ['197509', '159570', '105127', '90284', '84952', '84682', '84088', '82591', '79228', '77951', '77648', '77551', '76957', '76892', '76689', '74218', '73629', '73253', '71726', '70802', '70454', '68808', '67868', '67427', '67247', '66145', '65611', '65395', '65361', '65343', '65325', '65323', '64913', '64393', '64040', '63940', '63780', '63643', '63372', '63325', '62798', '62485', '62384', '62338', '62299', '62188', '62086', '61991', '61952', '61831', '61707', '61445', '60990', '60860', '60522', '60043', '59888', '59652', '59538', '59309', '59295', '59208', '59098', '58946', '58726', '58681', '58601', '58565', '58472', '58421', '58384', '58261', '58225', '58032', '58010', '57929', '57800', '57790', '57694', '57452', '57334', '57144', '57138', '57129', '57068', '57051', '56740', '56616', '56446', '56176', '56039', '56008', '55872', '55863', '55681', '55605', '55168', '54980', '54856', '54438']

gold: This graph shows the per capita Real Gross Domestic Product ( GDP ) of the United States in 2018 , by metropolitan area . Only the 100 leading metro areas are shown here . In 2018 , Midland metropolitan area , in Texas , had a per capita Real GDP of about 197,509 chained ( 2012 ) U.S. dollars .
gold_template: This graph shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitleSubject[0] Product ( templateYLabel[3] ) of the templateTitle[2] in templateTitleDate[0] , templateTitle[9] templateXLabel[0] templateXLabel[1] . Only the 100 leading templateTitle[10] areas are shown here . In templateTitleDate[0] , templateXValue[0] templateXLabel[0] templateXLabel[1] , in Texas , had a templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of about templateYValue[max] templateYLabel[4] ( 2012 ) templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] of templateXValue[22] templateYLabel[2] in the templateTitle[0] in templateTitleDate[0] , templateTitle[2] templateXLabel[0] of templateXValue[0] . In templateTitleDate[0] , about templateYValue[max] templateScale templateYLabel[2] were enrolled in the templateTitle[0] .
generated: This statistic shows the Per of Des_Moines-West_Des_Moines_IA Real in the Per in 2018 , U.S. Metropolitan of Midland_TX .  In 2018 , about 197509 million Real were enrolled in the Per .

Example 814:
titleEntities: {'Subject': ['UK'], 'Date': ['2012', '2019']}
title: Number of female directors in FTSE 100 companies UK 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'female', 'directors']: ['292', '264', '259', '244', '233', '205', '169', '141']

gold: This statistic shows the total number of female directors working on the board of FTSE 100 companies in the United Kingdom ( UK ) from 2012 to 2019 . During the observed period the number of female directors ( both executive and non-executive ) in FTSE 100 companies increased by 151 to reach a total of 292 females holding a director position , as of June 2019 .
gold_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] working on the board of templateTitle[3] templateTitle[4] templateTitle[5] in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . During the observed period the templateYLabel[0] of templateYLabel[1] templateYLabel[2] ( both executive and non-executive ) in templateTitle[3] templateTitle[4] templateTitle[5] templatePositiveTrend by 151 to reach a total of templateYValue[idxmax(X)] females holding a director position , as of 2019 templateXValue[idxmax(Y)]

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , around templateYValue[max] thousand babies were born in templateTitleSubject[0] in templateXValue[idxmax(Y)] , an templatePositiveTrend from the previous templateXLabel[0] were templateYValue[1] thousand babies were born .
generated: This statistic displays the Number of female in UK from 2012 to 2019 .  According to the report , around 292 thousand babies were born in UK 2019 , an increase from the previous Year were 264 thousand babies were born .

Example 815:
titleEntities: {'Subject': ['Mattel'], 'Date': ['2019']}
title: Distribution of U.S. toy manufacturer Mattel 's sales by customer 2019
Data:
                0     1     2     3     4     5     6     7     8     9    10    11    12
Year         2019  2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007
Wal-Mart     1.01  1.07  1.02   1.1   1.0   1.1   1.2   1.2   1.2   1.1   1.0   1.1   1.1
Toys 'R' Us     0     0   0.4   0.6   0.6   0.6   0.7   0.7   0.7   0.8   0.7   0.7   0.7
Target       0.44  0.45  0.37   0.4   0.5   0.5   0.5   0.5   0.5   0.5   0.5   0.5   0.6 

gold: This statistic shows the net sales of the toy manufacturer Mattel by major customer from 2007 to 2019 . In 2019 , Mattel made approximately one billion U.S. dollars worth of sales to Wal-Mart ..
gold_template: This statistic shows the net templateTitle[6] of the templateTitle[2] templateTitle[3] templateTitleSubject[0] templateTitle[7] major templateTitle[8] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateTitleSubject[0] made approximately templateValue[1][0] templateScale templateTitle[1] dollars worth of templateTitle[6] to templateLabel[1][0] ..

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] operated templateTitle[6] templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] , templateTitle[6] templateTitle[7] . templateLabel[2][0] templateLabel[2][1] templateLabel[2][2] was the templateTitle[7] with largest templateTitle[0] of templateTitle[1] as of 2 , templateValue[0][0] , with templateValue[2][0] locations .
generated: This statistic shows the Distribution of U.S. operated sales Mattel manufacturer from 2007 to 2019 , sales by .  Toys 'R' Us was the by with largest Distribution of U.S. as of 2 , 2019 with 0 locations .

Example 816:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2017']}
title: Participants in gymnastics 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']: ['4.81', '5.38', '4.68', '4.62', '4.97', '5.4', '4.83', '4.82', '4.02', '3.88', '4.07', '3.63']

gold: The number of U.S. participants in gymnastics aged six years and older amounted to approximately 4.81 million in 2017 , down from the previous year 's participation by around 10 percent . A participant is defined as an individual who took part in gymnastics at least once in the given year . Gymnastics in the U.S.While participation has decreased , national interest in the sport is relatively high and estimated to keep increasing - in 2018 , it was estimated that around 6.8 million Americans were very interested in gymnastics .
gold_template: The templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] in templateTitle[1] aged six years and older amounted to approximately templateYValue[0] templateScale in templateXValue[max] , down templateTitle[3] the previous templateXLabel[0] 's participation by around 10 templateScale . A participant is defined as an individual who took part in templateTitle[1] at least once in the given templateXLabel[0] . templateTitle[1] in the U.S.While participation has templateNegativeTrend , national interest in the sport is relatively high and estimated to keep templatePositiveTrend - in 2018 , it was estimated that around 6.8 templateScale Americans were very interested in templateTitle[1] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged templateYValue[9] years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Number of participants in gymnastics U.S. in the from 2006 to 2017 .  In 2017 , the Number of participants ( aged 3.88 years and older ) in gymnastics U.S. amounted to approximately 4.81 millions .

Example 817:
titleEntities: {'Subject': ['European'], 'Date': ['2016']}
title: International overnight visitors in European city destinations in 2016
X_Axis['City']: ['London', 'Paris', 'Istanbul', 'Barcelona', 'Amsterdam', 'Milan', 'Rome', 'Vienna', 'Prague', 'Dublin']
Y_Axis['Number', 'of', 'international', 'overnight', 'visitors', 'in', 'millions']: ['19.06', '15.45', '9.16', '8.36', '8.36', '8.17', '7.09', '6.42', '6.11', '5.51']

gold: This statistic shows the number of international overnight visitors in the most popular European city destinations in 2016 . London had the largest number of international overnight visitors in 2016 with 19.06 million . In 2016 , London had the second most international overnight visitors .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the most popular templateTitleSubject[0] templateXLabel[0] templateTitle[5] in templateTitleDate[0] . templateXValue[0] had the largest templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] with templateYValue[max] templateScale . In templateTitleDate[0] , templateXValue[0] had the second most templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic provides a ranking of the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[5] on the templateYLabel[0] of templateYLabel[1] as of 2017 . At this point , the templateXValue[1] templateXValue[0] templateXValue[1] was ranked second among such templateTitle[4] in the templateTitle[2] , with a total of templateYValue[1] templateYLabel[1] .
generated: This statistic provides a ranking of the International overnight European city destinations on the Number of international as of 2017 .  At this point , the Paris London was ranked second among such city in the visitors , with a total of 15.45 international .

Example 818:
titleEntities: {'Subject': ['Turkey'], 'Date': ['2024']}
title: Total population of Turkey 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['88.03', '87.05', '86.06', '85.05', '84.04', '83.02', '82.0', '80.81', '79.82', '78.74', '77.7']

gold: The statistic shows the total population of Turkey from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population of Turkey amounted to about 82 million people . Population of Turkey Although total population increased on a yearly basis from 2004 to 2014 , population growth has slowly decreased annually as of 2011 , despite remaining positive .
gold_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] amounted to about templateYValue[6] templateScale people . templateTitle[1] of templateTitleSubject[0] Although templateTitle[0] templateTitle[1] templatePositiveTrend on a yearly basis from 2004 to templateXValue[min] , templateTitle[1] growth has slowly templateNegativeTrend annually as of 2011 , despite remaining positive .

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 Turkey from 2014 to 2024 .  In 2018 , the Total population of Turkey was estimated at approximately 82.0 millions Inhabitants .

Example 819:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Canoes - number of units sold in the U.S. 2013
X_Axis['Year']: ['2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Canoes', 'sold']: ['74100', '78600', '77800', '77100', '89600', '73700', '99600', '99900', '77200', '93900', '86700', '100000', '105800', '111800']

gold: The timeline depicts how many canoes were sold in each year from 2000 to 2013 . In 2010 , a total of 77,100 canoes were sold in the United States , a decrease of 14 percent compared to the previous year . More statistics and facts on recreational boating
gold_template: The timeline depicts how many templateYLabel[0] were templateYLabel[1] in each templateXLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , a total of templateYValue[3] templateYLabel[0] were templateYLabel[1] in the templateTitle[4] , a templateNegativeTrend of 14 templateScale compared to the previous templateXLabel[0] . More statistics and facts on recreational boating

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[3] , templateYLabel[1] and templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] people worked either part-time or full-time as templateTitle[1] templateTitle[4] , templateYLabel[1] or templateTitle[3] . Included are solo templateYLabel[1] without employees working part-time or full-time in their artistic trade .
generated: This statistic shows the Canoes of number sold , and U.S. in the 2013 from 2000 to 2013 .  In 2013 , 111800 people worked either part-time or full-time as number U.S. , sold or .  Included are solo sold without employees working part-time or full-time in their artistic trade .

Example 820:
titleEntities: {'Subject': ['Barclays'], 'Date': ['2019']}
title: Employment figures Barclays group 2019 , by country
X_Axis['Country']: ['United_Kingdom', 'India', 'United_States', 'Singapore', 'Germany', 'Isle_of_Man', 'Japan', 'Czechia', 'Hong_Kong', 'Switzerland', 'Lithuania', 'Ireland', 'Jersey', 'Monaco', 'Italy', 'France', 'UAE', 'Spain', 'Mexico', 'Canada', 'Guernsey', 'Luxembourg', 'Israel', 'China', 'Portugal', 'The_Netherlands', 'Brazil', 'Sweden', 'Other_']
Y_Axis['Employee', 'numbers']: ['48241', '18537', '10142', '970', '779', '480', '471', '462', '441', '276', '273', '261', '251', '197', '180', '171', '170', '80', '75', '66', '66', '47', '41', '25', '13', '12', '7', '7', '4']

gold: As of 2019 , Barclays Group , one of the UK 's largest banks , employed approximately 48.2 thousand people across the country . The UK division accounted for almost 60 percent of all employees of the banking group . India and the United States accounted for the next highest employment figures globally .
gold_template: As of templateTitleDate[0] , templateTitleSubject[0] templateTitle[3] , one of the UK 's largest banks , employed approximately templateYValue[max] thousand people across the templateXLabel[0] . The UK division accounted for almost 60 templateScale of all employees of the banking templateTitle[3] . templateXValue[1] and the templateXValue[0] templateXValue[2] accounted for the next highest templateTitle[0] templateTitle[1] globally .

generated_template: This statistic shows the proportion of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] only ( excludes templateYLabel[1] templateYLabel[2] templateYLabel[3] both templateYLabel[4] templateYLabel[5] and eyeglasses ) in templateTitle[6] templateTitleSubject[0] templateTitle[8] in templateTitleDate[0] . In this year , templateXValue[1] , templateXValue[2] and templateXValue[0] had the highest proportion of templateYLabel[1] wearing templateYLabel[4] templateYLabel[5] with approximately templateYValue[max] templateScale doing so . This was followed by templateXValue[3] and templateXValue[4] with templateYValue[3] templateScale of the respective populations wearing templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the proportion of numbers only ( excludes numbers both and eyeglasses ) in country Barclays in 2019 .  In this year , India United_States and United_Kingdom had the highest proportion of numbers wearing with approximately 48241 million doing so .  This was followed by Singapore and Germany with 970 % of the respective populations wearing numbers .

Example 821:
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 templateTitle[2] templateTitleSubject[0] franchise 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 templateTitle[2] templateTitleSubject[0] are owned by William DeWitt Jr. , who bought the templateYLabel[0] for 150 templateScale templateYLabel[3] templateYLabel[4] in 1996 .
generated: This graph depicts the value of the San San Francisco Giants franchise of Major League Baseball from 2002 to 2019 .  In 2019 , the Franchise had an estimated value of 3000 million U.S. dollars .  The San San Francisco Giants are owned by William DeWitt Jr. , who bought the Franchise for 150 million U.S. dollars in 1996 .

Example 822:
titleEntities: {'Subject': ['LinkedIn'], 'Date': ['2013', '2016']}
title: LinkedIn : unique mobile visiting members 2013 to 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"]
Y_Axis['Number', 'of', 'mobile', 'visiting', 'members', 'in', 'millions']: ['63', '61', '57', '55', '51', '49', '45', '42', '38', '35', '31', '29', '26', '20']

gold: This timeline displays the number of unique mobile visiting members to social network LinkedIn . As of the second quarter of 2016 , LinkedIn had an average of 63 million unique visiting members via mobile . These accounted for 59 percent of all unique visiting members .
gold_template: This timeline displays the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] to social network templateTitleSubject[0] . As of the second templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] had an average of templateYValue[max] templateScale templateTitle[1] templateYLabel[2] templateYLabel[3] via templateYLabel[1] . These accounted for 59 templateScale of all templateTitle[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateTitleDate[min] to the third templateXLabel[0] of templateTitleDate[max] . As of the fourth templateXLabel[0] of templateTitleDate[max] , templateYValue[0] templateScale of templateYLabel[1] templateYLabel[2] templatePositiveTrend from the previous templateXLabel[0] .
generated: This statistic shows the Number of mobile visiting in the LinkedIn from 2013 to the third Quarter of 2016 .  As of the fourth Quarter of 2016 , 63 millions of mobile visiting increased from the previous Quarter .

Example 823:
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: The ranking shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[3] in the templateTitleSubject[0] between the fourth templateLabel[0][0] of templateTitleDate[min] and the fourth templateLabel[0][0] of templateTitleDate[max] . In the fourth templateLabel[0][0] of templateTitleDate[max] , templateLabel[4][0] 's templateTitle[0] templateTitle[1] was just templateValue[2][3] templateScale .
generated: The ranking shows the Daily active of Snapchat in the Snapchat between the fourth Quarter of 2014 and the fourth Quarter of 2019 .  In the fourth Quarter of 2019 , Rest 's Daily active was just 61 % .

Example 824:
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: The statistic shows the templateTitle[0] opinion on templateTitle[2] templateTitle[3] among Italians in templateTitleDate[0] . According to the data , templateYValue[0] templateScale of the templateYLabel[1] used an templateXValue[0] , while templateYValue[max] templateScale did not . templateYValue[min] templateScale of templateYLabel[1] said they did n't templateXValue[last] if they used templateXValue[0] blocking software .
generated: The statistic shows the Public opinion on adoption rights among Italians in 2014 .  According to the data , 63 % of the respondents used an Yes_should , while 63 % did not .  2 % of respondents said they did n't No_opinion if they used Yes_should blocking software .

Example 825:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Age of U.S. video game players in 2019
X_Axis['Year']: ['Under_18_years', '18_to_35_years', '36_to_49_years', '50_years_and_older']
Y_Axis['Share', 'of', 'respondents']: ['21', '40', '18', '21']

gold: Video gaming is no longer a hobby exclusively enjoyed by the young . As generations have grown up with video games a normal part of life , the age of the average gamer also increases . While 40 percent of video game players still come from the 18 to 35 age demographic , some 21 percent are 50 years and older .
gold_template: templateTitle[2] gaming is no longer a hobby exclusively enjoyed by the young . As generations have grown up with templateTitle[2] games a normal part of life , the templateTitle[0] of the average gamer also increases . While templateYValue[max] templateScale of templateTitle[2] templateTitle[3] templateTitle[4] still come from the templateXValue[0] to templateXValue[1] templateTitle[0] demographic , some templateYValue[last] templateScale are templateXValue[last] templateXValue[0] and templateXValue[last] .

generated_template: This statistic shows the templateScale of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] a templateTitle[6] as of 2018 , sorted templateTitle[8] templateTitle[9] templateTitle[10] . According to the survey , templateYValue[max] templateScale of templateTitleSubject[0] templateTitle[2] aged templateXValue[0] to templateXValue[0] had templateTitle[5] a photograph of themselves and uploaded it to a social media website .
generated: This statistic shows the percentage of U.S. video game players 2019 a as of 2018 , sorted 2019 .  According to the survey , 40 % of U.S. video aged Under_18_years to had 2019 a photograph of themselves and uploaded it to a social media website .

Example 826:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2019']}
title: Employment by economic sector in South Korea 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   4.68   4.73   4.78   4.82   5.11   5.58   5.98   6.13   6.37    6.6  7.01
Industry     24.84  24.96  25.07  24.99   25.2  24.77  24.52  24.58  24.85  25.02  24.5
Services     70.48  70.31  70.14  70.19  69.69  69.65   69.5  69.29  68.78  68.38  68.5 

gold: The statistic shows the distribution of employment in South Korea by economic sector from 2009 to 2019 . In 2019 , 4.68 percent of the employees in South Korea were active in the agricultural sector , 24.84 percent in industry and 70.48 percent in the services 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 templateLabel[3][0] 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 Korea by economic sector from 2009 to 2019 .  In 2019 , 4.68 % of the employees in South Korea were active in the agricultural sector , 24.84 % in Industry and 70.48 % in the service sector .

Example 827:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2012']}
title: U.S. geology - number of earthquakes from 2000 to 2012
X_Axis['Year']: ['2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Number', 'of', 'earthquakes']: ['3836', '5237', '8497', '4264', '3618', '2791', '2783', '3685', '3550', '2946', '3876', '2261', '2342']

gold: This statistic shows the number of earthquakes in the United States from 2000 to 2012 . 2,342 earthquakes were recorded in the United States in the year 2000 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitle[0] templateTitle[4] templateXValue[min] to templateXValue[max] . templateYValue[idxmin(X)] templateYLabel[1] were recorded in the templateTitle[0] in the templateXLabel[0] templateXValue[min] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , a total of templateYValue[idxmax(X)] templateYLabel[1] templateTitle[1] were recorded in the templateTitleSubject[0] .
generated: The statistic shows the Number of geology earthquakes in the U.S. from 2000 to 2012 .  In 2012 , a total of 3836 earthquakes geology were recorded in the U.S. .

Example 828:
titleEntities: {'Subject': ['European'], 'Date': ['2019']}
title: Active social media penetration in European countries 2019
X_Axis['Country']: ['Albania', 'Andorra', 'Austria', 'Belarus', 'Belgium', 'Bosnia_&_Herzegovina', 'Bulgaria', 'Croatia', 'Czechia', 'Denmark', 'Estonia', 'Finland', 'France', 'Germany', 'Gibraltar', 'Greece', 'Guernsey', 'Hungary', 'Iceland', 'Republic_of_Ireland', 'Italy', 'Kosovo', 'Latvia', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Malta', 'Moldova', 'Monaco', 'Montenegro', 'Netherlands', 'Norway', 'Poland', 'Portugal', 'Romania', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'The_Faroe_Islands', 'Ukraine', 'United_Kingdom_(UK)']
Y_Axis['Active', 'social', 'media', 'penetration']: ['51', '65', '50', '40', '65', '49', '54', '48', '54', '71', '57', '59', '58', '46', '72', '54', '58', '62', '83', '66', '59', '40', '51', '44', '59', '62', '88', '30', '99', '60', '64', '71', '47', '65', '56', '43', '50', '53', '60', '72', '51', '75', '39', '67']

gold: Monaco is the European nation with the highest active social media penetration , as of January 2019 . Monaco 's unique status as a small wealthy nation obviously plays a role in this rate . Malta , the country with the second highest rate , has an active social media penetration rate that is 11 percent lower .
gold_template: templateXValue[28] is the templateTitleSubject[0] nation with the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] , as of 2019 . templateXValue[28] 's unique status as a small wealthy nation obviously plays a role in this rate . templateXValue[26] , the templateXLabel[0] with the second highest rate , has an templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] rate that is 11 templateScale lower .

generated_template: In templateTitleDate[0] , templateXValue[0] was the leader in templateYLabel[0] of official templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] with templateYValue[max] out of 6,859 templateYLabel[1] templateYLabel[2] . Second and third come templateXValue[1] and templateXValue[2] with templateYValue[1] and templateYValue[2] official templateYLabel[1] templateYLabel[2] . The templateYLabel[0] of official templateYLabel[1] templateYLabel[2] in templateXValue[0] is in decline .
generated: In 2019 , Albania was the leader in Active of official social media in European with 99 out of 6,859 social media .  Second and third come Andorra and Austria with 65 and 50 official social media .  The Active of official social media in Albania is in decline .

Example 829:
titleEntities: {'Subject': ['European Union'], 'Date': []}
title: Fresh orange production volume in the European Union 2016/17 , by country
X_Axis['Country']: ['Spain', 'Italy', 'Greece', 'Portugal', 'Cyprus']
Y_Axis['Volume', 'in', '1,000', 'tons']: ['3731', '1500', '920', '344', '30']

gold: In 2018/2019 , Spain was the leading producer of fresh oranges in the European Union ( EU28 ) , with over 3.7 million tons of fresh oranges produced . The Spanish production was more than two times the production of Italy , the second largest producer of oranges . The other three producers in the EU produced less than one million tons during this year .
gold_template: In 2018/2019 , templateXValue[0] was the leading producer of templateTitle[0] oranges in the templateTitleSubject[0] ( EU28 ) , with over 3.7 templateScale templateYLabel[2] of templateTitle[0] oranges produced . The Spanish templateTitle[2] was more than two times the templateTitle[2] of templateXValue[1] , the second largest producer of oranges . The other three producers in the EU produced less than one templateScale templateYLabel[2] during this year .

generated_template: This statistic shows the templateScale of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleDate[0] , by templateYValue[2] templateScale of the population . According to the survey , templateYValue[max] templateScale of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[2] .
generated: This statistic shows the percentage of European Union 1,000 volume European Union 2016/17 by in , by 920 % of the population .  According to the survey , 3731 % of European Union 1,000 tons in Greece .

Example 830:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2018']}
title: Age structure in Denmark 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          16.46  16.49   16.6  16.84  17.03  17.28  17.57  17.81  17.95  18.16  18.35
15-64 years         63.73  63.85  63.98  64.11   64.4  64.69  64.94  65.16  65.38  65.54  65.69
65 years and older  19.81  19.66  19.42  19.05  18.57  18.03  17.49  17.03  16.67   16.3  15.96 

gold: This statistic shows the age structure in Denmark from 2008 to 2018 . In 2018 , about 16.46 percent of Denmark '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 Denmark from 2008 to 2018 .  In 2018 , about 16.46 % of Denmark 's total population were aged 0 to 14 years .

Example 831:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010', '2017']}
title: Halloween costume expenditure in the U.S. 2010 - 2017 , by type
Data:
                        0     1     2     3     4     5     6     7
Year                 2017  2016  2015  2014  2013  2012  2011  2010
Adult costumes       1.68  1.54  1.22  1.38  1.22   1.4  1.21  0.99
Children's costumes  1.23  1.17  0.95  1.06  1.04   1.1   1.0  0.84
Pet costumes         0.44  0.42  0.35  0.35  0.33  0.37  0.31  0.22 

gold: No Halloween celebration is complete without Halloween costumes . In 2017 , consumers in the United States spent around 1.68 billion U.S. dollars on adult Halloween costumes , up from 1.54 billion U.S. dollars in the previous year . Halloween Expenditure in the U.S .
gold_template: No templateTitle[0] celebration is complete without templateTitle[0] templateLabel[1][1] . In templateValue[0][0] , consumers in the templateTitle[3] spent around templateValue[1][0] templateScale templateTitleSubject[0] dollars on templateLabel[1][0] templateTitle[0] templateLabel[1][1] , up from templateValue[1][1] templateScale templateTitleSubject[0] dollars in the previous templateLabel[0][0] . templateTitle[0] templateTitle[2] in the templateTitle[3] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] and the templateTitleSubject[0] templateTitle[6] from templateValue[0][last] to templateValue[0][0] . The figures refer to those younger than 25 years . In templateValue[0][0] , the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] amounted to templateValue[2][0] templateScale .
generated: This statistic shows the Halloween costume expenditure in the U.S. and the U.S. by from 2010 to 2017 .  The figures refer to those younger than 25 years .  In 2017 , the Halloween costume expenditure in the U.S. amounted to 1.23 billion .

Example 832:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2001', '2019']}
title: Average daily temperatures in the United Kingdom ( UK ) 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['Degrees', 'Celsius']: ['10.5', '10.6', '10.6', '10.3', '10.3', '10.9', '9.7', '9.8', '10.7', '9.0', '10.1', '10.0', '10.5', '10.8', '10.5', '10.6', '10.6', '10.8', '10.2']

gold: The average daily temperature in the United Kingdom ( UK ) has remained relatively stable since 2001 , with temperatures rarely straying below 10 degrees Celsius . In 2019 , the UK had and average daily temperature of 10.5 degrees Celsius . The highest average daily temperature during the period shown was recorded in 2014 at 10.9 degrees Celsius .
gold_template: The templateTitle[0] templateTitle[1] temperature in the templateTitleSubject[0] ( templateTitleSubject[1] ) has remained relatively stable since templateXValue[min] , with templateTitle[2] rarely straying below templateYValue[3] templateYLabel[0] templateYLabel[1] . In templateXValue[max] , the templateTitleSubject[1] had and templateTitle[0] templateTitle[1] temperature of templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] . The highest templateTitle[0] templateTitle[1] temperature during the period shown was recorded in templateXValue[5] at templateYValue[max] templateYLabel[0] templateYLabel[1] .

generated_template: This statistic shows the total templateYLabel[1] of templateTitle[0] templateYLabel[0] in the templateYLabel[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] templateYLabel[1] of templateYLabel[0] stood at around templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] . The templateTitle[3] were ranked as third leading templateTitle[0] producing country worldwide in 2014/2015 .
generated: This statistic shows the total Celsius of Average Degrees in the Celsius from 2001 to 2019 .  In 2019 , Degrees Celsius of Degrees stood at around 10.5 % Celsius .  The United were ranked as third leading Average producing country worldwide in 2014/2015 .

Example 833:
titleEntities: {'Subject': ['FDI'], 'Date': ['1994', '2018']}
title: Foreign direct investment ( FDI ) from the U.S. into NAFTA 1994 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     24
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
Mexico  117.88  109.67  100.73  101.33   94.48   86.43  104.39    85.6   85.75   84.05   87.44   91.05   82.97   73.69   63.38   56.85    56.3  52.54   39.35   37.15  26.66  24.05  19.35  16.87  16.97
Canada  401.87  391.21  365.38  361.95  370.22  370.26  366.71  330.04  295.21  274.81  246.48  250.64  205.13  231.84  214.93  187.95  166.47  152.6  132.47  119.59   98.2  96.63  89.59   83.5  74.22 

gold: This statistic shows foreign direct investments of the United States into NAFTA countries from 1994 to 2018 . In 2018 , there was approximately 402 billion U.S. dollars worth of direct investments in Canada from the U.S .
gold_template: This statistic shows templateTitle[0] templateTitle[1] investments of the templateTitle[5] templateTitle[6] templateTitle[7] countries templateTitle[4] templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , there was approximately templateValue[2][0] templateScale templateTitle[5] dollars worth of templateTitle[1] investments in templateLabel[2][0] templateTitle[4] the templateTitle[5] .

generated_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: The statistic shows the number of FDI direct from 1994 to 2018 .  At the end of 2018 , 401.87 million people were Canada direct .  Canada Canada Canada 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 .

Example 834:
titleEntities: {'Subject': ['Terrorism'], 'Date': []}
title: Terrorism - number of hostages taken by region
X_Axis['Country']: ['Africa', 'South_Asia', 'Near_East', 'East_Asia_and_Pacific', 'Western_Hemisphere', 'Europe_and_Eurasia']
Y_Axis['Number', 'of', 'hostages', 'taken']: ['2651', '1748', '1206', '246', '190', '9']

gold: The statistic shows the number of hostages taken by terrorists in 2010 by region . 2.651 people were taken hostage by terrorists in Africa .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[4] terrorists in 2010 templateTitle[4] templateTitle[5] . 2.651 people were templateYLabel[2] hostage templateTitle[4] terrorists in templateXValue[0] .

generated_template: This statistic 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: This statistic shows the percentage of Number hostages in the Terrorism and hostages taken in .  Africa has 2651 million Number hostages .

Example 835:
titleEntities: {'Subject': ['Penguin Random House'], 'Date': ['2005', '2018']}
title: Number of employees of Penguin Random House 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']: ['10351', '10615', '10594', '10582', '12812', '11838', '5712', '5343', '5264', '5432', '5779', '5764', '5804', '5395']

gold: This statistic shows the number of employees of Penguin Random House from 2005 to 2018 . In 2018 , 10,351 people were employed by the company worldwide , down from 10,615 a year earlier .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] House from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] people were employed by the company worldwide , down from templateYValue[1] a templateXLabel[0] earlier .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] worldwide . In templateXValue[max] , templateTitleSubject[0] employed templateYValue[0] templateYValue[idxmax(X)] .
generated: This statistic shows the Number of employees Penguin Random House from 2005 to 2018 worldwide .  In 2018 , Penguin Random House employed 10351 .

Example 836:
titleEntities: {'Subject': ['China'], 'Date': ['2017']}
title: Export of goods from China 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Exports', 'in', 'billion', 'U.S.', 'dollars']: ['2263.33', '2097.63', '2273.47', '2342.29', '2209.01', '2048.71', '1898.38', '1577.75', '1201.61', '1430.69', '1220.46']

gold: The statistic shows the export of goods from China from 2007 to 2017 . In 2017 , goods worth approximately 2.26 trillion U.S. dollars had been exported from China . Export of goods from China – additional information According to the graph at hand , China 's exports have been growing steadily over the past decade , except in 2009 when financial crisis and global economic downturn slowed down global trade .
gold_template: The statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[2] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[1] worth approximately templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] had been exported templateTitle[2] templateTitleSubject[0] . templateTitle[0] of templateTitle[1] templateTitle[2] templateTitleSubject[0] – additional information According to the graph at hand , templateTitleSubject[0] 's templateYLabel[0] have been templatePositiveTrend steadily over the past decade , except in templateXValue[8] when financial crisis and global economic downturn slowed down global trade .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] templateTitleSubject[0] plc in the United Kingdom ( UK ) between templateXValue[min] and templateXValue[max] . Passenger numbers for the UK based airline have been templatePositiveTrend since templateXValue[8] and reached templateYValue[max] templateScale templateTitle[2] templateXValue[idxmax(Y)] . This figure excludes the company 's subsidiary BA CityFlyer .
generated: This statistic shows the total Exports of billion U.S. from China plc in the United Kingdom ( UK ) between 2007 and 2017 .  Passenger numbers for the UK based airline have been increasing since 2009 and reached 2342.29 billion from 2014 .  This figure excludes the company 's subsidiary BA CityFlyer .

Example 837:
titleEntities: {'Subject': ['Finland'], 'Date': ['2017', '2018']}
title: Monthly average temperatures in Finland 2017 to 2018
Data:
                 0        1        2        3        4        5        6        7        8        9       10       11       12       13       14       15       16       17       18
Month      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
Helsinki      -0.8      3.8      7.6     13.8     18.5     21.0     15.3     14.4      4.9     -3.7     -7.1     -1.2      1.8      4.0      5.8     12.1     16.3     15.9     13.5
Sodankylä     -8.6     -0.9     -0.6      7.6     13.4     19.9     10.8      9.9      0.0    -10.6    -14.9    -11.7    -10.3     -5.6      0.3      7.0     11.8     14.9     10.1 

gold: This statistic shows the monthly average temperatures in Helsinki and Sodankylä in Finland from June 2017 to December 2018 . In July 2018 , the average temperature in Helsinki , the capital of Finland , amounted to 21 degrees Celsius . The corresponding figure measured in Sodankylä in Northern Finland was 19.9 degrees Celsius .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateLabel[1][0] and templateLabel[2][0] in templateTitleSubject[0] from 2017 to 2018 . In 2018 , the templateTitle[1] temperature in templateLabel[1][0] , the capital of templateTitleSubject[0] , amounted to templateValue[1][5] degrees Celsius . The corresponding figure measured in templateLabel[2][0] in Northern templateTitleSubject[0] was templateValue[2][5] degrees Celsius .

generated_template: This statistic shows the templateTitle[0] of VAT and/or PAYE based templateTitle[6] in the templateTitle[1] templateTitle[2] , templateTitle[4] templateTitle[5] and other reservation service and related activities sector in the United Kingdom from templateTitleDate[min] to templateTitleDate[max] , templateTitle[8] templateLabel[0][0] size band . As of 2019 , there were templateValue[7][min] templateTitle[6] with a templateLabel[0][0] of more than 5 templateValue[0][5] templateLabel[0][1] in this sector .
generated: This statistic shows the Monthly of VAT and/or PAYE based 2018 in the average temperatures , 2017 2018 and other reservation service and related activities sector in the United Kingdom from 2017 to 2018 , Month size band .  As of 2019 , there were -14.9 2018 with a Month of more than 5 Jul_'18 Month in this sector .

Example 838:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Busiest U.S. airports - number of passengers 2018
X_Axis['U.S.', 'airport']: ['Atlanta_GA_(ATL)', 'Los_Angeles_CA_(LAX)', 'Chicago_IL_(ORD)', 'Dallas_TX_(DFW)', 'Denver_CO_(DEN)', 'New_York_NY_(JFK)', 'San_Francisco_CA_(SFO)', 'Seattle_WA(SEA)', 'Las_Vegas_NV_(LAS)', 'Orlando_FL_(MCO)', 'Newark_NJ_(EWR)', 'Charlotte_NC_(CLT)', 'Phoenix_AZ_(PHX)', 'Houston_TX_(IAH)', 'Miami_FL_(MIA)', 'Boston_MA_br>(BOS)']
Y_Axis['Passengers', 'enplanements', 'in', 'millions']: ['52.26', '43.27', '40.04', '32.92', '31.68', '30.75', '28.0', '24.08', '24.01', '23.33', '22.89', '22.33', '21.88', '21.27', '21.1', '20.07']

gold: In 2018 , Atlanta International Airport was the busiest airport in the United States , transporting over 52.2 million passengers . In that same year , Atlanta was also the main hub of Delta Air Lines . Delta Air Lines – additional information U.S.-based Delta Air Lines is the third major airline and legacy carrier in the United States , with a 17.2 percent market share .
gold_template: In templateTitleDate[0] , templateXValue[0] International templateXLabel[1] was the templateTitle[0] templateXLabel[1] in the templateTitle[1] , transporting over 52.2 templateScale templateYLabel[0] . In that same year , templateXValue[0] was also the main hub of Delta Air Lines . Delta Air Lines – additional information U.S.-based Delta Air Lines is the third major airline and legacy carrier in the templateTitle[1] , with a 17.2 templateScale market share .

generated_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[0] was the sixth largest templateTitle[2] templateXLabel[0] templateXLabel[1] in the world , with a value of templateYValue[5] templateScale .
generated: This statistic shows the enplanements of the U.S. airports number passengers in 2018 , U.S. airport .  In that year , the Atlanta_GA_(ATL) was the sixth largest airports U.S. airport in the world , with a value of 30.75 millions .

Example 839:
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 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 U.S. an active U.S. or subscription in the items as of 2017 , sorted lost 2015 .  According to the source , 10 % of Credit_card who subscribe to online video or music subscriptions had a U.S. or subscription as of 2017 .

Example 840:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013', '2020']}
title: Total federal drug control spending in the U.S. 2013 to 2020
X_Axis['Year']: ['2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018_(Final)', '2019_(Enacted)', '2020_(Request)']
Y_Axis['Total', 'spending', 'in', 'million', 'U.S.', 'dollars']: ['24365.5', '24504.1', '23809.6', '25733.1', '25892.9', '26874.0', '28813.3', '33358.7', '33312.3', '34577.3']

gold: This statistic depicts the total federal drug control spending in the United States from FY 2013 to FY 2020 , in million U.S. dollars . The largest amount of federal drug control spending is projected for fiscal year 2020 with some 34.6 billion U.S. dollars requested for drug control funding .
gold_template: This statistic depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] in the templateTitle[5] from FY templateXValue[2] to FY templateXValue[last] , in templateScale templateYLabel[3] templateYLabel[4] . The largest amount of templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] is projected for fiscal templateXLabel[0] templateXValue[last] with some templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] requested for templateTitle[2] templateTitle[3] funding .

generated_template: This statistic shows the average 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: This statistic shows the average spending generated U.S. drug control Total rights drug 2011 to 2016 and corresponding spending forecasts for the years 2015 to 2020_(Request) .  In 2011 , total revenues drug Total were 24365.5 million U.S. dollars .

Example 841:
titleEntities: {'Subject': ['Mount Rushmore National Memorial'], 'Date': ['2008', '2019']}
title: Number of visitors to the Mount Rushmore National 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']: ['1.96', '2.31', '2.44', '2.43', '2.43', '2.14', '2.16', '2.19', '2.08', '2.33', '2.26', '1.79']

gold: This statistic shows the number of recreational visitors to the Mount Rushmore National Memorial in the United States from 2008 to 2019 . The number of visitors to the Mount Rushmore National Memorial amounted to approximately 1.96 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[0] templateYValue[idxmax(X)] in templateXValue[max] .

generated_template: In templateXValue[max] , templateTitleSubject[0] Park saw nearly templateYValue[0] and a half templateScale templateYLabel[1] during the templateXLabel[0] . In templateXValue[3] , the templateTitleSubject[0] saw its largest volume of templateYLabel[1] accounting for about templateYValue[max] templateScale . templateTitleSubject[0] Park templateTitleSubject[0] Park is a large templateTitleSubject[0] forest located in central California .
generated: In 2019 , Mount Rushmore National Memorial Park saw nearly 1.96 and a half millions visitors during the Year .  In 2016 , the Mount Rushmore National Memorial saw its largest volume of visitors accounting for about 2.44 millions .  Mount Rushmore National Memorial Park Mount Rushmore National Memorial is a large Mount Rushmore National Memorial forest located in central California .

Example 842:
titleEntities: {'Subject': ['United States'], 'Date': ['2018']}
title: Favorite online dating website or app in the United States 2018 , by age group
Data:
                    0             1          2        3         4                   5       6       7                             8           9     10    11
unknown        Tinder  PlentyofFish  Match.com  OkCupid  EHarmony  Coffee_Meets_Bagel  Bumble  Grindr  A_religious_based_dating_app  Hinge_Down  Happn  None
18 - 29 years      14             8          7        6         4                   3       3       3                             2           2      1    47
30 - 44 years       5             7          9        4         5                   3       2       2                             2           1      1    59
45 - 54 years       1             4          7        2         3                   1       1       0                             4           1      0    75
55 - 64 years       1             1          7        1         5                   0       1       2                             3           0      0    79
65 and over         0             1          2        1         4                   0       0       0                             4           0      0    88 

gold: This statistic presents the most popular dating apps and websites according to online users in the United States , sorted by age group . During the January 2018 survey period , 14 percent of respondents aged 14 to 29 years stated that Tinder was their favorite online dating app or website .
gold_template: This statistic presents the most popular templateValue[0][8] apps and websites according to templateTitle[1] users in the templateTitleSubject[0] , sorted templateTitle[8] templateTitle[9] templateTitle[10] . During the 2018 survey period , templateValue[1][0] templateScale of respondents aged templateValue[1][0] to templateLabel[1][1] templateLabel[1][2] stated that templateValue[0][0] was their templateTitle[0] templateTitle[1] templateValue[0][8] or templateTitle[3] .

generated_template: This statistic shows the results of survey among templateTitleSubject[0] on whether they consider themselves as of 2012 . During the survey period , it was found that templateValue[1][1] templateScale of respondents used templateValue[0][1] templateTitle[0] .
generated: This statistic shows the results of survey among United States on whether they consider themselves as of 2012 .  During the survey period , it was found that 8 % of respondents used PlentyofFish Favorite .

