Example 1:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2018']}
title: Urbanization in Belgium 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['98', '97.96', '97.92', '97.88', '97.83', '97.79', '97.74', '97.7', '97.65', '97.6', '97.56']

gold: This statistic shows the degree of urbanization in Belgium from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 98 percent of Belgium '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)] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: The statistic in the degree of templateXValue[min] in templateTitleSubject[0] from shows to cities and details the where of the dropped templateXValue[max] , living in the This templateXValue[max] of a approximately . templateXValue[min] of the total templateXValue[max] in the This templateXValue[max] of a approximately .
generated: The statistic in the degree of 2008 in Belgium from shows to cities and details the where of the dropped 2018 , living in the This 2018 of a approximately . 2008 of the total 2018 in the This 2018 of a approximately .

Example 2:
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] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] the templateTitle[0] .

generated_template: The statistic displays the templateXLabel[0] of templateXLabel[1] templateTitle[2] per Product in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . It 69 percent of templateTitleSubject[0] throughout were templateTitleSubject[1] users with a templateXValue[0] or their templateXValue[0] transplant by templateXValue[0] . templateXValue[2] templateTitle[1] templateXValue[2] – additional templateXValue[5] templateXValue[2] templateTitle[1] , which refers to templateTitle[1] services via templateXValue[2] 24 , is a growing phenomenon templateXValue[6] over the world .
generated: The statistic displays the Brand of EIFS per Product in the EIFS ( STUCCO ) in 2018 . It 69 percent of EIFS throughout were STUCCO users with a Dryvit or their Dryvit transplant by Dryvit . Omega Products used – additional Parex/La Habra Omega Products used , which refers to used services via Omega Products 24 , is a growing phenomenon TEC over the world .

Example 3:
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)] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the development of templateTitleSubject[0] templateYValue[idxmax(X)] templateYLabel[0] ) between templateXValue[min] and details the percentage of the million templateXLabel[0] . In the most recent state , the templateYLabel[0] of templateTitleSubject[0] was about templateYValue[max] templateXValue[max] This that .
generated: The statistic shows the development of Pennsylvania 712.1 Real ) between 2000 and details the percentage of the million Year . In the most recent state , the Real of Pennsylvania was about 712.1 2018 This that .

Example 4:
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] templateYLabel[2] templateTitleSubject[0] templateTitle[7] . templateXValue[4] was ranked fifth with templateYValue[4] templateYLabel[2] templateTitle[7] .

generated_template: The statistic gives a ranking of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] . templateXValue[0] accounting the top spot with a templateXLabel[0] value of approximately templateYValue[max] percent , followed templateTitle[6] templateXValue[1] with a templateYLabel[0] of templateYValue[1] percent .
generated: The statistic gives a ranking of the Leading video gaming brands Twitter by in 2018 . PlayStation accounting the top spot with a Brand value of approximately 15.63 percent , followed by Xbox with a Brand of 12.87 percent .

Example 5:
titleEntities: {'Subject': ['UK'], 'Date': ['2010', '2018']}
title: Number of people employed in the defense industry in the UK 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Employees', 'in', 'thousands']: ['135', '140', '142', '142', '144', '148', '154', '159', '160']

gold: There were approximately 135,000 people directly employed by the defense industry in the United Kingdom in 2018 . This was a decrease of 31 percent since the beginning of the reporting period in 2010 . According to ADS - the UK trade organization representing the aerospace/space , defense and security sectors , the country is currently the second largest exporter of defense equipment and services in the world .
gold_template: There were approximately templateYValue[min] templateTitle[1] directly templateTitle[2] by the templateTitle[3] templateTitle[4] in the United Kingdom in templateXValue[idxmin(Y)] . This was a decrease of 31 percent since the beginning of the reporting period in templateXValue[min] . According to ADS - the templateTitleSubject[0] trade organization representing the aerospace/space , templateTitle[3] and security sectors , the country is currently the second largest exporter of templateTitle[3] equipment and services in the world .

generated_template: The statistic depicts the templateTitleSubject[0] templateYLabel[0] of This templateTitle[3] from templateXValue[min] to 2027 . In templateXValue[min] , the templateTitleSubject[0] templateYLabel[0] of This 's templateXValue[idxmax(Y)] was templateYValue[idxmin(X)] thousand This 's templateXValue[idxmax(Y)] .
generated: The statistic depicts the UK Employees of This defense from 2010 to 2027 . In 2010 , the UK Employees of This 's 2010 was 160 thousand This 's 2010 .