Example 843:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1999', '2017']}
title: U.S. lemon imports and exports 1999 to 2017
Data:
                       0       1      2      3       4      5      6       7       8      9     10     11     12      13      14      15      16      17      18
Year                1999    2000   2001   2002    2003   2004   2005    2006    2007   2008   2009   2010   2011    2012    2013    2014    2015    2016    2017
Imports            25160   34127  36351  27901   34461  35400  35245   65656   64214  37565  40848  56278  50939   38152   56996   85544   83851  110252  110828
Domestic exports  106249  110373  99906  99566  101603  97982  97445  115862  155668  91459  91445  97275  92081  104031  122420  109663  107193  106827   88537 

gold: This statistic shows the total amount of imports and domestic exports of lemons in the United States from 1999 to 2017 . According to the report , U.S. imports of lemons amounted to approximately 83,851 metric tons in 2015 .
gold_template: This statistic shows the total amount of templateTitle[2] and templateLabel[2][0] templateTitle[3] of lemons in the templateTitle[0] from templateValue[0][0] to templateValue[0][last] . According to the report , templateTitleSubject[0] templateTitle[2] of lemons amounted to approximately templateValue[1][16] metric tons in templateValue[0][16] .

generated_template: This statistic shows the total amount of templateTitle[2] and templateLabel[2][0] templateTitle[3] of oranges in the templateTitle[0] from templateValue[0][0] to templateValue[0][last] . According to the report , templateTitleSubject[0] templateTitle[2] of oranges amounted to approximately templateValue[1][16] metric tons in templateValue[0][16] .
generated: This statistic shows the total amount of imports and Domestic exports of oranges in the U.S. from 1999 to 2017 .  According to the report , U.S. imports of oranges amounted to approximately 83851 metric tons in 2015 .

Example 844:
titleEntities: {'Subject': ['Tampa Bay Lightnings'], 'Date': ['2005', '2019']}
title: Tampa Bay Lightnings ' 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']: ['140', '146', '124', '127', '121', '97', '72', '88', '87', '76', '80', '84', '85', '82']

gold: This graph depicts the annual National Hockey League revenue of the Tampa Bay Lightning from the 2005/06 season to the 2018/19 season . The revenue of the Tampa Bay Lightning amounted to 140 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] Lightning from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] Lightning amounted to templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] Wild from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] Wild amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .
generated: This graph depicts the annual National Hockey League Revenue of the Tampa Bay Lightnings Wild from the 2005/06 season to the 2018/19 season .  The Revenue of the Tampa Bay Lightnings Wild amounted to 146 million U.S. dollars in the 2017/18 season .

Example 845:
titleEntities: {'Subject': ['Trinidad', 'Tobago'], 'Date': ['2024']}
title: Inflation rate in Trinidad and Tobago 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']: ['2.55', '2.41', '2.19', '1.9', '1.45', '0.95', '1.02', '1.88', '3.07', '4.66', '5.7', '5.19', '9.28', '5.1', '10.53', '7.01', '12.04', '7.88', '8.31', '6.88', '3.76', '3.78', '4.14', '5.6', '3.52', '3.41', '5.6', '3.66', '3.29', '5.27', '8.81', '10.79', '6.49', '3.8', '11.06', '11.43', '7.76', '10.75', '7.69', '7.62', '13.34']

gold: This statistic shows the average inflation rate in Trinidad and Tobago from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Trinidad and Tobago amounted to about 1.02 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] and templateTitleSubject[1] 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 Trinidad from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Trinidad amounted to about 1.02 % compared to the previous Year .

Example 846:
titleEntities: {'Subject': ['Nintendo Switch'], 'Date': ['2019']}
title: Top selling Nintendo Switch games worldwide in 2019
X_Axis['Month']: ['Mario_Kart_8_Deluxe', 'Super_Smash_Bros._Ultimate', 'Super_Mario_Odyssey', 'The_Legend_of_Zelda:__Breath_of_the_Wild', "Pokémon:_Let's_Go_Pikachu!/_Pokémon:_Let's_Go_Eevee!", 'Splatoon_2', 'Super_Mario_Party', 'New_Super_Mario_Bros._U_Deluxe', 'Super_Mario_Maker_2', "The_Legend_of_Zelda:_Link's_Awakening"]
Y_Axis['Sales', 'in', 'million', 'units']: ['19.01', '15.71', '15.38', '14.54', '11.28', '9.28', '7.59', '4.59', '3.93', '3.13']

gold: This statistic shows the top selling Nintendo Switch titles worldwide as of September 2019 . With 19.01 million units sold , Mario Kart 8 Deluxe was the top selling Nintendo Switch game as of September 2019 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitleSubject[0] titles templateTitle[5] as of 2019 . With templateYValue[max] templateScale templateYLabel[2] sold , templateXValue[0] 8 templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitleSubject[0] game as of 2019 .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] titles templateTitle[5] as of 2019 . With templateYValue[max] templateScale templateYLabel[2] sold templateTitle[5] , templateXValue[0] 7 was the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] game as of 2019 .
generated: This statistic shows the Top selling Nintendo Switch Switch titles worldwide as of 2019 .  With 19.01 million units sold worldwide , Mario_Kart_8_Deluxe 7 was the Top selling Nintendo Switch Switch game as of 2019 .

Example 847:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: U.S. per capita consumption of fresh berries 2000 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['10.25', '10.03', '9.85', '9.69', '9.02', '8.61', '8.48', '7.51', '7.13', '7.04', '6.46', '6.24', '5.91', '5.21', '4.75', '5.31']

gold: This statistic depicts the per capita consumption of fresh berries in the United States from 2000 to 2015 . The berries category comprises strawberries , raspberries , blueberries and cranberries . In 2015 , the U.S. consumption of fresh berries amounted to about 10.25 pounds per capita .
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] . The templateTitle[5] category comprises strawberries , raspberries , blueberries and cranberries . In templateXValue[max] , the templateTitleSubject[0] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to about templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[0] templateYLabel[1] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] ( green beans ) in the templateTitle[0] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] ( green beans ) amounted to about templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of fresh ( green beans ) in the U.S. from 2000 to 2015 .  The U.S. Per capita consumption of fresh ( green beans ) amounted to about 10.25 pounds in 2015 .

Example 848:
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: This statistic shows the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded online by Harris Interactive in 2014 , asking the templateYLabel[1] where they usually templateTitle[3] their shampoo and/or templateTitle[6] . Some templateYValue[3] templateScale of templateTitleSubject[0] adults indicated that they buy their shampoo/conditioner templateXValue[3] .
generated: This statistic shows the results of a survey among Great Britain adult tea .  The survey was fielded online by Harris Interactive in 2014 , asking the respondents where they usually consumed their shampoo and/or 2014 .  Some 19 % of Great Britain adults indicated that they buy their shampoo/conditioner Fruit .

Example 849:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2006', '2018']}
title: Number of road deaths in Belgium 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['604', '609', '670', '762', '745', '764', '827', '884', '850', '956', '980', '1094', '1106']

gold: There were 604 road deaths recorded in Belgium in 2018 . Belgium had been able to decrease the number of people fatally injured on its roads by nearly half since 2006 . 2011 and 2015 were the only years in which the number of fatal accidents increased .
gold_template: There were templateYValue[min] templateTitle[1] templateTitle[2] recorded in templateTitleSubject[0] in templateXValue[idxmin(Y)] . templateTitleSubject[0] had been able to templateNegativeTrend the templateYLabel[0] of people fatally injured on its roads by nearly half since templateXValue[min] . templateXValue[7] and templateXValue[3] were the only years in which the templateYLabel[0] of fatal accidents templatePositiveTrend .

generated_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: In 2018 , 604 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 604 such incidences .  Belgium was one of the safest countries in Europe for road users .

Example 850:
titleEntities: {'Subject': ['Nepal'], 'Date': ['2017']}
title: Fertility rate in Nepal 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['1.97', '2.03', '2.1', '2.18', '2.26', '2.35', '2.44', '2.54', '2.64', '2.75', '2.87']

gold: This statistic shows the fertility rate of Nepal 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 in Nepal was 1.97 children per woman .
gold_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] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

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] 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: This statistic shows the Fertility rate in Nepal 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 Nepal 's population amounted to 1.97 children per woman .

Example 851:
titleEntities: {'Subject': ['Azerbaijan'], 'Date': ['2024']}
title: Inflation rate in Azerbaijan 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']: ['3.5', '3.5', '3.3', '3.2', '3', '2.8', '2.33', '12.84', '12.44', '4.05', '1.45', '2.42', '1.01', '7.75', '5.66', '1.35', '20.78', '16.59', '8.23', '9.58', '6.71', '2.12', '2.76', '1.77', '1.81', '-8.53', '-0.77', '3.67', '19.8', '411.76', '1664']

gold: This statistic shows the average inflation rate in Azerbaijan from 1994 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Azerbaijan amounted to about 2.33 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 Azerbaijan from 1994 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Azerbaijan amounted to about 2.33 % compared to the previous Year .

Example 852:
titleEntities: {'Subject': ['Number'], 'Date': ['2019']}
title: Number of top 500 supercomputers worldwide 2019 , by country
X_Axis['Country']: ['China', 'United_States', 'Japan', 'France', 'United_Kingdom', 'Germany', 'Ireland', 'Netherlands', 'Canada', 'Rest_of_world']
Y_Axis['Number', 'of', 'supercomputers']: ['219', '116', '29', '19', '18', '14', '13', '13', '8', '51']

gold: As of June 2019 , 219 of the world 's 500 most powerful supercomputers were located in China , a figure which nearly doubled that of its nearest competitor , the United States , which accounted for an additional 116 supercomputers . Together , the two nations account for around two-thirds of the world 's most powerful supercomputers . Supercomputers As the name suggests , a supercomputer is a machine with massive computing power , capable of working with huge amounts of data and performing incredibly complex computations .
gold_template: As of 2019 , templateYValue[max] of the templateXValue[last] 's templateTitle[2] most powerful templateYLabel[1] were located in templateXValue[0] , a figure which nearly doubled that of its nearest competitor , the templateXValue[1] , which accounted for an additional templateYValue[1] templateYLabel[1] . Together , the two nations account for around two-thirds of the templateXValue[last] 's most powerful templateYLabel[1] . templateYLabel[1] As the name suggests , a supercomputer is a machine with massive computing power , capable of working with huge amounts of data and performing incredibly complex computations .

generated_template: This statistic provides a ranking of templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] in selected regions worldwide in templateTitleDate[0] . In the country , templateTitle[2] output templatePositiveTrend templateTitle[4] templateYValue[2] templateScale in templateTitleDate[0] . That year , templateTitle[1] templateTitle[2] output was estimated to be around templateYValue[max] templateScale templateXValue[2] dollars .
generated: This statistic provides a ranking of 500 supercomputers Number in selected regions worldwide in 2019 .  In the country , 500 output increase worldwide 29 % in 2019 .  That year , top 500 output was estimated to be around 219 billion Japan dollars .

Example 853:
titleEntities: {'Subject': ['Syria'], 'Date': ['2018']}
title: Infant mortality rate in Syria 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['14.0', '14.0', '14.1', '14.1', '14.1', '14.1', '14.2', '13.9', '13.8', '14.0', '14.3']

gold: The statistic shows the infant mortality rate in Syria from 2008 to 2018 . In 2018 , the infant mortality rate in Syria was at about 14 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[0] 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 Syria from 2008 to 2018 .  In 2018 , the Infant mortality rate in Syria was at about 14.0 Deaths per 1,000 live births .

Example 854:
titleEntities: {'Subject': ['Zimbabwe'], 'Date': ['2018']}
title: Share of economic sectors in GDP in Zimbabwe 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, forestry, and fishing  12.08   9.66   7.87   8.28   8.75   7.14   8.04   8.67   9.61  10.74  19.02
Industry (incl. construction)       32.48  25.79  22.12  22.36  23.72   23.8  25.33  21.72  20.69  21.45  30.48
Services                            45.68  55.08  60.41  58.65  57.59  58.33  55.09   57.9  57.83  54.92  48.55 

gold: This statistic shows the share of economic sectors in gross domestic product ( GDP ) in Zimbabwe from 2008 to 2018 . In 2018 , the share of agriculture in Zimbabwe 's gross domestic product was 12.08 percent , industry contributed approximately 32.48 percent and the services sector contributed about 45.68 percent .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] in gross domestic product ( templateTitle[3] ) in templateTitleSubject[0] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , the templateTitle[0] of agriculture 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] templateTitle[1] of templateTitleSubject[0] inhabitants from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , about templateValue[1][min] templateScale of inhabitants were aged 0 to 14 years , while approximately templateValue[2][0] templateScale were aged 15 to 64 , and templateValue[3][0] templateScale of templateTitleSubject[0] inhabitants were aged templateLabel[3][1] or older .
generated: This statistic shows the Share economic of Zimbabwe inhabitants from 2008 to 2018 .  In 2018 , about 7.14 % of inhabitants were aged 0 to 14 years , while approximately 32.48 % were aged 15 to 64 , and 45.68 % of Zimbabwe inhabitants were aged Services or older .

Example 855:
titleEntities: {'Subject': ['March'], 'Date': ['2', '2020']}
title: COVID-19 cases worldwide as of March 2 , 2020 , by country
X_Axis['Country']: ['Total_(worldwide)', 'China', 'Republic_of_Korea', 'Italy', 'Iran_(Islamic_Republic_of)', 'Cases_on_an_international_conveyance_(Japan)', 'Japan', 'Germany', 'Singapore', 'France', 'United_States_of_America', 'Kuwait', 'Bahrain', 'Spain', 'Thailand', 'United_Kingdom', 'Australia', 'Malaysia', 'Switzerland', 'United_Arab_Emirates', 'Norway', 'Iraq', 'Canada', 'Viet_Nam', 'Sweden', 'Netherlands', 'Lebanon', 'Austria', 'Israel', 'Croatia', 'Greece', 'Oman', 'Finland', 'Mexico', 'Pakistan', 'Denmark', 'India', 'Czechia', 'Romania', 'Georgia', 'Philippines', 'Azerbaijan', 'Qatar', 'Indonesia', 'Iceland', 'Egypt', 'Brazil', 'Russian_Federation', 'Armenia', 'Ecuador', 'Dominican_Republic', 'Estonia', 'Ireland', 'Lithuania', 'Luxembourg', 'Monaco', 'Algeria', 'New_Zealand', 'Cambodia', 'North_Macedonia', 'San_Marino', 'Nepal', 'Sri_Lanka', 'Afghanistan', 'Nigeria', 'Belarus', 'Belgium']
Y_Axis['Number', 'of', 'cases']: ['88948', '80174', '4212', '1689', '978', '706', '254', '129', '106', '100', '62', '56', '47', '45', '42', '36', '27', '24', '24', '21', '19', '19', '19', '16', '14', '13', '10', '10', '7', '7', '7', '6', '6', '5', '4', '4', '3', '3', '3', '3', '3', '3', '3', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']

gold: As of March 2 , 2020 , the outbreak of the coronavirus disease ( COVID-19 ) had been confirmed in 65 countries , with the overwhelming majority of cases reported in China . The virus had infected 88,948 people worldwide , and the number of deaths had totaled 3,043 . The most severely affected countries outside of China were the Republic of Korea and Italy .
gold_template: As of templateTitleSubject[0] templateYValue[43] , templateTitleDate[0] , the outbreak of the coronavirus disease ( templateTitle[0] ) had been confirmed in 65 countries , with the overwhelming majority of templateXValue[5] reported in templateXValue[1] . The virus had infected templateYValue[max] people templateTitle[2] , and the templateYLabel[0] of deaths had totaled 3,043 . The most severely affected countries outside of templateXValue[1] were the templateXValue[2] of templateXValue[2] and templateXValue[3] .

generated_template: The statistic shows the degree of templateTitle[3] templateTitleSubject[0] in templateTitle[5] templateTitle[6] worldwide . According to the templateTitleSubject[0] Index , templateXValue[last] occupied the last place in templateTitle[3] templateTitleSubject[0] with templateYValue[min] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . templateXValue[1] and templateXValue[0] were ranked first and second with templateYValue[max] and templateYValue[1] out of 100 templateYLabel[0] templateYLabel[1] respectively .
generated: The statistic shows the degree of March in 2020 by worldwide .  According to the March Index , Belgium occupied the last place in March with 1 Number cases in 2 .  China and Total_(worldwide) were ranked first and second with 88948 and 80174 out of 100 Number cases respectively .

Example 856:
titleEntities: {'Subject': ['U.S.'], 'Date': []}
title: Number of international students in the U.S. , by country of origin 2018/19
X_Axis['Country']: ['China', 'India', 'South_Korea', 'Saudi_Arabia', 'Canada', 'Vietnam', 'Taiwan', 'Japan', 'Brazil', 'Mexico', 'Nigeria', 'Nepal', 'Iran', 'United_Kingdom', 'Turkey', 'Kuwait', 'Germany', 'France', 'Indonesia', 'Bangladesh', 'Colombia', 'Pakistan', 'Venezuela', 'Malaysia', 'Spain']
Y_Axis['Number', 'of', 'international', 'students']: ['369548', '202014', '52250', '37080', '26122', '24392', '23369', '18105', '16059', '15229', '13423', '13229', '12142', '11146', '10159', '9195', '9191', '8716', '8356', '8249', '8060', '7957', '7760', '7709', '7262']

gold: This statistic shows the number of international students that were studying in the United States as of 2019 . The numbers are ordered by countries of origin . In the academic year 2018/19 , there were 369,548 international students from China studying in the United States .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] that were studying in the templateXValue[13] States as of 2019 . The numbers are ordered templateTitle[4] countries of templateTitle[6] . In the academic year templateTitle[7] , there were templateYValue[max] templateYLabel[1] templateYLabel[2] from templateXValue[0] studying in the templateXValue[13] States .

generated_template: This statistic shows the top templateYValue[16] templateTitle[0] in the world templateTitle[1] the templateTitle[2] number of templateYLabel[1] templateYLabel[2] in templateTitleDate[0] . In templateTitleDate[0] , there were about templateYValue[2] templateScale Muslims living in templateXValue[1] .
generated: This statistic shows the top 9191 Number in the world international the students number of international students in .  In , there were about 52250 % Muslims living in India .

Example 857:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2004', '2008']}
title: U.S. market share of men 's electric shaver manufacturer 2004 to 2008
Data:
                                           0                  1                                  2       3                       4     5       6
unknown  Spectrum_Brands_(Remington/Rayovac)  Philips_(Norelco)  Proctor_&_Gamble_(Gillette/Braun)  Conair  Matsushita_(Panasonic)  Wahl  Others
2004                                      33                 44                                 14       0                       5     3       1
2008                                      41                 34                                 10       8                       5     0       2 

gold: The statistic illustrates the market share of men 's electric shaver manufacturers in the U.S. in 2004 and 2008 . In 2008 , Spectrum Brands had a electric shaver market share of 41 percent and was the U.S. shipment leader for men 's electric shavers .
gold_template: The statistic illustrates the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] manufacturers in the templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . In templateTitleDate[max] , templateValue[0][0] had a templateTitle[5] templateTitle[6] templateTitle[1] templateTitle[2] of templateValue[2][max] templateScale and was the templateTitleSubject[0] shipment leader for templateTitle[3] templateTitle[4] templateTitle[5] shavers .

generated_template: This statistic shows templateTitle[0] and templateTitle[1] on templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] . The survey revealed that templateValue[1][0] templateScale of respondents are budgeting templateValue[1][last] thousand templateTitleSubject[0] dollars or templateValue[0][0] for their templateTitle[2] renovation .
generated: This statistic shows U.S. and market on share men in the 's in 2004 .  The survey revealed that 33 % of respondents are budgeting 1 thousand U.S. dollars or Spectrum_Brands_(Remington/Rayovac) for their share renovation .

Example 858:
titleEntities: {'Subject': ['India'], 'Date': ['2017']}
title: Life expectancy of men in India 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Life', 'expectancy', 'at', 'birth', 'in', 'years']: ['68.0', '67.75', '67.47', '67.17', '66.84', '66.49', '66.12', '65.72', '65.32', '64.91', '64.5']

gold: The statistic shows the life expectancy of men at birth in India from 2007 to 2017 . In 2017 , the average life expectancy of men at birth in India was about 68 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[max] templateYLabel[3] .

generated_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: The statistic shows the Life expectancy of men at birth in India from 2007 to 2017 .  In 2017 , the average Life expectancy of men at birth in India was about 68.0 years .

Example 859:
titleEntities: {'Subject': ['Apple'], 'Date': ['2006', '2014']}
title: Global Apple iPod revenue 2006 to 2014
X_Axis['Fiscal', 'quarter']: ["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", "Q4_'06", "Q3_'06", "Q2_'06", "Q1_'06"]
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['0.41', '0.44', '0.46', '0.97', '0.57', '0.73', '0.96', '2.14', '0.82', '1.06', '1.21', '2.53', '1.1', '1.33', '1.6', '3.43', '1.48', '1.55', '1.86', '3.39', '1.56', '1.49', '1.67', '3.37', '1.66', '1.68', '1.82', '4.0', '1.62', '1.57', '1.69', '3.43', '1.56', '1.5', '1.71', '2.91']

gold: This graph shows the amount of revenue that Apple generated through sales of the iPod each quarter from the first quarter of 2006 . In the first quarter of 2013 , sales increased again compared to previous quarters and revenue from this product came to more than 2 billion U.S. dollars . As of the first quarter of 2014 however , revenue was down by more than half to 970 million U.S. dollars .
gold_template: This graph shows the amount of templateYLabel[0] that templateTitleSubject[0] generated through sales of the templateTitle[2] each templateXLabel[1] from the first templateXLabel[1] of templateTitleDate[min] . In the first templateXLabel[1] of 2013 , sales templatePositiveTrend again compared to previous quarters and templateYLabel[0] from this product came to more than templateYValue[7] templateScale templateYLabel[2] templateYLabel[3] . As of the first templateXLabel[1] of templateTitleDate[max] however , templateYLabel[0] was down by more than half to templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: In templateTitleDate[0] , templateXValue[0] was the most expensive templateTitleSubject[0] city in the world , with an templateYLabel[0] of about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . In the same year , templateTitleSubject[0] generated its templateYLabel[0] of templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] .
generated: In 2006 , Q4_'14 was the most expensive Apple city in the world , with an Revenue of about 4.0 billion U.S. dollars .  In the same year , Apple generated its Revenue of 0.46 billion U.S. dollars .

Example 860:
titleEntities: {'Subject': ['Life Time Fitness'], 'Date': ['2012', '2017']}
title: Health/Fitness club companies - Life Time Fitness , Inc. revenue 2012 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['1593.21', '1475.31', '1403.97', '1290.62', '1205.9', '1127.0']

gold: The statistic depicts the revenue of the Life Time Fitness , Inc. company from 2012 to 2017 . In 2017 , the health club chain generated a revenue of around 1.59 billion U.S. dollars . In 2017 , the Minnesota ( USA ) based company operated 130 clubs .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Fitness , templateTitle[6] company from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the health templateTitle[1] chain generated a templateYLabel[0] of around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . In templateXValue[idxmax(Y)] , the Minnesota ( USA ) based company operated 130 clubs .

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] Company generated a total amount of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the club Life Time Fitness from 2012 to 2017 .  In 2017 , the Life Time Fitness Company generated a total amount of 1593.21 million U.S. dollars .

Example 861:
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: In 2019 , it was found that templateYValue[max] templateScale of adults in the templateTitleSubject[0] aged between 18 and 29 years used templateTitle[0] . This templateTitle[7] templateTitle[8] was the microblogging service 's biggest audience in the templateTitleSubject[0] , followed templateTitle[6] a 27 templateScale templateTitle[1] templateTitle[2] among 30 to 49-year-olds . templateTitle[0] users in the templateTitleSubject[0] As of the first quarter of templateTitleDate[0] , templateTitle[0] had 68 templateScale monthly active users in the templateTitleSubject[0] .
generated: In 2019 , it was found that 22 % of adults in the U.S. aged between 18 and 29 years used U.S. This age group was the microblogging service 's biggest audience in the U.S. , followed by a 27 million consumers who among 30 to 49-year-olds .  U.S. users in the U.S. As of the first quarter of 2019 , U.S. had 68 million monthly active users in the U.S. .

Example 862:
titleEntities: {'Subject': ['Frequency'], 'Date': ['2015']}
title: Frequency U.S. consumers eat ethnic cuisine 2015 , by menu type
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
unknown                    Italian  Mexican  Chinese  Regional_American  Mediterranean  Sushi_/_sashimi  Fusion  Japanese_(other_than_sushi)  Regional_ethnic  Spanish  Middle_Eastern  Southwest_Asian  Belgian  Thai  German  Franch  Vietnamese  Greek  Indian  Latin_American  Caribbean  Korean  Russian_/_Eastern_European  Peruvian  Scandinavian  Brazilian_/_Argentine  Ethiopian
Eat at least once a month       61       50       36                 30             17               13      13                           13               11        9               9                9        8     8       7       7           7      6       6               6          5       4                           4         4             3                      2          2
Eat a few times a year          26       31       42                 28             24               17      24                           28               23       22              35               16       27    17      30      27          10     24      12              43         17       8                          13        11             9                      8          2 

gold: This statistic shows how often U.S. consumers eat ethnic cuisine by menu type in 2015 . During the survey , 50 percent of survey respondents said they eat Mexican cuisine at least once a month .
gold_template: This statistic shows how often templateTitle[1] templateTitle[2] templateTitle[3] templateValue[0][8] templateTitle[5] templateTitle[7] templateTitle[8] templateTitle[9] in templateTitleDate[0] . During the survey , templateValue[1][1] templateScale of survey respondents said they templateTitle[3] templateValue[0][1] templateTitle[5] at templateLabel[1][1] templateLabel[1][2] a templateLabel[1][3] .

generated_template: This statistic provides information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] and templateTitle[4] of all time as of October templateTitleDate[0] . During the survey period , it was found that templateValue[1][1] templateScale of respondents stated that they would be their templateTitleSubject[0] templateTitle[1] .
generated: This statistic provides information on the Frequency U.S. consumers eat and ethnic of all time as of October 2015 .  During the survey period , it was found that 50 % of respondents stated that they would be their Frequency U.S. .

Example 863:
titleEntities: {'Subject': ['Fastest'], 'Date': ['2020']}
title: Fastest growing internet populations 2020
X_Axis['Country']: ['Rep._of_the_Congo', 'Dem._Rep._of_the_Congo', 'Samoa', 'Iraq', 'Kiribati', 'Guinea-Bissau', 'India', 'Egypt', 'Burundi', 'Central_Africal_Rep.', 'Oman', 'Pakistan', 'Indonesia', 'Kenya', 'Zambia', 'Saudi_Arabia', 'Cambodia', 'Tajikistan', 'Haiti', 'Libya']
Y_Axis['Year-on-year', 'growth']: ['126', '122', '86', '55', '39', '26', '23', '22', '21', '20', '18', '17', '17', '16', '16', '15', '15', '15', '13', '13']

gold: This statistic presents the fastest growing online populations based on relative year-on-year user growth as of January 2020 . During the measured period , it was found that online audiences in the Republic of the Congo grew 126 percent in total .
gold_template: This statistic presents the templateTitleSubject[0] templateTitle[1] online templateTitle[3] based on relative templateYLabel[0] user templateYLabel[1] as of 2020 . During the measured period , it was found that online audiences in the Republic of the templateXValue[0] templatePositiveTrend templateYValue[max] templateScale in total .

generated_template: This statistic shows the templateYLabel[1] of premium templateTitle[2] templateTitle[3] templateTitle[4] ( per 50gram ) across templateTitleSubject[0] as at 2016 . templateTitle[2] templateTitle[3] templateTitle[4] was substantially more expensive in the templateXValue[0] with templateYLabel[0] figures at templateYValue[max] British pounds .
generated: This statistic shows the growth of premium internet populations 2020 ( per 50gram ) across Fastest as at 2016 .  internet populations 2020 was substantially more expensive in the Rep._of_the_Congo with Year-on-year figures at 126 British pounds .

Example 864:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2011', '2019']}
title: Facebook : number of daily active users worldwide 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['Number', 'of', 'users', 'in', 'millions']: ['1657', '1623', '1587', '1562', '1523', '1495', '1471', '1449', '1401', '1368', '1325', '1284', '1227', '1179', '1128', '1090', '1038', '1007', '968', '936', '890', '864', '829', '802', '757', '728', '699', '665', '618', '584', '552', '526', '483', '457', '417', '372']

gold: How many daily active users does Facebook have ? During the fourth quarter of 2019 , Facebook reported almost 1.66 billion daily active users ( DAU ) . Overall , daily active users accounted for 66 percent of monthly active users . Facebook audience reach With over 2.5 billion monthly active users , Facebook is the most popular social network worldwide .
gold_template: How many templateTitle[2] templateTitle[3] templateYLabel[1] does templateTitleSubject[0] have ? During the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] reported almost templateYValue[max] templateScale templateTitle[2] templateTitle[3] templateYLabel[1] ( DAU ) . Overall , templateTitle[2] templateTitle[3] templateYLabel[1] accounted for 66 templateScale of monthly templateTitle[3] templateYLabel[1] . templateTitleSubject[0] audience reach With over 2.5 templateScale monthly templateTitle[3] templateYLabel[1] , templateTitleSubject[0] is the most popular social network templateTitle[5] .

generated_template: As of the third templateXLabel[0] of templateTitleDate[max] , templateTitle[0] had a combined templateYValue[max] templateScale templateTitle[2] templateTitle[3] templateYLabel[1] in the country and Canada . templateTitle[0] user data – additional information templateTitle[0] 's data use policy or privacy policy as of the recent part of templateTitleDate[max] . As of the third quarter of templateTitleDate[max] , the templateYLabel[0] of templateTitleSubject[1] 's templateTitle[3] amounted to templateYValue[0] templateScale .
generated: As of the third Quarter of 2019 , Facebook had a combined 1657 millions daily active users in the country and Canada .  Facebook user data – additional information Facebook 's data use policy or privacy policy as of the recent part of 2019 .  As of the third quarter of 2019 , the Number of Facebook 's active amounted to 1657 millions .

Example 865:
titleEntities: {'Subject': ['Panama'], 'Date': ['2024']}
title: Unemployment rate in Panama 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Unemployment', 'rate']: ['5.77', '5.77', '5.77', '5.8', '5.91', '6.11', '5.96', '6.13', '5.49', '5.05', '4.82']

gold: This statistic shows the unemployment rate in Panama from 2014 to 2018 , with projections up until 2024 . In 2018 , the unemployment rate in Panama was 5.96 percent .
gold_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 templateYValue[6] templateScale .

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] . templateYLabel[0] occurs when people are without work , it is also known as joblessness . In order that the prevalence of templateYLabel[0] can be measured , a calculation is made by the division of the number of unemployed individuals by all individuals currently in the labor force , this yields a templateScale templateYLabel[1] .
generated: This statistic shows the Unemployment rate in Panama from 2014 to 2018 , with projections up until 2024 .  Unemployment occurs when people are without work , it is also known as joblessness .  In order that the prevalence of Unemployment can be measured , a calculation is made by the division of the number of unemployed individuals by all individuals currently in the labor force , this yields a percentage rate .

Example 866:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2024']}
title: National debt of the Netherlands 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'billion', 'U.S.', 'dollars']: ['415.71', '420.82', '425.23', '429.19', '432.09', '435.25', '446.23', '462.24', '482.4', '490.76', '502.62']

gold: This statistic shows the national debt of the Netherlands from 2014 to 2018 , with projections up until 2024 . In 2018 , the national debt in the Netherlands was around 466 billion U.S. dollars . For comparison , the Greek debt amounted to approximately 375 billion U.S. dollars that same year .
gold_template: This 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 around 466 templateScale templateYLabel[3] templateYLabel[4] . For comparison , the Greek templateYLabel[1] amounted to approximately 375 templateScale templateYLabel[3] templateYLabel[4] that same templateXLabel[0] .

generated_template: The 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] amounted to around templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the National debt of Netherlands from 2014 to 2018 , with projections up until 2024 .  In 2018 , the National debt of Netherlands amounted to around 446.23 billion U.S. dollars .

Example 867:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: U.S. governors - number by political party affiliation 1990 to 2019
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  2000  1995  1990
Democratic     23    16    16    18    18    21    20    20    20    26    28    28    28    22    22    22    23    18    19    29
Republican     27    33    33    31    31    29    30    29    29    24    22    22    22    28    28    28    27    30    30    21
Independent     0     1     1     1     1     0     0     1     1     0     0     0     0     0     0     0     0     2     1     0 

gold: This graph shows the number of governors in the U.S. by their political party affiliation from 1990 to 2019 . A total of 23 democratic and 27 republican governors were in office in 2019 .
gold_template: This graph shows the templateTitle[2] of templateTitle[1] in the templateTitleSubject[0] templateTitle[3] their templateTitle[4] templateTitle[5] templateTitle[6] from templateValue[0][last] to templateValue[0][0] . A total of templateValue[1][0] templateLabel[1][0] and templateValue[2][0] templateLabel[2][0] templateTitle[1] were in office in templateValue[0][0] .

generated_template: This statistic shows the share of templateTitleSubject[0] templateTitle[1] templateTitle[2] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][last] , there were a templateLabel[1][0] of templateValue[1][last] unprovoked templateTitleSubject[0] templateTitle[1] on humans templateTitle[2] . templateValue[2][last] of these unprovoked templateTitle[1] were templateLabel[2][0] .
generated: This statistic shows the share of U.S. governors number from 1990 to 2019 .  In 1990 , there were a Democratic of 29 unprovoked U.S. governors on humans number .  21 of these unprovoked governors were Republican .

Example 868:
titleEntities: {'Subject': ['Afghanistan'], 'Date': ['2018']}
title: Infant mortality rate in Afghanistan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['47.9', '49.5', '51.2', '53.1', '55.1', '57.3', '59.5', '61.9', '64.3', '66.8', '69.4']

gold: The statistic shows the infant mortality rate in Afghanistan from 2008 to 2018 . In 2018 , the infant mortality rate in Afghanistan was at about 47.9 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 Afghanistan from 2008 to 2018 .  In 2018 , the Infant mortality rate in Afghanistan was at about 47.9 Deaths per 1,000 live births .

Example 869:
titleEntities: {'Subject': ['Value M As'], 'Date': ['2009', '2019']}
title: Value of M & As in the U.S. healthcare and life sciences sector 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Value', 'of', 'transactions', 'in', 'billion', 'U.S.', 'dollars']: ['168.1', '241.7', '232.0', '277.9', '576.0', '236.6', '108.2', '101.6', '141.4', '110.6', '174.6']

gold: This statistic shows the value of merger and acquisition transactions in the United States healthcare and life sciences industry from 2009 to 2019 YTD . In 2016 , aggregate transactions totaled some 278 billion U.S. dollars . M & A transactions provide investors with an exit strategy from smaller and private companies .
gold_template: This statistic shows the templateYLabel[0] of merger and acquisition templateYLabel[1] in the templateTitle[3] templateTitle[4] and templateTitle[5] templateTitle[6] industry from templateXValue[min] to templateXValue[max] YTD . In templateXValue[3] , aggregate templateYLabel[1] totaled some templateYValue[3] templateScale templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] templateTitle[2] A templateYLabel[1] provide investors with an exit strategy from smaller and private companies .

generated_template: This statistic shows the templateScale of the templateYLabel[1] templateYLabel[2] living in urban areas in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] was living in cities and urban areas .
generated: This statistic shows the billion of the transactions billion living in urban areas in Value M As from 2009 to 2019 .  In 2019 , 168.1 billion of the transactions billion of Value M As was living in cities and urban areas .

Example 870:
titleEntities: {'Subject': ['European'], 'Date': ['2016']}
title: Selected European countries ranked by retail banking customer satisfaction 2016
X_Axis['Country']: ['Netherlands', 'Czech_Republic', 'Austria', 'Switzerland', 'Portugal', 'Germany', 'Poland', 'Sweden', 'Italy', 'United_Kingdom', 'Finland', 'Belgium', 'Denmark', 'Norway', 'France', 'Spain']
Y_Axis['Share', 'of', 'customers', 'with', 'positive', 'experience']: ['70.6', '67', '66.8', '64.8', '63', '62.3', '61.6', '60.7', '59.5', '58.4', '58.2', '56.7', '55.9', '53.9', '52.3', '35.7']

gold: This statistic illustrates the share of customers with a positive retail banking experience in the leading selected European banking systems ( countries ) as of 2016 . Approximately 70.6 percent of surveyed bank customers in the Netherlands indicated high levels of satisfaction , ranking the country highest among European banking locations in 2016 . This was followed by the Czech Republic , with 67 percent of bank customers with a positive experience throughout the year .
gold_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] a templateYLabel[3] templateTitle[5] templateTitle[6] templateYLabel[4] in the leading templateTitle[0] templateTitleSubject[0] templateTitle[6] systems ( templateTitle[2] ) as of templateTitleDate[0] . Approximately templateYValue[max] templateScale of surveyed bank templateYLabel[1] in the templateXValue[0] indicated high levels of templateTitle[8] , ranking the templateXLabel[0] highest among templateTitleSubject[0] templateTitle[6] locations in templateTitleDate[0] . This was followed templateTitle[4] the templateXValue[1] , templateYLabel[2] templateYValue[1] templateScale of bank templateYLabel[1] templateYLabel[2] a templateYLabel[3] templateYLabel[4] throughout the year .

generated_template: This statistic shows the an estimate of templateTitle[1] templateYLabel[0] worldwide , from the 2017 fiscal year to fiscal year 2021 , templateTitle[3] select templateXLabel[0] . The templateXValue[0] is projected to spend about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] on drones between 2017 and 2021 , making it the templateXLabel[0] with the greatest expenditure on drones .
generated: This statistic shows the an estimate of European Share worldwide , from the 2017 fiscal year to fiscal year 2021 , ranked select Country .  The Netherlands is projected to spend about 70.6 % positive experience on drones between 2017 and 2021 , making it the Country with the greatest expenditure on drones .

Example 871:
titleEntities: {'Subject': ['Ukraine'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) of Ukraine 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', 'in', 'billion', 'U.S.', 'dollars']: ['204.49', '193.32', '182.52', '172.23', '161.87', '150.4', '130.86', '112.13', '93.31', '90.49', '130.57', '179.57', '175.71', '163.16', '136.01', '117.08', '181.31', '143.26', '107.77', '86.0', '64.75', '50.1', '42.34', '37.86', '32.33', '32.66', '43.32', '51.87', '46.08', '38.28', '38.01']

gold: This statistic shows the Ukrainian gross domestic product ( GDP ) from 1994 to 2018 in billion U.S. dollars , with projections up until 2024 . In 2018 , Ukraine 's GDP amounted to approximately 130.86 billion U.S. dollars . See the Russian GDP for comparison .
gold_template: This statistic shows the Ukrainian templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) from templateXValue[min] to templateXValue[6] in templateScale templateYLabel[2] templateYLabel[3] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] amounted to approximately templateYValue[6] templateScale templateYLabel[2] templateYLabel[3] . See the Russian templateYLabel[0] for comparison .

generated_template: This timeline shows templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[0] in templateTitleSubject[0] was at around templateYValue[min] templateScale US templateYLabel[3] .
generated: This timeline shows GDP in Ukraine from 1994 to 2018 , with projections up until 2024 .  In 2018 , GDP in Ukraine was at around 32.33 billion US dollars .

Example 872:
titleEntities: {'Subject': ['Detroit Lions'], 'Date': ['2019']}
title: Average regular season home attendance of the Detroit Lions 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Average', 'attendance']: ['61342', '62795', '64137', '60792', '61347', '63024', '63796', '63769', '63742', '56285', '49395', '54497']

gold: This graph depicts the average regular season home attendance of the Detroit Lions from 2008 to 2019 . In 2019 , the average attendance at home games of the Detroit Lions was 61,342 .
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)] templateTitleSubject[0] average templateTitle[3] templateYLabel[1] - additional information The templateTitleSubject[0] ' templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] has remained relatively constant in recent years , with the templateYLabel[0] in the templateXValue[max] templateTitle[2] standing at templateYValue[idxmax(X)] .
generated: This graph depicts the Average regular season home attendance of the Detroit Lions from 2008 to 2019 .  In 2019 , the Average attendance at home games of the Detroit Lions was 61342 Detroit Lions average home attendance - additional information The Detroit Lions ' Average regular season home attendance has remained relatively constant in recent years , with the Average in the 2019 season standing at 61342 .