Example 6:
titleEntities: {'Subject': ['Dublin'], 'Date': ['2019']}
title: Prime office rental prices in Dublin Q1 2015-Q3 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", "Q1_'18_", "Q2_'18", "Q3_'18", "Q4_'18", "Q1_'19", "Q2_'19", "Q3_'19"]
Y_Axis['Cost', 'per', 'square', 'meter', 'in', 'euros']: ['538', '552', '592', '619', '619', '619', '646', '646', '646', '646', '673', '673', '-', '673', '673', '-', '673', '673', '673']

gold: The statistic displays the rental costs per square meter of prime office spaces in Dublin , Ireland , from the first quarter of 2015 to the third quarter of 2019 . It can be seen that the price of prime office properties in Dublin grew over time , reaching 673 euros per square meter per year as of the 3rd quarter of 2017 and remaining constant up until the third quarter 2019 .
gold_template: The statistic displays the templateTitle[2] costs templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] spaces in templateTitleSubject[0] , Ireland , from the first templateXLabel[0] of 2015 to the third templateXLabel[0] of templateTitleDate[0] . It can be seen that the price of templateTitle[0] templateTitle[1] properties in templateTitleSubject[0] grew over time , reaching templateYValue[last] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year as of the 3rd templateXLabel[0] of 2017 and remaining constant up until the third templateXLabel[0] templateTitleDate[0] .

generated_template: The statistic gives information on templateTitleSubject[0] that templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] from the second templateXLabel[0] of 2012 to the fourth templateXLabel[0] of templateTitleDate[max] . As of the last reported templateXLabel[0] , the website that templateYLabel[0] templateTitle[1] amounted to templateYValue[0] templateXValue[max] This 's .
generated: The statistic gives information on Dublin that Cost rental prices Dublin from the second Quarter of 2012 to the fourth Quarter of 2019 . As of the last reported Quarter , the website that Cost office amounted to 538 Q4 '18 This 's .

Example 7:
titleEntities: {'Subject': ['Orioles'], 'Date': ['2001', '2018']}
title: Baltimore Orioles 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']: ['251', '252', '253', '239', '245', '198', '206', '179', '175', '171', '174', '166', '158', '156', '148', '129', '129', '133']

gold: The statistic depicts the revenue of the Baltimore Orioles from 2001 to 2018 . In 2018 , the revenue of the Major League Baseball franchise amounted to 251 million U.S. dollars.The Baltimore Orioles are owned by Peter Angelos , who bought the franchise for 173 million U.S. dollars in 1993 .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitle[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] dollars.The templateTitle[0] templateTitleSubject[0] are owned by Peter Angelos , who bought the franchise for 173 templateYLabel[1] templateYLabel[2] templateYLabel[3] in 1993 .

generated_template: the statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the templateXValue[7] Football League , from templateXValue[min] to templateTitleDate[max] . In the million templateXLabel[0] season , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateXValue[max] This . templateTitleSubject[0] is a professional basketball team of the templateXValue[7] Football League ( templateTitleSubject[1] ) .
generated: the statistic depicts the Revenue of the Orioles , a franchise of the 2011 Football League , from 2001 to 2018 . In the million Year season , the Revenue of the Orioles was 253 2018 This . Orioles is a professional basketball team of the 2011 Football League ( Orioles ) .

Example 8:
titleEntities: {'Subject': ['Wisconsin'], 'Date': ['2000', '2018']}
title: Wisconsin - 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']: ['11', '11.3', '11.8', '12.1', '13.2', '13.5', '13.2', '13.1', '13.2', '12.4', '10.4', '10.8', '11', '10.2', '10.7', '10.5', '9.7', '9.8', '8.9']

gold: This graph shows the poverty rate in Wisconsin from 2000 to 2018 . In 2018 , 11 percent of Wisconsin 's population lived below the poverty line .
gold_template: This graph shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateTitleDate[max] . In templateTitleDate[max] , about percent of templateTitleSubject[0] that population 2016 below the templateTitleSubject[0] line .
generated: The statistic shows the Wisconsin rate in Wisconsin from 2000 to 2018 . In 2018 , about percent of Wisconsin that population 2016 below the Wisconsin line .

Example 9:
titleEntities: {'Subject': ['Ultra'], 'Date': ['2019']}
title: Ultra high net worth individuals - distribution by region 2019
X_Axis['Region']: ['North_America', 'Europe', 'Asia-Pacific', 'China', 'Latin_America', 'India', 'Africa']
Y_Axis['Number', 'of', 'UHNW', 'individuals']: ['84054', '33551', '22657', '18132', '4460', '4376', '804']

gold: This statistic shows the regional distribution of ultra high net worth individuals in 2019 . About 84,054 individuals with net assets of at least 50 million U.S. dollars were residing in North America in 2019 . That is about 50 percent of the total number of UHNW individuals worldwide .
gold_template: This statistic shows the regional templateTitle[5] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] in templateTitleDate[0] . About templateYValue[max] templateYLabel[2] with templateTitle[2] assets of at least 50 million U.S. dollars were residing in templateXValue[0] in templateTitleDate[0] . That is about 50 percent of the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] worldwide .

generated_template: The statistic displays the templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitle[2] between 1982 and 500 , 2020 , templateTitle[4] templateXLabel[0] and ethnicity of the shooter ( s ) . Between 1982 and 2020 , templateYValue[max] internet of 118 templateTitle[0] templateTitle[1] were initiated templateTitle[4] templateXValue[0] templateTitle[3] .
generated: The statistic displays the Number of Ultra high in the net between 1982 and 500 , 2020 individuals Region and ethnicity of the shooter ( s ) . Between 1982 and 2020 , 84054 internet of 118 Ultra high were initiated individuals North America worth .

Example 10:
titleEntities: {'Subject': ['Chicago Blackhawks', 'NHL'], 'Date': ['2014/15', '2014/15']}
title: Average ticket price Chicago Blackhawks ( NHL ) 2014/15
X_Axis['Year']: ['2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['78.8', '72.95', '62.88', '55.72', '55.39', '55.39', '46.8', '52.22', '34.88', '34.88']

gold: This graph depicts the average ticket price of Chicago Blackhawks games within the National Hockey League from 2005/06 to 2014/15 In the 2005/06 season , the average ticket price was 34.88 U.S. dollars . The Blackhawks play their home games at the United Center .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] games within the National Hockey League from templateXValue[last] to templateXValue[0] In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] play their home games at the United Center .

generated_template: The graph depicts the templateYLabel[0] The templateXValue[max] for templateTitleSubject[0] games of the templateXValue[7] Basketball internet from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateXValue[max] was templateYValue[min] This 's .
generated: The graph depicts the Average 2014/15 for Chicago Blackhawks games of the 2007/08 Basketball internet from 2005/06 to 2014/15 . In the 2005/06 season , the Average 2014/15 was 34.88 This 's .

Example 11:
titleEntities: {'Subject': ['Audi'], 'Date': ['2002', '2018']}
title: Audi - total assets 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['Total', 'assets', 'in', 'million', 'euros']: ['65598', '63680', '61090', '56763', '50769', '45156', '40401', '37019', '30772', '26550', '26056', '22578', '18910', '16112', '14904', '14063', '12650']

gold: This statistic shows Audi 's total assets from the fiscal year of 2002 to the fiscal year of 2018 . In the fiscal year of 2018 , Audi held total assets of around 65.6 billion euros ( or about 74.43 billion US dollars ) .
gold_template: This statistic shows templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , templateTitleSubject[0] held templateYLabel[0] templateYLabel[1] of around templateYValue[max] templateYLabel[2] templateYLabel[3] ( or about 74.43 templateYLabel[2] US dollars ) .

generated_template: The statistic shows the average templateYLabel[0] of the templateTitleSubject[0] Yankees templateTitleSubject[0] from templateXValue[min] to had , and a forecast reason for templateXValue[1] . In templateXValue[min] , the templateTitleSubject[0] generated a total of templateYValue[min] templateXValue[max] This that .
generated: The statistic shows the average Total of the Audi Yankees from 2002 to had , and a forecast reason for 2017 . In 2002 , the Audi generated a total of 12650 2018 This that .