Example 873:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Twitter usage frequency in the United States 2019
X_Axis['Response']: ['Several_times_a_day', 'Daily', 'Several_times_a_week', 'Weekly', 'Several_times_a_month', 'Monthly', 'Less_often', "Don't_know"]
Y_Axis['Share', 'of', 'respondents']: ['29', '20', '22', '11', '8', '5', '7', '0']

gold: As of the third quarter of 2019 , it was found that 29 percent of Twitter users in the United States accessed the social platform several times a day . A further 11 percent of respondents claimed that they used Twitter on a weekly basis .
gold_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[3] templateScale of templateYLabel[1] claimed that they used templateTitle[0] on a templateXValue[3] basis .

generated_template: This statistic presents the templateTitle[2] of users in the templateTitleSubject[0] accessing templateTitle[0] . As of the third quarter of templateTitleDate[0] , it was found that templateYValue[max] templateScale of templateTitle[4] templateTitle[0] users accessed the social platform templateXValue[0] a templateXValue[0] . templateTitle[0] is the most popular social media site in the templateTitle[4] .
generated: This statistic presents the frequency of users in the United States accessing Twitter .  As of the third quarter of 2019 , it was found that 29 % of States Twitter users accessed the social platform Several_times_a_day a .  Twitter is the most popular social media site in the States .

Example 874:
titleEntities: {'Subject': ['Super Bowl TV'], 'Date': ['1990', '2020']}
title: Super Bowl TV ratings in the U.S. 1990 to 2020
X_Axis['Year']: ['20', '19', '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', '91', '90']
Y_Axis['Super', 'Bowl', 'TV', 'rating']: ['41.6', '41.1', '43.1', '45.3', '46.6', '47.5', '46.4', '46.3', '47.0', '46.0', '45.0', '42.0', '43.1', '42.6', '41.6', '41.1', '41.4', '40.7', '40.4', '40.4', '43.3', '40.2', '44.5', '43.3', '46.0', '41.3', '45.5', '45.1', '40.3', '41.9', '39.0']

gold: TV ratings of the Super Bowl declined for the fourth consecutive year in 2019 . The New England Patriots ' victory over the Los Angeles Rams received the joint-lowest household TV rating in the United States since Super Bowl XXXVII in 2003 . In 2020 , however , the ratings picked up slightly as the Kansas City Chiefs ' stunning fourth quarter comeback victory against the San Francisco 49ers scored a rating of 41.6 .
gold_template: templateYLabel[2] templateTitle[3] of the templateYLabel[0] templateYLabel[1] declined for the fourth consecutive templateXLabel[0] in 2019 . The New England Patriots ' victory over the Los Angeles Rams received the joint-lowest household templateYLabel[2] templateYLabel[3] in the templateTitle[4] since templateYLabel[0] templateYLabel[1] XXXVII in 2003 . In templateTitleDate[max] , however , the templateTitle[3] picked up slightly as the Kansas City Chiefs ' stunning fourth quarter comeback victory against the San Francisco 49ers scored a templateYLabel[3] of templateYValue[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . By templateXValue[max] , around templateYValue[idxmax(X)] templateScale people were counted in the templateTitle[0] .
generated: This statistic shows the Super of Bowl in the Super from 0 to 99 .  By 99 , around 40.2 million people were counted in the Super .

Example 875:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Most followed sports leagues in the U.S. 2019
X_Axis['Response']: ['NFL', 'MLB', 'NBA', 'NHL', 'MLS', "I_don't_follow_any_of_these_leagues"]
Y_Axis['Share', 'of', 'respondents']: ['33', '16', '10', '5', '3', '32']

gold: There are widely considered to be four major professional men 's sports leagues in the United States and Canada - NFL , NBA , MLB , and NHL . The professional soccer league ( MLS ) has also achieved some popularity in the United States in recent years . During a 2019 survey , 33 percent of respondents stated that the National Football League , NFL , was their favorite men 's U.S. professional sports league to follow .
gold_template: There are widely considered to be four major professional men 's templateTitle[2] templateXValue[last] in the templateTitle[4] and Canada - templateXValue[0] , templateXValue[2] , templateXValue[1] , and templateXValue[3] . The professional soccer league ( templateXValue[4] ) has also achieved some popularity in the templateTitle[4] in recent years . During a templateTitleDate[0] survey , templateYValue[max] templateScale of templateYLabel[1] stated that the National Football League , templateXValue[0] , was their favorite men 's templateTitleSubject[0] professional templateTitle[2] league to templateXValue[last] .

generated_template: This statistic shows the results of a templateTitleDate[0] survey regarding patriotism in the templateTitle[4] . The templateYLabel[1] were asked how proud they are to be an templateTitleSubject[0] . In templateTitleDate[0] , some templateYValue[max] templateScale of survey templateYLabel[1] stated they were templateXValue[0] proud to be an templateTitleSubject[0] .
generated: This statistic shows the results of a 2019 survey regarding patriotism in the U.S. The respondents were asked how proud they are to be an U.S. In 2019 , some 33 % of survey respondents stated they were NFL proud to be an U.S. .

Example 876:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016']}
title: Tea consumption by social class in the United Kingdom ( UK ) 2016
Data:
                 0                     1               2           3
unknown  Black_tea  Fruit_and_herbal_tea  Specialty_bags  Green_bags
ABC1            48                    63              66          59
C2DE            52                    37              34          41 

gold: This statistic displays preferences for different types of tea in the United Kingdom in 2016 , by social class.Class differences are less apparent with black tea , consumed by 48 percent of classes ABC1 and 52 percent of classes C2DE respectively . Specialty bags , however , are only consumed by 34 percent of respondents from the C2DE grouping but by 66 percent of ABC1 .
gold_template: This statistic displays preferences for different types of templateValue[0][0] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[2] templateTitle[3] class.Class differences are less apparent with templateValue[0][0] , consumed templateTitle[2] templateValue[1][0] templateScale of classes templateLabel[1][0] and templateValue[2][0] templateScale of classes templateLabel[2][0] respectively . templateValue[0][2] , however , are only consumed templateTitle[2] templateValue[2][2] templateScale of respondents from the templateLabel[2][0] grouping but templateTitle[2] templateValue[1][2] templateScale of templateLabel[1][0] .

generated_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: This survey shows the voter by social Barack United Kingdom and Mitt class in the 2016 consumption as of October 28 , Kingdom UK 2016 .  If the elections were held that day , about 66 % of Specialty_bags or African American voters would vote social Barack United Kingdom .

Example 877:
titleEntities: {'Subject': ['Egypt'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Egypt 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']: ['453.73', '419.83', '397.35', '376.54', '353.0', '302.26', '249.56', '236.53', '332.48', '332.08', '305.57', '288.01', '278.77', '247.73', '230.02', '198.32', '170.8', '137.06', '112.9', '94.13', '82.86', '85.16', '90.26', '102.27', '104.75', '95.04', '89.19', '79.77', '71.11', '63.26', '54.55', '49.53', '44.17', '48.43', '96.09', '115.36', '92.53', '77.36', '54.08', '48.84', '41.89']

gold: The statistic shows gross domestic product ( GDP ) in Egypt 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 Egypt 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 878:
titleEntities: {'Subject': ['United Arab Emirates'], 'Date': ['2018']}
title: Age structure of the population in the United Arab Emirates 2018
Data:
                       0      1      2      3      4      5      6      7      8      9     10
Age groups          2018   2017   2016   2015   2014   2013   2012   2011   2010   2009   2008
Ages 0-14           14.6   14.5  14.39  14.16  13.93  13.73  13.58  13.42  13.16  13.66  14.28
Ages 15-64         84.31  84.46  84.63   84.9  85.17  85.41  85.62  85.83  86.16  85.65  85.01
Ages 65 and older   1.09   1.03   0.99   0.93    0.9   0.86    0.8   0.74   0.69   0.69   0.71 

gold: The statistic shows the age structure among the population of the United Arab Emirates ( UAE ) from 2008 to 2018 . In 2018 , 14.6 percent of the UAE 's total population were aged 0 to 14 years .
gold_template: The statistic shows the templateLabel[0][0] templateTitle[1] among the templateTitle[2] of the templateTitleSubject[0] Emirates ( UAE ) from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of the UAE 's total templateTitle[2] were aged 0 to templateValue[1][2] years .

generated_template: This statistic shows the global templateTitle[1] of templateTitle[2] around the templateLabel[5][1] between templateValue[0][last] and 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 .
generated: This statistic shows the global structure of population around the 65 between 2008 and 2018 , Arab Emirates .  population generated a structure of 1.83 billion British pounds in 2016 the country alone .

Example 879:
titleEntities: {'Subject': ['Italy'], 'Date': ['2014', '2029']}
title: Total contribution of travel and tourism to GDP in Italy 2014 to 2029
X_Axis['Year']: ['2029', '2028', '2027', '2026', '2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'contribution', 'in', 'billion', 'euros']: ['331.0', '321.5', '311.5', '301.5', '291.5', '281.8', '272.1', '262.7', '252.6', '244.1', '237.8', '232.2', '224.3', '214.8', '210.5', '197.0']

gold: In 2019 , the contribution of travel and tourism to the Italian gross domestic product amounted to 237.8 billion euros . The industry , which is one of the most important ones for the country 's economy , constituted about 13.3 percent of the Italian GDP in the year considered and is predicted to reach 14.3 percent in 2029 . In search of the Italian dolce vita Every year more and more international visitors come to Italy to discover the real Italian dolce vita – breathtaking landscapes , rich history , great art , interesting culture and delectable cuisine .
gold_template: In templateXValue[10] , the templateYLabel[1] of templateTitle[2] and templateTitle[3] to the Italian gross domestic product amounted to templateYValue[10] templateScale templateYLabel[3] . The industry , which is one of the most important ones for the country 's economy , constituted about 13.3 templateScale of the Italian templateYLabel[0] in the templateXLabel[0] considered and is predicted to reach 14.3 templateScale in templateXValue[max] . In search of the Italian dolce vita Every templateXLabel[0] more and more international visitors come to templateTitleSubject[0] to discover the real Italian dolce vita – breathtaking landscapes , rich history , great art , interesting culture and delectable cuisine .

generated_template: This statistic shows the average templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] per annum from templateXValue[min] to templateXValue[max] . The largest templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] was in templateXValue[12] with a total production of templateYValue[max] templateScale templateYLabel[2] of templateTitle[2] . Since then the total templateYLabel[0] of templateTitle[2] templateTitle[3] has declined .
generated: This statistic shows the average GDP of Italy travel tourism per annum from 2014 to 2029 .  The largest GDP of Italy travel tourism was in 2017 with a total production of 331.0 billion of travel .  Since then the total GDP of travel tourism has declined .

Example 880:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2016']}
title: Average age of cars on the road in the United Kingdom ( UK ) , 2000 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Average', 'age']: ['7.7', '7.8', '7.8', '7.7', '7.59', '7.44', '7.25', '7.06', '6.93', '6.79', '6.73', '6.7', '6.69', '6.78', '6.88', '7.01', '7.11']

gold: This graph shows the average age of cars on the road in the United Kingdom ( UK ) between 2000 and 2016 , including projections for 2015 and 2016 . It can be seen that before the recession the average age of cars in the UK was falling , however , since the recession the average age has risen drastically and will probably continue to rise . These figures show that people were preferring to hold onto their cars for longer rather than sell them to buy new cars .
gold_template: This graph shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] on the templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) between templateXValue[min] and templateXValue[max] , including projections for templateXValue[1] and templateXValue[max] . It can be seen that before the recession the templateYLabel[0] templateYLabel[1] of templateTitle[2] in the templateTitleSubject[1] was templateNegativeTrend , however , since the recession the templateYLabel[0] templateYLabel[1] has risen drastically and will probably continue to rise . These figures show that people were preferring to hold onto their templateTitle[2] for longer rather than sell them to buy new templateTitle[2] .

generated_template: According to a survey conducted by the Organization for Economic Cooperation and Development ( OECD ) , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] steadily templateNegativeTrend between the years templateXValue[min] and templateXValue[4] , going to templatePositiveTrend again to templateYValue[min] within twelve years . Nevertheless , this trend was abruptly reverted during the first templateXLabel[0] of the Hollande Presidency : the templateYLabel[0] templateYLabel[1] jumped from templateYValue[min] in templateXValue[idxmin(Y)] to templateYValue[max] in templateXValue[idxmax(Y)] . The templateYLabel[0] templateYLabel[1] for Public templateYLabel[1] in templateTitleSubject[0] have templatePositiveTrend during Hollande 's presidency During the first templateXLabel[0] of the former French president François Hollande Presidency , the templateYLabel[0] templateYLabel[1] of the templateYLabel[0] templateYLabel[1] of publicly templatePositiveTrend at an abrupt pace , going from 928 in templateXValue[4] to 1,458 templateYLabel[1] in templateXValue[3] .
generated: According to a survey conducted by the Organization for Economic Cooperation and Development ( OECD ) , the Average age in United Kingdom steadily decreased between the years 2000 and 2012 , going to increase again to 6.69 within twelve years .  Nevertheless , this trend was abruptly reverted during the first Year of the Hollande Presidency : the Average age jumped from 6.69 in 2004 to 7.8 in 2015 .  The Average age for Public age in United Kingdom have increased during Hollande 's presidency During the first Year of the former French president François Hollande Presidency , the Average age of the Average age of publicly increased at an abrupt pace , going from 928 in 2012 to 1,458 age in 2013 .

Example 881:
titleEntities: {'Subject': ['Australia'], 'Date': ['2024']}
title: Public debt of Australia in relation to gross domestic product ( GDP ) 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'public', 'debt', 'to', 'GDP']: ['36.64', '37.94', '39.65', '41.27', '42.28', '41.76', '41.37', '41.07', '40.46', '37.69', '34.03']

gold: The statistic shows Australia 's public debt from 2014 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . The debt-to-GDP ratio includes the debts of central and local government , communities and municipalities as well as social security . In 2018 , Australia 's public debt was around 41.37 percent of its GDP .
gold_template: The statistic shows templateTitleSubject[0] 's templateYLabel[1] templateYLabel[2] 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 debt-to-GDP templateYLabel[0] includes the debts of central and local government , communities and municipalities as well as social security . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[1] templateYLabel[2] was around templateYValue[6] templateScale of its templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of the European Union in the templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] based on purchasing-power-parity from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateYLabel[0] of the European Union in the templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] based on purchasing-power-parity amounted to an estimated templateYValue[6] templateScale . The templateTitleSubject[0] GDP amounted to 13.92 templateScale euros in templateXValue[min] .
generated: This statistic shows the Ratio of the European Union in the debt GDP based on purchasing-power-parity from 2014 to 2024 .  In 2018 , the Ratio of the European Union in the debt GDP based on purchasing-power-parity amounted to an estimated 41.37 % .  The Australia GDP amounted to 13.92 billion euros in 2014 .

Example 882:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2017', '2019']}
title: Inflation rate ( CPI ) in the United Kingdom ( UK ) 2017 to 2019
X_Axis['Month']: ["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"]
Y_Axis['Inflation', 'rate']: ['1.5', '1.5', '1.7', '1.7', '2.1', '2', '2', '2.1', '1.9', '1.9', '1.8', '2.1', '2.3', '2.4', '2.4', '2.7', '2.5', '2.4', '2.4', '2.4', '2.5', '2.7', '3', '3', '3.1']

gold: The Consumer Price Index ( CPI ) rate of the United Kingdom in November 2019 was 1.5 percent , which together with the previous month , was the lowest rate recorded in this two year period . Between November 2017 and November 2019 the CPI rate was at it 's highest in November of 2017 , when an inflation rate of 3.1 percent was recorded .
gold_template: The Consumer Price Index ( templateTitle[2] ) templateYLabel[1] of the templateTitleSubject[0] in 2019 was templateYValue[min] templateScale , which together with the previous templateXLabel[0] , was the lowest templateYLabel[1] recorded in this templateYValue[min] year period . Between 2017 and 2019 the templateTitle[2] templateYLabel[1] was at it 's highest in of templateTitleDate[min] , when an templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale was recorded .

generated_template: The statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] in templateTitleDate[0] , by templateTitle[5] templateTitle[6] of . The templateYLabel[0] templateTitle[1] templateYLabel[1] in templateTitleDate[0] was valued at templateYValue[max] in the same period . templateTitle[1] – additional information The templateTitle[1] templateYLabel[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templatePositiveTrend over the past years , the templateYLabel[0] templateTitle[1] templateYLabel[1] templateNegativeTrend to templateYValue[12] in 2015 .
generated: The statistic shows the Inflation rate in the United Kingdom in 2017 , by UK 2017 of .  The Inflation rate in 2017 was valued at 3.1 in the same period .  rate – additional information The rate Inflation in the United Kingdom increased over the past years , the Inflation rate dropped to 2.3 in 2015 .

Example 883:
titleEntities: {'Subject': ['GO U.S.'], 'Date': ['2016']}
title: Pokémon GO players in the U.S. 2016 , by age
X_Axis['Year']: ['13-17_years', '18-29_years', '30-50_years', '50_years_and_older']
Y_Axis['Share', 'of', 'users']: ['22', '46', '25', '6']

gold: The graph shows the distribution of Pokémon GO players in the United States as of July 2016 , broken down by age group . It was found that in the measured period , 46 percent of Pokémon GO users were between the ages of 18 and 29 . It was measured that the popular AR mobile game had 27 million active users in the U.S. in 2017 , with 10 million playing the game on iOS devices , and 17 million using it on Android mobiles .
gold_template: The graph shows the distribution of templateTitle[0] templateTitleSubject[0] templateTitle[2] in the templateTitle[3] as of 2016 , broken down templateTitle[5] templateTitle[6] group . It was found that in the measured period , templateYValue[max] templateScale of templateTitle[0] templateTitleSubject[0] templateYLabel[1] were between the ages of 18 and 29 . It was measured that the popular AR mobile game had 27 templateScale active templateYLabel[1] in the templateTitleSubject[0] in 2017 , with 10 templateScale playing the game on iOS devices , and 17 templateScale using it on Android mobiles .

generated_template: This statistic shows the templateScale of internet templateYLabel[1] in the templateTitle[1] who use templateTitle[2] as of 2017 , sorted templateTitle[5] templateTitle[6] templateTitle[7] . As of the measured period , over templateYValue[0] templateScale of 15 to 24 templateXLabel[0] old templateTitleSubject[0] internet templateYLabel[1] accessed online mail .
generated: This statistic shows the percentage of internet users in the GO who use players as of 2017 , sorted by age .  As of the measured period , over 22 % of 15 to 24 Year old GO U.S. internet users accessed online mail .

Example 884:
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: 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 the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] , templateTitle[1] templateYLabel[0] templateYLabel[1] , followed by templateXValue[1] and templateXValue[2] . The overall quarterly templateYLabel[2] templateYLabel[3] in the country can be found here .
generated: This statistic shows the 1043247 Countries largest the number cosmetic procedures in the world 2018 to .  Over the past decade , United_States has the Country largest the number cosmetic procedures , largest Number procedures , followed by Brazil and Mexico .  The overall quarterly procedures in the country can be found here .

Example 885:
titleEntities: {'Subject': ['Global'], 'Date': ['2020']}
title: Global medical alert systems/PERS market between 2013 and 2020
X_Axis['Year']: ['2020', '2018', '2013']
Y_Axis['Market', 'volume', 'in', 'billion', 'U.S.', 'dollars']: ['2.0', '1.85', '1.23']

gold: The statistic shows the global medical alert systems/PERS ( personal emergency response system ) market volume in 2013 , and provides a forecast for 2018 and 2020 . In 2013 , the global market size for such medical systems stood at some 1.23 billion U.S. dollars . Until 2020 , the market is expected to increase to some two billion U.S. dollars .
gold_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] ( personal emergency response system ) templateYLabel[0] templateYLabel[1] in templateXValue[min] , and provides a forecast for templateXValue[1] and templateXValue[max] . In templateXValue[min] , the templateTitleSubject[0] templateYLabel[0] size for such templateTitle[1] systems stood at some templateYValue[idxmin(X)] templateScale templateYLabel[3] templateYLabel[4] . Until templateXValue[max] , the templateYLabel[0] is expected to templatePositiveTrend to some templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_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: The statistic shows the projected Global alert systems/PERS Market from 2013 to 2020 .  Market of alert systems/PERS are forecast to total around 2.0 billion units in 2020 .

Example 886:
titleEntities: {'Subject': ['London'], 'Date': ['2013']}
title: London : occupancy rate of hotels by quarter up to 2013
X_Axis['Quarter']: ['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['Occupancy', 'rate']: ['81.1', '85.6', '82.4', '75.2', '81', '91.9', '84.2', '77', '81.9', '87.4', '85.2', '75.8']

gold: The statistic illustrates the occupancy rate of hotels in London by quarter from 2011 to 2013 . In the first quarter of 2011 the occupancy rate of hotels in London was 75.8 percent . London hotel occupancy rate - additional information The highest occupancy rate for London hotels during this timeframe was forecasted for the third quarter of 2012 at 91.9 percent .
gold_template: The statistic illustrates the templateYLabel[0] templateYLabel[1] of templateTitle[3] in templateTitleSubject[0] templateTitle[4] templateXLabel[0] from 2011 to templateTitleDate[0] . In the first templateXLabel[0] of 2011 the templateYLabel[0] templateYLabel[1] of templateTitle[3] in templateTitleSubject[0] was templateYValue[last] templateScale . templateTitleSubject[0] hotel templateYLabel[0] templateYLabel[1] - additional information The highest templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] templateTitle[3] during this timeframe was forecasted for the third templateXLabel[0] of 2012 at templateYValue[max] templateScale .

generated_template: This statistic illustrates a forecast of the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitleSubject[1] from third templateXLabel[0] templateXValue[10] to second templateXLabel[0] templateXValue[0] . It is forecast that there will be templateYValue[max] templateScale templateTitle[1] templateTitle[2] templateYLabel[0] as of templateXLabel[0] two templateXValue[0] .
generated: This statistic illustrates a forecast of the London of occupancy rate in the London from third Quarter Q2_´11 to second Quarter Q4_´13 .  It is forecast that there will be 91.9 % occupancy rate as of Quarter two Q4_´13 .

Example 887:
titleEntities: {'Subject': ['Serbia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Serbia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['4', '4', '4', '4', '3.99', '3.5', '4.3', '2.05', '3.34', '1.78', '-1.59']

gold: The statistic shows the growth in real GDP in Serbia from 2014 to 2018 , with projections up until 2024 . In 2018 , Serbia 's real gross domestic product increased by around 4.3 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 between 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 Serbia from between 2014 to 2018 , with projections up until 2024 .  In 2018 , Serbia 's real Gross domestic product increased by around 4.3 % compared to the previous Year .

Example 888:
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 amount of persons arrested in templateTitleSubject[0] and templateTitleSubject[1] from fiscal templateLabel[0][0] templateValue[0][last] to templateValue[0][0] , templateTitle[5] templateTitle[6] templateTitle[7] . The peak templatePositiveTrend templateValue[1][2] templateScale of the respondents aged between 30 and 34 templateValue[0][0] had the same templateLabel[0][0] .
generated: This statistic shows the amount of persons arrested in Bundesliga and from fiscal Year VFL_Wolfsburg to FSV_Mainz_05 , 2019/2020 .  The peak increased 184€ % of the respondents aged between 30 and 34 FSV_Mainz_05 had the same Year .

Example 889:
titleEntities: {'Subject': ['Dacia', 'United Kingdom', 'UK'], 'Date': ['2016', '2019']}
title: Dacia 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']: ['2357', '1963', '1287', '4300', '1031', '1445', '2935', '2647', '2060', '7443', '1008', '2475', '1890', '2237', '1778', '2470', '811', '1366', '2000', '2516', '1919', '4013', '1048', '2121', '983', '1466', '1625', '4459', '769', '1460', '2845', '1939', '1397', '5773', '439', '1994', '1891', '1405', '2280', '5247', '845', '1386']

gold: Dacia sold 2,357 cars in the United Kingdom in December 2019 . This was an increase of roughly 24.7 percent compared to the same month in the previous year . Overall , car sales of the Romanian automobile manufacturer amounted to 30,951 units in 2019 , up 28 percent compared to 2018 .
gold_template: templateTitleSubject[0] templateYLabel[1] templateYValue[0] cars in the templateTitleSubject[1] in 2019 . This was an templatePositiveTrend of roughly 24.7 templateScale compared to the same templateXLabel[0] in the previous year . Overall , templateTitle[1] templateTitle[2] of the Romanian automobile manufacturer amounted to 30,951 templateYLabel[0] in templateTitleDate[max] , up 28 templateScale compared to 2018 .

generated_template: templateTitle[0] templateYLabel[1] templateYValue[3] cars in the templateTitleSubject[0] ( UK ) in 2019 , translating to a market share of 8.9 templateScale . In the past four years , templateTitle[2] volume remained stable , with the most profitable months coming in and of each year . templateTitle[2] in and are often considerably higher , as these are the months in which the Driver & Vehicle Licensing Agency ( DVLA ) issues new registration plates .
generated: Dacia sold 4300 cars in the Dacia ( UK ) in 2019 , translating to a market share of 8.9 % .  In the past four years , sales volume remained stable , with the most profitable months coming in and of each year .  sales in and are often considerably higher , as these are the months in which the Driver & Vehicle Licensing Agency ( DVLA ) issues new registration plates .

Example 890:
titleEntities: {'Subject': ['Bulgaria'], 'Date': ['2015']}
title: Median age of the population in Bulgaria 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']: ['48.1', '49.1', '49.0', '48.1', '47.1', '46.0', '44.6', '43.4', '42.3', '41.1', '39.7', '38.2', '36.5', '35.3', '34.2', '33.7', '33.2', '32.0', '30.4', '28.9', '27.3']

gold: This statistic shows the median age of the population in Bulgaria 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 Bulgaria 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 891:
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 average templateTitle[0] templateTitle[1] in templateValue[0][0] for those born in templateTitleDate[0] , by gender and region . The average templateTitle[0] templateTitle[1] across 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 Global internet in Europe for those born in 2019 , by gender and region .  The average Global internet across the whole continent was 73.6 years for Male and 71 years for Female .  The average Global internet globally was 70 years for Male and 75 years for Female in 2019 .

Example 892:
titleEntities: {'Subject': ['Swiss'], 'Date': ['2000', '2018']}
title: Export value of Swiss watches 2000 - 2018 , by price category
Data:
                  0      1      2      3      4      5      6     7     8     9
Year           2018   2017   2016   2015   2014   2013   2012  2010  2005  2000
0-200 CHF      0.95   0.98   1.11   1.24   1.25   1.27   1.33  1.16  1.07  1.23
200-500 CHF     1.3   1.34   1.29   1.42   1.56   1.49   1.32  1.04  0.84  1.04
500-3000 CHF   4.51    4.2    4.0   4.16   4.34   4.39   4.45  3.86  4.29  3.86
3000+ CHF     13.18  12.26  11.86  13.41  13.84  13.46  13.09  9.09  5.22  3.15 

gold: The graph presents the export value of Swiss watches from 2000 to 2018 , by price category . In 2018 , the export value of Swiss watches within the 0 - 200 CHF price category was .95 billion Swiss francs .
gold_template: The graph presents the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[3] from templateValue[0][last] to templateValue[0][0] , templateTitle[6] templateTitle[7] templateTitle[8] . In templateValue[0][0] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[3] within the 0 - 200 templateLabel[1][1] templateTitle[7] templateTitle[8] was templateValue[1][min] templateScale templateTitleSubject[0] francs .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] operated the country templateTitle[3] in the templateTitle[4] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , there were around 9,172,000 templateTitle[2] templateTitle[3] ( including templateTitle[3] and heifers that have calved ) in the templateTitle[4] .
generated: This statistic shows the Export of value operated the country watches in the 2000 from to 2018 .  In 2018 , there were around 9,172,000 Swiss watches ( including watches and heifers that have calved ) in the 2000 .

Example 893:
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: This statistic shows the total annual growth of the total templateTitle[0] templateYLabel[3] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[2] templateTitle[1] templateTitle[2] to the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the total annual growth of the total dollars in the Olive from 2016 to 2019 .  In 2019 , the U.S. sales Olive to the Olive Garden amounted to 4.29 billion dollars .

Example 894:
titleEntities: {'Subject': ['Activision Blizzard'], 'Date': ['2005', '2018']}
title: Activision Blizzard 's net annual revenue 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Net', 'revenue', 'in', 'million', 'U.S.', 'dollars']: ['7500', '7017', '6608', '4664', '4408', '4583', '4856', '4755', '4447', '4279', '3026', '1349', '1018', '780']

gold: This graph depicts Activision Blizzard 's net annual revenue from 2005 to 2018 . In 2018 , Activision 's annual revenue amounted to 7.15 billion U.S. dollars . Activision Blizzard publishes games such as Call of Duty , Guitar Hero , StarCraft and World of Warcraft .
gold_template: This graph depicts templateTitleSubject[0] 's templateYLabel[0] templateTitle[4] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[2] templateTitle[4] templateYLabel[1] amounted to 7.15 templateScale templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] publishes games such as Call of Duty , Guitar Hero , StarCraft and World of Warcraft .

generated_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] generated approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic represents Activision Blizzard 's Net revenue between the fiscal Year of 2005 and the fiscal Year of 2018 .  In the fiscal Year of 2018 , Activision Blizzard Blizzard Net revenue generated approximately 7500 million U.S. dollars .

Example 895:
titleEntities: {'Subject': ['GO Steam'], 'Date': ['2016', '2020']}
title: CS : GO peak concurrent player number on Steam 2016 to 2020
X_Axis['Month']: ["Jan_'20", "Dec_'19", "Nov_'19", "Oct_'19", "Sept_'19", "Aug_'19", "Jul'_19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sept_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Aprl_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sept_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17", "Dec_'16", "Nov_'16", "Oct_'16", "Sept_'16", "Aug_'16", "Jul_'16", "Jun_'16", "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16"]
Y_Axis['Number', 'of', 'players', 'in', 'thousands']: ['817.23', '767.06', '758.41', '747.94', '720.05', '647.46', '578.93', '587.72', '588.45', '621.61', '680.07', '654.07', '684.51', '746.55', '546.03', '565.97', '583.03', '454.37', '426.01', '420.26', '454.48', '523.26', '672.5', '686.59', '715.85', '598.41', '601.88', '639.97', '665.37', '595.78', '624.79', '614.62', '692.97', '709.84', '742.36', '744.47', '814.62', '662.46', '627.12', '661.99', '638.36', '599.1', '636.06', '579.11', '668.61', '850.49', '737.6', '738.97', '667.43']

gold: In January 2020 , Counter-Strike : Global Offensive ( CS : GO ) reached 817.23 thousand concurrent players on Steam . The game reached its record high peak concurrent player number in April 2016 , at nearly 850.5 thousand gamers playing at the same time . Counter Strike still going strong Despite the fact that CS : GO was released some seven years ago in August 2012 , the game is still very popular online and has developed a cult following .
gold_template: In 2020 , Counter-Strike : Global Offensive ( templateTitle[0] : templateTitleSubject[0] ) reached templateYValue[0] thousand templateTitle[3] templateYLabel[1] on templateTitleSubject[0] . The game reached its record high templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] in 2016 , at nearly templateYValue[max] thousand gamers playing at the same time . Counter Strike still going strong Despite the fact that templateTitle[0] : templateTitleSubject[0] was released some seven years ago in 2012 , the game is still very popular online and has developed a cult following .

generated_template: The statistic shows information on the monthly templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] of Grand Theft Auto templateTitleSubject[0] on templateTitleSubject[0] worldwide as of 2020 . In 2020 , templateTitle[0] templateTitleSubject[0] reached templateYValue[max] thousand templateTitle[3] templateYLabel[1] on templateTitleSubject[0] .
generated: The statistic shows information on the monthly Number of peak concurrent players of Grand Theft Auto GO Steam on worldwide as of 2020 .  In 2020 , CS GO Steam reached 850.49 thousand concurrent players on GO Steam .

Example 896:
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] 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 Share of rural at France establishments in France from 2006 to 2018 .  In 2018 , the Share of rural in travel France ( including both international and domestic tourists ) amounted to approximately 19.5 % .

Example 897:
titleEntities: {'Subject': ['Household'], 'Date': ['2013', '2020']}
title: Household appliances consumption value worldwide 2013 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Consumption', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['588.83', '567.22', '546.39', '526.08', '506.16', '482.37', '458.02', '428.17']

gold: The statistic shows the global consumption value of household appliances from 2013 to 2020 . In 2013 , the worldwide consumption of household appliances amounted to 428.17 billion U.S. dollars .
gold_template: The statistic shows the global templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[4] templateYLabel[0] of templateTitleSubject[0] templateTitle[1] amounted to templateYValue[idxmin(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows templateYLabel[0] of templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was approximately templateYValue[3] templateScale , up from templateYValue[idxmin(X)] templateScale in the previous templateXLabel[0] .
generated: This statistic shows Consumption of billion in Household from 2013 to 2020 .  In 2018 , the Consumption of value billion in Household was approximately 526.08 billion , up from 428.17 billion in the previous Year .

Example 898:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Top U.S. states by number of active dentists 2017
X_Axis['State']: ['California', 'Texas', 'New_York', 'Florida', 'Illinois', 'Pennsylvania', 'New_Jersey', 'Ohio', 'Michigan', 'Massachusetts']
Y_Axis['Number', 'of', 'dentists']: ['30638', '14917', '14777', '10816', '8730', '7724', '6991', '6143', '6093', '5670']

gold: In total there are about 61 active dentists per every 100,000 people living in the United States , though there is significant variability in the number of dentists available depending on the state . As of 2017 , California , Texas and New York had the largest number of active dentists working . California was ranked first with over 30 thousand active dentists .
gold_template: In total there are about 61 templateTitle[5] templateYLabel[1] per every 100,000 people living in the templateTitle[1] , though there is significant variability in the templateYLabel[0] of templateYLabel[1] available depending on the templateXLabel[0] . As of templateTitleDate[0] , templateXValue[0] , templateXValue[1] and templateXValue[2] had the largest templateYLabel[0] of templateTitle[5] templateYLabel[1] working . templateXValue[0] was ranked first with over 30 thousand templateTitle[5] templateYLabel[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the largest templateYLabel[0] of templateTitle[3] templateTitle[4] templateYLabel[2] templateTitle[6] in the templateTitle[7] in templateTitleDate[0] . According to the source , Connecticut was the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[2] templateTitle[6] in templateTitleDate[0] templateTitle[1] templateYValue[max] templateYLabel[1] to every templateYLabel[3] thousand templateYLabel[5] .
generated: This statistic shows the Top U.S. the largest Number of by number dentists in the 2017 in .  According to the source , Connecticut was the State U.S. the states by number dentists in 2017 U.S. 30638 dentists to every dentists thousand .

Example 899:
titleEntities: {'Subject': ['United States'], 'Date': ['1990', '2018']}
title: United States - birth 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['Birth', 'rate', 'per', '1,000', 'of', 'population']: ['11.6', '11.8', '12.2', '12.4', '12.5', '12.4', '12.6', '12.7', '13.0', '13.5', '14.0', '14.3', '14.3', '14.0', '14.0', '14.1', '14.0', '14.1', '14.4', '14.2', '14.3', '14.2', '14.4', '14.6', '15.0', '15.4', '15.8', '16.2', '16.7']

gold: Over the past 30 years , the birth rate in the United States has been steadily declining , and in 2018 , there were 11.6 births per 1,000 of the population . In 1990 , this figure stood at 16.7 births per 1,000 of the population . Demographics have an impact The average birth rate in the U.S. may be falling , but when broken down along ethnic and economic lines , a different picture is painted : Native Hawaiian and other Pacific Islander women saw the highest birth rate in 2018 among all ethnicities , and White and Asian women both saw the lowest birth rate .
gold_template: Over the past 30 years , the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] has been steadily declining , and in templateXValue[max] , there were templateYValue[idxmax(X)] births templateYLabel[2] 1,000 of the templateYLabel[4] . In templateXValue[min] , this figure stood at templateYValue[idxmin(X)] births templateYLabel[2] 1,000 of the templateYLabel[4] . Demographics have an impact The average templateYLabel[0] templateYLabel[1] in the templateTitle[1] may be templateNegativeTrend , but when broken down along ethnic and economic lines , a different picture is painted : Native Hawaiian and other Pacific Islander women saw the highest templateYLabel[0] templateYLabel[1] in templateXValue[idxmin(Y)] among all ethnicities , and White and Asian women both saw the lowest templateYLabel[0] templateYLabel[1] .

generated_template: In templateXValue[max] , there were about templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] in the templateTitle[4] with a templateTitle[2] mother . This is an templatePositiveTrend from templateXValue[min] levels , when there were about templateYValue[min] templateScale templateYLabel[1] templateYLabel[2] with a templateTitle[2] mother . templateTitle[2] parenthood The typical family is comprised of two parents and at least one child .
generated: In 2018 , there were about 11.6 % rate per in the 1990 with a birth mother .  This is an increase from 1990 levels , when there were about 11.6 % rate per with a birth mother .  birth parenthood The typical family is comprised of two parents and at least one child .

Example 900:
titleEntities: {'Subject': ['eBay'], 'Date': ['2017', '2019']}
title: eBay : annual net revenue 2017 - 2019 , by category
Data:
                                            0                                                    1                    2                                   3                                                4                5                    6
Month  Marketplace_-_net_transaction_revenues  Marketplace_-_marketing_services_and_other_revenues  [TOTAL]_Marketplace  StubHub_-_net_transaction_revenues  StubHub_-_marketing_services_and_other_revenues  [TOTAL]_StubHub  [TOTAL]_Classifieds
2017                                     6809                                                 1192                 8001                                1011                                               18             1029                  897
2018                                     7416                                                 1225                 8641                                1068                                               15             1083                 1022
2019                                     7578                                                 1060                 8638                                1057                                               64             1121                 1061 

gold: In 2019 , eBay 's marketplaces net revenue was 7.58 billion US dollars , the majority of which were generated via transaction revenues . StubHub generated 1.12 billion U.S. dollars in segment revenues and Classifieds was still the smallest business segment with 1.06 billion U.S. dollars in annual revenues .
gold_template: In templateTitleDate[max] , templateTitleSubject[0] 's marketplaces templateValue[0][0] templateTitle[3] was 7.58 templateScale US dollars , the majority of which were generated via templateValue[0][0] . templateValue[0][3] generated 1.12 templateScale U.S. dollars in segment templateValue[0][0] and templateValue[0][6] was still the smallest business segment with 1.06 templateScale U.S. dollars in templateTitle[1] templateValue[0][0] .

generated_template: This statistic shows the leading templateTitleSubject[0] templateTitle[2] based on templateTitle[3] templateTitle[4] from 2014 to templateTitleDate[max] . In templateValue[0][0] , some 234.7 templateScale pounds of templateTitle[3] were produced in templateTitleDate[max] . China was the biggest templateTitle[3] producer worldwide in that year .
generated: This statistic shows the leading eBay net based on revenue 2017 from 2014 to 2019 .  In Marketplace_-_net_transaction_revenues , some 234.7 million pounds of revenue were produced in 2019 .  China was the biggest revenue producer worldwide in that year .

Example 901:
titleEntities: {'Subject': ['Survey'], 'Date': ['2013']}
title: Survey on tattoos and piercings on the job 2013
X_Axis['Response']: ['Visible_tattoos', 'Body_piercings']
Y_Axis['Share', 'of', 'respondents']: ['42', '55']

gold: This statistic shows the results of a survey on what the respondents consider worse - visible tattoos or body piercings in the workplace . According to 42 percent of respondents , visible tattoos are worse .
gold_template: This statistic shows the results of a templateTitleSubject[0] on what the templateYLabel[1] consider worse - templateXValue[0] or templateXValue[last] in the workplace . According to templateYValue[min] templateScale of templateYLabel[1] , templateXValue[0] are worse .

generated_template: This statistic shows the templateYLabel[0] of internet templateTitle[8] in the templateTitle[0] who use another device templateXValue[0] TV or templateXValue[last] video to templateXValue[0] as of 2017 . During the survey period , it was found that templateYValue[max] templateScale of templateTitle[0] templateTitle[7] adults were templateTitle[2] templateTitle[3] templateTitle[8] , accessing content on their smartphones , tablets or computers during regular templateXValue[0] consumption .
generated: This statistic shows the Share of internet 2013 in the Survey who use another device Visible_tattoos TV or Body_piercings video to Visible_tattoos as of 2017 .  During the survey period , it was found that 55 % of Survey 2013 adults were piercings job 2013 , accessing content on their smartphones , tablets or computers during regular Visible_tattoos consumption .