Example 12:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2010', '2017']}
title: Grocery market growth year-on-year in Great Britain 2010 to 2017
X_Axis['Year']: ['28_Nov_2010', '27_Nov_2011', '25_Nov_2012', '10_Nov_2013', '09_Nov_2014', '09_Nov_2015', '06_Nov_2016', '05_Nov_2017']
Y_Axis['Percentage', 'growth', '(year-on-year)']: ['2.5', '4.2', '3.2', '3.2', '-0.2', '0.5', '0.8', '3.8']

gold: In the three months leading up to November 5 , 2017 , the grocery market in Great Britain grew by 3.8 percent compared to the same period a year earlier . Compared to the growth during the last three years this constitutes a sizeable increase . The entire grocery market amounted to 185.2 billion British pounds in 2017 .
gold_template: In the templateYValue[0] months leading up to 5 , templateXValue[last] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] grew by templateYValue[last] percent compared to the same period a templateXLabel[0] earlier . Compared to the templateYLabel[1] during the last templateYValue[0] years this constitutes a sizeable increase . The entire templateTitle[0] templateTitle[1] amounted to 185.2 billion British pounds in templateXValue[last] .

generated_template: The statistic shows templateTitle[3] templateTitle[4] templateTitle[5] ' responses to a survey question tournament from templateXValue[min] to templateTitleDate[max] , about increases in templateTitle[4] This . In templateXValue[3] , templateTitle[0] templateTitle[4] templateYLabel[0] was America at around templateYValue[min] templateXValue[max] This 's in templateTitleSubject[0] worldwide .
generated: The statistic shows year-on-year Great Britain ' responses to a survey question tournament from 05 Nov 2017 to 2017 , about increases in Great This . In 10 Nov 2013 , Grocery Great Percentage was America at around -0.2 28 Nov 2010 This 's in Great Britain worldwide .

Example 13:
titleEntities: {'Subject': ['Brooklyn Nets'], 'Date': ['2001', '2019']}
title: Brooklyn Nets ' revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['304', '290', '273', '223', '220', '212', '190', '84', '89', '89', '92', '98', '102', '93', '87', '93', '94', '91']

gold: The statistic shows the revenue of the New Jersey Nets franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise is 304 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the New Jersey templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise is templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: the statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the templateXValue[7] Basketball Association franchise an to templateYValue[max] the previous templateXLabel[0] . templateTitleSubject[0] that templateTitleSubject[0] are a professional basketball franchise of the templateXValue[7] Basketball Association ( 900 ) , Peace the league as templateXValue[6] expansion team in 1968 .
generated: the statistic shows the Revenue of the Brooklyn Nets franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the 11/12 Basketball Association franchise an to 304 the previous Year . Brooklyn Nets that are a professional basketball franchise of the 11/12 Basketball Association ( 900 ) , Peace the league as 12/13 expansion team in 1968 .

Example 14:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. consumers who have personally experienced hacking 2018
X_Axis['Response']: ['Yes_more_than_once', 'Yes_but_only_once', 'No_never', "Don't_know_/_can't_recall"]
Y_Axis['Share', 'of', 'respondents']: ['14', '22', '51', '12']

gold: This statistic presents the share of internet users in the United States who have ever had any of their online accounts hacked . During the October 2018 survey , 14 percent of respondents stated that their online accounts had been hacked more than once .
gold_template: This statistic presents the templateYLabel[0] of internet users in the templateTitle[0] templateTitle[2] templateTitle[3] ever had any of their online accounts hacked . During the October templateTitleDate[0] survey , templateYValue[0] percent of templateYLabel[1] stated that their online accounts had been hacked templateXValue[0] once .

generated_template: The statistic shows the results of a survey among female templateTitleSubject[0] stood Capital students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . season to the source , templateYValue[max] percent of female students in the templateTitle[1] were templateTitle[5] templateTitle[6] as of 2013 .
generated: The statistic shows the results of a survey among female U.S. stood Capital students have personally experienced hacking . season to the source , 51 percent of female students in the consumers were experienced hacking as of 2013 .

Example 15:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. exports - top trading partners 2018
X_Axis['Country']: ['Canada', 'Mexico', 'China', 'Japan', 'United_Kingdom', 'Germany', 'Korea_South', 'Netherlands', 'Brazil', 'Hong_Kong', 'France', 'Singapore', 'India', 'Belgium', 'Taiwan']
Y_Axis['Export', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['298.7', '265.0', '120.3', '75.0', '66.2', '57.7', '56.3', '49.4', '39.5', '37.5', '36.3', '33.1', '33.1', '31.4', '30.2']

gold: This graph shows the largest trading partners for the United States in 2018 , by value of exported trade goods . In 2018 , the United States exported trade goods worth about 39.5 billion U.S. dollars to Brazil .
gold_template: This graph shows the largest templateTitle[3] templateTitle[4] for the templateXValue[4] States in templateTitleDate[0] , by templateYLabel[1] of exported trade goods . In templateTitleDate[0] , the templateXValue[4] States exported trade goods worth about templateYValue[8] templateYLabel[2] templateYLabel[3] templateYLabel[4] to templateXValue[8] .

generated_template: The statistic shows the templateTitle[3] templateTitle[4] of the templateTitle[0] templateTitle[1] in templateTitleDate[0] , based on this volume . In templateTitleDate[0] , templateXValue[0] they about templateYValue[max] templateXValue[max] This 's of templateTitle[1] .
generated: The statistic shows the trading partners of the U.S. exports in 2018 , based on this volume . In 2018 , Canada they about 298.7 Canada This 's of exports .

Example 16:
titleEntities: {'Subject': ['Kansas'], 'Date': ['2000', '2018']}
title: Kansas - 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']: ['12', '11.9', '12.1', '13', '13.6', '14', '14', '13.8', '13.6', '13.4', '11.3', '11.2', '12.4', '11.7', '10.5', '10.8', '12.1', '11.3', '9.5']

gold: This statistic shows the poverty rate in Kansas from 2000 to 2018 . In 2018 , about 12 percent of Kansas ' 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)] percent of templateTitleSubject[0] ' templateYLabel[1] lived below the templateTitle[1] line .

generated_template: The statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateTitleDate[max] . For instance , there were a templateTitle[1] in the most recent state in the templateTitle[1] .
generated: The statistic shows the poverty rate in Kansas from 2000 to 2018 . For instance , there were a poverty in the most recent state in the poverty .

Example 17:
titleEntities: {'Subject': ['Economic'], 'Date': ['2016']}
title: Economic loss due to major droughts worldwide up to 2016
X_Axis['Drought']: ['United_States_June_2012', 'China_P_Rep_January_1994', 'United_States_January_2011', 'Australia_1981', 'Brazil_January_2014', 'Spain_September_1990', 'China_P_Rep_October_2009', 'United_States_July_2002', 'Iran_Islam_Rep_April_1999', 'Spain_April_1999']
Y_Axis['Economic', 'loss', 'in', 'billion', 'U.S.', 'dollars']: ['20.0', '13.8', '8.0', '6.0', '5.0', '4.5', '3.6', '3.3', '3.3', '3.2']

gold: This statistic shows the economic harm as a result of droughts in certain countries in the period from 1900 to 2016  . The drought in China in 1994 caused an economic loss of almost 13.8 billion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] harm as a result of templateTitle[4] in certain countries in the period from 1900 to templateTitleDate[0] . The templateXLabel[0] in templateXValue[1] in templateXValue[1] caused an templateYLabel[0] templateYLabel[1] of almost templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows a ranking of the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[5] on the templateYLabel[0] of templateXValue[max] 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] templateXValue[max] This .
generated: The statistic shows a ranking of the Economic loss major droughts worldwide on the Economic of Brazil January 2014 as of 2017 . At this point , the China P Rep January 1994 United States June 2012 was ranked second among such droughts in the due , with a total of 13.8 Brazil January 2014 This .