Example 902:
titleEntities: {'Subject': ['Electronics'], 'Date': ['2014', '2018']}
title: Electronics components total available market worldwide 2014 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['363', '348', '331', '316', '303']

gold: This statistic shows the electronics components total available market size worldwide from 2014 to 2018 . In 2014 , the electronics components total available market had a size of 303 billion U.S. dollars .
gold_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] had a size of templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The timeline presents data on the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] sales templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . The source estimates that the templateTitleSubject[0] VR templateTitle[4] market size in templateXValue[max] will be worth templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The timeline presents data on the components total available market sales Revenue worldwide from 2014 to 2018 .  The source estimates that the Electronics VR market size in 2018 will be worth 363 billion U.S. dollars .

Example 903:
titleEntities: {'Subject': ['Share'], 'Date': ['2017']}
title: Share of people worldwide who believed vaccines can cause autism as of 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      28            29      30         31            32           33     34      35      36   37
Country     Argentina  Australia  Belgium  Brazil  Canada  Chile  China  Colombia  Denmark  France  Germany  Great_Britain  Hong_Kong  Hungary  India  Indonesia  Israel  Italy  Japan  Malaysia  Mexico  Montenegro  Netherlands  New_Zealand  Norway  Peru  Philippines  Poland  Russia  Saudi_Arabia  Serbia  Singapore  South_Africa  South_Korea  Spain  Sweden  Turkey  USA
True               10         14        9      10      16     16     19        14       22      17       12             20         22       10     44         23      29     14     12        30      14          44           11           16      18    17           28      17      14            24      27         20            30           19      8      25      31   19
Don't know         28         27       51      35      32     31     50        44       36      48       36             35         50       42     36         32      35     38     63        35      28          30           40           31      23    39           33      38      47            51      40         48            35           52     39      24      47   29
False              62         59       40      54      52     53     31        42       42      35       52             45         28       48     20         45      36     48     25        34      58          26           49           53      59    45           39      45      39            25      34         32            34           30     53      52      22   52 

gold: This statistic is based on a survey by Ipsos MORI and shows the percentage of respondents in select countries worldwide who believed some vaccines cause autism in healthy children as of 2017 . It was found that around 44 percent of respondents in India believed vaccines can cause autism , despite this claim being widely discredited .
gold_template: This statistic is based on a survey by Ipsos MORI and shows the templateScale of respondents in select countries templateTitle[2] templateTitle[3] templateTitle[4] some templateTitle[5] templateTitle[6] templateTitle[7] in healthy children as of templateTitleDate[0] . It was found that around templateValue[1][14] templateScale of respondents in templateValue[0][14] templateTitle[4] templateTitle[5] can templateTitle[6] templateTitle[7] , despite this claim being widely discredited .

generated_template: This statistic depicts templateTitleSubject[0] templateTitle[1] on templateTitle[2] templateTitle[3] in templateTitleDate[0] . In templateValue[0][0] , templateValue[3][0] templateScale of residents believe that templateTitle[2] templateTitle[3] among adults is morally templateLabel[3][0] . templateTitle[3] before marriage templateValue[1][11] out of templateValue[1][24] people in templateValue[0][0] , templateValue[0][1] , templateValue[0][2] , and templateValue[0][4] believed that engaging in templateTitle[3] before marriage was templateLabel[1][0] templateLabel[2][0] templateLabel[2][0] .
generated: This statistic depicts Share people on worldwide who in 2017 .  In Argentina , 62 % of residents believe that worldwide who among adults is morally False .  who before marriage 20 out of 18 people in Argentina , Australia Belgium , and Canada believed that engaging in who before marriage was True Don't .

Example 904:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Travelers in the U.S. who find family vacation planning stressful in 2014 , by gender
X_Axis['Response']: ['Women', 'Men']
Y_Axis['Share', 'of', 'respondents']: ['74', '67']

gold: This statistic shows the share of travelers who find family vacation planning stressful in the United States as of May 2014 , by gender . During the survey , 74 percent of women said that they found family vacation planning stressful .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[1] as of 2014 , templateTitle[9] templateTitle[10] . During the survey , templateYValue[max] templateScale of templateXValue[0] said that they found templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the templateYLabel[0] of internet templateTitle[8] in the templateTitle[0] who use another device templateXValue[0] TV or templateXValue[last] video to templateXValue[0] as of 2017 . During the survey period , it was found that templateYValue[max] templateScale of templateTitle[0] templateTitle[7] adults were templateTitle[2] templateTitle[3] templateTitle[8] , accessing content on their smartphones , tablets or computers during regular templateXValue[0] consumption .
generated: This statistic shows the Share of internet 2014 in the Travelers who use another device Women TV or Men video to Women as of 2017 .  During the survey period , it was found that 74 % of Travelers stressful adults were who find 2014 , accessing content on their smartphones , tablets or computers during regular Women consumption .

Example 905:
titleEntities: {'Subject': ['Bible U.S.'], 'Date': ['2017']}
title: Preferred Bible version in the U.S. 2017
X_Axis['Response']: ['King_James_Version', 'New_International_Version', 'English_Standard_Version', 'New_King_James_Version', 'Amplified', 'Christian_Community', 'New_American_Standard', 'New_Living_Translation', 'Revised_Standard', 'Contemporary_English_Version', 'New_American_Bible', 'All_others_(1_or_less_combined)', 'Not_sure']
Y_Axis['Share', 'of', 'respondents']: ['31', '13', '9', '7', '7', '4', '3', '2', '2', '2', '2', '9', '8']

gold: The graph presents data on the popularity of the versions of the Bible read in the United States as of January 2017 . During the survey , 31 percent of the respondents stated they most often read the King James Version of the Bible . During the same survey , 32 percent of respondents stated that they had never read the Bible , whilst 16 percent stated that they read the Bible every day .
gold_template: The graph presents data on the popularity of the versions of the templateXValue[10] read in the templateTitle[3] as of 2017 . During the survey , templateYValue[max] templateScale of the templateYLabel[1] stated they most often read the templateXValue[0] Version of the templateXValue[10] . During the same survey , 32 templateScale of templateYLabel[1] stated that they had never read the templateXValue[10] , whilst 16 templateScale stated that they read the templateXValue[10] every day .

generated_template: This statistic shows the results of a survey among the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] in templateTitleSubject[1] as of 2018 . The survey found that templateXValue[0] was templateTitleSubject[1] 's templateTitle[0] loved templateTitleSubject[0] princess with templateYValue[min] out of templateYValue[max] British adults choosing her as their favorite . templateXValue[1] and templateXValue[2] were second and third templateTitle[0] templateTitle[1] at templateYValue[2] and templateYValue[1] templateScale .
generated: This statistic shows the results of a survey among the Preferred Bible Bible U.S. U.S. in Bible U.S. as of 2018 .  The survey found that King_James_Version was Bible U.S. 's Preferred loved Bible U.S. princess with 2 out of 31 British adults choosing her as their favorite .  New_International_Version and English_Standard_Version were second and third Preferred Bible at 9 and 13 % .

Example 906:
titleEntities: {'Subject': ['Asus'], 'Date': ['2016', '2019']}
title: Global Asus quarterly sales share 2016 - 2019 , by product
Data:
                         0       1       2       3       4       5       6       7       8       9      10      11      12      13
unknown             1Q_'16  2Q_'16  3Q_'16  4Q_'16  1Q_'17  2Q_'17  3Q_'17  4Q_'17  1Q_'18  2Q_'18  3Q_'18  4Q_'18  1Q_'19  2Q_'19
PC                      66      65      65      63      62      63      63      63      61      60      62      74      70      70
Component & others       0       0       0       0       0       0       0       0       0       0       0       0      30      30
Mobile                  17      18      17      20      20      17      15      15      11      15      16       0       0       0
Component               14      14      15      14      15      17      19      19      25      21      18      21       0       0
IoT                      3       3       3       3       3       3       3       3       3       4       4       0       0       0
Others                   0       0       0       0       0       0       0       0       0       0       0       5       0       0 

gold: This statistic presents the quarterly sales share of the Taiwanese tech company Asus by product , from 2016 to 2018 . In the second quarter of 2019 , 70 percent of Asus ' global sales came from the PC segment .
gold_template: This statistic presents the templateTitle[2] templateTitle[3] templateTitle[4] of the Taiwanese tech company templateTitleSubject[0] templateTitle[7] templateTitle[8] , from templateTitleDate[min] to 2018 . In the second quarter of templateTitleDate[max] , templateValue[1][last] templateScale of templateTitleSubject[0] ' templateTitle[0] templateTitle[3] came from the templateLabel[1][0] segment .

generated_template: The statistic provides data on templateTitle[0] templateValue[0][0] templateTitle[2] among consumers in the templateTitle[3] as of 2018 , sorted templateTitle[5] templateTitle[6] group . According to the source , templateValue[1][max] templateScale of respondents aged templateValue[6][last] to templateValue[6][idxmax(1)] years old stated that templateValue[0][0] was their templateTitle[0] templateValue[0][0] genre , compared to templateValue[1][8] templateScale of respondents aged 65 or above . Country templateValue[0][0] in the templateTitle[3] – additional information In 2012 , country templateValue[0][0] topped the list ; 27.6 templateScale of respondents picked it among their templateTitle[0] templateTitle[2] .
generated: The statistic provides data on Global 1Q_'16 quarterly among consumers in the sales as of 2018 , sorted 2016 2019 group .  According to the source , 74 % of respondents aged 0 to 74 years old stated that 1Q_'16 was their Global 1Q_'16 genre , compared to 61 % of respondents aged 65 or above .  Country 1Q_'16 in the sales – additional information In 2012 , country 1Q_'16 topped the list ; 27.6 % of respondents picked it among their Global quarterly .

Example 907:
titleEntities: {'Subject': ['Number'], 'Date': ['2011']}
title: Number of casinos worldwide 2011 by region
X_Axis['Country']: ['North_America', 'Western_Europe', 'Eastern_Europe', 'South_America', 'Africa', 'Caribbean', 'Far_East', 'Central_America', 'Central_Asia', 'Oceania', 'South_Asia', 'Middle_East']
Y_Axis['Number', 'of', 'casinos']: ['1623', '682', '479', '199', '157', '124', '103', '93', '42', '25', '15', '5']

gold: This statistic shows the number of casinos worldwide by region in 2011 . In that year , North America was the region with the most casinos with 1,623 . In 2016 , the U.S. casino gaming market had revenues of 71.1 billion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[4] templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[5] with the most templateYLabel[1] with templateYValue[max] . In 2016 , the U.S. casino gaming market had revenues of 71.1 templateScale U.S. dollars .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] in templateTitleDate[0] , broken down templateTitle[3] templateTitle[4] . That year , there were a total of approximately two templateScale templateYLabel[1] in templateXValue[0] .
generated: This statistic shows the Number of casinos in Number 2011 , broken down 2011 by .  That year , there were a total of approximately two million casinos in North_America .

Example 908:
titleEntities: {'Subject': ['Latin America Caribbean'], 'Date': ['2018']}
title: Latin America & the Caribbean : athletes in 2018 Winter Games , by country
X_Axis['Country']: ['Total', 'Puerto_Rico', 'Ecuador', 'Bolivia', 'Jamaica', 'Mexico', 'Colombia', 'Argentina', 'Chile', 'Brazil']
Y_Axis['Number', 'of', 'athletes']: ['37', '1', '1', '2', '3', '4', '4', '6', '7', '9']

gold: This statistic presents the number of athletes from Latin America and the Caribbean participating in the 2018 PyeongChang Winter Games , sorted by country . Out of a total of 37 athletes from this region , nine were from Brazil and seven from Chile .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] from templateTitleSubject[0] and the templateTitleSubject[0] participating in the templateTitleDate[0] PyeongChang templateTitle[6] templateTitle[7] , sorted templateTitle[8] templateXLabel[0] . Out of a templateXValue[0] of templateYValue[max] templateYLabel[1] from this region , templateYValue[last] were from templateXValue[last] and templateYValue[8] from templateXValue[8] .

generated_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: This statistic shows the share of Caribbean athletes Latin Latin America Caribbean athletes 2018 in , Winter Country .  In 2018 , Caribbean Latin Latin America Caribbean athletes in Total accounted for around 37 % of the world 's total Caribbean athletes grid-connected Latin America Caribbean athletes .

Example 909:
titleEntities: {'Subject': ['California'], 'Date': ['2000', '2018']}
title: Per capita real GDP of California 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', 'real', 'GDP', 'in', 'chained', '2012', 'U.S.', 'dollars']: ['68803', '66262', '63785', '62347', '59871', '58015', '56492', '55565', '55147', '54827', '57675', '58030', '57527', '55547', '53474', '51780', '50002', '49385', '50310']

gold: This statistic shows the per capita real Gross Domestic Product of California from 2000 to 2018 . In 2018 , the per capita real GDP of California stood at 68,803 chained 2012 U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[max] templateYValue[idxmax(X)] templateXValue[6] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Per capita real Gross Domestic Product of California from 2000 to 2018 .  In 2018 , the Per capita real GDP of California stood at 68803 2012 U.S. dollars .

Example 910:
titleEntities: {'Subject': ['Atlanta', 'Braves'], 'Date': ['2006', '2019']}
title: Atlanta Braves 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']: ['29.44', '31.71', '19.38', '19.14', '18.53', '17.32', '16.69', '19.38', '17.05', '17.05', '17.05', '17.17', '17.07']

gold: This graph depicts the average ticket price for Atlanta Braves games in Major League Baseball from 2006 to 2019 . In 2019 , the average ticket price was at 29.44 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] templateTitleSubject[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 Atlanta games in Major League Baseball from 2006 to 2019 .  In 2019 , the Average ticket price was at 29.44 U.S. dollars .

Example 911:
titleEntities: {'Subject': ['Latin America Caribbean'], 'Date': ['2017']}
title: Latin America & the Caribbean : population density 2017 , by country
X_Axis['Country']: ['Barbados', 'Haiti', 'Puerto_Rico', 'Grenada', 'El_Salvador', 'Saint_Lucia', 'Saint_Vincent_and_the_Grenadines', 'Trinidad_and_Tobago', 'Jamaica', 'Antigua_and_Barbuda', 'Dominican_Republic', 'Saint_Kitts_and_Nevis', 'Guatemala', 'Cuba', 'Dominica', 'Costa_Rica', 'Honduras', 'Ecuador', 'Mexico', 'Panama', 'Nicaragua', 'Colombia', 'Bahamas', 'Venezuela', 'Regional_average', 'Peru', 'Brazil', 'Chile', 'Uruguay', 'Paraguay', 'Belize', 'Argentina', 'Bolivia', 'Guyana', 'Suriname']
Y_Axis['Inhabitants', 'per', 'square', 'kilometer']: ['664.46', '398.45', '376.23', '317.13', '307.81', '293.19', '281.79', '266.89', '266.88', '231.85', '222.87', '212.87', '157.83', '110.41', '98.57', '96.08', '82.81', '66.94', '66.44', '55.13', '51.67', '44.22', '39.5', '36.25', '32.14', '25.13', '25.04', '24.28', '19.75', '17.14', '16.43', '16.18', '10.2', '3.95', '3.61']

gold: The statistic depicts the population density in Latin America and the Caribbean in 2017 , by country . In Argentina , the population density was estimated at around 16.2 people per square kilometer .
gold_template: The statistic depicts the templateTitle[4] templateTitle[5] in templateTitleSubject[0] and the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In templateXValue[31] , the templateTitle[4] templateTitle[5] was estimated at around templateYValue[31] people templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: Out of all templateTitleSubject[0] templateTitle[3] , templateXValue[0] had the highest templateTitle[0] rate as of templateTitleDate[0] , at templateYValue[max] templateScale . The templateXLabel[0] with the second highest templateTitle[0] rate was the templateXValue[1] , with templateYValue[1] templateScale . The significance of the templateTitleSubject[0] The templateTitleSubject[0] , or the Organisation for Economic Co-operation and Development , was founded in 1948 and is made up of 36 member templateTitle[3] .
generated: Out of all Latin America Caribbean Caribbean , Barbados had the highest Latin rate as of 2017 , at 664.46 % .  The Country with the second highest Latin rate was the Haiti , with 398.45 % .  The significance of the Latin America Caribbean , or the Organisation for Economic Co-operation and Development , was founded in 1948 and is made up of 36 member Caribbean .

Example 912:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2016', '2019']}
title: Clothing , footwear and textiles retail store sales in Great Britain 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', 'Sept_16', 'Aug_16', 'July_16', 'June_16', 'May_16', 'Apr_16', 'Mar_16', 'Feb_16', 'Jan_16']
Y_Axis['Retail', 'sales', 'in', 'thousand', 'GBP']: ['6413719', '4103639', '3684260', '4570809', '3755191', '4000259', '4735522', '3534680', '3614324', '4110860', '2979949', '3182274', '6562771', '4164350', '3610727', '4412748', '3624307', '3942695', '4571299', '3507200', '3274802', '3862707', '2851154', '3047687', '6357075', '4094224', '3585331', '4446848', '3681833', '3866902', '4579545', '3462317', '3385237', '3983710', '2904247', '3052591', '6275529', '3991563', '3650714', '4170853', '3547220', '3809540', '4298271', '3333118', '3126633', '3750005', '2778610', '2996428']

gold: This statistic shows the value of total retail sales volumes through textile , clothing and footwear stores in Great Britain monthly , from January 2016 to December 2019 . In December 2019 , approximately 6.4 billion British pounds worth of items were sold through textile , clothing and footwear stores in Britain . In sales value terms ( amount spent ) , the Office for National Statistics ( ONS ) reports slightly lower figures .
gold_template: This statistic shows the value of total templateYLabel[0] templateYLabel[1] volumes through textile , templateTitle[0] and templateTitle[1] stores in templateTitleSubject[0] monthly , from 2016 to 2019 . In 2019 , approximately templateYValue[0] templateScale British pounds worth of items were sold through textile , templateTitle[0] and templateTitle[1] stores in templateTitleSubject[0] . In templateYLabel[1] value terms ( amount spent ) , the Office for National Statistics ( ONS ) reports slightly lower figures .

generated_template: This statistic shows the total number of templateTitleSubject[0] motorcycles templateYLabel[1] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 to 2019 . and recorded the highest templateTitle[3] , which were the months when the Driver and Vehicle Licensing Agency issued new registration plates for cars and motorcycles . In 2019 , templateTitleSubject[0] templateYLabel[1] templateYValue[0] motorcycles in the templateTitleSubject[1] .
generated: This statistic shows the total number of Great Britain motorcycles sales in the Great Britain ( ) between 2016 to 2019 .  and recorded the highest retail , which were the months when the Driver and Vehicle Licensing Agency issued new registration plates for cars and motorcycles .  In 2019 , Great Britain sales 6413719 motorcycles in the Great Britain .

Example 913:
titleEntities: {'Subject': ['Global'], 'Date': ['2015', '2019']}
title: Global online retail website visits duration 2015 - 2019 , by device
Data:
                 0       1       2       3       4       5       6       7       8       9      10      11      12      13      14      15
Quarter     Q2_'15  Q3_'15  Q4_'15  Q1_'16  Q2_'16  Q3_'16  Q4_'16  Q1_'17  Q2_'17  Q3_'17  Q4_'17  Q1_'18  Q2_'18  Q3_'18  Q4_'18  Q1_'19
Desktop          6       6       7       7       6       6       7       7       6       5       5       5       5       5       6       5
Smartphone       6       5       6       6       5       5       6       6       5       4       4       4       4       4       4       4
Tablet           7       7       7       7       7       7       8       8       7       5       5       5       5       5       5       5 

gold: This statistic presents the retail website visit duration worldwide as of the first quarter 2019 , sorted by device . During the most recently measured period , global online shopping audiences spent an average of five minutes on retail websites per desktop visit .
gold_template: This statistic presents the templateTitle[2] templateTitle[3] visit templateTitle[5] worldwide as of the first templateLabel[0][0] templateTitleDate[max] , sorted templateTitle[8] templateTitle[9] . During the most recently measured period , templateTitleSubject[0] templateTitle[1] shopping audiences spent an average of templateValue[1][last] minutes on templateTitle[2] websites per templateLabel[1][0] visit .

generated_template: This statistic illustrates the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] according to smartphone users in the templateTitleSubject[0] ( templateTitleSubject[1] ) from the first templateLabel[0][0] of templateTitleDate[min] to the third templateLabel[0][0] of templateTitleDate[max] . During the survey period , it was found that templateValue[1][0] templateScale of respondents templateTitle[4] , up from templateValue[1][last] templateScale said the same templateLabel[0][0] .
generated: This statistic illustrates the online retail website visits according to smartphone users in the Global ( ) from the first Quarter of 2015 to the third Quarter of 2019 .  During the survey period , it was found that 6 percent of respondents visits , up from 5 percent said the same Quarter .

Example 914:
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
unknown  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 templateTitle[9] listed which had seen its templateTitle[3] of templateTitle[4] cars templatePositiveTrend .

generated_template: templateValue[0][0] cars were the most expensive automobiles sold in the templateTitleSubject[0] in templateTitleDate[max] . With an templateTitle[3] price tag of templateValue[2][max] euros , the templateTitle[1] maker ranked ahead of fellow German manufacturer templateValue[0][1] . The only templateTitle[1] templateTitle[8] which had seen its templateTitle[4] templateNegativeTrend since templateTitleDate[min] was Citroen .
generated: Volvo cars were the most expensive automobiles sold in the EU in 2017 .  With an share price tag of 79 euros , the car maker ranked ahead of fellow German manufacturer BMW .  The only car by which had seen its diesel dropped since 2013 was Citroen .

Example 915:
titleEntities: {'Subject': ['Myanmar'], 'Date': ['2024']}
title: Inflation rate in Myanmar 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']: ['5.54', '5.54', '5.91', '6.47', '6.7', '7.84', '5.94', '4.62', '9.1', '7.26', '5.14', '5.76', '0.36', '6.83', '5.92', '3.69', '20.91', '34.44', '16.55', '6.89', '8.01']

gold: Inflation in Myanmar dropped to a five-year low in 2017 , settling at 4.62 percent . This is down from a fairly high spike in 2016 , expected to converge to a steady state around 6 percent in the coming years . For a developing economy , this is an acceptable level , though Myanmar 's central bankers would probably prefer one or two percentage points less .
gold_template: templateYLabel[0] in templateTitleSubject[0] templateNegativeTrend to a five-year low in templateXValue[7] , settling at templateYValue[7] templateScale . This is down from a fairly high spike in templateXValue[8] , expected to converge to a steady state around templateYValue[0] templateScale in the coming years . For a developing economy , this is an acceptable level , though templateTitleSubject[0] 's central bankers would probably prefer one or two templateScale points less .

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 Myanmar from 2004 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Myanmar amounted to about 5.94 % compared to the previous Year .

Example 916:
titleEntities: {'Subject': ['Washington Redskins'], 'Date': ['2019']}
title: Average ticket price of the Washington Redskins 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']: ['103.39', '117.13', '119.52', '102.0', '102.0', '94.8', '79.13', '79.13', '79.13', '79.13', '79.13', '79.13', '79.13']

gold: This graph depicts the average ticket price for Washington Redskins games in the National Football League from 2006 to 2019 . In 2019 , the average ticket price was at 103.39 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] 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 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: This graph depicts the Average ticket price for Washington Redskins games in the National Football League from 2006 to 2019 .  In 2019 , the Average ticket price was at 103.39 U.S. dollars .

Example 917:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Time children spend in outside-school activities in the U.S. 2014 , by grade
Data:
                                   0               1
unknown               Elementary/K-5  Secondary/6-12
Free/leisure time               17.8            20.2
Scheduled activities             4.1             4.3 

gold: This statistic shows the number of hours children spend in outside-school activities in the United States as of June 2014 , by school grade . During the survey , parents reported that their elementary school-aged children spent an average of 4.1 hours on scheduled activities such as lessons , tutoring , and non-school sports .
gold_template: This statistic shows the number of hours templateTitle[1] templateTitle[2] in templateTitle[3] templateTitle[4] in the templateTitle[5] as of 2014 , templateTitle[7] school templateTitle[8] . During the survey , parents reported that their elementary school-aged templateTitle[1] spent an average of templateValue[2][0] hours on templateLabel[2][0] templateTitle[4] such as lessons , tutoring , and non-school sports .

generated_template: The statistic shows the templateTitle[0] of templateTitle[3] templateTitle[5] templateTitle[6] in the online computer game templateTitleSubject[0] of templateTitleSubject[0] as of 2019 . Approximately templateValue[1][last] templateScale of all templateTitle[3] in the templateValue[0][1] templateValue[0][0] were members of the templateLabel[1][0] . In the beginning , when a player generates an avatar for himself they need to choose a race .
generated: The statistic shows the Time of outside-school U.S. 2014 in the online computer game U.S. of as 2019 .  Approximately 20.2 % of all outside-school in the Secondary/6-12 Elementary/K-5 were members of the Free/leisure .  In the beginning , when a player generates an avatar for himself they need to choose a race .

Example 918:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2014']}
title: Number of MRI scanners in the United Kingdom ( UK ) 2000 to 2014
X_Axis['Year']: ['2014', '2013', '2012', '2011', '2010_', '2009', '2008_', '2007', '2006', '2005', '2004', '2003_', '2002_', '2001_', '2000']
Y_Axis['Number', 'of', 'scanners']: ['467', '462', '456', '441', '411', '0', '340', '0', '342', '326', '300', '271', '296', '367', '331']

gold: This statistic displays the number of magnetic resonance imaging ( MRI ) units in the United Kingdom ( UK ) from 2000 to 2014 . In 2014 there were 467 MRI scanners in the UK , an increase from 2013 .
gold_template: This statistic displays the templateYLabel[0] of magnetic resonance imaging ( templateTitle[1] ) units in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[last] to templateXValue[0] . In templateXValue[0] there were templateYValue[max] templateTitle[1] templateYLabel[1] in the templateTitleSubject[1] , an templatePositiveTrend from templateXValue[1] .

generated_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . Globally , there were templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The major nations conducting templateYLabel[1] templateYLabel[2] include Russia , the country , the member states of ESA .
generated: This statistic shows United Kingdom MRI Number scanners from 2000 to 2014 .  Globally , there were 467 Number scanners in 2014 .  The major nations conducting scanners include Russia , the country , the member states of ESA .

Example 919:
titleEntities: {'Subject': ['Welsh Assembly'], 'Date': ['1999', '2011']}
title: Welsh Assembly elections : turnout rates 1999 to 2011
X_Axis['Year']: ['1999', '2003', '2007', '2011']
Y_Axis['Turnout', 'rate']: ['46.4', '38.2', '43.5', '41.8']

gold: This statistic shows the voter turnout rates for constituency votes in the Welsh Assembly elections from 1999 to 2011 . Over the last four elections there was a variation in voter turnout of 8.2 percent . The peak , in 1999 , was followed in 2003 by the lowest turnout rate of this period .
gold_template: This statistic shows the voter templateYLabel[0] templateTitle[4] for constituency votes in the templateTitleSubject[0] elections from templateXValue[min] to templateXValue[max] . Over the last four templateTitle[2] there was a variation in voter templateYLabel[0] of 8.2 templateScale . The peak , in templateXValue[min] , was followed in templateXValue[1] by the lowest templateYLabel[0] templateYLabel[1] of this period .

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] templateTitle[9] group . During that period of time , templateYValue[max] templateScale of templateYLabel[2] between the ages of 18 and 29 years used templateTitle[5] networks .
generated: This statistic shows the Turnout of rate in the Assembly turnout were using 1999 networks as of 2019 , sorted 2011 group .  During that period of time , 46.4 % of rate between the ages of 18 and 29 years used 1999 networks .

Example 920:
titleEntities: {'Subject': ['BBC News', 'United Kingdom', 'UK'], 'Date': ['2019']}
title: BBC News viewers reached quarterly in the United Kingdom ( UK ) Q1 2012-Q2 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', 'Q4_2012', 'Q3_2012', 'Q2_2012', 'Q1_2012']
Y_Axis['Viewers', 'in', 'thousands']: ['18044', '18858', '18023', '16914', '17052', '19104', '20164', '20186', '22251', '21458', '21335', '21451', '22500', '20743', '25590', '20829', '21890', '22965', '21747', '22454', '22211', '25085', '24102', '23640', '25072', '25825', '26710', '24758', '24497', '25228']

gold: The BBC News TV channel has seen a decline in reach over recent years in the United Kingdom . In the second quarter of 2019 , there were just over 18 million viewers . Five years previously , the number of viewers stood at 25 million .
gold_template: The templateTitleSubject[0] TV channel has seen a decline in reach over recent years in the templateTitleSubject[1] . In the second templateXLabel[0] of templateXValue[0] , there were just over templateYValue[0] templateScale templateYLabel[0] . Five years previously , the number of templateYLabel[0] stood at templateYValue[21] templateScale .

generated_template: The templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] and templateTitle[4] templateTitle[5] amounted to approximately templateYValue[max] templateScale templateYLabel[1] in the third templateXLabel[0] of templateXValue[0] . templateTitle[0] templateTitle[1] sector templateYLabel[0] 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: The BBC News Viewers of reached and quarterly United amounted to approximately 26710 thousands in the third Quarter of Q2_2019 .  BBC News sector Viewers in the Kingdom BBC News sector Viewers in the Kingdom has been steadily decreasing 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 Kingdom real estate market was quite stable .

Example 921:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2012']}
title: Perceived influences on consumer purchasing of eyewear in the U.S. 2012
X_Axis['Response']: ['Doctor_recommendation', 'Optician/sales_rep_recommendation', 'Price', 'Insurance', 'Product_features', 'Word_of_mouth', 'Brand/designer_name', 'Coupon/sales', 'Internet']
Y_Axis['Share', 'of', 'respondents']: ['27', '19', '18', '11', '9', '8', '5', '2', '0.3']

gold: This statistic depicts the perceived influences on consumer purchasing of eyewear in the United States in 2012 . The survey revealed that 27 percent of the respondents are influenced into eyewear purchases as a result of doctor recommendations.Eyewear in the United StatesAfter suffering three consistent years of value sales declines , the eyewear market in the US began to emerge from the depths of the economic recession in 2011 and 2012 . While the economy remains weaker than it was before the recession , GDP growth , a declining unemployment rate , and rising levels of consumer confidence led consumers to once again invest in vision correction products.Innovative new product launches and pent-up consumer demand to update the style and prescription of their spectacles has helped retail value sales of eyewear to finally return to their pre-recessionary levels in 2012.Contact lenses once again outperformed spectacles in 2012 , continuing a trend that has been ongoing for many years .
gold_template: This statistic depicts the templateTitle[0] templateTitle[1] on templateTitle[2] templateTitle[3] of templateTitle[4] in the templateTitle[5] in templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of the templateYLabel[1] are influenced into templateTitle[4] purchases as a result of templateXValue[0] recommendations.Eyewear in the United StatesAfter suffering three consistent years of value sales declines , the templateTitle[4] market in the US began to emerge from the depths of the economic recession in 2011 and templateTitleDate[0] . While the economy remains weaker than it was before the recession , GDP growth , a declining unemployment rate , and templatePositiveTrend levels of templateTitle[2] confidence led consumers to once again invest in vision correction products.Innovative new templateXValue[4] launches and pent-up templateTitle[2] demand to update the style and prescription of their spectacles has helped retail value sales of templateTitle[4] to finally return to their pre-recessionary levels in 2012.Contact lenses once again outperformed spectacles in templateTitleDate[0] , continuing a trend that has been ongoing for many years .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2014 . During the survey , templateYValue[2] templateScale of templateYLabel[1] said that templateXValue[2] was their templateXValue[last] game to play at casinos .
generated: This statistic shows the Perceived influences consumer purchasing eyewear U.S. in the 2012 as of 2014 .  During the survey , 18 % of respondents said that Price was their Internet game to play at casinos .

Example 922:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Daily online activities of adult U.S. internet users 2017 , by age
Data:
                            0                                     1                                           2                      3                4                                             5                                 6                      7                    8                                        9                            10                                        11                                     12                 13                        14
unknown  Buy_a_product_online  Buy_or_make_a_reservation_for_travel  Check_weather_reports_and_forecasts_online  Do_any_banking_online  Get_news_online  Look_for_news_and_information_about_politics  Participate_in_an_online_auction  Send_instant_messages  Send_or_read_e-mail  Use_a_search_engine_to_find_information  Use_a_social_networking_site  Use_classified_ads_sites_like_Craigslist  Watch_a_video_on_a_video-sharing_site  None_of_the_above  I_don’t_use_the_Internet
18-29                      49                                    13                                          50                     49               57                                            29                                12                     49                   77                                       67                            74                                        16                                     55                  1                         0
30-59                      49                                    22                                          57                     55               64                                            39                                11                     43                   86                                       72                            67                                        17                                     40                  1                         1
60+                        41                                    15                                          61                     55               58                                            40                                 8                     24                   91                                       70                            49                                        12                                     20                  2                         0 

gold: This graph shows the typical daily online activities of internet users in the United States as of February 2017 , sorted by age group . During the survey period , 50 percent of respondents aged 18 to 29 years checked the weather online on a daily basis .
gold_template: This graph shows the typical templateTitle[0] templateValue[0][0] templateTitle[2] of templateValue[0][14] templateTitle[6] in the templateTitle[4] as of 2017 , sorted templateTitle[8] templateTitle[9] group . During the survey period , templateValue[1][2] templateScale of respondents aged 18 to templateValue[1][5] years checked the templateValue[0][2] templateValue[0][0] on a templateTitle[0] basis .

generated_template: This statistic shows the results of a survey among adult Americans on whether they rate the templateTitleSubject[0] of the templateTitle[1] templateTitle[2] in the templateTitleSubject[0] today as excellent , good , templateLabel[2][0] templateLabel[2][1] , or templateLabel[1][0] . The survey was conducted in templateValue[0][0] each year . In templateValue[0][0] , templateValue[1][0] templateScale of respondents rated the templateTitle[1] templateTitle[2] in the templateTitleSubject[0] as `` templateLabel[1][0] '' .
generated: This statistic shows the results of a survey among adult Americans on whether they rate the U.S. of the online activities in the U.S. today as excellent , good 30-59 , or 18-29 .  The survey was conducted in Buy_a_product_online each year .  In Buy_a_product_online , 49 % of respondents rated the online activities in the U.S. as `` 18-29 '' .

Example 923:
titleEntities: {'Subject': ['HSBC'], 'Date': ['2007', '2019']}
title: Profits of HSBC 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Profits', 'in', 'billion', 'U.S.', 'dollars']: ['8.71', '15.03', '11.88', '3.45', '15.1', '14.71', '17.8', '15.33', '17.94', '14.19', '6.69', '6.5', '20.46']

gold: HSBC posted a profit of 8.7 billion U.S. dollars in 2019 , down from 15 billion U.S. dollars in 2018 . In the same time period , operating income of the bank increased from 63.6 billion U.S. dollars to 71 billion U.S. dollars . HSBC bank HSBC is a British banking and financial services company and one of the major players on the global banking market .
gold_template: templateTitleSubject[0] posted a profit of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] , down from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[1] . In the same time period , operating income of the bank templatePositiveTrend from 63.6 templateScale templateYLabel[2] templateYLabel[3] to 71 templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] bank templateTitleSubject[0] is a British banking and financial services company and one of the major players on the global banking market .

generated_template: This statistic shows the total templateTitle[0] templateYLabel[0] in the NFL ( templateTitleSubject[0] League ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] in the templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the total Profits in the NFL ( HSBC League ) from 2007 to 2019 .  In 2019 , the Profits in the HSBC amounted to approximately 8.71 billion U.S. dollars .

Example 924:
titleEntities: {'Subject': ['EU'], 'Date': ['2013']}
title: EU : used car sales in 2013 , per 1,000 population
X_Axis['Country']: ['Spain', 'Italy', 'Portugal', 'Belgium', 'France', 'Germany', 'Denmark', 'Austria', 'Netherlands', 'Sweden', 'UK']
Y_Axis['Number', 'of', 'used', 'car', 'sales', 'per', 'thousand', 'population']: ['35', '42', '49', '78', '81', '88', '97', '98', '102', '107', '116']

gold: This statistic displays the volume of used car sales in selected European countries in 2013 , per thousand population . The United Kingdom ( UK ) is the biggest used car market among the displayed countries , handling approximately eight percent more used cars than Sweden in 2013 .
gold_template: This statistic displays the volume of templateYLabel[1] templateYLabel[2] templateYLabel[3] in selected European countries in templateTitleDate[0] , templateYLabel[4] thousand templateYLabel[6] . The United Kingdom ( templateXValue[last] ) is the biggest templateYLabel[1] templateYLabel[2] market among the displayed countries , handling approximately eight templateScale more templateYLabel[1] cars than templateXValue[9] in templateTitleDate[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in selected templateTitleSubject[0] American and Caribbean countries in templateTitleDate[0] . That year , there were over 6,000 templateYLabel[1] in operation in templateXValue[0] , while templateXValue[last] had less than one hundred .
generated: This statistic shows the Number of used in selected EU American and Caribbean countries in 2013 .  That year , there were over 6,000 used in operation Spain , while UK had less than one hundred .

Example 925:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005', '2018']}
title: Organic food sales in the U.S. from 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['47.9', '45.2', '43.0', '39.75', '35.95', '32.34', '29.02', '26.34', '24.12', '22.5', '21.57', '18.19', '15.63', '13.26']

gold: This statistic depicts organic food sales in the United States from 2005 to 2018 . In 2018 , organic food sales in the United States amounted to about 47.9 billion U.S. dollars . Organic food Organic foods are foods that are manufactured using organic farming standards .
gold_template: This statistic depicts templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] amounted to about templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] . templateTitle[0] templateTitle[1] templateTitle[0] foods are foods that are manufactured using templateTitle[0] farming standards .

generated_template: This statistic presents the annual templateYLabel[0] templateTitle[4] in the templateTitle[1] templateTitle[2] in the templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[7] reached a templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic presents the annual Sales from in the food sales in the Organic between 2005 and 2018 .  In 2018 , food sales U.S. 2018 reached a Sales of 47.9 billion U.S. dollars .

Example 926:
titleEntities: {'Subject': ['Coronavirus', 'Italy'], 'Date': []}
title: Coronavirus ( COVID-19 ) deaths in Italy as of March 2 , 2020 , by region
X_Axis['Month']: ['Lombardy', 'Emilia-Romagna', 'Veneto', 'Marche', 'Liguria']
Y_Axis['Number', 'of', 'deaths']: ['55', '18', '3', '2', '1']

gold: After entering Italy , Coronavirus ( COVID-19 ) has been spreading fast . As of March 3 , 2020 , twelve deaths were reported by the authorities , 55 in the region of Lombardy , 18 in the region of Emilia-Romagna , and three in the region of Veneto . The total number of cases reported in the country exceeded two thousand .
gold_template: After entering templateTitleSubject[1] , templateTitleSubject[0] ( templateTitle[1] ) has been spreading fast . As of templateTitle[4] templateYValue[2] , templateTitleDate[0] , twelve templateYLabel[1] were reported templateTitle[7] the authorities , templateYValue[max] in the templateTitle[8] of templateXValue[0] , templateYValue[1] in the templateTitle[8] of templateXValue[1] , and templateYValue[2] in the templateTitle[8] of templateXValue[2] . The total templateYLabel[0] of cases reported in the country exceeded templateYValue[3] thousand .

generated_template: As of 2019 , templateYValue[max] thousand templateYLabel[1] were living in templateTitleSubject[1] . Between templateXValue[min] and templateXValue[2] , templateYValue[min] thousand templateYLabel[1] were living in templateTitleSubject[0] . The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] in templateTitleSubject[0] in the country .
generated: As of 2019 , 55 thousand deaths were living in Italy .  Between Lombardy and Veneto , 1 thousand deaths were living in Coronavirus .  The Number of deaths in Coronavirus the country .

Example 927:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2002']}
title: Commodity prices of wheat in the United Kingdom ( UK ) 2002 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Price', 'per', 'tonne', 'in', 'GBP']: ['127.15', '143.06', '175.95', '179.26', '169.17', '123.76', '107.05', '137.87', '120.97', '78.88', '67.43', '80.3', '79.32', '65.02']

gold: This statistic shows the average price per tonne of wheat in the United Kingdom ( UK ) between 2002 and 2015 by year , according to published agricultural and commodity price figures . In 2012 , the price of wheat was 179.26 British Pound Sterling ( GBP ) per tonne .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) between templateXValue[min] and templateXValue[max] by templateXLabel[0] , according to published agricultural and templateTitle[0] templateYLabel[0] figures . In templateXValue[3] , the templateYLabel[0] of templateTitle[2] was templateYValue[max] British Pound Sterling ( templateYLabel[3] ) templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[3] , templateYLabel[1] and templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] people worked either part-time or full-time as templateTitle[1] templateTitle[4] , templateYLabel[1] or templateTitle[3] . Included are solo templateYLabel[1] without employees working part-time or full-time in their artistic trade .
generated: This statistic shows the Price of prices United , per and Kingdom in the UK from 2002 to 2015 .  In 2015 , 179.26 people worked either part-time or full-time as prices Kingdom , per or United .  Included are solo per without employees working part-time or full-time in their artistic trade .

Example 928:
titleEntities: {'Subject': ['China Construction Bank'], 'Date': ['2018']}
title: Total assets of the China Construction Bank 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Total', 'assets', 'in', 'billion', 'yuan']: ['23222.69', '22124.38', '20963.71', '18349.49', '16774.09', '15363.21', '13972.83', '12281.8', '10810.3', '9623.4', '7555.5']

gold: This statistic shows the total assets of the China Construction Bank from 2008 to 2018 . The Chinese Construction Bank is one of the four major banks in China . This statistic depicts the total assets as reported on December 31 of each year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Bank from templateXValue[min] to templateXValue[max] . The Chinese templateTitleSubject[0] is one of the four major banks in templateTitleSubject[0] . This statistic depicts the templateYLabel[0] templateYLabel[1] as reported on 31 of each templateXLabel[0] .

generated_template: This statistic shows the templateScale of the templateYLabel[1] templateYLabel[2] living in urban areas in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] was living in cities and urban areas .
generated: This statistic shows the billion of the assets billion living in urban areas in China Construction Bank from 2008 to 2018 .  In 2018 , 23222.69 billion of the assets billion of China Construction Bank was living in cities and urban areas .

Example 929:
titleEntities: {'Subject': ['RPI', 'UK'], 'Date': ['2017', '2023']}
title: Retail price index ( RPI ) forecast UK 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Percentage', 'change']: ['3.1', '3.1', '3.2', '3.1', '3.1', '3.5', '3.6']

gold: This statistic shows the predicted change in retail prices index ( RPI ) in the United Kingdom ( UK ) between 2017 and 2023 . A slight and steady decline is forecasted to occur during the period from 2018 to 2023 .
gold_template: This statistic shows the predicted templateYLabel[1] in templateTitle[0] prices templateTitle[2] ( templateTitleSubject[0] ) in the United Kingdom ( templateTitleSubject[1] ) between templateXValue[min] and templateXValue[max] . A slight and steady decline is forecasted to occur during the period from templateXValue[5] to templateXValue[max] .

generated_template: The templateTitle[0] templateTitle[1] templateTitle[2] of the United Kingdom is forecasted to be generally stable in the near future , and only templateYLabel[1] by templateScale of between templateYValue[min] and templateYValue[1] templateScale up until templateXValue[idxmin(Y)] . In early templateXValue[4] the templateTitle[3] rate was even lower than this at 1.8 templateScale . Peak of 4.5 in 2011 The templateTitle[0] templateTitle[1] templateTitle[2] is the most important measure of inflation within an economy and calculates how the cost of a typical basked of templateTitle[0] goods changes over time .
generated: The Retail price index of the United Kingdom is forecasted to be generally stable in the near future , and only change by percentage of between 3.1 and percentage up until 2023 .  In early 2019 the RPI rate was even lower than this at 1.8 percentage .  Peak of 4.5 in 2011 The Retail price index is the most important measure of inflation within an economy and calculates how the cost of a typical basked of Retail goods changes over time .

Example 930:
titleEntities: {'Subject': ['Companies'], 'Date': ['2014']}
title: Companies ' market share global smartwatch market 2014
X_Axis['Company']: ['Samsung', 'Lenovo', 'LG', 'Pebble', 'Garmin', 'Sony', 'Fitbit', 'Withings', 'Polar', 'Asus', 'Other']
Y_Axis['Market', 'share']: ['23', '10', '7', '7', '7', '6', '6', '4', '2', '2', '26']

gold: The statistic shows a market share breakdown of the global smartwatch / consumer wearables market in 2014 . Samsung 's share of the smartwatch market was 23 percent . The smartwatch market - additional information The wearable technology industry is a new and expanding market , with rapidly growing revenues and innovative new players entering the market .
gold_template: The statistic shows a templateYLabel[0] templateYLabel[1] breakdown of the templateTitle[4] templateTitle[5] / consumer wearables templateYLabel[0] in templateTitleDate[0] . templateXValue[0] 's templateYLabel[1] of the templateTitle[5] templateYLabel[0] was templateYValue[0] templateScale . The templateTitle[5] templateYLabel[0] - additional information The wearable technology industry is a new and expanding templateYLabel[0] , with rapidly templatePositiveTrend revenues and innovative new players entering the templateYLabel[0] .

generated_template: This statistic provides information on the projected templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] in selected templateTitleSubject[0] templateTitle[5] in templateTitleDate[0] . During this period of time , experts expect templateYValue[max] templateScale of templateYLabel[1] sales in the templateXValue[0] to be generated templateTitle[0] .
generated: This statistic provides information on the projected Companies Market of share in selected Companies smartwatch in 2014 .  During this period of time , experts expect 26 % of share sales in the Samsung to be generated Companies .

Example 931:
titleEntities: {'Subject': ['Russia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) 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['GDP', 'in', 'billion', 'U.S.', 'dollars']: ['1940.44', '1858.61', '1782.06', '1714.31', '1657.54', '1637.89', '1657.29', '1579.29', '1280.52', '1356.84', '2058.31', '2289.24', '2202.67', '2044.62', '1632.84', '1309.17', '1778.39', '1391.68', '1059.99', '817.75', '632.77', '460.75', '369.94', '328.28', '278.08', '209.77', '290.23', '433.62', '419.52', '335.66', '296.47']

gold: This statistic shows the gross domestic product ( GDP ) in Russia from 1994 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: This statistic 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] . 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 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: The statistic shows Gross domestic product ( GDP ) billion U.S. in Russia 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 .

Example 932:
titleEntities: {'Subject': ['Play'], 'Date': ['2009', '2019']}
title: Google Play : number of available apps 2009 to 2019
X_Axis['Month']: ["Dec_'19", "Sep_'19", "Jun_'19", "Mar_'19", "Dec_'18", "Sep_'18", "Jun_'18", "Mar_'18", "Dec_'17", "Sep_'17", "Jun_'17", "Mar_'17", "Dec_'16", "Sep_'16", "Feb_'16", "Nov_'15", "Jul_'15", "Feb_'15", "Jul_'14", "Jul_'13", "Apr_'13", "Oct_'12", "Sep_'12", "Jun_'12", "May_'12", "Feb_'12", "Dec_'11", "Aug_'11", "Jul_'11", "Apr_'11", "Oct_'10", "Jul_'10", "Apr_'10", "Mar_'10", "Dec_'09"]
Y_Axis['Number', 'of', 'available', 'apps']: ['2900000', '2800000', '2700000', '2600000', '2600000', '2600000', '3300000', '3600000', '3500000', '3300000', '3000000', '2800000', '2600000', '2400000', '2000000', '1800000', '1600000', '1400000', '1300000', '1000000', '850000', '700000', '675000', '600000', '500000', '450000', '400000', '300000', '250000', '200000', '100000', '70000', '38000', '30000', '16000']

gold: This statistic shows the number of available applications in the Google Play Store from December 2009 to December 2019 . The number of available apps in the Google Play Store was most recently placed at 2.9 million apps , after surpassing 1 million apps in July 2013 . Google Play was originally launched in October 2008 under the name Android Market .
gold_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_template: As of 2019 , the value of templateYLabel[1] templateTitle[2] and coins in templateTitle[4] in the United Kingdom reached approximately templateYValue[0] templateScale British pounds . This was an templatePositiveTrend of over 1.4 templateScale British pounds as compared to 2017 . When broken down by denomination , the twenty-pound note accounted for the highest share of templateTitle[2] in templateTitle[4] .
generated: As of 2019 , the value of available number and coins in apps the United Kingdom reached approximately 2900000 million British pounds .  This was an increase of over 1.4 billion British pounds as compared to 2017 .  When broken down by denomination , the twenty-pound note accounted for the highest share of number in apps .

Example 933:
titleEntities: {'Subject': ['Qatar'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Qatar 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.83', '2.09', '2.36', '3.02', '2.76', '1.97', '1.49', '1.58', '2.13', '3.66', '3.98']

gold: The statistic shows the growth in real GDP in Qatar from 2014 to 2024  . In 2018 , Qatar 's real gross domestic product increased by around 1.49 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[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[min] 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 between 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 Qatar from between 2014 to 2018 , with projections up until 2024 .  In 2018 , Qatar 's real Gross domestic product increased by around 1.49 % compared to the previous Year .

Example 934:
titleEntities: {'Subject': ['California'], 'Date': ['2011', '2018']}
title: Ice cream production in California and other U.S. states 2011 to 2018
Data:
                        0       1       2       3       4       5       6       7
Year                 2011    2012    2013    2014    2015    2016    2017    2018
California         142136  131321  127508  129002  126095  118087  105820   66328
Other U.S. states  657722  670544  663854  638845  649117  668760  656842  429382 

gold: This statistic shows the leading U.S. states in production of regular hard ice cream from 2011 to 2018 . In 2018 , California produced around 66 thousand gallons of ice cream , while the other U.S. states all together produced around 669 thousand gallons .
gold_template: This statistic shows the leading templateTitle[5] templateTitle[6] in templateTitle[2] of regular hard templateTitle[0] templateTitle[1] from templateValue[0][0] to templateValue[0][last] . In templateValue[0][last] , templateTitleSubject[0] produced around templateValue[1][last] thousand gallons of templateTitle[0] templateTitle[1] , while the templateTitle[4] templateTitle[5] templateTitle[6] all together produced around templateValue[2][5] thousand gallons .

generated_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: This statistic shows the other U.S. for Other light-emitting diode ( California ) cream production in the California from 2011 to 2018 , by other area .  The target for Other states for 2018 is expected to reach decreased to 429382 California dollars per square meter of California cream produced .

Example 935:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. grill ownership by type of grill 2018
X_Axis['Response']: ['Gas_grill', 'Charcoal_grill', 'Electric_grill']
Y_Axis['Share', 'of', 'respondents']: ['64', '44', '9']

gold: This statistic shows the results of a survey conducted by the Hearth , Patio & Barbecue Association ( HPBA ) in July and August 2018 . During the survey , U.S. consumers were asked about which types of grills they owned . Approximately 64 percent of the respondents indicated ownership of a gas grill .
gold_template: This statistic shows the results of a survey conducted templateTitle[3] the Hearth , Patio & Barbecue Association ( HPBA ) in and 2018 . During the survey , templateTitleSubject[0] consumers were asked about which types of grills they owned . Approximately templateYValue[max] templateScale of the templateYLabel[1] indicated templateTitle[2] of a templateXValue[0] .

generated_template: The statistic illustrates the answers to the following survey question : `` The templateTitle[2] templateXValue[0] templateTitle[5] will probably cost a thousand euros . templateXValue[last] you willing to pay that ? '' As of templateTitleDate[0] , roughly 20 templateScale of the templateYLabel[1] said to templateXValue[0] the templateXValue[0] from templateTitleSubject[0] when it is released , even if it templateTitle[6] them a thousand euros . However , more than half of the templateYLabel[1] said the price is templateXValue[1] absurd for an templateTitleSubject[0] templateXValue[0] .
generated: The statistic illustrates the answers to the following survey question : `` The ownership Gas_grill grill will probably cost a thousand euros .  Electric_grill you willing to pay that ? '' As of 2018 , roughly 20 % of the respondents said to Gas_grill the from U.S. when it is released , even if it 2018 them a thousand euros .  However , more than half of the respondents said the price is Charcoal_grill absurd for an U.S. Gas_grill .

Example 936:
titleEntities: {'Subject': ['ThyssenKrupp'], 'Date': ['2003', '2019']}
title: ThyssenKrupp - net sales 2003 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']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['42.0', '41.53', '42.97', '39.26', '42.78', '41.21', '39.78', '47.05', '49.09', '42.62', '40.56', '53.43', '51.72', '47.13', '42.93', '37.3', '33.49']

gold: ThyssenKrupp generated roughly 42 billion euros in net sales in the fiscal year ended September 30 , 2019 . Net sales were up around 460 million euros or one percent , compared to the previous year . ThyssenKrupp 's net loss in 2019 came to 260 million euros .
gold_template: templateTitleSubject[0] generated roughly templateYValue[0] templateScale templateYLabel[2] in templateTitle[1] templateTitle[2] in the fiscal templateXLabel[0] ended 30 , templateTitleDate[max] . templateTitle[1] templateTitle[2] were up around 460 templateScale templateYLabel[2] or one templateScale , compared to the previous templateXLabel[0] . templateTitleSubject[0] 's templateTitle[1] loss in templateTitleDate[max] came to 260 templateScale templateYLabel[2] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] between the templateXValue[last] season . In templateXValue[0] , the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[1] amounted to approximately templateYValue[max] templateScale .
generated: The statistic shows the Revenue of net billion euros in the ThyssenKrupp between the 2002/03 season .  In 2018/19 , the Revenue of net billion euros in the ThyssenKrupp amounted to approximately 53.43 billion .

Example 937:
titleEntities: {'Subject': ['United States'], 'Date': ['2018', '2018']}
title: Population of the United States by sex and age 2018
Data:
                    0       1         2         3         4         5         6         7         8         9        10        11        12        13        14        15        16           17
Age in years  Under_5  5_to_9  10_to_14  15_to_19  20_to_24  25_to_29  30_to_34  35_to_39  40_to_44  45_to_49  50_to_54  55_to_59  60_to_64  65_to_69  70_to_74  75_to_79  80_to_84  85_and_over
Male            10.13   10.32     10.66     10.77      11.2     12.02     11.19     10.79       9.8     10.26     10.28     10.67      9.73      8.03      6.21      4.14      2.59         2.33
Female           9.68    9.88     10.22     10.32     10.67     11.54     10.94     10.77      9.92     10.48     10.61     11.27      10.6      9.05      7.19      5.12      3.54         4.22 

gold: There were about 11.2 million males between the ages of 20 and 24-years-old in the United States in 2018 . In the same year , there were about about 10.67 million females between the ages of 20 and 24-years-old in the U.S . The American Dream The United States is well known the world over for being a diverse country with many different ethnicities .
gold_template: There were about templateValue[1][4] templateScale males between the ages of templateValue[0][4] and 24-years-old in the templateTitleSubject[0] in templateTitleDate[0] . In the same year , there were about templateValue[1][11] templateScale females between the ages of templateValue[0][4] and 24-years-old in the templateTitle[2] . The American Dream The templateTitleSubject[0] is well known the world templateValue[0][17] for being a diverse country with many different ethnicities .

generated_template: This statistic displays the total templateTitle[0] templateTitle[1] in the United Kingdom ( templateTitleSubject[0] ) templateTitle[3] templateTitle[4] in templateTitleDate[0] . In this year the mobile templateTitle[0] templateTitle[1] was templateValue[1][0] years for the templateTitle[3] and templateValue[2][0] years for templateLabel[2][0] in templateTitleDate[0] .
generated: This statistic displays the total Population United in the United Kingdom ( United States ) by sex in 2018 .  In this year the mobile Population United was 10.13 years for the by and 9.68 years for Female in 2018 .

Example 938:
titleEntities: {'Subject': ['European'], 'Date': ['2018']}
title: European insurance : number of companies 2018 , 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
Country   Germany  United_Kingdom  Sweden  Ireland  France  Spain  Netherlands  Switzerland  Italy  Denmark  Luxembourg  Austria  Belgium  Poland  Turkey  Finland  Portugal  Liechtenstein  Bulgaria  Greece  Cyprus  Romania  Czechia  Hungary  Croatia  Slovenia  Slovakia  Malta  Estonia  Latvia
Domestic      528             402     280      270     264    215          147          145    100       99          97       93       67      60      59       51        39             38        37      36      32       29       28       25       18        16        15     11       10       6
Total         528               0     315        0       0   1155            0            0   1265        0           0      111        0       0      61      801       609             38         0      53     590       41       48       30       20        22       669     62        0     540 

gold: In 2018 , There were almost 7 thousand insurance companies operating in Europe . In terms of domestic companies , only Germany and the United Kingdom had more than 400 hundred companies operating . Although Germany had the highest number of companies operating in their insurance market , the volume of insurance firms in 2018 was less than one third of the number seen in 2011 .
gold_template: In templateTitleDate[0] , There were almost 7 thousand templateTitle[1] templateTitle[3] operating in Europe . In terms of templateLabel[1][0] templateTitle[3] , only templateValue[0][0] and the templateValue[0][1] had more than 400 templateValue[1][8] templateTitle[3] operating . Although templateValue[0][0] had the highest templateTitle[2] of templateTitle[3] operating in their templateTitle[1] market , the volume of templateTitle[1] firms in templateTitleDate[0] was less than one third of the templateTitle[2] seen in 2011 .

generated_template: The table shows the European Union on templateTitle[0] templateValue[0][0] in templateTitleSubject[0] . In templateTitleDate[0] , some templateValue[1][0] templateScale people were templateLabel[1][0] cargo and templateValue[2][0] templateScale respectively . templateLabel[2][0] was templateLabel[2][0] templateLabel[2][1] , the number of templateLabel[2][0] templateTitle[3] in templateTitleSubject[0] .
generated: The table shows the European Union on European Germany in European .  In 2018 , some 528 million people were Domestic cargo and 528 million respectively .  Total was Total , the number of Total companies in European .

Example 939:
titleEntities: {'Subject': ['Warner'], 'Date': ['2018']}
title: Consumer product revenue of Warner Bros. 2018 , by segment
Data:
                       0
Year                2018
Television product  3621
Theatrical product  4002
Games and other     1080 

gold: The consumer product revenue of Warner Bros. amounted to 8.7 billion U.S. dollars in 2018 , over four billion of which was derived from the company 's theatrical product . AT & T acquired Time Warner in June 2018 and assigned Warner Bros. , Home Box Office ( HBO ) , and Turner to their new WarnerMedia segment .
gold_template: The templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] templateTitle[4] amounted to 8.7 templateScale U.S. dollars in templateValue[0][last] , over four templateScale of which was derived from the company 's templateLabel[2][0] templateTitle[1] . AT & T acquired Time templateTitleSubject[0] in 2018 and assigned templateTitleSubject[0] templateTitle[4] , Home Box Office ( HBO ) , and Turner to their new WarnerMedia templateTitle[7] .

generated_template: This statistic shows the templateLabel[1][0] templateTitle[2] of the templateTitleSubject[1] templateTitle[0] templateTitle[1] to the templateTitleSubject[0] templateTitle[3] in templateValue[0][last] and templateValue[0][0] . In templateValue[0][0] , the templateTitle[2] of the templateTitleSubject[0] templateTitle[1] is forecast to grow from the templateYLabel[0] of the entire templateTitle[2] of the templateTitleSubject[1] templatePositiveTrend by approximately templateYValue[max] templateScale .
generated: This statistic shows the Television revenue of the Warner Consumer product to the Warner in 2018 and .  In 2018 , the revenue of the Warner product is forecast to grow from the of entire revenue of the Warner increase by approximately percent .

Example 940:
titleEntities: {'Subject': ['Number'], 'Date': ['2008', '2013']}
title: Number of commercial 3D-printing machines worldwide 2008 to 2013
X_Axis['Year']: ['2013', '2008']
Y_Axis['Number', 'of', 'commercial', '3D-printing', 'machines']: ['23000', '355']

gold: This statistic shows the number of commercial 3D printing machines that were in use in 2008 and again in 2013 . By 2013 , there were more than 23,000 machines in use which was a significant increase compared to 5 years earlier signifying an up and coming trend .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] 3D printing templateYLabel[3] that were in use in templateXValue[min] and again in templateXValue[max] . By templateXValue[max] , there were more than templateYValue[idxmax(X)] templateYLabel[3] in use which was a significant templatePositiveTrend compared to 5 years earlier signifying an up and coming trend .

generated_template: This statistic shows the templateYLabel[0] of migrant worker templateYLabel[1] templatePositiveTrend up away from their parents in templateTitleSubject[0] in templateXValue[min] and templateXValue[max] . The 6th National Population Census of the Republic of templateTitleSubject[0] estimated that templateYValue[max] templateScale templateTitle[1] templateYLabel[1] until the age of 17 templatePositiveTrend up without their parents .
generated: This statistic shows the Number of migrant worker commercial growing up away from their parents in Number 2008 and 2013 .  The 6th National Population Census of the Republic of Number estimated that 23000 thousand commercial until the age of 17 grew up without their parents .

Example 941:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018', '2024']}
title: Forecast of smartphone user numbers in Italy 2018 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018']
Y_Axis['Smartphone', 'users', 'in', 'millions']: ['47.57', '47.04', '46.34', '45.46', '44.36', '43.06', '41.55']

gold: In 2024 , the number of monthly active smartphone users in Italy is projected to reach 47.57 million individuals . This would be an increase of approximately six million new users compared to 2018 . The penetration rate is also expected to increase .
gold_template: In templateXValue[max] , the number of monthly active templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] is projected to reach templateYValue[idxmax(X)] templateScale individuals . This would be an templatePositiveTrend of approximately six templateScale new templateYLabel[1] compared to templateXValue[min] . The penetration rate is also expected to templatePositiveTrend .

generated_template: This statistic displays the development in templateYLabel[0] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateXValue[min] with a templateTitle[0] from templateXValue[5] to templateXValue[max] . In templateXValue[min] , the number of templateYLabel[0] templateYLabel[1] amounted to templateYValue[idxmin(X)] templateScale . In the same templateXLabel[0] , templateYLabel[0] penetration rate was at 86.95 templateScale .
generated: This statistic displays the development in Smartphone user numbers in Italy 2018 with a Forecast from 2019 to 2024 .  In 2018 , the number of Smartphone users amounted to 41.55 millions .  In the same Year , Smartphone penetration rate was at 86.95 millions .

Example 942:
titleEntities: {'Subject': ['Total'], 'Date': ['2014', '2015']}
title: Total and mobile messenger app retention rates 2014 to 2015
Data:
                        0          1          2          3           4
Year            1st_month  2nd_month  3rd_month  6th_month  12th_month
Average                36         29         25         18          11
Messaging apps         68         67         65         62          62 

gold: This statistic shows the average retention rates of messaging apps compared to the average of all apps as of January 2015 . During they survey period it was found that the average 12 month messaging app retention rate was 62 percent . The average app retention rate at this point was 11 percent .
gold_template: This statistic shows the templateLabel[1][0] templateTitle[4] templateTitle[5] of templateLabel[2][0] templateLabel[2][1] compared to the templateLabel[1][0] of all templateLabel[2][1] as of 2015 . During they survey period it was found that the templateLabel[1][0] 12 templateValue[0][0] templateLabel[2][0] templateTitle[3] templateTitle[4] rate was templateValue[2][last] templateScale . The templateLabel[1][0] templateTitle[3] templateTitle[4] rate at this point was templateValue[1][last] templateScale .

generated_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: This graph displays the percentage of Americans app were rates in 2014 , distinguished 2015 and .  In 2014 , 47.51 % of the Average Americans , aged 12th_month 1st_month and 12th_month , were rates .

Example 943:
titleEntities: {'Subject': ['Super Bowl'], 'Date': ['2013', '2019']}
title: Share of people planning to attend or throw a Super Bowl party 2013 to 2019
Data:
                                        0     1     2     3     4     5     6
Year                                 2013  2014  2015  2016  2017  2018  2019
Yes, plan to throw a party           16.6  16.2  17.6  17.7    18    18    17
Yes, plan to attend a party          25.2    26  25.9  28.6  27.2  27.5    24
Plan to watch at a bar / restaurant   4.3   4.5   5.5   4.3     5     5     5
No                                   53.9  53.4  51.1  49.4  49.8  49.5    54 

gold: Since 2013 , around 50 percent of people in the United States have not planned on throwing or attending a Super Bowl party . The number of people planning to attend a party dropped from 28.6 percent in 2016 to 24 percent in 2019 . Super spending on Super Bowl The Super Bowl is one of the biggest events on the sporting calendar , taking place annually on the first Sunday of February .
gold_template: Since templateValue[0][0] , around templateValue[4][4] templateScale of templateTitle[1] in the country have not planned on throwing or attending a templateTitleSubject[0] party . The number of templateTitle[1] templateTitle[2] to templateTitle[3] a templateTitle[7] templateNegativeTrend from templateValue[2][3] templateScale in templateValue[0][3] to templateValue[2][last] templateScale in templateValue[0][last] . templateTitleSubject[0] spending on templateTitleSubject[0] The templateTitleSubject[0] is one of the biggest events on the sporting calendar , taking place annually on the first Sunday of .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateValue[0][0] to templateValue[0][last] , templateTitle[7] the templateTitle[8] of the templateTitle[9] . In templateValue[0][0] , templateValue[5][0] marathons took place in the templateTitleSubject[0] .
generated: The statistic shows the Share of people planning in the Super Bowl from 2013 to 2019 , party the 2013 of the 2019 .  In 2013 , 53.9 marathons took place in the Super Bowl .

Example 944:
titleEntities: {'Subject': ['Siemens AG'], 'Date': ['2010', '2019']}
title: Siemens AG - revenue 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Revenue', 'in', 'million', 'euros']: ['86849', '83044', '82863', '79644', '75636', '71227', '73445', '74734', '69607', '65067']

gold: Siemens AG has managed to increase its annual revenue from 2018 by three percent , meaning that annual revenue surpassed 86.8 billion euros in the 2019 fiscal year . Restructure and redirection Revenue grew in 2019 at Siemens in spite of a struggling power and gas division , which will be spun off by the end of 2020 . In fact , revenue from this division dropped from around 18 billion euros in 2018 to about 17.66 billion euros in 2019 .
gold_template: templateTitleSubject[0] has managed to templatePositiveTrend its annual templateYLabel[0] from templateXValue[1] by three templateScale , meaning that annual templateYLabel[0] surpassed templateYValue[max] templateScale templateYLabel[2] in the templateXValue[idxmax(Y)] fiscal templateXLabel[0] . Restructure and redirection templateYLabel[0] templatePositiveTrend in templateXValue[max] at templateTitleSubject[0] in spite of a struggling power and gas division , which will be spun off by the end of 2020 . In fact , templateYLabel[0] from this division templateNegativeTrend from around 18 templateScale templateYLabel[2] in templateXValue[1] to about 17.66 templateScale templateYLabel[2] in templateXValue[max] .

generated_template: The statistic illustrates the sales of the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in 2010 and a forecast for the years templateTitle[5] templateXValue[max] . According to CLSA Asia-Pacific Market , templateTitle[1] sales templateYLabel[0] in templateTitleSubject[0] will amount to around templateYValue[max] templateScale templateYLabel[2] in templateXValue[idxmax(Y)] .
generated: The statistic illustrates the sales of the AG revenue 2010 in Siemens AG 2010 and a forecast for the years 2019 .  According to CLSA Asia-Pacific Market , AG sales Revenue in Siemens AG will amount to around 86849 million euros in 2019 .

Example 945:
titleEntities: {'Subject': ['Spain'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth in Spain 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'rate', 'compared', 'to', 'previous', 'year']: ['1.59', '1.65', '1.67', '1.7', '1.85', '2.18', '2.58', '2.98', '3.17', '3.65', '1.38']

gold: This statistic shows the growth of the real gross domestic product ( GDP ) in Spain 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: This 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 depicts templateTitleSubject[0] 's real 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: The statistic depicts Spain 's real 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 .

Example 946:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Top U.S. states based on hay production 2017
X_Axis['State']: ['Texas', 'Nebraska', 'Kansas', 'California', 'Oklahoma', 'Missouri', 'Kentucky', 'Idaho', 'Montana', 'South_Dakota']
Y_Axis['Production', 'in', 'thousand', 'tons']: ['10350', '6159', '6042', '6028', '5998', '5985', '5325', '5128', '4880', '4785']

gold: This statistic shows the U.S. states where the most hay was produced in 2017 . Nebraska came second with an estimated hay production amount of around 6.2 million tons of hay in that year .
gold_template: This statistic shows the templateTitleSubject[0] templateTitle[2] where the most templateTitle[4] was produced in templateTitleDate[0] . templateXValue[1] came second with an estimated templateTitle[4] templateYLabel[0] amount of around templateYValue[1] templateScale templateYLabel[2] of templateTitle[4] in that year .

generated_template: This statistic shows the templateTitle[0] templateTitle[2] templateTitle[3] in the templateTitle[1] in templateTitleDate[0] , templateTitle[4] on templateYLabel[1] templateYLabel[2] . templateXValue[0] ranked the highest with a templateTitle[5] templateTitle[6] of templateYValue[max] templateScale templateYLabel[1] , followed by templateXValue[1] with templateYValue[1] templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Top states based in the U.S. in 2017 , hay on thousand tons .  Texas ranked the highest with a production 2017 of 10350 thousand , followed by Nebraska with 6159 thousand tons .

Example 947:
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 total templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale templateTitle[1] templateTitle[2] templateYLabel[1] were living in templateTitleSubject[0] , compared to templateYValue[8] templateScale in templateXValue[8] .
generated: This statistic shows the total Annual of beauty in YouTube from 2006 to 2018 .  In 2018 , about 169.0 billions annual beauty were living in YouTube , compared to 5.0 billions in 2010 .

Example 948:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita 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['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['3952.1', '3664.17', '3406.14', '3171.51', '2954.64', '2739.82', '2551.12', '2353.36', '2172.01', '2085.71', '2047.43', '1898.9', '1750.76', '1532.31', '1297.23', '1181.45', '1154.49', '920.46', '796.93', '699.68', '603.67', '489.03', '440.21', '413.34', '401.57', '374.72', '360.93', '361.91', '337.52', '288.87', '230.31', '189.26', '144.15', '113.65', '98.03', '97.16', '365.89', '674.88', '556.02', '251.2', '822.55']

gold: In 2018 , GDP per capita in Vietnam amounted to around 2,551 U.S. dollars . 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: In templateXValue[6] , templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[3] templateYLabel[4] . 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] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was around templateYValue[7] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] 's economy templateYLabel[0] templateYLabel[1] templateYLabel[2] is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the templateYLabel[0] and dividing it by the total population in the country .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in Vietnam from 1984 to 2018 , with projections up until 2024 .  In 2018 , the Gross domestic product per capita in Vietnam was around 2353.36 U.S. dollars .  Vietnam 's economy GDP per capita is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the GDP and dividing it by the total population in the country .

Example 949:
titleEntities: {'Subject': ['Oregon'], 'Date': ['2000', '2018']}
title: Oregon - 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']: ['12.6', '13.2', '13.3', '15.4', '16.6', '16.7', '17.2', '17.5', '15.8', '14.3', '13.6', '12.9', '13.3', '14.1', '14.1', '13.9', '13.2', '13.4', '13.2']

gold: This graph shows the poverty rate in Oregon from 2000 to 2018 . 12.6 percent of Oregon 's population lived below the poverty line in 2018 .
gold_template: This graph shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] templateTitle[3] templateXValue[min] to templateXValue[max] . templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line in templateXValue[idxmin(Y)] .

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 Oregon from 2000 to 2018 .  In 2018 , about 12.6 percentage of Oregon 's population lived below the poverty line .

Example 950:
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 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: This statistic shows the share of tournaments runners-up Runner-up World Cup runners-up country in 1930 , Country .  In 1930 , tournaments Runner-up World Cup runners-up by in Germany accounted for around 4 % of the world 's total tournaments runners-up grid-connected World Cup runners-up .

Example 951:
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: In templateTitleDate[0] , templateValue[0][0] had the highest templateTitleSubject[0] templateTitle[1] in the world at templateValue[1][0] templateScale , and templateLabel[2][0] templateLabel[2][1] use was nearly ubiquitous . In the majority of countries where more than 90 templateScale owned a templateLabel[2][0] templateLabel[2][1] , templateTitleSubject[0] templateTitle[1] is higher than 70 templateScale , which is somewhat of an indicator for an templateValue[0][10] economy . templateTitleSubject[0] user numbers are templatePositiveTrend The number of global templateTitleSubject[0] users has constantly been templatePositiveTrend ever since the first smartphones hit the market , surpassing the three templateScale mark for the first time in 2019 .
generated: In 2016 , Germany had the highest Europe in the world at 40514123 million , and Female use was nearly ubiquitous .  In the majority of countries where more than 90 % owned a Female , Europe is higher than 70 % , which is somewhat of an indicator for an Greece economy .  Europe user numbers are increasing The number of global Europe users has constantly been increasing ever since the first smartphones hit the market , surpassing the three million mark for the first time in 2019 .

Example 952:
titleEntities: {'Subject': ['Americans'], 'Date': ['2001', '2018']}
title: Americans ' moral stance towards doctor-assisted suicide from 2001 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  2008  2007  2006  2005  2004  2003  2002  2001
Morally acceptable      54    57    53    56    52    45    45    45    46    51    49    50    49    53    45    50    49
Morally wrong           42    37    41    37    43    49    48    48    46    44    44    41    46    41    49    44    40
Depends on situation     3     4     4     4     3     4     4     5     6     3     5     6     4     3     5     4     8 

gold: This statistic shows the moral stance of Americans regarding doctor-assisted suicide from 2001 to 2018 . In 2018 , 54 percent of respondents stated that they think doctor-assisted suicide is morally acceptable , while 3 percent said it depends on the situation .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] regarding templateTitle[5] templateTitle[6] templateTitle[7] templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateValue[1][0] templateScale of respondents stated that they think templateTitle[5] templateTitle[6] is templateLabel[1][0] templateLabel[1][1] , while templateValue[3][0] templateScale said it templateLabel[3][0] on the templateLabel[3][1] .

generated_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: The statistic shows the number of Americans ' from 2001 to 2018 .  At the end of 2018 , 42 million people were Morally wrong ' .  Morally wrong wrong 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 .

Example 953:
titleEntities: {'Subject': ['UK'], 'Date': ['1970', '2018']}
title: UK households : ownership of landline telephones 1970 to 2018
X_Axis['Year']: ['2017-18', '2016-17', '2015-16', '2014-15', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1990', '1985', '1980', '1975', '1970']
Y_Axis['Percentage', 'of', 'respondents']: ['85', '89', '88', '88', '88', '89', '88', '88', '87', '88', '90', '89', '92', '93', '92', '94', '94', '93', '95', '95', '94', '93', '92', '91', '87', '81', '72', '52', '35']

gold: The statistic shows the percent of households in the United Kingdom ( UK ) that own a landline telephone . In 1970 , when this survey initiated , 35 percent of households owned a telephone . As of 2018 , that number had increased significantly to 85 percent of households .
gold_template: The statistic shows the templateScale of templateTitle[1] in the United Kingdom ( templateTitleSubject[0] ) that own a templateTitle[3] telephone . In templateXValue[last] , when this survey initiated , templateYValue[min] templateScale of templateTitle[1] owned a telephone . As of templateTitleDate[max] , that number had templatePositiveTrend significantly to templateYValue[0] templateScale of templateTitle[1] .

generated_template: Just five templateScale of houses in the templateTitleSubject[0] ( templateTitleSubject[1] ) were not heated using a templateTitle[1] templateTitle[2] system , as of templateXValue[max] . The share of houses using a templateTitle[1] templateTitle[2] system climbed steadily until templateXValue[13] , templatePositiveTrend from templateYValue[18] templateScale in the templateXLabel[0] templateXValue[18] . 86 templateScale of properties use gas as the fuel for their templateTitle[1] templateTitle[2] system .
generated: Just five percentage of houses in the UK ( ) were not heated using a households ownership system , as of 2017-18 .  The share of houses using a households ownership system climbed steadily until 2005 , increasing from 95 percentage in the Year 2000 .  86 percentage of properties use gas as the fuel for their households ownership system .

Example 954:
titleEntities: {'Subject': ['Apple Music'], 'Date': ['2018']}
title: Apple Music users in the U.S. 2018 , by age
X_Axis['Year']: ['18-24', '25-34', '35-44', '45-54', '55+']
Y_Axis['Share', 'of', 'users']: ['17', '23', '22', '15', '23']

gold: This statistic gives information on the share of Apple Music in the United States as of February 2018 , by age . During this month , 17 percent of Apple Music users were aged between 18 and 24 .
gold_template: This statistic gives information on the templateYLabel[0] of templateTitleSubject[0] in the templateTitle[3] as of 2018 , templateTitle[5] templateTitle[6] . During this month , templateYValue[0] templateScale of templateTitleSubject[0] users were aged between 18 and 24 .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] group . During that period of time , templateYValue[max] templateScale of templateYLabel[1] between 30 and 49 years used the social networking site .
generated: This statistic shows the Share of users in the Apple Music who were using Apple as of 2019 , sorted age group .  During that period of time , 23 % of users between 30 and 49 years used the social networking site .

Example 955:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2012', '2014']}
title: Number of PC online gamers in Malaysia 2012 to 2014
X_Axis['Year']: ['2014', '2013', '2012']
Y_Axis['Number', 'of', 'gamers', 'in', 'millions']: ['9.9', '8.7', '7.62']

gold: The statistic presents the projected number of PC online gamers in Malaysia from 2012 to 2014 . NIKO Media Research projected the number would rise from 7.62 million in 2012 to 9.9 million in 2014 .
gold_template: The statistic presents the projected templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . NIKO Media Research projected the templateYLabel[0] would rise from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .

generated_template: This statistic displays the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] in templateXValue[min] and templateXValue[1] , with a forecast for templateXValue[max] . According the calculations , there were templateYValue[idxmin(X)] templateScale templateTitleSubject[0] templateYLabel[1] in SEA in templateXValue[idxmin(Y)] and this templateYLabel[0] is expected to grow to templateYValue[idxmax(X)] templateScale by the end of templateXValue[idxmax(Y)] .
generated: This statistic displays the Number of Malaysia gamers in Malaysia 2012 and 2013 , with a forecast for 2014 .  According the calculations , there were 7.62 millions Malaysia gamers in SEA 2012 and this Number is expected to grow 9.9 millions by the end of 2014 .

Example 956:
titleEntities: {'Subject': ['Global'], 'Date': ['2015', '2022']}
title: Global solar power market value 2015 to 2022
X_Axis['Year']: ['2022', '2015']
Y_Axis['Industry', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['422', '86']

gold: The global solar energy market is expected to reach 422 billion U.S. dollars in 2022 , in comparison to 86 billion U.S. dollars in 2015 . Solar energy is one of the most popular renewable energy sources and in recent years more capacity was deployed than traditional energy sources . The success related to the solar energy segment can be attributed to the declining cost of installing solar photovoltaic systems .
gold_template: The templateTitleSubject[0] templateTitle[1] energy templateTitle[3] is expected to reach templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] , in comparison to templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmin(Y)] . templateTitle[1] energy is one of the most popular renewable energy sources and in recent years more capacity was deployed than traditional energy sources . The success related to the templateTitle[1] energy segment can be attributed to the declining cost of installing templateTitle[1] photovoltaic systems .

generated_template: This statistic shows the templateYLabel[0] of migrant worker templateYLabel[1] templatePositiveTrend up away from their parents in templateTitleSubject[0] in templateXValue[min] and templateXValue[max] . The 6th National Population Census of the Republic of templateTitleSubject[0] estimated that templateYValue[max] templateScale templateTitle[1] templateYLabel[1] until the age of 17 templatePositiveTrend up without their parents .
generated: This statistic shows the Industry of migrant worker value growing up away from their parents in Global 2015 and 2022 .  The 6th National Population Census of the Republic of Global estimated that 422 billion solar value until the age of 17 grew up without their parents .

Example 957:
titleEntities: {'Subject': ['Protestant'], 'Date': ['2010']}
title: Largest Mainline Protestant churches in the U.S. 2010 , by congregations
X_Axis['Mainline', 'Protestant', 'church']: ['United_Methodist_Church', 'Presbyterian_Church_(U.S.A.)', 'Evangelical_Lutheran_Church_in_America', 'Episcopal_Church', 'American_Baptist_Churches_in_the_U.S.A.', 'United_Church_of_Christ', 'All_other_Mainline_Protestant']
Y_Axis['Number', 'of', 'congregations']: ['33323', '10487', '9846', '6794', '5243', '5225', '6842']

gold: This graph shows the largest churches among Mainline Protestant denominations in the United States in 2010 , by number of congregations . In 2010 , the United Methodist Church was the largest church , with 33,323 congregations .
gold_template: This graph shows the templateTitle[0] templateXValue[4] among templateXValue[last] denominations in the templateXValue[0] States in templateTitleDate[0] , templateTitle[6] templateYLabel[0] of templateYLabel[1] . In templateTitleDate[0] , the templateXValue[0] Church was the templateTitle[0] templateXValue[0] , with templateYValue[max] templateYLabel[1] .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] in templateTitleDate[0] , by templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , templateYValue[max] templateScale of the templateXValue[2] community was templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the total Number of congregations in the Protestant in 2010 , by Mainline Protestant .  In 2010 , 33323 % of the Evangelical_Lutheran_Church_in_America community was congregations .