Example 18:
titleEntities: {'Subject': ['Germany'], 'Date': ['2000', '2013']}
title: Trade union density in Germany from 2000 to 2013
X_Axis['Year']: ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013']
Y_Axis['Percentage', 'of', 'employees']: ['24.57', '23.75', '23.51', '23.02', '22.17', '21.68', '20.72', '19.89', '19.13', '18.93', '18.56', '18.49', '18.3', '18.13']

gold: This statistic shows the percentage of employees that were members of a trade union ( trade union density ) in Germany from 2000 to 2013 . Over this 14 year period , the percentage fell by a total of 6.44 percentage points .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] that were members of a templateTitle[0] templateTitle[1] ( templateTitle[0] templateTitle[1] templateTitle[2] ) in templateTitleSubject[0] templateTitle[4] templateXValue[min] to templateXValue[max] . Over this 14 templateXLabel[0] period , the templateYLabel[0] fell by a total of 6.44 templateYLabel[0] points .

generated_template: The statistic gives information on the This templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[12] and gives forecasts for the following templateXLabel[0] . In the most founded period , the templateTitle[0] templateTitle[1] templateTitle[2] increased from the previous templateXLabel[0] to templateYValue[max] percent . The is expected to grow to templateYValue[max] percent in increase .
generated: The statistic gives information on the This density in Germany from 2000 to 2012 and gives forecasts for the following Year . In the most founded period , the Trade union density increased from the previous Year to 24.57 percent . The is expected to grow 24.57 percent in increase .

Example 19:
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)] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: The statistic in the degree of templateXValue[min] in templateTitleSubject[0] from shows to cities . templateXValue[min] means the templateYLabel[0] of the dropped templateXValue[max] in the This templateXValue[max] of a approximately . In the This templateXValue[max] of the This templateXValue[max] of a approximately .
generated: The statistic in the degree of 2008 in Finland from shows to cities . 2008 means the Share of the dropped 2018 in the This 2018 of a approximately . In the This 2018 of the This 2018 of a approximately .

Example 20:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2010', '2019']}
title: Twitter : number of monthly active users 2010 to 2019
X_Axis['Quarter']: ["Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10", "Q2_'10", "Q1_'10"]
Y_Axis['Number', 'of', 'monthly', 'active', 'users', 'in', 'millions']: ['330.0', '321.0', '326.0', '335.0', '336.0', '330.0', '330.0', '326.0', '327.0', '318.0', '317.0', '313.0', '310.0', '305.0', '307.0', '304.0', '302.0', '288.0', '284.0', '271.0', '255.0', '241.0', '231.7', '218.0', '204.0', '185.0', '167.0', '151.0', '138.0', '117.0', '101.0', '85.0', '68.0', '54.0', '49.0', '40.0', '30.0']

gold: How many people use Twitter ? As of the first quarter of 2019 , Twitter averaged 330 million monthly active users , a decline from its all-time high of 336 MAU in the first quarter of 2018 . As of the first quarter of 2019 , the company switched its user reporting metric to monetizable daily active users ( mDAU ) . Twitter Twitter is a social networking and microblogging service , enabling registered users to read and post short messages called tweets .
gold_template: How many people use templateTitleSubject[0] ? As of the first templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] averaged templateYValue[0] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] , a decline from its all-time high of templateYValue[max] MAU in the first templateXLabel[0] of 2018 . As of the first templateXLabel[0] of templateTitleDate[max] , the company switched its user reporting metric to monetizable daily templateYLabel[2] templateYLabel[3] ( mDAU ) . templateTitleSubject[0] is a social networking and microblogging service , enabling registered templateYLabel[3] to read and post short messages called tweets .

generated_template: The statistic shows a timeline with the amount of templateXValue[max] templateTitle[4] templateTitleSubject[0] This as of the first templateXLabel[0] of templateTitleDate[max] . Excluding the country , the templateYLabel[0] of templateXValue[max] templateTitle[4] templateTitleSubject[0] This amounted to templateYValue[max] 's , up from templateYValue[1] 's in the preceding templateXLabel[0] .
generated: The statistic shows a timeline with the amount of Q4 '18 users Twitter This as of the first Quarter of 2019 . Excluding the country , the Number of Q4 '18 users Twitter This amounted to 336.0 's , up from 321.0 's in the preceding Quarter .

Example 21:
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] templateYLabel[0] 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] templateYLabel[0] 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: The statistic shows the templateTitle[3] of templateTitleSubject[0] templateTitle[1] templateYLabel[0] in selected countries . As of 2012 , templateXValue[0] had the highest templateYLabel[0] of templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] with an templateYLabel[0] of templateYValue[max] templateXValue[max] .
generated: The statistic shows the degree of Percentage population Percent in selected countries . As of 2012 , Canada had the highest Percent of Percentage population in the Percentage with an Percent of 48 Canada .

Example 22:
titleEntities: {'Subject': ['Projected'], 'Date': ['1950', '2100']}
title: Projected global median age 1950 to 2100
X_Axis['Year']: ['2100', '2095', '2090', '2085', '2080', '2075', '2070', '2065', '2060', '2055', '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']: ['41.9', '41.3', '40.8', '40.3', '39.7', '39.2', '38.7', '38.2', '37.6', '36.9', '36.2', '35.4', '34.6', '33.9', '33.0', '32.0', '30.9', '29.6', '28.5', '27.4', '26.3', '25.1', '24.0', '23.3', '22.6', '21.9', '21.5', '22.0', '22.6', '23.1', '23.6']

gold: This statistic shows the median age of the world population from 1950 to 2100 . By 2100 , the global median age is projected to be 41.9 years of age .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the world population from templateXValue[min] to templateXValue[max] . By templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] is templateTitleSubject[0] to be templateYValue[idxmax(X)] years of templateYLabel[1] .

generated_template: The statistic shows the Ukrainian templateYLabel[0] of templateTitle[1] templateTitle[2] in the templateTitle[3] from templateXValue[min] to had , with projections up years templateTitleDate[max] . In had , templateTitleSubject[0] that templateYLabel[0] of templateTitle[1] templateTitle[2] an to approximately templateYValue[7] percent templateXValue[max] to the This templateXLabel[0] .
generated: The statistic shows the Ukrainian Median of global median in the age from 1950 to had , with projections up years 2100 . In had , Projected that Median of global median an to approximately 38.2 percent 2100 to the This Year .

Example 23:
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)] templateYLabel[0] cars and light vehicles produced worldwide were equipped with an templateTitle[2] templateTitle[3] . It is templateTitle[4] that this number will increase to about templateYValue[idxmax(X)] templateYLabel[0] automobiles produced worldwide in templateXValue[idxmax(Y)] .

generated_template: The statistic shows the templateTitle[3] templateYLabel[0] held by templateTitleSubject[0] as of templateTitle[1] templateTitle[2] and templateTitle[3] from templateXValue[min] to templateXValue[4] and a forecast reason years templateTitleDate[max] . In templateXValue[2] , templateTitle[0] templateTitle[3] are expected to one to not percent compared to the previous templateXLabel[0] .
generated: The statistic shows the transmission Million held by Automobile as of production automatic and transmission from 2010 to 2014 and a forecast reason years 2010 . In 2012 , Automobile transmission are expected to one not percent compared to the previous Year .

Example 24:
titleEntities: {'Subject': ['General Motors'], 'Date': ['2015', '2019']}
title: General Motors - adjusted EBIT 2015 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015']
Y_Axis['EBIT', '(adjusted;', 'in', 'million', 'U.S.dollars)']: ['8393', '11783', '12844', '12848', '11449']

gold: In 2019 , General Motors produced earnings before interests and taxes of around 8.4 billion U.S. dollars , down from almost 11.8 billion U.S. dollars in 2018 . GM 's earnings were affected by falling vehicle sales , particularly in China .
gold_template: In templateXValue[max] , templateTitleSubject[0] produced earnings before interests and taxes of around templateYValue[min] templateYLabel[2] U.S. dollars , down from almost templateYValue[1] templateYLabel[2] U.S. dollars in templateXValue[1] . GM 's earnings were affected by falling vehicle sales , particularly in China .

generated_template: The statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per Product in the templateTitleSubject[0] between templateXValue[min] and templateXValue[1] , with a forecast for 2018 . In templateXValue[min] , the templateTitle[0] of templateTitle[2] templateTitle[3] in the templateTitleSubject[0] an to templateYValue[max] templateXValue[max] This 's . The is expected to one to one to them and one production in the templateXLabel[0] increase .
generated: The statistic displays the General of Motors adjusted EBIT per Product in the General Motors between 2015 and 2018 , with a forecast for 2018 . In 2015 , the General of adjusted EBIT in the General Motors an to 12848 2019 This 's . The is expected to one them and one production in the Year increase .