Example 958:
titleEntities: {'Subject': ['Global'], 'Date': ['2018']}
title: Global retail sales share of organic food 2018 , by country
X_Axis['Country']: ['United_States', 'Germany', 'France', 'China', 'Italy', 'Canada', 'United_Kingdom', 'Switzerland', 'Other']
Y_Axis['Retail', 'sales', 'share']: ['42', '11', '9', '8', '4', '3', '3', '3', '17']

gold: This statistic shows the organic food retail sales share worldwide in 2018 , broken down by country . According to the report , the United States accounted for approximately 42 percent of global organic retail sales that year .
gold_template: This statistic shows the templateTitle[4] templateTitle[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] worldwide in templateTitleDate[0] , broken down templateTitle[7] templateXLabel[0] . According to the report , the templateXValue[0] accounted for approximately templateYValue[max] templateScale of templateTitleSubject[0] templateTitle[4] templateYLabel[0] templateYLabel[1] that year .

generated_template: This statistic shows the templateYLabel[0] of templateXValue[0] templateTitleSubject[0] templateYLabel[1] templateTitle[4] as of 2019 , templateTitle[6] templateTitle[7] . There were templateYValue[2] templateXValue[3] templateYLabel[1] within the templateXValue[0] templateTitleSubject[0] templateXValue[0] templateXValue[2] group in templateTitleDate[0] .
generated: This statistic shows the Retail of United_States Global sales organic as of 2019 , 2018 by .  There were 9 China sales within the United_States Global France group in 2018 .

Example 959:
titleEntities: {'Subject': ['JPMorgan Chase'], 'Date': ['2018']}
title: Total assets of JPMorgan Chase 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Total', 'assets', 'in', 'billion', 'U.S.', 'dollars']: ['2622.53', '2533.6', '2490.97', '2351.7', '2572.27', '2415.69', '2359.14', '2265.79', '2117.61', '2031.99', '2175.05', '1562.15', '1351.52']

gold: This statistic presents the development of total assets of JPMorgan Chase from 2006 to 2018 . The total assets of JPMorgan Chase increased from approximately 1.35 trillion U.S. dollars in 2006 to approximately 2.62 trillion U.S. dollars in 2018 .
gold_template: This statistic presents the development of templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templatePositiveTrend from approximately templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmin(Y)] to approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[2] was at templateYValue[idxmax(X)] templateScale per game in the templateTitle[6] .
generated: This statistic presents the Total assets billion for JPMorgan Chase 2006 2018 in the 2018 from 2006 to 2018 .  In 2018 , the Total billion was at 2622.53 billion per game in the 2018 .

Example 960:
titleEntities: {'Subject': ['U.S. Super Bowl Sunday'], 'Date': ['2019']}
title: Planned average spending of U.S. consumers on Super Bowl Sunday 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Planned', 'spending', 'in', 'U.S.', 'dollars']: ['81.0', '81.0', '75.0', '82.19', '89.05', '79.12', '82.3', '76.37', '71.51']

gold: Adults in the United States plan on spending an average of 81 U.S. dollars on Super Bowl Sunday , with food and beverage purchases the most common among those who plan on watching the game . Football fans spend billions More than 50 percent of adults in the United States did not plan on throwing or attending a Super Bowl party in 2019 . In spite of this , the total planned Super Bowl spending in the United States was estimated to be around 15 billion U.S. dollars in 2019 .
gold_template: Adults in the templateTitle[3] plan on templateYLabel[1] an templateTitle[1] of templateYValue[0] templateYLabel[2] templateYLabel[3] on templateTitleSubject[0] Sunday , with food and beverage purchases the most common among those who plan on watching the game . Football fans spend templateScale More than 50 templateScale of adults in the templateTitle[3] did not plan on throwing or attending a templateTitleSubject[0] party in templateXValue[max] . In spite of this , the total templateYLabel[0] templateTitleSubject[0] spending in the templateTitle[3] was estimated to be around 15 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] at 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: This statistic shows the Planned of U.S. at universities in the U.S. Super Bowl Sunday ( ) from 2011 to 2019 .  The Planned of U.S. peaked in 2017 .  The lower figures in 2013 and 2014 may be connected to the rise of the tuition fee limit in 2013 to 9,000 British pounds per Year .

Example 961:
titleEntities: {'Subject': ['United States'], 'Date': ['2018']}
title: United States crime rate 2018 , by type of crime
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['Rate', 'per', '100,000', 'inhabitants']: ['2362.9', '1695.5', '429.7', '394.9', '249.2', '237.7', '98.6', '41.7', '5.3']

gold: This statistic shows the crime rate in the United States in 2018 , by type of crime . In 2018 , the violent crime rate was at 394.9 cases per 100,000 inhabitants .
gold_template: This statistic shows the templateXValue[0] templateYLabel[0] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] of templateXValue[0] . In templateTitleDate[0] , the templateXValue[3] templateXValue[0] templateYLabel[0] was at templateYValue[3] cases templateYLabel[1] 100,000 templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateXValue[0] was templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the United States crime rate in the United States in 2018 , type crime .  In 2018 , the United States crime rate in the Property_crime__(total) was 2362.9 Rate per 100,000 inhabitants .

Example 962:
titleEntities: {'Subject': ['Philippines'], 'Date': ['2024']}
title: Inflation rate 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['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['3', '3', '3', '3.11', '2.25', '2.51', '5.21', '2.85', '1.25', '0.67', '3.6', '2.58', '3.01', '4.76', '4.11', '3.23', '9.31', '2.78', '6.25', '7.7', '5.96', '3.45', '3.06', '6.77', '3.92', '5.97', '9.25', '5.56', '7.49', '6.84', '10.37', '6.69', '8.73', '19.33', '13.2', '11.37', '12.23', '3.04', '-0.33', '23.22', '46.67']

gold: The Philippines ' inflation rate seems to have leveled off after peaking at more than 5 percent in 2018 and jumping up and down a few years prior . The Philippines are considered `` newly industrialized '' , but the economy relies on remittances from nationals overseas , and the services sector generates most of its GDP . Emerging and soon to develop ? After switching from agriculture to services and manufacturing , the Philippines are now an emerging economy , i.e .
gold_template: The templateTitleSubject[0] ' templateYLabel[0] templateYLabel[1] seems to have leveled off after peaking at more than templateYValue[6] templateScale in templateXValue[6] and jumping up and down a few years prior . The templateTitleSubject[0] are considered `` newly industrialized '' , but the economy relies on remittances from nationals overseas , and the services sector generates most of its GDP . Emerging and soon to develop ? After switching from agriculture to services and manufacturing , the templateTitleSubject[0] are now an emerging economy , i.e .

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 Philippines from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Philippines amounted to about 5.21 % compared to the previous Year .

Example 963:
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: 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 Revenue of billion at 2007 establishments in Yandex from 2007 to 2019 .  In 2019 , the Revenue of billion in travel 2007 ( including both international and domestic tourists ) amounted to approximately 175.39 billion .

Example 964:
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] of templateYLabel[1] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . The templateYLabel[0] of templateYLabel[1] in the templateTitle[3] has templatePositiveTrend tenfold during this time , from templateYValue[min] templateYLabel[1] in templateXValue[idxmin(Y)] to templateYValue[0] templateYLabel[1] in templateTitleDate[max] .
generated: This statistic shows the Surplus/deficit of trillion in the Budget from 2000 to .  The Surplus/deficit of trillion in the surplus has increased tenfold during this time , from -1.41 trillion in '09 to -0.53 trillion in 2000 .

Example 965:
titleEntities: {'Subject': ['Guatemala'], 'Date': ['2019']}
title: Employment by economic sector in Guatemala 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  29.19  29.26  29.32  29.38  31.88  31.53  30.88  32.26  37.57  37.04  36.12
Industry     20.62  20.76   20.9  21.01   18.9  19.46  18.16   19.5  19.88  19.77  20.28
Services     50.19  49.98  49.79  49.61  49.23  49.01  50.96  48.25  42.55  43.19   43.6 

gold: The statistic shows the distribution of employment in Guatemala by economic sector from 2009 to 2019 . In 2019 , 29.19 percent of the employees in Guatemala were active in the agricultural sector , 20.62 percent in industry and 50.19 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 Guatemala by economic sector from 2009 to 2019 .  In 2019 , 29.19 % of the employees in Guatemala were active in the agricultural sector , 20.62 % in Industry and 50.19 % in the service sector .

Example 966:
titleEntities: {'Subject': ['China'], 'Date': ['2012', '2024']}
title: Unemployment rate in China 2012 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Unemployment', 'rate']: ['3.8', '3.8', '3.8', '3.8', '3.8', '3.8', '3.8', '3.9', '4.02', '4.05', '4.09', '4.05', '4.09']

gold: The statistic shows the unemployment rate for China from 2012 to 2017 , with projections up until 2024 . In 2017 , China 's unemployment rate ranged at about 3.9 percent . Unemployment rate in China – additional information From the statistic at hand one can see that the unemployment rate in China has been fairly constant at around four percent over the last few years .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] ranged at about templateYValue[7] templateScale . templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] – additional information From the statistic at hand one can see that the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] has been fairly constant at around templateYValue[min] templateScale over the last few years .

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] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at around templateYValue[6] templateScale .
generated: The statistic shows the Unemployment rate in China from 2012 to 2018 , with projections up until 2024 .  In 2018 , the Unemployment rate in China was at around 3.8 % .

Example 967:
titleEntities: {'Subject': ['Number'], 'Date': ['2006']}
title: Number of car sharing users worldwide 2006 to 2025
X_Axis['Year']: ['2025_(forecast)', '2015', '2014', '2013', '2012', '2010', '2008', '2006']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['36.0', '7.0', '4.94', '3.48', '2.34', '1.16', '0.67', '0.35']

gold: This statistic shows the global number of car sharing users from 2006 to 2015 , and a forecast for 2025 . The term car sharing stands for the organized communal use of one or more cars . In 2015 , there were roughly 7 million car sharing users worldwide .
gold_template: This statistic shows the global templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] from templateXValue[last] to templateXValue[1] , and a forecast for templateXValue[0] . The term templateTitle[1] templateTitle[2] stands for the organized communal use of templateYValue[5] or more cars . In templateXValue[1] , there were roughly templateYValue[1] templateScale templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[4] .

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 users in Number from 2006 to 2025_(forecast) .  In 2025_(forecast) , the Number of users in Number is expected to reach 36.0 millions , up from 1.16 millions in 2010 .

Example 968:
titleEntities: {'Subject': ['Germany'], 'Date': ['2014', '2016']}
title: Market share of the leading search engines in Germany 2014 to 2016
Data:
              0     1      2         3        4          5       6       7     8           9       10
unknown  Google  Bing  Yahoo  T-Online  Ask.com  AOL_Suche  Yandex  WEB.DE   GMX  Search.com  Conduit
2014       91.2   3.5    1.4         1      0.5        0.5       0     0.6   0.5         0.5      0.3
2015      94.84  2.59   1.66      0.75     0.18       0.13       0    0.05  0.02        0.02     0.01
2016      94.52  4.16   0.98      0.12     0.12       0.08    0.06       0     0           0        0 

gold: This statistic shows a ranking of the most used search engines in Germany from 2014 to 2016 . In 2016 , Google had a market share of 94.52 percent , ranking first . That year , the three most frequently searched terms on Google in Germany ( as of May 2016 ) were prince ( 1,000 percent increase in search volume ) , vatertag ( Father 's Day , 850 percent increase ) , muttertag ( Mother 's Day , 650 percent increase ) .
gold_template: This statistic shows a ranking of the most used templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateValue[0][0] had a templateTitle[0] templateTitle[1] of templateValue[3][max] templateScale , ranking first . That year , the templateValue[2][1] most frequently searched terms on templateValue[0][0] in templateTitleSubject[0] ( as of 2016 ) were prince ( 1,000 templateScale templatePositiveTrend in templateTitle[3] volume ) , vatertag ( Father 's Day , 850 templateScale templatePositiveTrend ) , muttertag ( Mother 's Day , 650 templateScale templatePositiveTrend ) .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[min] and templateTitleDate[max] . As of templateTitleDate[max] , templateValue[2][max] templateScale of respondents said their templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] technology was templateValue[0][0] .
generated: The statistic shows the Germany share leading search engines in 2014 and 2016 .  As of 2016 , 94.84 % of respondents said their Germany share leading search technology was Google .

Example 969:
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: templateTitle[2] is the most popular team sport in templateTitleSubject[0] across the board . Of templateTitle[0] aged 5 to 10 , roughly templateValue[1][0] templateScale play templateTitle[2] at least on a monthly basis , which is only surpassed by swimming . At age 11 to 15 templateTitle[2] becomes even more popular with approximately 44 templateScale in this age group playing .
generated: cricket is the most popular team sport in England across the board .  Of Children aged 5 to 10 , roughly 5.3 % play cricket at least on a monthly basis , which is only surpassed by swimming .  At age 11 to 15 cricket becomes even more popular with approximately 44 % in this age group playing .

Example 970:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2018', '2018']}
title: Most popular technology brands on Facebook 2018
X_Axis['Platform']: ['Facebook', 'Facebook_for_Every_Phone', 'Samsung_Mobile_USA', 'Instagram', 'Microsoft_Windows', 'Netflix', 'Samsung_Mobile', 'Intel', 'PlayStation', 'iTunes']
Y_Axis['Number', 'of', 'fans', 'in', 'millions']: ['208.14', '208.14', '156.25', '54.31', '47.15', '44.57', '43.48', '38.88', '38.57', '30.46']

gold: This statistic shows the most popular technology brands in Facebook in April 2018 , as measured in Facebook fans . Sony 's PlayStation brand had accumulated approximately 38.57 million fans on the social network .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateXValue[0] in 2018 , as measured in templateXValue[0] templateYLabel[1] . Sony 's templateXValue[8] brand had accumulated approximately templateYValue[8] templateScale templateYLabel[1] on the social network .

generated_template: The statistic shows the ten most popular television templateTitle[5] in the templateTitle[0] based on their templateTitle[2] of templateYLabel[1] . In 2016 , templateXValue[0] was ranked first with a templateTitle[1] templateTitle[2] of templateYValue[max] templateScale of total templateYLabel[1] .
generated: The statistic shows the ten most popular television 2018 in the Most based on their technology of fans .  In 2016 , Facebook was ranked first with a popular technology of 208.14 millions of total fans .

Example 971:
titleEntities: {'Subject': ['Egypt'], 'Date': ['2019']}
title: Unemployment rate in Egypt 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']: ['11.29', '11.44', '11.77', '12.41', '13.05', '13.11', '13.15', '12.6', '11.85', '11.85', '9.09', '8.52', '8.8', '10.49', '11.2', '10.32', '11.01', '10.01', '9.26', '8.98', '7.95']

gold: This statistic shows the unemployment rate in Egypt from 1999 to 2019 . In 2019 , the unemployment rate in Egypt was at approximately 11.29 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 Egypt from 1999 to 2019 .  In 2019 , the Unemployment rate in Egypt was at approximately 11.29 % .

Example 972:
titleEntities: {'Subject': ['United States'], 'Date': []}
title: United States number of ski areas operating , per state 2018/19
X_Axis['State']: ['New_York', 'Michigan', 'Wisconsin', 'Colorado', 'New_Hampshire', 'California', 'Pennsylvania', 'Vermont', 'Minnesota', 'Maine', 'Idaho', 'Utah', 'Montana', 'Washington', 'Massachusetts', 'Oregon', 'Wyoming', 'Alaska', 'New_Mexico', 'North_Carolina', 'Nevada', 'West_Virginia', 'Connecticut', 'Illinois', 'Ohio', 'Virginia', 'Iowa', 'South_Dakota', 'Arizona', 'New_Jersey', 'North_Dakota', 'Missouri', 'Indiana', 'Tennessee', 'Maryland', 'Rhode_Island', 'Alabama']
Y_Axis['Number', 'of', 'ski', 'areas']: ['51', '43', '31', '31', '30', '30', '26', '25', '20', '20', '17', '14', '14', '14', '13', '12', '10', '9', '9', '6', '5', '5', '5', '5', '5', '5', '3', '3', '3', '2', '2', '2', '2', '1', '1', '1', '1']

gold: This graph depicts the number of ski areas operating in the U.S. during the 2018/2019 winter season , per state . During that season , there were three operating ski areas in Arizona .
gold_template: This graph depicts the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[5] in the templateTitle[1] during the 2018/2019 winter season , templateTitle[6] templateXLabel[0] . During that season , there were templateYValue[26] templateTitle[5] templateYLabel[1] templateYLabel[2] in templateXValue[28] .

generated_template: As of 2020 , templateXValue[0] had the most templateYLabel[2] templateYLabel[3] in the templateTitle[2] , with templateYValue[max] templateYLabel[0] templateYLabel[3] since templateTitleDate[min] . The source defines a templateYLabel[2] shooting as a shooting where templateYValue[7] or more people were killed . Firearms in the templateTitleSubject[0] templateYLabel[2] templateYLabel[3] in the templateTitle[2] are disturbingly common .
generated: As of 2020 , New_York had the most areas in the number , with 51 Number areas since .  The source defines a areas shooting as a shooting where 25 or more people were killed .  Firearms in the United States areas in the number are disturbingly common .

Example 973:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019', '2019']}
title: U.S. government - annual receipts by quarter 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", "Q3_'12", "Q2_'12", "Q1_'12"]
Y_Axis['Receipts', 'in', 'trillion', 'U.S.', 'dollars']: ['5.78', '5.78', '5.69', '5.6', '5.62', '5.54', '5.5', '6.59', '5.53', '5.45', '5.48', '5.42', '5.39', '5.33', '5.31', '5.44', '5.3', '5.3', '5.22', '5.12', '5.1', '5.08', '5.01', '4.98', '4.79', '4.97', '4.69', '4.44', '4.32', '4.3', '4.27']

gold: This statistic shows the total annual receipts of the U.S. government from 2012 to 2019 , by quarter . The total annual receipts of the government added up to about 5.78 trillion U.S. dollars in the third quarter of 2019 .
gold_template: This statistic shows the total templateTitle[2] templateYLabel[0] of the templateYLabel[2] templateTitle[1] from 2012 to templateTitleDate[0] , templateTitle[4] templateXLabel[0] . The total templateTitle[2] templateYLabel[0] of the templateTitle[1] added up to about templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in the third templateXLabel[0] of templateTitleDate[0] .

generated_template: This statistic illustrates the templateTitle[1] templateTitle[2] templateYLabel[0] of templateTitleSubject[0] Inc . The online commerce and payments platform 's templateYLabel[0] in the second templateXLabel[0] of templateTitleDate[max] was templateYValue[last] templateScale US templateYLabel[3] , a 7 templateScale templatePositiveTrend from the first templateXLabel[0] of the previous year . templateYLabel[1] of the previous year , templateTitleSubject[0] had templateYValue[last] templateScale in the templateTitle[3] .
generated: This statistic illustrates the government annual Receipts of U.S. Inc .  The online commerce and payments platform 's Receipts in the second Quarter of 2019 was 4.27 trillion US dollars , a 7 trillion increase from the first Quarter of the previous year .  trillion of the previous year , U.S. had 4.27 trillion in the receipts .

Example 974:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2013', '2019']}
title: Expenditure on police services United Kingdom ( UK ) 2013 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']
Y_Axis['Expenditure', 'in', 'billion', 'GBP']: ['17.99', '17.5', '16.37', '16.57', '16.66', '16.35', '17.58', '18.24', '18.58', '19.3']

gold: This statistic shows public sector expenditure on police services in the United Kingdom from 2009/10 to 2018/19 . Expenditure on police services fell between 2009/10 and 2013/14 by approximately 3 billion British pounds . From 2013/14 until 2016/17 police expenditure fluctuated between a low of 16.35 billion in 2013/14 and a high of 16.66 billion in 2014/15 .
gold_template: This statistic shows public sector templateYLabel[0] on templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . templateYLabel[0] on templateTitle[1] templateTitle[2] templateNegativeTrend between templateXValue[last] and templateXValue[5] by approximately templateDelta[9,5] templateScale British pounds . From templateXValue[5] until templateXValue[2] templateTitle[1] templateYLabel[0] fluctuated between a low of templateYValue[min] templateScale in templateXValue[idxmin(Y)] and a high of templateYValue[4] templateScale in templateXValue[4] .

generated_template: The templateTitleSubject[0] spent approximately templateYValue[0] templateScale British pounds on its prison system in templateXValue[0] , an templatePositiveTrend when compared to the previous templateXLabel[0] . Despite this , the templateTitleSubject[0] is still spending around 40 templateScale pounds less than it did in templateXValue[8] , due mainly to the austerity policies pursued by the coalition Government of the time.Decline in officer numbers As of 2019 , there were around 22.63 thousand prison officers working in England and Wales , a seven-year high and an templatePositiveTrend of 1.59 thousand people from templateXValue[2] . The number of prison officers has naturally followed a similar pattern to the levels of funding provided by the government , so as funding templateNegativeTrend after 2010 , so too did officer numbers .
generated: The United Kingdom spent approximately 17.99 billion British pounds on its prison system in 2018/19 , an increase when compared to the previous Year .  Despite this , the United Kingdom is still spending around 40 billion pounds less than it did in 2010/11 , due mainly to the austerity policies pursued by the coalition Government of the time.Decline in officer numbers As of 2019 , there were around 22.63 thousand prison officers working in England and Wales , a seven-year high and an increase of 1.59 thousand people from 2016/17 .  The number of prison officers has naturally followed a similar pattern to the levels of funding provided by the government , so as funding fell after 2010 , so too did officer numbers .

Example 975:
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 976:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Fertility rate in Denmark 2008 to 2018
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Fertility', 'rate']: ['1.7', '1.73', '1.75', '1.79', '1.71', '1.69', '1.67', '1.73', '1.75', '1.9', '1.84']

gold: This statistic shows the fertility rate in Denmark from 2009 to 2019 . The fertility rate rose to 1.9 children per woman in 2010 but started to decline since then . In 2019 , the rate was 1.7 children .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] templatePositiveTrend to templateYValue[max] children per woman in templateXValue[idxmax(Y)] but started to decline since then . In templateXValue[max] , the templateYLabel[1] was templateYValue[idxmax(X)] children .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] has declined in this time period from templateYValue[max] children per woman in templateXValue[8] to templateYValue[idxmax(X)] in templateXValue[idxmin(Y)] .
generated: This statistic shows the Fertility rate in Denmark from 2009 to 2019 .  The Fertility rate has declined in this time period from 1.9 children per woman in 2011 to 1.7 in 2013 .

Example 977:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2007', '2019']}
title: Number of fire-related fatalities in Great Britain 2007 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['Number', 'of', 'fatalities']: ['316', '400', '328', '366', '324', '326', '349', '397', '408', '425', '404', '458', '430', '470', '483', '576', '522', '583', '554']

gold: There were 316 fire-related fatalities in Great Britain during 2018/19 , 84 fewer than occurred in 2017/18 when there were 400 . In the early 2000s , the annual number of fire fatalities was consistently over 500 , with numbers gradually falling throughout that decade . In 2011/12 the number of fatalities related to fire fell below 400 for the first time , and with the exception of 2017/18 remained below 300 for the rest of the 2010s .
gold_template: There were templateYValue[min] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] during templateXValue[0] templateXValue[idxmin(Y)] 84 fewer than occurred in templateXValue[1] when there were templateYValue[1] . In the early 2000s , the annual templateYLabel[0] of fire templateYLabel[1] was consistently over 500 , with numbers gradually templateNegativeTrend throughout that decade . In templateXValue[7] the templateYLabel[0] of templateYLabel[1] related to fire templateNegativeTrend below templateYValue[1] for the first time , and with the exception of templateXValue[1] remained below 300 for the rest of the 2010s .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] and templateTitle[4] , Canada , from templateTitleDate[min] to templateXValue[max] . In 2018 - templateTitleDate[max] , there were templateYValue[max] new templateYLabel[1] to templateTitleSubject[0] and templateTitle[4] .
generated: This statistic shows the Number of fire-related fatalities in Great Britain and Britain , Canada from 2007 to 2018/19 .  In 2018 - 2019 , there were 583 new fatalities to Great Britain and Britain .

Example 978:
titleEntities: {'Subject': ['United States'], 'Date': ['2018']}
title: Tinder usage frequency in the United States 2018
X_Axis['Response']: ['Several_times_a_day', 'Once_a_day', 'Once_every_few_days', 'Once_a_week', 'Once_every_few_weeks', 'Less_often_than_every_few_weeks', 'Never']
Y_Axis['Share', 'of', 'respondents']: ['2', '3', '3', '2', '2', '3', '84']

gold: This statistic presents the frequency of Tinder usage in the United States as of January 2018 . During an online survey conducted by We Are Flint , it was found that three percent of internet users in the United States accessed the dating app once every few days .
gold_template: This statistic presents the templateTitle[2] of templateTitle[0] templateTitle[1] in the templateTitleSubject[0] as of 2018 . During an online survey conducted by We Are Flint , it was found that templateYValue[1] templateScale of internet users in the templateTitleSubject[0] accessed the dating app templateXValue[1] templateXValue[2] days .

generated_template: This statistic shows the results of a survey , conducted in 2016 in Canada , on templateXValue[5] templateTitle[3] templateTitle[4] templateTitle[5] . According to templateYValue[max] templateScale of surveyed templateTitleSubject[0] , their top resolution templateTitle[6] templateTitleDate[0] was to templateXValue[0] fitness and templateXValue[0] .
generated: This statistic shows the results of a survey , conducted in 2016 Canada , on Less_often_than_every_few_weeks United States 2018 .  According to 84 % of surveyed United States , their top resolution 2018 was to Several_times_a_day fitness and Several_times_a_day .

Example 979:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2018']}
title: Age structure in Indonesia 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          26.55  26.91  27.23  27.45  27.77  28.04  28.27  28.52  28.83  28.91  29.04
15-64 years         67.59   67.4  67.24  67.17  66.95  66.77  66.61  66.43  66.21  66.12     66
65 years and older   5.86   5.68   5.52   5.38   5.28   5.19   5.12   5.04   4.96   4.97   4.95 

gold: This statistic shows the age structure in Indonesia from 2008 to 2018 . In 2018 , about 26.55 percent of Indonesia '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 Indonesia from 2008 to 2018 .  In 2018 , about 26.55 % of Indonesia 's total population were aged 0 to 14 years .

Example 980:
titleEntities: {'Subject': ['OPPO'], 'Date': ['2015', '2019']}
title: Global smartphone unit shipments of OPPO 2015 - 2019 , by quarter
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"]
Y_Axis['Unit', 'shipments', 'in', 'millions']: ['30.6', '31.2', '29.5', '23.1', '29.2', '29.9', '29.4', '24.6', '27.3', '30.6', '28.0', '25.8', '31.6', '25.8', '22.6', '19.7', '14.4', '11.4', '9.6', '7.3']

gold: The statistic depicts the global smartphone shipments of OPPO from the first quarter of 2015 to the fourth quarter of 2019 . In the fourth quarter of 2019 , OPPO 's smartphone shipments amounted to 30.6 million units .
gold_template: The statistic depicts the templateTitle[0] templateTitle[1] templateYLabel[1] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] 's templateTitle[1] templateYLabel[1] amounted to templateYValue[0] templateScale units .

generated_template: How many paid templateYLabel[1] does templateTitleSubject[0] have ? As of the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] had templateYValue[max] templateScale templateTitle[2] templateYLabel[1] worldwide , up from templateYValue[1] 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: How many paid shipments does OPPO have ? As of the fourth Quarter of 2019 , OPPO had 31.6 millions unit shipments worldwide , up from 31.2 millions in the corresponding Quarter of Q4_'18 .  OPPO smartphone subscriber base has increased dramatically in the last few years , more than doubling in just three years .  OPPO and competitors OPPO is a music streaming service originally founded in 2006 Sweden .

Example 981:
titleEntities: {'Subject': ['USA'], 'Date': ['1990', '2018']}
title: USA - reported larceny-theft rate 1990 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", "'91", "'90"]
Y_Axis['Rate', 'per', '100,000', 'population']: ['1594.6', '1695.5', '1745.4', '1783.6', '1821.5', '1901.9', '1965.4', '1974.1', '2005.8', '2064.5', '2166.1', '2185.4', '2213.2', '2288.0', '2362.3', '2416.5', '2450.7', '2484.6', '2477.3', '2550.7', '2729.0', '2886.6', '2979.7', '3043.8', '3026.7', '3032.4', '3103.0', '3228.8', '3194.8']

gold: This graph shows the reported larceny-theft rate in the United States from 1990 to 2018 . In 2018 , the nationwide rate was 1,594.6 cases per 100,000 of the population .
gold_template: This graph shows the templateTitle[1] templateTitle[2] templateYLabel[0] in the country from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the nationwide templateYLabel[0] was templateYValue[min] cases templateYLabel[1] 100,000 of the templateYLabel[3] .

generated_template: This graph shows the templateTitle[1] templateTitle[2] templateYLabel[0] in the country from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the nationwide templateYLabel[0] was templateYValue[min] cases templateYLabel[1] 100,000 of the templateYLabel[3] .
generated: This graph shows the reported larceny-theft Rate in the country from 1990 to 2018 .  In 2018 , the nationwide Rate was 1594.6 cases per 100,000 of the population .

Example 982:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2017']}
title: Facebook reactions on top shared content 2017
X_Axis['Response']: ['Love', 'Haha', 'Wow', 'Sad', 'Angry']
Y_Axis['Share', 'of', 'reactions']: ['41', '28', '15', '12', '5']

gold: This statistic presents the reaction usage in top shared posts on Facebook in September 2017 . During the measured period , Love was the most popular Facebook reaction on top shared posts on the social network .
gold_template: This statistic presents the reaction usage in templateTitle[2] templateTitle[3] posts on templateTitleSubject[0] in 2017 . During the measured period , templateXValue[0] was the most popular templateTitleSubject[0] reaction on templateTitle[2] templateTitle[3] posts on the social network .

generated_template: This statistic provides a templateTitle[6] of who was targeted in attacks committed by templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] between templateTitleDate[min] to templateTitleDate[max] . Between templateTitleDate[min] and templateTitleDate[max] , templateYValue[max] of the 49 templateYLabel[1] of attacks by templateTitle[0] supremacists were members of a templateXValue[0] .
generated: This statistic provides a 2017 of who was targeted in attacks committed by Facebook reactions top in the shared between 2017 to .  Between 2017 and , 41 of the 49 reactions of attacks by Facebook supremacists were members of a Love .

Example 983:
titleEntities: {'Subject': ['Rio Tinto'], 'Date': ['2015', '2019']}
title: Rio Tinto 's revenue by segment 2015 to 2019
Data:
                         0      1      2      3      4
Year                  2015   2016   2017   2018   2019
Iron ore             15305  14605  18466  18731  24075
Aluminum             10117   9458  11005  12191  10340
Copper & diamonds     5592   4524   4842   6468   5815
Energy & minerals     7140   6734   7549   5451   5150
Other operations         0      0     10      9     18
Energy                   0      0      0      0      0
Copper                7705      0      0      0      0
Copper and coal       7705      0      0      0      0
Diamonds & minerals   3674      0      0      0      0 

gold: This statistic shows the revenue of Rio Tinto from 2015 to 2019 , sorted by segment . The Rio Tinto Group is an multinational exploration , development , production and processing corporation . It is headquartered in London , UK .
gold_template: This statistic shows the templateTitle[3] of templateTitleSubject[0] from templateValue[0][0] to templateValue[0][last] , sorted templateTitle[4] templateTitle[5] . The templateTitleSubject[0] Group is an multinational exploration , development , production and processing corporation . It is headquartered in London , UK .

generated_template: This statistic shows the number of templateTitle[0] templateTitle[1] to templateTitle[2] templateTitle[3] in the templateTitle[4] from templateValue[0][0] to templateValue[0][last] . In templateValue[0][last] , there were a total of templateValue[1][last] templateTitle[0] templateTitle[1] to templateTitle[2] templateTitle[3] in the templateTitle[4] .
generated: This statistic shows the number of Rio Tinto to 's revenue in the by from 2015 to 2019 .  In 2019 , there were a total of 24075 Rio Tinto to 's revenue in the by .

Example 984:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: Number of part-time employees 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', 'part-time', 'workers', 'in', 'millions']: ['26.94', '27.19', '27.37', '27.68', '27.34', '27.59', '27.62', '27.66', '27.31', '27.35', '27.24', '25.33', '24.96', '24.74', '24.71', '24.73', '24.41', '23.79', '23.36', '23.04', '23.19', '23.26', '23.22', '23.17', '23.22', '23.29', '21.15', '20.83', '20.53', '20.13']

gold: This statistic shows the number of part-time employees in the United States from 1990 to 2019 . In 2019 , about 26.94 million people were employed on a part-time basis .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] in the templateTitle[3] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , about templateYValue[0] templateScale people were employed on a templateYLabel[1] basis .

generated_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: 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 , 26.94 refugees were admitted to the employees .

Example 985:
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: templateXValue[0] was by far the templateXLabel[0] templateTitle[1] the largest amount of templateTitle[3] templateTitle[4] in templateTitleDate[0] . Some templateYValue[max] templateScale of global templateTitle[3] templateTitle[4] templateYLabel[1] came from templateXValue[0] . templateXValue[0] is able to export the most templateTitle[3] templateTitle[4] worldwide due to the fact that it also has the templateXValue[last] 's largest reserves of templateTitle[3] ore. What is templateTitle[3] templateTitle[4] ? templateTitle[3] ores are rocks from which metallic templateTitle[3] can be extracted for profit .
generated: Apple_Inc. was by far the Company profit the largest amount of leading 10 in 2013 .  Some 28.3 % of global leading 10 profit came from Apple_Inc. Apple_Inc. is able to export the most leading 10 worldwide due to the fact that it also has the Kimberly-Clark_Corporation 's largest reserves of leading ore. What is leading 10 ? leading ores are rocks from which metallic leading can be extracted for profit .

Example 986:
titleEntities: {'Subject': ['Mobile'], 'Date': ['2019']}
title: Mobile share of organic search engine traffic 2019 , by platform
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  Q3_'15  Q2_'15  Q1_'15  Q4_'14  Q3_'14  Q2_'14  Q1_'14  Q4_'13
Google          61      64      63      62      61      60      59      57      56      57      54      56      54      51      49      48      47      47      47      44      41      38      36      34
Yahoo           49      53      52      50      51      51      50      48      42      48      49      49      49      47      47      44      44      46      43      49      47      40      39      32
Bing            23      25      23      23      22      22      22      19      18      18      18      18      18      19      20      22      29      30      31      29      28      19      16      15
DuckDuckGo      58      65      62      62       0       0       0       0      46       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0 

gold: As of the fourth quarter of 2019 , 49 percent of Yahoo 's U.S. organic search traffic originated from mobile devices . Google had the highest share of organic mobile search traffic with 61 percent , slightly ahead of DuckDuckGo with 58 percent .
gold_template: As of the fourth templateLabel[0][0] of templateTitleDate[0] , templateValue[1][14] templateScale of templateLabel[2][0] 's U.S. templateTitle[2] templateTitle[3] templateTitle[5] originated from templateTitleSubject[0] devices . templateLabel[1][0] had the highest templateTitle[1] of templateTitle[2] templateTitleSubject[0] templateTitle[3] templateTitle[5] with templateValue[1][0] templateScale , slightly ahead of templateLabel[4][0] with templateValue[4][0] templateScale .

generated_template: The statistic shows the most popular templateTitle[3] templateTitle[4] in the templateTitleSubject[0] store ranked templateTitle[7] number of downloads . In the fourth templateLabel[0][0] of templateTitleDate[max] , templateLabel[5][0] apps were the fifth-most popular category with templateValue[5][last] templateScale downloads during the measured period . Gaming apps were ranked first with 8.59 templateScale templateTitle[3] downloads .
generated: The statistic shows the most popular search engine in the Mobile store ranked by number of downloads .  In the fourth Quarter of 2019 , DuckDuckGo apps were the fifth-most popular category with 0 million downloads during the measured period .  Gaming apps were ranked first with 8.59 million search downloads .

Example 987:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: Per capita consumption of wheat flour 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']: ['132.1', '131.8', '131.7', '133.0', '134.7', '135.0', '134.3', '132.5', '134.8', '134.7', '136.6', '138.3', '135.8', '134.4', '134.6', '136.8', '136.9', '141.1', '146.3']

gold: The timeline shows the per capita consumption of wheat flour in the United States from 2000 to 2018 . The U.S. per capita consumption of wheat flour amounted to 131.8 pounds in 2017 .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] 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[1] 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 approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of flour U.S. in the Per from 2000 to 2018 .  According to the report , the U.S. Per capita consumption of flour U.S. amounted to approximately 132.1 pounds in 2018 .

Example 988:
titleEntities: {'Subject': ['CAGR'], 'Date': ['2010', '2015']}
title: Global railway technology market - CAGR by region 2010 to 2015
X_Axis['Region']: ['South/Central_America', 'North_America', 'CIS', 'Asia', 'Africa/Middle_East', 'Western_Europe', 'Eastern_Europe', 'Australia/Pacific']
Y_Axis['Compound', 'annual', 'growth', 'rate']: ['6.8', '5', '4.9', '4.3', '4', '3.3', '2.9', '2.3']

gold: The statistic shows the compound annual growth rate of the global railway technology market from 2010 to 2015 , by region . With an annual growth rate of 6.8 percent , South and Central America will have the highest compound growth between 2010 and 2015 .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateTitleDate[min] to templateTitleDate[max] , templateTitle[5] templateXLabel[0] . With an templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYValue[max] templateScale , South and Central templateXValue[0] will have the highest templateYLabel[0] templateYLabel[2] between templateTitleDate[min] and templateTitleDate[max] .

generated_template: This statistic shows the age-standardized templateTitle[0] of templateTitle[1] templateYLabel[1] in the templateTitle[4] as of templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , templateXValue[0] was the templateTitle[0] templateXLabel[0] with the highest templateYLabel[0] of templateTitle[1] at templateYValue[max] templateScale templateYLabel[1] .
generated: This statistic shows the age-standardized Global of railway annual in the CAGR as of 2010 , region .  In 2010 , South/Central_America was the Global Region with the highest Compound of railway at 6.8 % annual .

Example 989:
titleEntities: {'Subject': ['Indian'], 'Date': ['2015']}
title: Total revenues of the Indian sports market 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Sports', 'market', 'revenue', 'in', 'million', 'U.S.', 'dollars']: ['1899', '1831', '1648', '1530', '1534', '1489', '1185', '1095', '916', '856']

gold: The Indian sports industry has grown dramatically over the last few years . The industry became one of the fastest growing with a margin of around ten percent over the global average . The total revenue of the industry was projected to be around 1.9 million U.S. dollars in 2015 .
gold_template: The templateTitleSubject[0] templateYLabel[0] industry has grown dramatically over the last few years . The industry became one of the fastest templatePositiveTrend with a margin of around ten templateScale over the global average . The templateTitle[0] templateYLabel[2] of the industry was projected to be around 1.9 templateScale templateYLabel[4] templateYLabel[5] in templateXValue[max] .

generated_template: This statistic shows the total amount of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] generated templateYLabel[0] person templateYLabel[0] templateYLabel[5] between templateXValue[min] and templateXValue[max] . In the templateTitle[0] , an average of templateYValue[0] templateScale of templateTitle[1] templateTitle[2] templateTitle[3] were generated daily templateYLabel[0] person in templateXValue[max] .
generated: This statistic shows the total amount of Indian revenues sports generated person Sports dollars between 2006 and 2015 .  In the Total , an average of 1899 million of revenues Indian sports were generated daily Sports person in 2015 .

Example 990:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: Civilian labor force participation rate 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['Labor', 'force', 'participation', 'rate']: ['63.1', '62.9', '62.9', '62.8', '62.7', '62.9', '63.2', '63.7', '64.1', '64.7', '65.4', '66', '66', '66.2', '66', '66', '66.2', '66.6', '66.8', '67.1', '67.1', '67.1', '67.1', '66.8', '66.6', '66.6', '66.3', '66.4', '66.2', '66.5']

gold: This graph shows the civilian labor force participation rate in the United States from 1990 to 2019 . In 2019 , about 63.1 percent of the American population , eligible to work , participated in the job market .
gold_template: This graph shows the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , about templateYValue[0] templateScale of the American population , eligible to work , participated in the job market .

generated_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: This statistic shows the Labor 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 , 63.1 refugees were admitted to the force .

Example 991:
titleEntities: {'Subject': ['Salaries'], 'Date': []}
title: Salaries of heads of state and government
X_Axis['Month']: ['Lee_Hsien_Loong_(Singapore)', 'Mwai_Kibaki_(Kenya)', 'Barack_Obama_(United_States)', 'Nicolas_Sarkozy_(France)', 'Stephen_Harper_(Canada)', 'Brian_Cowen_(Ireland)', 'Julia_Gillard_(Australia)', 'Angela_Merkel_(Germany)', 'Naoto_Kan_(Japan)', 'Jacob_Zuma_(South_Africa)', 'John_Key_(New_Zealand)', 'David_Cameron_(United_Kingdom)']
Y_Axis['Annual', 'salary', '(in', 'U.S.', 'dollars)']: ['2183500', '427900', '400000', '302400', '296400', '287900', '286800', '283600', '273700', '272300', '271800', '215400']

gold: This statistic shows the annual salaries of heads of state and government leaders in selected countries in 2010  . U.S. President Barack Obama earned 400,000 . U.S. dollars in 2010 .
gold_template: This statistic shows the templateYLabel[0] templateTitleSubject[0] of templateTitle[1] of templateTitle[2] and templateTitle[3] leaders in selected countries in 2010 . templateYLabel[3] President templateXValue[2] earned templateYValue[2] . templateYLabel[3] dollars in 2010 .

generated_template: This statistic shows the templateTitle[1] templateXValue[1] templateTitle[2] as of 2013 , templateTitle[4] templateYLabel[0] templateYLabel[1] . In 2011 , templateXValue[6] owned templateYValue[6] templateScale in templateXValue[1] stakes . templateXValue[0] owned 24 templateScale of templateXValue[1]
generated: This statistic shows the heads Mwai_Kibaki_(Kenya) state as of 2013 , government Annual salary .  In 2011 , Julia_Gillard_(Australia) owned 286800 million in Mwai_Kibaki_(Kenya) stakes .  Lee_Hsien_Loong_(Singapore) owned 24 % of Mwai_Kibaki_(Kenya) . 

Example 992:
titleEntities: {'Subject': ['Maritime'], 'Date': ['2019']}
title: Maritime trade routes - containerized cargo flows 2019
X_Axis['Trade', 'route']: ['Trans-Pacific', 'Europe-Asia-Europe', 'Translatlantic']
Y_Axis['Cargo', 'flow', 'in', 'million', 'TEUs']: ['30', '25', '8']

gold: Over 30 million twenty-foot equivalent units ( TEU ) of cargo are expected to be transported across the Pacific Ocean in 2019 , making Trans-Pacific routes the largest shipping zone in terms of load size . As a result of growing globalization , the volume of seaborne trade reached some 11 billion metric tons in 2016 . Trade boom in Asia Asian economies are experiencing a boom .
gold_template: Over templateYValue[max] templateScale twenty-foot equivalent units ( TEU ) of templateYLabel[0] are expected to be transported across the Pacific Ocean in templateTitleDate[0] , making templateXValue[0] templateTitle[2] the largest shipping zone in terms of load size . As a result of templatePositiveTrend globalization , the volume of seaborne templateXLabel[0] reached some 11 templateScale metric tons in 2016 . templateXLabel[0] boom in Asia Asian economies are experiencing a boom .

generated_template: This statistic depicts the templateScale of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitleDate[0] , by the templateXLabel[0] of templateXValue[0] . templateYValue[max] templateScale of templateYLabel[1] with templateXValue[last] and templateXValue[last] templateXLabel[1] used templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] .
generated: This statistic depicts the million of Maritime flow containerized cargo flows 2019 route in 2019 , by the Trade of Trans-Pacific .  30 million of flow with Translatlantic and route used cargo flows 2019 in .

Example 993:
titleEntities: {'Subject': ['Ecuador'], 'Date': ['2019']}
title: Employment by economic sector in Ecuador 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  27.27  27.48  27.69  26.94  26.19  25.28  26.65  27.98  27.91  27.88  28.71
Industry     18.53  18.54  18.55  18.63  19.66  19.96  19.08  17.52  17.95  18.49  18.79
Services      54.2  53.98  53.75  54.43  54.15  54.77  54.27   54.5  54.15  53.63   52.5 

gold: The statistic shows the distribution of employment in Ecuador by economic sector from 2009 to 2019 . In 2019 , 27.27 percent of the employees in Ecuador were active in the agricultural sector , 18.53 percent in industry and 54.2 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 Ecuador by economic sector from 2009 to 2019 .  In 2019 , 27.27 % of the employees in Ecuador were active in the agricultural sector , 18.53 % in Industry and 54.2 % in the service sector .

Example 994:
titleEntities: {'Subject': ['FDIC'], 'Date': ['2002', '2018']}
title: Number of FDIC-insured commercial banks in the U.S. 2002 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Number', 'of', 'banks']: ['4708', '4909', '5102', '5330', '5596', '5836', '6061', '6263', '6506', '6813', '7061', '7262', '7380', '7507', '7612', '7750', '7870']

gold: In 2018 , there were 4,708 FDIC-insured commercial banks in the United States . The FDIC , of Federal Deposit Insurance Corporation , is an agency that insures the banking system in the U.S . The number of such registered banks has been declining since 2002 , when it there were over 7,800 FDIC-insured banks in the country .
gold_template: In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitle[4] . The FDIC , of Federal Deposit Insurance Corporation , is an agency that insures the banking system in the templateTitle[4] . The templateYLabel[0] of such registered templateYLabel[1] has been declining since templateXValue[min] , when it there were over 7,800 templateTitle[1] templateYLabel[1] in the country .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were around templateYValue[max] thousand templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] , up from templateYValue[15] templateScale in templateXValue[min] .
generated: This statistic shows the Number of FDIC-insured banks 2018 2002 to 2018 .  In 2018 , there were around 7870 thousand banks , up from 7750 thousand in 2002 .

Example 995:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1964', '2012']}
title: Job approval ratings for incumbent U.S. presidents seeking re-election from 1964 to 2012
Data:
                          0                     1                   2                       3                    4                   5                  6                    7                     8
Year     2012:_Barack_Obama  2004:_George_W._Bush  1996:_Bill_Clinton  1992:_George_H.W._Bush  1984:_Ronald_Reagan  1980:_Jimmy_Carter  1976:_Gerald_Ford  1972:_Richard_Nixon  1964:_Lyndon_Johnson
January                  44                    60                  42                      46                   52                  56                 56                   49                    77
March                    46                    49                  54                      41                   54                  43                 43                   56                    77
June                     46                    49                  58                      37                   55                  32                 32                   59                    74
October                  50                    50                  58                      33                   58                  50                  0                    0                     0 

gold: This survey shows the Job approval ratings of incumbent U.S. presidents seeking re-election from 1964 to 2012 . In January 2012 , 44 percent of respondents said they would approve of U.S. President Barack Obama . During the month before election on November 6 , Barack Obama 's approval rating increased to 50 percent .
gold_template: This survey shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[4] templateTitleSubject[0] templateTitle[6] templateTitle[7] templateTitle[8] templateTitle[9] templateTitleDate[min] to templateTitleDate[max] . In templateLabel[1][0] templateTitleDate[max] , templateValue[1][0] templateScale of respondents said they would approve of templateTitleSubject[0] President templateValue[0][0] . During the month before election on 6 , templateValue[0][0] 's templateTitle[1] rating templatePositiveTrend to templateValue[4][0] templateScale .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateValue[5][0] to 24 templateLabel[0][0] olds in the templateTitleSubject[0] templateTitle[7] templateValue[0][last] to templateValue[0][0] , by participation rate . In templateValue[0][0] , templateValue[1][last] templateScale of the templateYLabel[1] to templateLabel[1][1] olds , templateLabel[1][1] and templateLabel[1][2] templateLabel[1][3] in the templateTitle[7] templateTitle[8] .
generated: This statistic shows the Job approval ratings for incumbent 50 to 24 Year olds in the U.S. seeking 1964:_Lyndon_Johnson to 2012:_Barack_Obama , by participation rate .  In 2012:_Barack_Obama , 77 % of the to January olds , January and in the seeking re-election .

Example 996:
titleEntities: {'Subject': ['Poland'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Poland 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']: ['777.24', '729.84', '685.94', '645.42', '606.73', '565.85', '585.82', '526.75', '471.84', '477.57', '545.28', '524.4', '500.85', '528.57', '479.16', '440.14', '533.8', '429.47', '344.76', '306.15', '255.28', '217.51', '198.68', '190.43', '171.28', '167.8', '172.05', '157.18', '156.68', '139.09', '103.68', '90.37', '88.71', '80.45', '62.08', '66.9', '68.61', '63.71', '73.68', '70.78', '75.51']

gold: Gross domestic product in Poland has been increasing since 2016 and is expected to reach approximately 645 billion U.S. dollars by 2021 . Poland 's economy tripled in size during the early 2000s , before being hit by the global financial crisis . After several years of fluctuation , Poland 's GDP recently reached an all-time high of around 586 billion U.S. dollars in 2018 .
gold_template: templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] has been templatePositiveTrend since templateXValue[8] and is expected to reach approximately templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] by templateXValue[3] . templateTitleSubject[0] 's economy tripled in size during the early 2000s , before being hit by the global financial crisis . After several years of fluctuation , templateTitleSubject[0] 's templateYLabel[0] recently reached an all-time high of around templateYValue[6] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[6] .

generated_template: templateTitle[0] templateTitle[1] templateTitle[2] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered an important indicator of the economic strength of a country . In templateXValue[6] , templateYLabel[0] in templateTitleSubject[0] amounted to around templateYValue[6] templateScale templateYLabel[2] templateYLabel[3] .
generated: Gross domestic product is the total value of all goods and services produced in a country in a Year .  It is considered an important indicator of the economic strength of a country .  In 2018 , GDP in Poland amounted to around 585.82 billion U.S. dollars .

Example 997:
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 between 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 between 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 998:
titleEntities: {'Subject': ['Slovakia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Slovakia 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', '2.75', '2.75', '2.75', '2.7', '2.6', '4.11', '3.19', '3.13', '4.18', '2.75']

gold: The statistic shows the growth in real GDP in Slovakia from 2014 to 2018 , with projections up until 2024 . In 2018 , Slovakia 's real gross domestic product grew by around 4.11 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 between 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 Slovakia from between 2014 to 2018 , with projections up until 2024 .  In 2018 , Slovakia 's real Gross domestic product increased by around 4.11 % compared to the previous Year .

Example 999:
titleEntities: {'Subject': ['Average'], 'Date': ['2014']}
title: Average prices for aluminum worldwide from 2014 to 2025
X_Axis['Year']: ['2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Price', 'in', 'nominal', 'U.S.', 'dollars', 'per', 'metric', 'ton']: ['1968', '1924', '1882', '1840', '1800', '1760', '1790', '2108', '1968', '1604', '1665', '1867']

gold: This statistic depicts the average annual prices for aluminum from 2014 through 2025  . In 2018 , the average price for aluminum stood at 2,108 nominal U.S. dollars per metric ton .
gold_template: This statistic depicts the templateTitleSubject[0] annual templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[5] templateXValue[min] through templateXValue[max] . In templateXValue[7] , the templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateTitle[3] stood at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: This statistic depicts the templateTitleSubject[0] annual templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[5] templateXValue[min] through templateXValue[max] . In templateXValue[7] , the templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateTitle[3] stood at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] templateYLabel[6] .
generated: This statistic depicts the Average annual prices for aluminum from 2014 through 2025 .  In 2018 , the Average Price for aluminum stood at 2108 nominal U.S. dollars per metric ton .

Example 1000:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017', '2023']}
title: U.S. fashion and accessories e-retail revenue 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['145831', '139576', '131000', '120797', '110605', '102153', '96245']

gold: This statistic represents U.S. revenue generated with online apparel and accessories retail sales between 2017 and 2023 . In 2018 , retail e-commerce revenues from apparel and accessories sales amounted to 102.15 billion U.S. dollars and are projected to increase to 145.8 billion U.S. dollars in 2023 . E-commerce revenue from apparel in the U.S. – additional information With retail e-commerce sales worldwide expected to reach just over four trillion U.S. dollars in 2020 , the global e-commerce industry appears to show no signs of slowing down .
gold_template: This statistic represents templateYLabel[2] templateYLabel[0] generated with online apparel and templateTitle[2] retail sales between templateXValue[min] and templateXValue[max] . In templateXValue[5] , retail e-commerce revenues from apparel and templateTitle[2] sales amounted to templateYValue[5] templateScale templateYLabel[2] templateYLabel[3] and are projected to templatePositiveTrend to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . E-commerce templateYLabel[0] from apparel in the templateYLabel[2] – additional information With retail e-commerce sales worldwide expected to reach just over four templateScale templateYLabel[2] templateYLabel[3] in templateXValue[3] , the global e-commerce industry appears to show no signs of slowing down .

generated_template: The statistic shows templateTitle[1] AR templateTitle[4] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] reached templateYValue[min] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] and is forecast to amount to around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] by templateXValue[idxmax(Y)] .
generated: The statistic shows fashion AR revenue worldwide from 2017 to 2023 .  fashion accessories e-retail revenue reached 96245 million U.S. dollars in 2017 and is forecast to amount around 145831 million U.S. dollars by 2023 .

Example 1001:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013', '2019']}
title: Total U.S. grain exports 2013 to 2019
X_Axis['Year']: ['2018/2019', '2017/2018', '2016/2017', '2015/2016', '2014/2015']
Y_Axis['Exports', 'in', 'million', 'metric', 'tons']: ['89.2', '89.0', '92.4', '78.7', '80.5']

gold: This statistic shows the total exports of grain from the United States from 2014 to 2019 . According to the report , approximately 92.4 million metric tons of grain were exported from the U.S. during the fiscal year 2016/2017 .
gold_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] from the templateTitle[1] from 2014 to templateTitleDate[max] . According to the report , approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[2] were exported from the templateTitleSubject[0] during the fiscal templateXLabel[0] templateXValue[idxmax(Y)] .

generated_template: The templateTitle[0] movie commercial commercial in the templateTitle[2] based on templateTitle[6] television 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: The Total movie commercial in the grain based on 2019 television Exports 2016/2017 the week ending U.S. 5 , 2013 was 2016/2017 war drama film ' 2018/2019 _ , with a 92.4 million metric dollar 2019 studio Universal Pictures .  Universal also spent 89.0 million metric tons on U.S. promotion of 'Dolittle ' _ .

Example 1002:
titleEntities: {'Subject': ['Marks Spencer'], 'Date': ['2010', '2019']}
title: Marks & Spencer revenue worldwide 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Revenue', 'in', 'million', 'GBP']: ['10377.3', '10698.2', '10622.0', '10555.4', '10311.4', '10309.7', '10026.8', '9934.3', '9740.3', '9536.6']

gold: This statistic shows the total global revenue of Marks & Spencer from 2010 to 2019 . In 2019 , the British retailer generated approximately 10.4 billion British pounds in revenue both in the United Kingdom ( UK ) and internationally . This is a decrease of almost 1 billion British pounds since 2010 .
gold_template: This statistic shows the total global templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the British retailer generated approximately templateYValue[0] templateScale British pounds in templateYLabel[0] both in the United Kingdom ( UK ) and internationally . This is a templateNegativeTrend of almost 1 templateScale British pounds since templateXValue[min] .

generated_template: In the fiscal templateXLabel[0] of templateXValue[max] , templateTitleSubject[0] transported passengers on a total of over 131.3 templateScale templateTitle[2] . The leading low-cost carrier had a capacity of 157.2 templateScale available seat templateTitle[2] in that same templateXLabel[0] , and as such was efficient in using its fleet to transport paying customers . Flying with templateTitleSubject[0] ' main hub , Las Vegas McCarran International Airport , saw a traffic of 17.5 templateScale templateTitleSubject[0] passengers in templateXValue[1] .
generated: In the fiscal Year of 2019 , Marks Spencer transported passengers on a total of over 131.3 million Spencer .  The leading low-cost carrier had a capacity of 157.2 million available seat Spencer in that same Year , and as such was efficient in using its fleet to transport paying customers .  Flying with Marks Spencer ' main hub , Las Vegas McCarran International Airport , saw a traffic of 17.5 million Marks Spencer passengers in 2018 .

Example 1003:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2004', '2008']}
title: U.S. market share of hand-held hair dryer manufacturer 2004 to 2008
Data:
              0              1       2                                    3       4
unknown  Conair  Helen_of_Troy  Revlon  Spectrum_Brands_(Rayovac_Remington)  Others
2004         50             36       0                                    4      10
2008         44             34      11                                    5       6 

gold: The statistic illustrates the market share of hand-held hair dryer manufacturers in the U.S. in 2004 and 2008 . In 2008 , Conair had a hand-held hair dryer market share of 44 percent and was the U.S. shipment leader for hand-held hair dryers .
gold_template: The statistic illustrates the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] manufacturers in the templateTitleSubject[0] in templateTitleDate[min] and templateTitleDate[max] . In templateTitleDate[max] , templateValue[0][0] had a templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[1] templateTitle[2] of templateValue[2][max] templateScale and was the templateTitleSubject[0] shipment leader for templateTitle[3] templateTitle[4] dryers .

generated_template: This statistic shows the results of a survey in templateTitleDate[0] among templateTitleSubject[0] adults by gender on the most templateTitle[0] issues to them in templateTitle[2] a templateTitle[3] or templateTitle[4] . During the survey , templateValue[1][1] templateScale of templateLabel[1][0] were of the opinion that finding someone with a templateValue[0][1] would be very templateTitle[0] to them in templateTitle[2] a templateTitle[3] or templateTitle[4] while templateValue[2][1] templateScale of templateLabel[2][0] were of the opinion that finding someone with a templateValue[0][1] would be very templateTitle[0] to them .
generated: This statistic shows the results of a survey in 2004 among U.S. adults by gender on the most U.S. issues to them in share a hand-held or hair .  During the survey , 36 percent of 2004 were of the opinion that finding someone with a Helen_of_Troy would be very U.S. to them in share a hand-held or hair while 34 percent of 2008 were of the opinion that finding someone with a Helen_of_Troy would be very U.S. to them .

Example 1004:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita 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['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['62108.69', '59106.56', '56436.49', '53960.65', '51892.08', '51241.91', '54356.07', '53412.28', '51599.45', '51126.77', '59528.9', '60069.35', '56978.61', '59454.4', '51925.6', '46047.31', '55592.67', '53185.65', '46145.74', '43048.11', '42407.83', '36936.66', '29566.18', '26970.43', '29287.8', '30601.86', '30180.21', '29932.76', '32614.25', '29911.82', '25672.07', '24034.57', '32142.44', '31075.22', '29792.96', '24985.75', '23951.92', '21282.67', '17574.09', '13345.57', '12780.66']

gold: The statistic shows gross domestic product ( GDP ) per capita in Sweden 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 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] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was around templateYValue[7] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] 's economy templateYLabel[0] templateYLabel[1] templateYLabel[2] is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the templateYLabel[0] and dividing it by the total population in the country .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in Sweden from 1984 to 2018 , with projections up until 2024 .  In 2018 , the Gross domestic product per capita in Sweden was around 53412.28 U.S. dollars .  Sweden 's economy GDP per capita is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the GDP and dividing it by the total population in the country .

Example 1005:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016']}
title: Weekly food spending in the United Kingdom ( UK ) 2016 , by age and category
Data:
                            0                  1                  2                  3              4
Year        18_-_24_years_old  25_-_34_years_old  35_-_44_years_old  45_-_54_years_old  55+_years_old
Groceries                  27                 38                 48                 55             54
Takeaways                  20                 18                 17                 15              8
Eating out                 11                 10                 10                 11             11 

gold: In 2016 , the average weekly expenditure by individuals in the United Kingdom ( UK ) exhibited generational differences . The expenditure on grocery purchases of food tended to increase with age , reaching an average 55 British pounds a week in the group of respondents aged 45 to 54 years old . Younger consumers commonly spent more money on takeaways .
gold_template: In templateTitleDate[0] , the average templateTitle[0] expenditure templateTitle[7] individuals in the templateTitleSubject[0] ( templateTitleSubject[1] ) exhibited generational differences . The expenditure on grocery purchases of templateTitle[1] tended to templatePositiveTrend with templateTitle[8] , reaching an average templateValue[1][3] British pounds a week in the group of respondents aged templateValue[0][3] to templateValue[0][3] templateValue[0][0] . Younger consumers commonly spent more money on templateLabel[2][0] .

generated_template: The statistic shows how templateLabel[1][1] the survey respondents , broken down templateTitle[8] templateTitle[9] templateTitle[10] , templateTitle[3] the templateTitleSubject[0] Association . templateValue[1][0] templateScale of the templateValue[3][2] to 29 year-old respondents said that they templateTitle[3] the NBA templateLabel[1][0] templateLabel[1][1] .
generated: The statistic shows how Groceries the survey respondents , broken down age category , United the United Kingdom Association .  27 % of the 10 to 29 year-old respondents said that they United the NBA Groceries .

Example 1006:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Home renovators in the U.S. 2018 , by age
X_Axis['Response']: ['25-34', '35-54', '55+']
Y_Axis['Share', 'of', 'respondents']: ['7', '38', '55']

gold: In 2018 , 38 percent of homeowners who renovated their home were between 35 and 54 years old in the United States . In 2018 , American homeowners said that they decided to renovate because they wanted to do it all along and finally had the time or financial means to do it . Home renovation Home renovation is when homeowners choose to remodel their home .
gold_template: In templateTitleDate[0] , templateYValue[1] templateScale of homeowners who renovated their templateTitle[0] were between 35 and 54 years old in the templateTitle[2] . In templateTitleDate[0] , American homeowners said that they decided to renovate because they wanted to do it all along and finally had the time or financial means to do it . templateTitle[0] renovation templateTitle[0] renovation is when homeowners choose to remodel their templateTitle[0] .

generated_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] templateTitle[8] . During that period of time , templateYValue[max] templateScale of templateYLabel[1] between 30 and 49 years used the social networking site .
generated: This statistic shows the Share of adults in the U.S. who were using Home as of 2019 , sorted age .  During that period of time , 55 % of respondents between 30 and 49 years used the social networking site .

Example 1007:
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 depicts templateTitleSubject[0] 's real 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: The statistic depicts Japan 's real 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 .

Example 1008:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: Value of U.S. honey 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', 'million', 'U.S.', 'dollars']: ['333.48', '334.17', '343.03', '327.18', '387.38', '320.08', '283.45', '261.85', '285.69', '215.67', '232.74', '159.76', '155.69', '160.99', '199.64', '252.05', '228.34', '132.99', '132.87']

gold: This statistic shows the value of honey production in the U.S. from 2000 to 2018 . According to the report , the value of honey production in the United States came to around 333.5 million U.S. dollars in 2018 .
gold_template: This statistic shows the templateTitle[0] of templateTitle[2] templateYLabel[0] in the templateYLabel[2] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] of templateTitle[2] templateYLabel[0] in the templateTitle[1] came to around templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] .

generated_template: This statistic 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: This statistic shows the Production of U.S. in the U.S. dollars permanent via dollars from 2000 to 2018 .  In the most recently reported period , close to 333.48 million U.S. had fixed dollars , up from close to 215.67 million in 2009 .  The U.S. are one of the biggest online markets worldwide .

Example 1009:
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: 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 templateLabel[2][0] 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: In 2018 , Agriculture contributed around 4.58 % to the Costa Rica 's gross , 19.47 % came from the Industry , and 68.35 % from the Services sector .  The UK is not a farmer 's market The vast majority of the UK 's gross 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 gross through travel and tourism – about 277 billion U.S. dollars – and the forecasts see an upwards trend .

Example 1010:
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 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[idxmin(Y)] .
generated: This statistic shows 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 2000 .

Example 1011:
titleEntities: {'Subject': ['General Electric', 'Healthcare'], 'Date': ['1997', '2018']}
title: General Electric profits in the Healthcare segment 1997 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2002', '1997']
Y_Axis['Profit', 'in', 'billion', 'U.S.', 'dollars']: ['3.7', '3.49', '3.21', '2.88', '3.05', '3.05', '2.92', '2.8', '2.7', '2.4', '2.9', '3.1', '1.6', '0.7']

gold: The statistic shows the profit of General Electric in the segment of Healthcare from 1997 to 2018 GE Healthcare is a division of GE Technology Infrastructure , which is itself a division of General Electric ( GE ) . In 2002 , the General Electric Healthcare segment generated a profit of 1.6 billion U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] in the templateTitle[4] of templateTitleSubject[1] from templateXValue[min] to templateXValue[max] GE templateTitleSubject[1] is a division of GE Technology Infrastructure , which is itself a division of templateTitleSubject[0] ( GE ) . In templateXValue[12] , the templateTitleSubject[0] Healthcare templateTitle[4] generated a templateYLabel[0] of templateYValue[12] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic presents the annual templateYLabel[0] of templateYLabel[1] templateYLabel[2] ( templateTitle[1] ) templateYLabel[4] one hundred thousand templateYLabel[7] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately 66.45 templateTitle[1] templateYLabel[4] hundred thousand templateYLabel[7] in templateTitleSubject[0] .
generated: This statistic presents the annual Profit of billion U.S. ( Electric ) dollars one hundred thousand dollars in General Electric from 1997 to 2018 .  In 2018 , there were approximately 66.45 Electric dollars hundred thousand dollars in General Electric .

Example 1012:
titleEntities: {'Subject': ['Washington Wizards', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Washington Wizards ( 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']: ['51.21', '32.7', '30.31', '32.24', '23.64', '24.52', '27.21', '29.14', '30.89', '46.83']

gold: This graph depicts the average ticket price for Washington Wizards games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 46.83 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 templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Washington Wizards games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Average ticket price was 51.21 U.S. dollars .

Example 1013:
titleEntities: {'Subject': ['Global'], 'Date': ['2016', '2027']}
title: Global big data revenue 2016 - 2027 , by type
Data:
             0     1     2     3     4     5     6     7     8     9    10    11
Year      2027  2026  2025  2024  2023  2022  2021  2020  2019  2018  2017  2016
Services    33    32    31    29    27    26    24    21    19    16    14    11
Hardware    24    23    22    20    19    18    16    15    14    12    10     9
Software    46    42    38    34    31    27    24    20    17    14    11     8 

gold: The statistic shows the revenue from the global big data market by major segment from 2016 to 2027 . In 2018 , the big data software market is estimated to be worth 14 billion U.S. dollars , while the market overall will be worth 42 billion U.S. dollars .
gold_template: The statistic shows the templateTitle[3] from the templateTitleSubject[0] templateTitle[1] templateTitle[2] market templateTitle[6] major segment from templateValue[0][last] to templateValue[0][0] . In templateValue[0][9] , the templateTitle[1] templateTitle[2] templateLabel[3][0] market is estimated to be worth templateValue[1][10] templateScale U.S. dollars , while the market overall will be worth templateValue[3][1] templateScale U.S. dollars .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] in templateTitleSubject[1] from templateValue[0][0] to templateValue[0][last] , templateTitle[8] templateTitle[9] templateTitle[10] . In templateValue[0][last] , the templateTitleSubject[0] Corporation generated templateValue[1][last] templateScale of its total templateTitle[0] templateTitle[5] its templateLabel[1][0] templateLabel[1][1] templateTitle[9] .
generated: This statistic shows the Global big of Global in from 2027 to 2016 , type .  In 2016 , the Global Corporation generated 11 % of its total Global 2027 its Services type .

Example 1014:
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 1015:
titleEntities: {'Subject': ['Most'], 'Date': ['2019']}
title: Most valuable nation brands 2019
X_Axis['Country']: ['United_States', 'China', 'Germany', 'Japan', 'United_Kingdom', 'France', 'India', 'Canada', 'South_Korea', 'Italy']
Y_Axis['Brand', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['27751', '19486', '4855', '4533', '3851', '3097', '2562', '2183', '2135', '2110']

gold: The United States was the world 's most valuable nation brand by a distance in 2019 , with a value of 27.8 trillion U.S. dollars . To give this figure some context , China was second in the ranking valued at 19.5 trillion U.S. dollars , substantially lower in value than that of the United States . Many of the world 's most valuable brands come from the United States .
gold_template: The templateXValue[0] was the world 's templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] by a distance in templateTitleDate[0] , with a templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . To give this figure some context , templateXValue[1] was second in the ranking valued at templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] , substantially lower in templateYLabel[1] than that of the templateXValue[0] . Many of the world 's templateTitleSubject[0] templateTitle[1] templateTitle[3] come from the templateXValue[0] .

generated_template: The statistic depicts the leading templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] , sorted templateTitle[5] templateXLabel[0] . The templateXValue[0] and templateXValue[1] both held more than 20 templateScale of the templateYLabel[0] templateYLabel[1] worldwide in that year .
generated: The statistic depicts the leading valuable the nation Brand value in 2019 , sorted 2019 Country .  The United_States and China both held more than 20 billion of the Brand value worldwide in that year .

Example 1016:
titleEntities: {'Subject': ['ExxonMobil', 'R D'], 'Date': ['2001', '2018']}
title: ExxonMobil 's R & D costs 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['Costs', 'in', 'million', 'U.S.', 'dollars']: ['1116', '1063', '1058', '1008', '971', '1044', '1042', '1044', '1012', '1050', '847', '814', '733', '712', '649', '618', '631', '603']

gold: This statistic outlines the research and development costs of ExxonMobil from 2001 to 2018 . In 2018 , ExxonMobil 's research and development costs amounted to 1.12 billion U.S. dollars . ExxonMobil is a United States-based multinational oil and gas corporation headquartered in Irving , Texas .
gold_template: This statistic outlines the research and development templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[1] research and development templateYLabel[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] is a United States-based multinational oil and gas corporation headquartered in Irving , Texas .

generated_template: This statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to about templateYValue[0] templateScale templateYLabel[4] .
generated: This statistic depicts the Costs million U.S. worldwide from 2001 to 2018 .  In 2018 , the ExxonMobil Costs million U.S. amounted to about 1116 million dollars .

Example 1017:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016']}
title: Most visited restaurant chains in the last year in the United Kingdom ( UK ) 2016
X_Axis['Cuisine', 'types']: ['Pizza_Hut', "Frankie_&_Benny's", 'Pizza_Express', "Nando's", 'TGI_Fridays', 'Beefeater_Grill', 'Bella_Italia', 'Wagamama', 'Cafe_Rouge', 'Prezzo']
Y_Axis['Share', 'of', 'consumers']: ['23', '22', '21', '20', '14', '13', '12', '11', '10', '9']

gold: This statistic presents the most visited restaurant chains in the United Kingdom ( UK ) , ranked by the share of consumers who have dined in them in the last 12 months , as of a survey published in 2016 . Pizza Hut was among the leading brands with 23 percent of respondents having visited in the last year .
gold_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) , ranked by the templateYLabel[0] of templateYLabel[1] who have dined in them in the templateTitle[4] templateYValue[6] months , as of a survey published in templateTitleDate[0] . templateXValue[0] was among the leading brands with templateYValue[max] templateScale of respondents having templateTitle[1] in the templateTitle[4] templateTitle[5] .

generated_template: This statistic provides information on the templateTitleSubject[0] templateTitle[1] online and tech templateTitle[3] in templateTitleDate[0] , based on templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Food delivery templateXLabel[0] templateXValue[0] went public in 2014 and was ranked first with a templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYValue[max] templateScale .
generated: This statistic provides information on the United Kingdom visited online and tech chains in 2016 , based on Share consumers .  Food delivery Cuisine Pizza_Hut went public in 2014 and was ranked first with a Share consumers of 23 million .

Example 1018:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Participants in indoor soccer 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']: ['5.23', '5.4', '5.12', '4.81', '4.53', '4.8', '4.9', '4.34', '4.93', '4.91', '4.74', '4.24', '4.7']

gold: This statistic shows the number of participants in indoor soccer in the United States from 2006 to 2018 . In 2018 , the number of participants ( aged six years and older ) in indoor soccer amounted to approximately 5.23 million . All in all , the number of soccer players in the United States seems to be on the rise .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[idxmax(X)] templateScale . All in all , the templateYLabel[0] of templateTitle[2] players in the templateTitle[3] seems to be on the rise .

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 . templateTitle[1] is a popular recreational activity with more than templateYValue[min] templateScale people partaking in templateTitle[1] activities in the templateTitle[2] each templateXLabel[0] .
generated: This statistic shows the Number of participants in indoor the soccer U.S. 2006 to 2018 .  In 2018 , the Number of participants ( aged six years and older ) in indoor amounted to approximately 5.23 millions .  indoor is a popular recreational activity with more than 4.24 millions people partaking in indoor activities in the soccer each Year .

Example 1019:
titleEntities: {'Subject': ['Leading'], 'Date': ['2019']}
title: Leading eSports players worldwide 2019 , by overall earnings
X_Axis['Player', 'Name']: ['N0tail_(Johan_Sundstein)', 'JerAx_(Jesse_Vainikka)', 'ana_(Anathan_Pham)', 'Ceb_(Sébastien_Debs)', 'Topson_(Topias_Taavitsainen)', 'KuroKy_(Kuro_Takhasomi)', 'Miracle-_(Amer_Barqawi)', 'MinD_ContRoL_(Ivan_Ivanov)', 'GH_(Maroun_Merhej)', 'SumaiL_(Sumail_Hassan)']
Y_Axis['Earnings', 'in', 'million', 'U.S.', 'dollars']: ['6.89', '6.47', '6.0', '5.49', '5.41', '5.13', '4.69', '4.48', '4.09', '3.59']

gold: The statistic presents the leading eSports players worldwide as of December 2019 , ranked by overall earnings . According to the estimates , Johan Sundstein , a player from Denmark , also known as N0tail , earned 6.89 million U.S. dollars throughout his recorded eSports gaming career . eSports gaming – additional information Video gaming is no longer a pastime of purported home-dwellers or arcade-loitering individuals ; the professionalization of this activity has now validated it as a career choice for talented players to earn millions and sign lucrative sponsorship deals .
gold_template: The statistic presents the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] as of 2019 , ranked templateTitle[5] templateTitle[6] templateYLabel[0] . According to the estimates , Johan Sundstein , a templateXLabel[0] from Denmark , also known as templateXValue[0] , earned templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] throughout his recorded templateTitle[1] gaming career . templateTitle[1] gaming – additional information Video gaming is no longer a pastime of purported home-dwellers or arcade-loitering individuals ; the professionalization of this activity has now validated it as a career choice for talented templateTitle[2] to earn templateScale and sign lucrative sponsorship deals .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] from templateTitleDate[min] to 2019 , templateTitle[8] National templateTitle[2] Insurance Program templateTitle[9] . The templateTitle[9] of the National templateTitle[2] Insurance Program as a consequence of damage caused templateTitle[8] floods following templateXValue[0] templateXValue[1] in 2017 , amounted to almost templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Leading eSports players worldwide in the Leading from 2019 to , earnings National players Insurance Program earnings .  The earnings of the National players Insurance Program as a consequence of damage caused earnings floods following N0tail_(Johan_Sundstein) JerAx_(Jesse_Vainikka) in 2017 , amounted to almost 6.47 million dollars .

Example 1020:
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)] templateTitleSubject[0] average templateTitle[3] templateYLabel[1] - additional information The templateTitleSubject[0] ' templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] has remained relatively constant in recent years , with the templateYLabel[0] in the templateXValue[max] templateTitle[2] standing at 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 Cleveland Browns average home attendance - additional information The Cleveland Browns ' Average regular season home attendance has remained relatively constant in recent years , with the Average in the 2019 season standing at 67431 .

Example 1021:
titleEntities: {'Subject': ['U.S. U.S.'], 'Date': ['2008', '2018']}
title: Wholesale sales of U.S. sports product industry in the U.S. 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2011', '2010', '2009', '2008']
Y_Axis['Wholsale', 'sales', 'in', 'billion', 'U.S.', 'dollars']: ['92.26', '90.22', '87.72', '85.98', '84.28', '81.4', '77.31', '74.19', '71.6', '75.0']

gold: The statistic depicts the manufacturers ( wholesale ) sales of sporting goods equipment , fitness equipment , sports apparel , athletic footwear , and sports licensed merchandise in the U.S. from 2008 to 2018 . In 2018 , wholesale sales amounted to about 92.26 billion U.S. dollars . Sporting goods manufacturers wholesale sales - additional information Wholesale sales of sporting goods in the U.S. reached 92.26 billion U.S. dollars in 2018 , the highest figure to-date and a eight billion U.S. dollar increase on the figure from 2014 .
gold_template: The statistic depicts the manufacturers ( templateTitle[0] ) templateYLabel[1] of sporting goods equipment , fitness equipment , templateTitle[3] apparel , athletic footwear , and templateTitle[3] licensed merchandise in the templateYLabel[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateYLabel[1] amounted to about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . Sporting goods manufacturers templateTitle[0] templateYLabel[1] - additional information templateTitle[0] templateYLabel[1] of sporting goods in the templateYLabel[3] reached templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] , the highest figure to-date and a eight templateScale templateYLabel[3] dollar templatePositiveTrend on the figure from templateXValue[4] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] against ships templateTitle[3] from templateXValue[min] to templateXValue[max] . There were templateYValue[idxmax(X)] such incidents in templateXValue[idxmin(Y)] . templateYLabel[1] templateYLabel[2] Although the term `` templateYLabel[1] '' may conjure up images of bearded men with eye patches , wooden legs and parrots who were convicted and buried centuries ago , templateYLabel[1] templateYLabel[2] are indeed posing a threat to today 's shipping lines all over the world .
generated: This statistic shows the Wholsale of sales billion against ships sports from 2008 to 2018 .  There were 92.26 such incidents in 2009 .  sales billion Although the term `` sales '' may conjure up images of bearded men with eye patches , wooden legs and parrots who were convicted and buried centuries ago , sales billion are indeed posing a threat to today 's shipping lines all over the world .

Example 1022:
titleEntities: {'Subject': ['FOMO'], 'Date': ['2013', '2013']}
title: Percentage of U.S. social networks who suffer from FOMO as of June 2013
X_Axis['Response']: ['Suffer_from_FOMO', "Don't_suffer_from_FOMO"]
Y_Axis['Share', 'of', 'respondents']: ['56', '44']

gold: This statistic provides information on the share of U.S. social networkers who are afraid of missing something such as an event , news or important status updates if they do n't keep an eye on their social networks . During a survey in June 2013 , it was found that 56 percent of U.S. social network users suffered from the fear of missing out , also known as FOMO .
gold_template: This statistic provides information on the templateYLabel[0] of templateTitle[1] templateTitle[2] networkers templateTitle[4] are afraid of missing something such as an event , news or important status updates if they do n't keep an eye on their templateTitle[2] templateTitle[3] . During a survey in templateTitle[8] templateTitleDate[0] , it was found that templateYValue[max] templateScale of templateTitle[1] templateTitle[2] network users suffered templateXValue[0] the fear of missing out , also known as templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] of internet templateTitle[8] in the templateTitle[0] who use another device templateXValue[0] TV or templateXValue[last] video to templateXValue[0] as of 2017 . During the survey period , it was found that templateYValue[max] templateScale of templateTitle[0] templateTitle[7] adults were templateTitle[2] templateTitle[3] templateTitle[8] , accessing content on their smartphones , tablets or computers during regular templateXValue[0] consumption .
generated: This statistic shows the Share of internet June in the Percentage who use another device Suffer_from_FOMO TV or Don't_suffer_from_FOMO video to Suffer_from_FOMO as of 2017 .  During the survey period , it was found that 56 % of Percentage FOMO adults were social networks June , accessing content on their smartphones , tablets or computers during regular Suffer_from_FOMO consumption .

Example 1023:
titleEntities: {'Subject': ['GDP'], 'Date': ['2024']}
title: Global gross domestic product ( GDP ) 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'in', 'billion', 'U.S.', 'dollars']: ['111569.46', '105786.19', '100409.01', '95353.02', '90519.61', '86598.83', '84929.51', '80262.15', '75823.65', '74779.48', '78944.49']

gold: The statistic shows global gross domestic product ( GDP ) from 2014 to 2018 , with projections up until 2024 . In 2018 , global GDP amounted to about 84.93 trillion U.S. dollars . Gross domestic product Gross domestic product , also known as GDP , is the accumulated value of all finished goods and services produced in a country , often measured annually .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[0] templateYLabel[0] amounted to about templateYValue[6] templateScale templateYLabel[2] templateYLabel[3] . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[1] templateTitle[2] templateTitle[3] , also known as templateYLabel[0] , is the accumulated value of all finished goods and services produced in a country , often measured annually .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] in templateScale templateYLabel[2] templateYLabel[3] . In templateXValue[6] , the EU 's templateYLabel[0] amounted to about templateYValue[6] templateScale templateYLabel[2] templateYLabel[3] . Brexit and the economy of the templateTitleSubject[0] The templateTitleSubject[0] is still recovering from the crisis in 2008 , but it is by no means making an impressive comeback and templateXValue[8] has not started out on the right foot either .
generated: This statistic shows Global gross domestic ( GDP ) of the GDP from 2014 to 2024 in billion U.S. dollars .  In 2018 , the EU 's GDP amounted to about 84929.51 billion U.S. dollars .  Brexit and the economy of the GDP is still recovering from the crisis in 2008 , but it is by no means making an impressive comeback and 2016 has not started out on the right foot either .

Example 1024:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2017']}
title: Leading comparison websites in the Netherlands 2017
X_Axis['Comparison', 'websites']: ['Kieskeurig.nl', 'Beslist.nl', 'Independer.nl', 'HoyHoy', 'Bellen.com', 'Easyswitch_and_Overstappen.nl']
Y_Axis['Share', 'of', 'respondents']: ['84', '78', '78', '28', '16', '8']

gold: Kieskeurig.nl was the most well-known price comparison website in the Netherlands , according to a survey from 2017 ( the only one held in the country about this topic ) . 84 percent of Dutch consumers knew the website for retail prices and product reviews . The insurance comparison website Independer.nl was also a familiar name .
gold_template: templateXValue[0] was the most well-known price templateXLabel[0] website in the templateTitleSubject[0] , according to a survey from templateTitleDate[0] ( the only one held in the country about this topic ) . templateYValue[max] templateScale of Dutch consumers knew the website for retail prices and product reviews . The insurance templateXLabel[0] website templateXValue[2] was also a familiar name .

generated_template: This statistic depicts the results of a survey in which templateTitle[5] templateTitle[4] were asked how templateXValue[4] they purchase templateXValue[last] . Some templateYValue[max] templateScale of templateYLabel[1] stated that they purchase templateXValue[last] a templateXValue[1] per templateXValue[2] , while templateYValue[1] templateScale of templateYLabel[1] reportedly purchase templateXValue[last] a templateXValue[1] per templateXValue[1] .
generated: This statistic depicts the results of a survey in which 2017 were asked how Bellen.com they purchase Easyswitch_and_Overstappen.nl .  Some 84 % of respondents stated that they purchase Easyswitch_and_Overstappen.nl a Beslist.nl per Independer.nl , while 78 % of respondents reportedly purchase Easyswitch_and_Overstappen.nl a Beslist.nl per .

Example 1025:
titleEntities: {'Subject': ['Michigan'], 'Date': ['2018']}
title: Real GDP of Michigan 2018 , by industry
X_Axis['Industry']: ['Manufacturing', 'Finance_insurance_real_estate_rental_and_leasing', 'Professional_and_business_services', 'Government_and_government_enterprises', 'Educational_services_health_care_and_social_assistance', 'Wholesale_trade', 'Retail_trade', 'Construction', 'Arts_entertainment_recreation_accommodation_and_food_services', 'Information', 'Transportation_and_warehousing', 'Other_services_(except_government_and_government_enterprises)', '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']: ['88.79', '76.55', '65.72', '47.6', '44.72', '32.33', '31.09', '16.71', '15.81', '14.24', '11.63', '10.14', '9.24', '4.23', '2.36']

gold: This graph shows the real value added to the Gross Domestic Product ( GDP ) of Michigan in 2018 , by industry . In 2018 , the construction industry added 16.71 billion chained 2012 U.S. dollars of value to the total state GDP .
gold_template: This graph shows the templateXValue[1] 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[7] templateXLabel[0] templateYLabel[2] templateYValue[7] templateScale templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the total state templateTitle[1] .

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[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] .
generated: This graph shows the Manufacturing value added to the Gross Domestic Product ( GDP ) of Michigan in 2018 , by Industry .  In 2018 , the Mining_quarrying_and_oil_and_gas_extraction Industry added 2.36 billion chained 2012 U.S. dollars of value to the state GDP .

Example 1026:
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 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] amounted to around templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the National debt of Ireland from 2014 to 2018 , with projections up until 2024 .  In 2018 , the National debt of Ireland amounted to around 226.92 billion U.S. dollars .

Example 1027:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2009', '2019']}
title: Number of homicides in Denmark 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'homicides']: ['48', '52', '54', '49', '46', '59', '41', '43', '49', '49', '56']

gold: In Denmark , the total number of reported criminal offences fluctuated in recent years . In 2019 , the number stood at close to 499 thousand crimes , and among them there were 52 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 .
gold_template: In templateTitleSubject[0] , the total templateYLabel[0] of 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_template: In templateXValue[max] , the harmonized templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale . This was the highest level of templateYLabel[0] reached since templateXValue[7] , when the templateYLabel[0] was at templateYValue[max] templateScale . Of the templateYValue[0] Benelux countries , the templateTitleSubject[0] saw the lowest templateYLabel[0] .
generated: In 2019 , the harmonized Number homicides in the Denmark was 48 % .  This was the highest level of Number reached since 2012 , when the Number was at 59 % .  Of the 48 Benelux countries , the Denmark saw the lowest Number .

Example 1028:
titleEntities: {'Subject': ['Kentucky'], 'Date': ['2000', '2018']}
title: Kentucky - 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']: ['16.9', '17.2', '18.5', '18.5', '19.1', '18', '19.4', '19.1', '19', '18.6', '17.3', '17.3', '17', '16.8', '17.4', '17.4', '15.6', '15.4', '16.4']

gold: This statistic shows the poverty rate in Kentucky from 2000 to 2018 . In 2018 , about 16.9 percent of Kentucky '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 Kentucky from 2000 to 2018 .  In 2018 , about 16.9 percentage of Kentucky 's population lived below the Poverty line .

Example 1029:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1970', '2014']}
title: Median household income in the U.S. 1970 - 2014 , by income tier
Data:
                          0       1       2       3       4
Year                   2014    2000    1990    1980    1970
Lower income class    24074   26496   23081   21336   18799
Middle income class   73392   76819   68856   61832   54682
Upper income class   174625  180769  153557  130150  118617 

gold: This statistic shows the median household income in the United States from 1970 to 2014 , by income tier . In 2014 , the median household income for the middle class stood at 73,392 U.S. dollars . This study defined middle class income households as those with an income between 67 and 200 percent of the U.S. median household income , after adjustment for household size .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] from templateValue[0][last] to templateValue[0][0] , templateTitle[6] templateTitle[2] templateTitle[8] . In templateValue[0][0] , the templateTitle[0] templateTitle[1] templateTitle[2] for the templateLabel[2][0] templateLabel[1][2] stood at templateValue[2][0] templateTitleSubject[0] dollars . This study defined templateLabel[2][0] templateLabel[1][2] templateTitle[2] households as those with an templateTitle[2] between 67 and 200 templateScale of the templateTitleSubject[0] templateTitle[0] templateTitle[1] templateTitle[2] , after adjustment for templateTitle[1] size .

generated_template: This statistic shows the consumer templateTitle[1] on templateTitle[0] templateTitle[7] product templateTitle[8] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateValue[0][0] to templateValue[0][2] , with a forecast estimate for templateValue[0][last] . In each templateLabel[0][0] during this period , templateTitle[1] on templateTitle[0] templateLabel[1][0] was the highest across the various categories , at an estimated templateValue[1][last] templateScale British pounds in templateValue[0][last] . In comparison , templateLabel[2][0] templateTitle[1] was estimated at templateValue[2][last] templateScale pounds that same templateLabel[0][0] , while templateTitle[1] on templateLabel[3][0] and templateLabel[4][0] was forecast to reach templateValue[3][last] templateScale pounds and templateValue[4][last] templateScale pounds , respectively .
generated: This statistic shows the consumer household on Median income product tier in the U.S. ( ) from 2014 to 1990 , with a forecast estimate for 1970 .  In each Year during this period , household on Median Lower was the highest across the various categories , at an estimated 18799 million British pounds in 1970 .  In comparison , Middle household was estimated at 54682 million pounds that same Year , while household on Upper and was forecast to reach 118617 million pounds and 118617 million pounds , respectively .

Example 1030:
titleEntities: {'Subject': ['Manufacturing'], 'Date': ['2016']}
title: Manufacturing costs in pharmaceutical industry by country 2016
X_Axis['Country']: ['Mexico', 'Canada', 'Netherlands', 'Italy', 'United_Kingdom', 'Australia', 'France', 'Germany', 'Japan', 'United_States']
Y_Axis['Manufacturing', 'costs', 'index', '(U.S.', '=', '100)']: ['82.9', '88.8', '89.9', '90.3', '90.8', '91.3', '91.8', '93.4', '93.6', '100']

gold: This statistic compares the manufacturing costs of the pharmaceutical industry in selected countries with costs in the United States in 2016 , based on a cost index . Manufacturing costs in all selected countries were less than in the United States , with costs in Mexico being 17.1 percent less than in the United States .
gold_template: This statistic compares the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] in selected countries with templateYLabel[1] in the templateXValue[4] templateXValue[last] in templateTitleDate[0] , based on a cost templateYLabel[2] . templateYLabel[0] templateYLabel[1] in all selected countries were less than in the templateXValue[4] templateXValue[last] , with templateYLabel[1] in templateXValue[0] being 17.1 templateScale less than in the templateXValue[4] templateXValue[last] .

generated_template: This statistic shows the global templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , the templateTitle[1] templateTitle[2] market in the templateXValue[0] generated templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] in templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the global costs pharmaceutical industry by in 2016 .  In that year , the costs pharmaceutical market in the Mexico generated 100 % = 100) in costs index .

Example 1031:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Top U.S. hospital operators based on number of hospitals 2019
X_Axis['Operator', '(City,', 'State)']: ['Hospital_Corporation_of_America_(Nashville_Tenn.)', 'Ascension_Health_(St._Louis)', 'CommonSpirit_Health_(Chicago_Ill.)', 'Community_Health_Systems_(Franklin_Tenn.)', 'Trinity_Health_(Livonia_Mich.)', 'LifePoint_Hospitals_(Brentwood_Tenn.)', 'Tenet_Healthcare_Corp._(Dallas_Tex.)', 'Vibra_Healthcare_(Mechanicsburg_Pa.)', 'Providence_Health_&_Services_(Renton_Wash.)', 'Atrium_Health_(Charlotte_N.C.)']
Y_Axis['Number', 'of', 'hospitals']: ['185', '151', '142', '105', '92', '86', '65', '65', '51', '50']

gold: This statistic depicts a ranking of the top U.S. for-profit hospital operators based on number of hospitals as of July 2019 . At this point , the Hospital Corporation of America , based in Nashville , Tennessee , was ranked first in the United States , with a total of 185 hospitals . Hospitals in the United States Currently , there are approximately 6,210 hospitals in the United States .
gold_template: This statistic depicts a ranking of the templateTitle[0] templateTitleSubject[0] for-profit templateXValue[0] templateTitle[3] templateTitle[4] on templateYLabel[0] of templateXValue[5] as of 2019 . At this point , the templateXValue[0] of templateXValue[0] , templateTitle[4] in Nashville , Tennessee , was ranked first in the templateTitle[1] , with a total of templateYValue[max] templateXValue[5] . templateXValue[5] in the templateTitle[1] Currently , there are approximately 6,210 templateXValue[5] in the templateTitle[1] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] the world 's templateTitle[4] templateTitle[5] templateTitle[3] 1900 to templateTitleDate[0] . The templateXLabel[0] in templateXValue[2] in 1973 claimed templateYValue[2] lives . Natural disasters Natural disasters , such as earthquakes , volcanic eruption , tsunamis , floods , tornados or templateTitle[5] affect people templateTitle[6] .
generated: This statistic shows the Number of hospitals hospital operators the world 's based number operators 1900 to 2019 .  The Operator in CommonSpirit_Health_(Chicago_Ill.) 1973 claimed 142 lives .  Natural disasters Natural , such as earthquakes , volcanic eruption , tsunamis floods , tornados or number affect people hospitals .

Example 1032:
titleEntities: {'Subject': ['Colorado'], 'Date': ['1992', '2018']}
title: Colorado - 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']: ['3.3', '2.7', '3.2', '3.9', '5', '6.9', '7.9', '8.4', '8.7', '7.3', '4.8', '3.7', '4.3', '5', '5.5', '6', '5.5', '3.8', '2.8', '3.1', '3.6', '3.5', '4.2', '4', '4.2', '5.3', '6']

gold: This statistic shows the unemployment rate in Colorado from 1992 to 2018 . In 2018 , the unemployment in Colorado was 3.3 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] in templateTitleSubject[0] was 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] , 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: This statistic displays the Unemployment rate in Colorado from 1992 to 2018 .  In 2018 , Unemployment rate in Colorado was 3.3 % .  You can access the monthly Unemployment rate for the country here .

Example 1033:
titleEntities: {'Subject': ['Estonia'], 'Date': ['2024']}
title: Inflation rate in Estonia 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']: ['2.1', '2.1', '2.2', '2.3', '2.4', '2.5', '3.41', '3.65', '0.8', '0.07', '0.48', '3.25', '4.22', '5.08', '2.74', '0.2', '10.61', '6.75', '4.44', '4.12', '3.04', '1.39', '3.59', '5.63', '3.93', '3.1', '8.78', '9.31', '19.74', '29', '47.66']

gold: This statistic shows the average inflation rate in Estonia from 1994 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Estonia had amounted to about 3.41 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 Estonia from 1994 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Estonia amounted to about 3.41 % compared to the previous Year .

Example 1034:
titleEntities: {'Subject': ['WhatsApp'], 'Date': ['2017']}
title: WhatsApp usage in the U.S. 2017 , by gender
Data:
                                       0       1
Sex                                 Male  Female
Today                                  8       5
Within the previous week               7       2
Within the previous month              5       4
Within the previous year               4       4
More than a year ago                   4       6
I have never used this application    72      78 

gold: This statistic presents the share of internet users in the United States who have used WhatsApp to communicate with others as of August 2017 . During the survey period , it was found that only two percent of female survey respondents had used WhatsApp to communicate within the previous week .
gold_template: templateLabel[6][4] statistic presents the share of internet users in the templateTitle[2] who templateLabel[6][1] templateLabel[6][3] templateTitleSubject[0] to communicate with others as of 2017 . During the survey period , it was found that only templateValue[2][last] templateScale of templateValue[0][1] survey respondents had templateLabel[6][3] templateTitleSubject[0] to communicate templateLabel[2][0] the templateLabel[2][1] templateLabel[2][2] .

generated_template: This statistic shows 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] templateTitle[8] 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: This statistic shows the share of respondents who have a WhatsApp usage U.S. or 2017 by in the Female in 2017 , gender of the WhatsApp .  Of respondents , 5 % of individuals in the WhatsApp had a WhatsApp usage U.S. or 2017 by .

Example 1035:
titleEntities: {'Subject': ['Philadelphia'], 'Date': ['2019']}
title: Player expenses ( payroll ) of Philadelphia Union 2019
X_Axis['Month']: ['Marco_Fabian', 'Alejandro_Bedoya', 'Sergio_Santos', 'Haris_Medunjanin', 'Jamiro_Monteiro', 'Andre_Blake', 'Kai_Wagner', 'Ilsinho', 'Kacper_Przybylko', 'Jack_Elliott', 'Raymon_Gaddis', 'Warren_Creavalle', 'Aurelien_Collin', 'Fafa_Picault', 'Carlos_Miguel_Coronel', 'Auston_Trusty', 'Fabinho_Alves', 'Brenden_Aaronson', 'Mark_McKenzie', 'Derrick_Jones', 'Cory_Burke', 'Matt_Freese', 'Olivier_Mbaizo', 'Anthony_Fontana', 'Matthew_Real', 'Michee_Ngalina']
Y_Axis['Payroll', 'in', 'thousand', 'U.S.', 'dollars']: ['2274.09', '1266.25', '668.5', '595.01', '569.2', '550.0', '360.15', '357.0', '277.0', '265.0', '190.0', '175.3', '175.0', '173.67', '148.43', '124.1', '120.0', '95.81', '82.23', '80.9', '79.72', '77.65', '70.88', '70.26', '57.23', '56.25']

gold: The statistic shows the player expenses ( payroll ) of the Philadelphia Union club of Major League Soccer by player in 2019 . Marco Fabian received a salary of 2.27 million U.S. dollars .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] templateTitle[4] club 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] templateTitle[4] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] received a salary of templateYValue[max] thousand templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Player expenses ( Payroll ) of the Philadelphia Union club of Major League Soccer by Player in 2019 .  Marco_Fabian received a salary of 2274.09 thousand U.S. dollars .

Example 1036:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Number of live births in Denmark 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'births']: ['61476', '61397', '61614', '58205', '56870', '55873', '57916', '58998', '63411', '62818', '65038']

gold: The total number of live births fluctuated in the past years in Denmark . Since 2013 , the number of live births increased generally . As of 2018 , there were approximately 61.5 thousand live births registered .
gold_template: The total templateYLabel[0] of templateTitle[1] templateYLabel[1] fluctuated in the past years in templateTitleSubject[0] . Since templateXValue[5] , the templateYLabel[0] of templateTitle[1] templateYLabel[1] templatePositiveTrend generally . As of templateXValue[max] , there were approximately templateYValue[0] thousand templateTitle[1] templateYLabel[1] registered .

generated_template: In templateXValue[max] , nearly 118,000 babies were born in templateTitleSubject[0] . This was the lowest templateYLabel[0] of templateYLabel[1] in the last decade . The templateYLabel[0] of children born in the country peaked in templateXValue[8] , at just over 129,000 .
generated: In 2018 , nearly 118,000 babies were born in Denmark .  This was the lowest Number of births in the last decade .  The Number of children born in the country peaked in 2010 , at just over 129,000 .

Example 1037:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2009', '2019']}
title: Passenger 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']: ['356036', '353729', '379393', '372318', '345108', '303948', '269599', '279899', '304984', '289684', '213408']

gold: In 2019 , over 356 thousand passenger cars were sold in Sweden . This was an increase of 0.65 percent compared to the previous year . During the past ten years , the amount fluctuated , peaking in 2017 at roughly 379 thousand units .
gold_template: In templateXValue[max] , over templateYValue[0] thousand templateTitle[0] cars were templateYLabel[1] in templateTitleSubject[0] . This was an templatePositiveTrend of 0.65 templateScale compared to the previous templateXLabel[0] . During the past ten years , the amount fluctuated , peaking in templateXValue[2] at roughly templateYValue[max] thousand templateYLabel[0] .

generated_template: In templateXValue[max] , the harmonized templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale . This was the highest level of templateYLabel[0] reached since templateXValue[7] , when the templateYLabel[0] was at templateYValue[max] templateScale . Of the templateYValue[0] Benelux countries , the templateTitleSubject[0] saw the lowest templateYLabel[0] .
generated: In 2019 , the harmonized Units sold in the Sweden was 356036 % .  This was the highest level of Units reached since 2012 , when the Units was at 379393 % .  Of the 356036 Benelux countries , the Sweden saw the lowest Units .

Example 1038:
titleEntities: {'Subject': ['Monsanto'], 'Date': ['2013', '2017']}
title: Monsanto 's number of regular and temporary workers worldwide 2013 to 2017
Data:
                       0                  1
unknown  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: This survey , conducted in the templateTitle[2] in 2014 , shows if respondents assess templateValue[0][0] 's political templateTitle[1] as being templateValue[0][0] and templateValue[0][0] than in the past . In templateTitleDate[0] , templateValue[2][max] templateScale of respondents thought that the current political templateTitle[1] is templateValue[0][0] and templateValue[0][0] than that of the past .
generated: This survey , conducted in the number in 2014 , shows if respondents assess Regular_workers 's political as being Regular_workers and than in the past .  In 2013 , 22400 % of respondents thought that the current political 's is Regular_workers and than that of the past .

Example 1039:
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: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] 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 templateXValue[0] templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the Number of Pinterest users in the Pinterest who were using Number as of 2019 , sorted gender .  During that period of time , 58.9 millions of Female users stated that they used the social networking site .

Example 1040:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': []}
title: Racist incidents recorded by the police in England and Wales 2015/16 - 2018/19
Data:
              0           1           2                         3              4                5           6              7           8      9
unknown  London  North_West  South_East  Yorkshire_and_the_Humber  West_Midlands  East_of_England  South_West  East_Midlands  North_East  Wales
2015/16   12784        9377        7012                      6437           6057             4192        3607           3930        2188   2613
2016/17   16016        8409        7078                      6275           6290             4845        3835           3012        2581   2080
2017/18   15537       11182        8833                      7350           6716             5470        4475           3725        2980   2298
2018/19   16037       12310       10609                      9329           7257             6202        4568           4136        3068   2676 

gold: This chart illustrates racist incidents recorded by the police in England and Wales from April 2015 to March 2019 , sorted by region . While the fewest racist incidents were reported in the North East and Wales , the most occurred in London with more than 16 thousand cases recorded in 2018/19 . North West experienced the second highest number of racist incidents .
gold_template: This chart illustrates templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the templateTitle[4] in templateValue[0][5] and templateValue[0][9] from 2015 to 2019 , sorted templateTitle[3] region . While the fewest templateTitle[0] templateTitle[1] were reported in the templateValue[0][1] templateValue[0][2] and templateValue[0][9] , the most occurred in templateValue[0][0] with more than templateValue[2][max] thousand cases templateTitle[2] in templateTitle[8] . templateValue[0][1] experienced the second highest number of templateTitle[0] templateTitle[1] .

generated_template: This statistic shows the templateValue[0][0] of templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) templateValue[0][0] on 31 , templateTitleDate[0] , templateTitle[5] templateTitle[6] templateValue[0][0] and templateTitle[8] . As of this date , there were templateLabel[7][2] templateValue[5][max] thousand adminstrative templateValue[0][2] and templateValue[0][4] that were aged between templateValue[1][last] and 59 .
generated: This statistic shows the London of by in the United Kingdom ( England ) London on 31 , England Wales London and 2018/19 .  As of this date , there were 2018/19 16037 thousand adminstrative South_East and West_Midlands that were aged between 2613 and 59 .

Example 1041:
titleEntities: {'Subject': ['Manchester United'], 'Date': ['2009', '2019']}
title: Average per game attendance Manchester United 2009 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']
Y_Axis['Average', 'attendance']: ['74879', '75102', '75305', '75279', '75335', '75203', '75530', '75387', '75104', '74864']

gold: Manchester United had an average attendance of 74,879 for their home games in the 2018/19 season , their lowest since 2009/10 . Despite the fall in numbers , Manchester United had the highest average attendance of all 20 English Premier League clubs in 2018/19 . The Theatre of Dreams Manchester United play their homes games at the largest stadium in the English Premier League : Old Trafford .
gold_template: templateTitleSubject[0] had an templateYLabel[0] templateYLabel[1] of templateYValue[0] for their home games in the templateXValue[0] season , their lowest since templateXValue[last] . Despite the fall in numbers , templateTitleSubject[0] had the highest templateYLabel[0] templateYLabel[1] of all 20 English Premier League clubs in templateXValue[0] . The Theatre of Dreams templateTitleSubject[0] play their homes games at the largest stadium in the English Premier League : Old Trafford .

generated_template: The statistic depicts 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: The statistic depicts the Average per game attendance at games of the Manchester United United 2009 from the 2009/10 season to the 2018/19 season .  In 2018/19 , the Average attendance at the games was at 74879 .

Example 1042:
titleEntities: {'Subject': ['Japan'], 'Date': ['2024']}
title: Budget balance in Japan in relation to gross domestic product ( GDP ) 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'to', 'GDP', 'ratio']: ['-1.96', '-1.78', '-1.77', '-1.95', '-2.16', '-2.95', '-3.21', '-3.17', '-3.69', '-3.81', '-5.64']

gold: The statistic shows the budget balance of Japan from 2014 to 2017 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . In 2017 , the state deficit of Japan was at about 3.17 percent of the gross domestic product .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[7] , the state deficit of templateTitleSubject[0] was at about 3.17 templateScale of the templateTitle[4] templateTitle[5] templateTitle[6] .

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 at around 29.98 templateScale templateYLabel[3] .
generated: The statistic shows the Budget balance in Japan 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 Japan was at around 29.98 % ratio .

Example 1043:
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 1044:
titleEntities: {'Subject': ['Northrop Grumman'], 'Date': ['2008', '2019']}
title: Northrop Grumman - expenditure on research and development 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Expenditures', 'in', 'million', 'U.S.', 'dollars']: ['953', '764', '639', '705', '712', '569', '507', '520', '543', '580', '588', '543']

gold: The statistic shows Northrop Grumman 's research and development expenditures from the fiscal year of 2008 to the fiscal year of 2019 . In the 2019 fiscal year , the Virginia-based aerospace and defense technology company incurred about 953 million U.S. dollars in research and development costs .
gold_template: The statistic shows templateTitleSubject[0] 's templateTitle[3] and templateTitle[4] templateYLabel[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the templateXValue[max] fiscal templateXLabel[0] , the Virginia-based aerospace and defense technology company incurred about templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateTitle[3] and templateTitle[4] costs .

generated_template: templateYLabel[0] templateYLabel[1] templateYLabel[2] ( AUM ) in templateTitleSubject[0] have grown almost annually from templateXValue[10] to templateXValue[max] to an estimated templateYValue[idxmax(X)] templateScale templateYLabel[4] . AUM , covers all client funds and templateYLabel[0] that are managed on their behalf by a financial institute . These asset templateYLabel[2] companies include mutual fund , venture capital firms and brokers .
generated: Expenditures million U.S. ( AUM ) in Northrop Grumman have grown almost annually from 2009 to 2019 an estimated 953 million dollars .  AUM , covers all client funds and Expenditures that are managed on their behalf by a financial institute .  These asset U.S. companies include mutual fund , venture capital firms and brokers .

Example 1045:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Child abuse in the U.S. - number of victims by gender 2018
X_Axis['Gender']: ['Girls', 'Boys', 'Unknown']
Y_Axis['Number', 'of', 'child', 'abuse', 'cases']: ['346957', '328281', '2291']

gold: This statistic shows the number of victims of child abuse in the United States in 2018 , by gender . In 2018 , about 328,281 boys were abused in the United States .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[4] of templateYLabel[1] templateYLabel[2] in the templateTitle[2] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[1] templateXValue[1] were abused in the templateTitle[2] .

generated_template: This statistic shows the templateYLabel[0] of adults templateYLabel[2] in the templateTitleSubject[1] who were using templateTitleSubject[0] as of 2019 , sorted templateTitle[6] templateXLabel[0] templateXLabel[1] . During that period of time , templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] had attained a templateXValue[1] degree used the photo sharing app .
generated: This statistic shows the Number of adults abuse in the U.S. who were using U.S. as of 2019 , sorted gender .  During that period of time , 346957 % of child abuse had attained a Boys degree used the photo sharing app .

Example 1046:
titleEntities: {'Subject': ['GDP'], 'Date': ['2007', '2018']}
title: Value added of manufacturing as a percentage of GDP 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Value', 'added', 'as', 'a', 'percentage', 'of', 'GDP']: ['11.4', '11.2', '11.1', '11.7', '11.7', '11.9', '11.9', '12', '12', '11.8', '12.2', '12.8']

gold: The statistic displays the value added of the U.S. manufacturing industry as a percentage of GDP between 2007 and 2018 . In 2018 , this sector accounted for 11.4 % of the nation 's Gross Domestic Product , making it one of the largest sectors of the U.S. economy .
gold_template: The statistic displays the templateYLabel[0] templateYLabel[1] of the U.S. templateTitle[2] industry as a templateScale of templateYLabel[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , this sector accounted for templateYValue[idxmax(X)] templateScale of the nation 's Gross Domestic Product , making it one of the largest sectors of the U.S. economy .

generated_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: This statistic shows the Value of added 2007 2018 in the manufacturing from 2007 to 2018 .  In 2018 , about 11.4 added in the GDP were 2007 to be adopted .

Example 1047:
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] , 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 Jacksonville Jaguars , a franchise of the National Football League , from 2001 to 2018 .  In 2018 , the Revenue of the Jacksonville Jaguars was 424 U.S. dollars .

Example 1048:
titleEntities: {'Subject': ['Nuclear'], 'Date': ['2030']}
title: Nuclear energy - global market size by segment through 2030
Data:
                          0                     1                                 2        3                        4                                                   5
unknown  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: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateValue[0][2] care templateValue[0][5] to templateLabel[1][0] and templateLabel[2][0] consumers in the templateTitleSubject[0] , according to a survey conducted in 2013 . templateValue[0][2] moisturising and hydrating templateTitle[2] were considered the templateTitle[0] templateTitle[1] templateTitle[2] templateValue[0][2] both genders , with templateValue[2][0] templateScale of women stating this as templateTitle[1] . The templateValue[0][3] moisturiser market takes up the largest proportion of the templateTitle[3] templateValue[0][2] care market with a share of 41.6 templateScale in templateTitleDate[0] .
generated: This statistic shows the Nuclear energy global of market Nuclear_power_plant_construction care Spent_nuclear_fuel_reprocessing_and_secondary_fuel to 2011 and 2030 consumers in the Nuclear , according to a survey conducted in 2013 .  Nuclear_power_plant_construction moisturising and hydrating global were considered the Nuclear energy global Nuclear_power_plant_construction both genders , with 300 % of women stating this as energy .  The Service moisturiser market takes up the largest proportion of the market Nuclear_power_plant_construction care market with a share of 41.6 % in 2030 .

Example 1049:
titleEntities: {'Subject': ['June'], 'Date': ['2019', '2019']}
title: Consumer confidence index of June 2019
X_Axis['Months', 'from', 'June', '2018', 'to', 'June', '2019']: ["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"]
Y_Axis['Index', 'points', '(2001', '=', '100)']: ['114.3', '113.6', '110.6', '0', '104.7', '101.6', '98.3', '102.2', '102.2', '101.9', '102.7', '102.9', '100.5']

gold: This statistic shows the consumer confidence index for Brazil from December 2017 to December 2018 . The index is composed of several different indices , including an assessment of one 's personal financial situation . In December 2018 , Brazil 's consumer confidence was at 114.3 points .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] for Brazil templateXLabel[1] 2017 to 2018 . The templateYLabel[0] is composed of several different indices , including an assessment of one 's personal financial situation . In 2018 , Brazil 's templateTitle[0] templateTitle[1] was at templateYValue[max] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , templateYValue[max] templateYLabel[1] templateYLabel[2] were templateTitle[5] in templateTitleDate[0] .
generated: This statistic shows the Index of points (2001 in the June in 2019 , Months from .  In 2019 , 114.3 points (2001 were 2019 in .

Example 1050:
titleEntities: {'Subject': ['NHL'], 'Date': ['2011', '2018']}
title: NHL sponsorship revenue 2011 to 2018
X_Axis['Year']: ['17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['559.5', '505.0', '477.0', '447.0', '409.0', '390.0', '372.0', '356.0']

gold: This statistic shows the worldwide sponsorship revenue of the National Hockey League ( teams/league ) from 2011 to 2018 . In 2017/18 , the global NHL league and team sponsorship revenue amounted to 559.5 million U.S. dollars .
gold_template: This statistic shows the worldwide templateTitle[1] templateYLabel[0] of the National Hockey League ( teams/league ) from templateTitleDate[min] to templateTitleDate[max] . In 2017/18 , the global templateTitleSubject[0] league and team templateTitle[1] templateYLabel[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic above shows templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitle[4] from templateXValue[last] to the second season . The company generated a total of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .
generated: The statistic above shows NHL sponsorship revenue in the 2018 from 10/11 to the second season .  The company generated a total of 559.5 million U.S. dollars in the 17/18 season .

Example 1051:
titleEntities: {'Subject': ['China'], 'Date': ['2008', '2018']}
title: Urbanization in China 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population']: ['59.58', '58.52', '57.35', '56.1', '54.77', '53.73', '52.57', '51.27', '49.95', '48.34', '46.99']

gold: The graph shows the percentage of the Chinese population living in urban areas until 2018 . In 2018 , about 59.8 percent of the total population lived in cities . Degree of urbanization in China – additional information Urbanization is generally defined as a process of people migrating from rural to urban areas , during which towns and cities are formed and increase in size .
gold_template: The graph shows the templateScale of the Chinese templateYLabel[2] living in templateYLabel[1] areas until templateXValue[max] . In templateXValue[max] , about 59.8 templateScale of the total templateYLabel[2] lived in cities . Degree of templateTitle[0] in templateTitleSubject[0] – additional information templateTitle[0] is generally defined as a process of people migrating from rural to templateYLabel[1] areas , during which towns and cities are formed and templatePositiveTrend in size .

generated_template: In templateXValue[max] , approximately a third of the total templateYLabel[2] in templateTitleSubject[0] lived in cities . The trend shows an templatePositiveTrend of templateTitle[0] by almost 4 templateScale in the last decade , meaning people have moved away from rural areas to find work and make a living in the cities . Leaving the field Over the last decade , templateTitle[0] in templateTitleSubject[0] has templatePositiveTrend by almost 4 templateScale , as more and more people leave the agricultural sector to find work in services .
generated: In 2018 , approximately a third of the total population in China lived in cities .  The trend shows an increase of Urbanization by almost 4 % in the last decade , meaning people have moved away from rural areas to find work and make a living in the cities .  Leaving the field Over the last decade , Urbanization in China has increased by almost 4 % , as more and people leave the agricultural sector to find work in services .

Example 1052:
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[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateTitle[0] templateTitle[1] home templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[1] was templateYValue[idxmax(X)] . • templateTitleSubject[0] templateTitleSubject[1] total home templateYLabel[1] • Major League Baseball templateYLabel[0] per game templateYLabel[1] • Major League Baseball total templateYLabel[1]
generated: 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 36090 .  • Milwaukee Brewers Milwaukee Brewers total home attendance • Major League Baseball Average per game attendance • Major League Baseball total attendance . 

Example 1053:
titleEntities: {'Subject': ['Puma'], 'Date': ['2013', '2019']}
title: Global R & D expenditure of Puma from 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['R&D', 'expenditure', 'in', 'million', 'euros']: ['61.7', '54.0', '53.4', '52.0', '56.7', '46.2', '47.9']

gold: Sportswear company Puma has increased its research and development expenditure since 2016 , with 61.7 million euros spent by the department in 2019 – an increase of 7.7 million euros on the previous year . What is the role of research and development ? As of December 2019 , Puma had 14,332 employees worldwide , of which 986 were employed in the field of research and development/product management . In a sportswear market that is constantly changing , Puma 's research and development team is responsible for providing insights into product design and new technologies .
gold_template: Sportswear company templateTitleSubject[0] has templatePositiveTrend its research and development templateYLabel[1] since templateXValue[3] , with templateYValue[max] templateScale templateYLabel[3] spent by the department in templateXValue[idxmax(Y)] – an templatePositiveTrend of 7.7 templateScale templateYLabel[3] on the previous templateXLabel[0] . What is the role of research and development ? As of 2019 , templateTitleSubject[0] had 14,332 employees worldwide , of which 986 were employed in the field of research and development/product management . In a sportswear market that is constantly changing , templateTitleSubject[0] 's research and development team is responsible for providing insights into product design and new technologies .

generated_template: This statistic displays templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] through templateXValue[max] . In templateXValue[max] , the internet company 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateScale templateYLabel[4] dollars . templateTitleSubject[0] is the main revenue generator of online business conglomerate Alphabet .
generated: This statistic displays Puma 's R&D expenditure from 2013 through 2019 .  In 2019 , the internet company 's R&D expenditure amounted to 61.7 million euros dollars .  Puma is the main revenue generator of online business conglomerate Alphabet .

Example 1054:
titleEntities: {'Subject': ['Americans'], 'Date': ['2019']}
title: Share of Americans who believe in true love in 2019
X_Axis['Response']: ['Yes', 'No']
Y_Axis['Share', 'of', 'respondents']: ['94', '6']

gold: This statistic shows the results of a survey conducted in the United States in 2017 on whether the respondents believe in love , or not . During the survey , some 94 percent of respondents stated they believe in true love .
gold_template: This statistic shows the results of a survey conducted in the country in 2017 on whether the templateYLabel[1] templateTitle[3] in templateTitle[5] , or not . During the survey , some templateYValue[max] templateScale of templateYLabel[1] stated they templateTitle[3] in templateTitle[4] templateTitle[5] .

generated_template: The statistic shows templateYLabel[0] of templateTitle[1] users in the templateTitle[0] in templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . During the survey period , it was found that templateYValue[max] templateScale of the templateXValue[last] templateYLabel[1] were templateTitle[1] users . Overall , templateYValue[min] templateScale of the templateTitleSubject[0] templateYLabel[1] accessed the templateTitle[1] .
generated: The statistic shows Share of Americans users in the Share in 2019 , sorted love 2019 .  During the survey period , it was found that 94 % of the No respondents were Americans users .  Overall , 6 % of the Americans respondents accessed the Americans .

Example 1055:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1975', '2017']}
title: U.S. hospital occupancy rate 1975 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2000', '1995', '1990', '1980', '1975']
Y_Axis['Hospital', 'occupancy', 'rate']: ['65.9', '65.8', '65.5', '64.8', '64.7', '65.2', '66.5', '66.6', '67.8', '68.2', '68.3', '66.1', '65.7', '69.5', '77.7', '76.7']

gold: In 2017 , the occupancy rate of hospitals in the U.S. stood at 65.9 percent . Hospitals are a cornerstone of health services in any country and also contribute considerably to the economy . Nevertheless , the number of hospitals in the U.S. has decreased in recent years .
gold_template: In templateXValue[max] , the templateYLabel[1] templateYLabel[2] of hospitals in the templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateScale . Hospitals are a cornerstone of health services in any country and also contribute considerably to the economy . Nevertheless , the number of hospitals in the templateTitleSubject[0] has templateNegativeTrend in recent years .

generated_template: This statistic represents 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: This statistic represents the Hospital of cars rate by U.S. in between 1975 and 2017 .  European occupancy of the U.S. cars increased from 400 thousand occupancy rate in 1975 to over 77.7 thousand occupancy rate by 1980 .  In 2017 , there were 65.9 thousand occupancy of U.S. cars rate in U.S. .

Example 1056:
titleEntities: {'Subject': ['Bangladesh'], 'Date': ['2019']}
title: Employment by economic sector in Bangladesh 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  39.71  40.15   40.6  42.66  43.44  44.29     45  45.77  46.57  47.31  47.54
Industry     20.53  20.47  20.42  20.46  19.93  19.45  19.05  18.55  18.07  17.63  16.81
Services     39.76  39.38  38.98  36.87  36.64  36.26  35.93  35.68  35.35  35.07  35.65 

gold: The statistic shows the distribution of employment in Bangladesh by economic sector from 2009 to 2019 . In 2019 , 39.71 percent of the employees in Bangladesh were active in the agricultural sector , 20.53 percent in industry and 39.76 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 Bangladesh by economic sector from 2009 to 2019 .  In 2019 , 39.71 % of the employees in Bangladesh were active in the agricultural sector , 20.53 % in Industry and 39.76 % in the service sector .

Example 1057:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2018']}
title: Computers and accessories : U.S. imports 2002 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   2009   2008   2007   2006   2005   2004   2003   2002
Computers             77.57  68.91  60.88  63.26  63.99  64.69  65.77  64.9  55.29  40.76  40.67  40.77  33.77  29.57   25.0  20.37  16.14
Computer Accessories  64.79   59.5  53.64  56.98  57.98   56.5  56.47  54.8  61.95   53.1  60.21  63.03  67.58  63.74  63.64  56.15  59.01 

gold: This statistic illustrates the total value of imports of computers and computer accessories , peripherals , and parts into the United States from 2002 to 2018 . In 2018 , computers accessories , peripherals , and parts worth around 64.79 billion U.S. dollars were imported to the United States .
gold_template: This statistic illustrates the total value of templateTitle[3] of templateTitle[0] and templateLabel[2][0] templateTitle[1] , peripherals , and parts into the templateTitle[2] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][0] , templateTitle[0] templateTitle[1] , peripherals , and parts worth around templateValue[2][0] templateScale templateTitleSubject[0] dollars were imported to the templateTitle[2] .

generated_template: This statistic shows the total templateTitle[0] of templateTitle[1] and templateTitle[2] templateTitle[3] in the templateTitle[4] from templateValue[0][last] to templateValue[0][0] . In templateValue[0][last] , there were around 9,172,000 templateTitle[2] templateTitle[3] ( including templateTitle[3] and heifers that have calved ) in the templateTitle[4] .
generated: This statistic shows the total Computers of accessories and U.S. imports in the 2002 from to 2018 .  In 2002 , there were around 9,172,000 U.S. imports ( including imports and heifers that have calved ) in the 2002 .