Example 25:
titleEntities: {'Subject': ['Germany'], 'Date': ['2012', '2028']}
title: Travel and tourism 's total contribution to GDP in Germany 2012 to 2028
X_Axis['Year']: ['2028', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Value', 'in', 'billion', 'euros']: ['411.0', '358.3', '348.1', '342.2', '336.2', '325.7', '315.9', '318.0']

gold: This statistic presents the total contribution of travel and tourism to GDP in Germany from 2012 to 2018 , with a forecast for 2028 . Over this period , the contribution of the travel and tourism industry to GDP in Germany has increased , reaching around 348.1 billion euros in 2017 .
gold_template: This statistic presents the templateTitle[3] templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[1] , with a forecast for templateXValue[max] . Over this period , the templateTitle[4] of the templateTitle[0] and templateTitle[1] industry to templateTitle[5] in templateTitleSubject[0] has increased , reaching around templateYValue[2] templateYLabel[1] templateYLabel[2] in templateXValue[2] .

generated_template: templateTitle[0] and templateTitle[1] have Europe be of the leading engines of growth for the observed economy , Development an ongoing one in the templateTitle[5] templateTitle[4] over the last years and projected to reach approximately templateYValue[1] templateXValue[max] templateTitle[0] in templateXValue[1] . templateTitleSubject[0] ranked second on the much templateTitle[1] Organization templateTitle[2] list of most visited countries in the world , with its number of international visitors amounting to nearly 82 templateXValue[max] in templateXValue[2] . A popular holiday traffic for Europeans , templateTitleSubject[0] this is be of slight templateTitle[2] death holiday holiday mainly in templateXValue[1] – the United Kingdom , Germany and France appeared in the leading positions of the largest number of international visitors to templateTitleSubject[0] .
generated: Travel and tourism have Europe be of the leading engines of growth for the observed economy , Development an ongoing one in the GDP contribution over the last years and projected to reach approximately 358.3 2028 Travel in 2018 . Germany ranked second on the much tourism Organization 's list of most visited countries in the world , with its number of international visitors amounting to nearly 82 2028 in 2017 . A popular holiday traffic for Europeans , Germany this is be of slight 's death holiday mainly in 2018 – the United Kingdom , Germany and France appeared in the leading positions of the largest number of international visitors to Germany .

Example 26:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2000']}
title: Argentina : internet penetration 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'individuals', 'using', 'internet']: ['74.29', '70.97', '68.04', '64.7', '59.9', '55.8', '51', '45', '34', '28.11', '25.95', '20.93', '17.72', '16.04', '11.91', '10.88', '9.78', '7.04']

gold: This statistic gives information on the internet penetration in Argentina from 2000 to 2017 . In 2017 , about 74.3 percent of the Argentinean population accessed the internet , a considerable increase when compared to only seven percent in 2000 .
gold_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[max] percent of the Argentinean population accessed the templateYLabel[3] , a considerable increase when compared to only templateYValue[min] percent in templateXValue[idxmin(Y)] .

generated_template: In the U.S. state of templateTitleSubject[0] , there were approximately templateYValue[0] This templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] 's amounted in this in templateTitleDate[0] . Since templateXValue[min] , than figure up been steadily increasing to the templateXLabel[0] templateXValue[10] in which it also reached its peak with around 213 thousand This and 100 percent . 2019.The , the templateTitleSubject[0] templateTitle[1] templateTitle[2] in the templateTitle[3] has been more until 100 percent of the From population in templateXValue[1] .
generated: In the U.S. state of Argentina , there were approximately 74.29 This internet penetration and 2000 2017 's amounted in this 2000 . Since 2000 , than figure up been steadily increasing to the Year 2007 in which it also reached its peak with around 213 thousand This and 100 percent . 2019.The , the Argentina internet penetration in the 2000 has been more until 100 percent of the From population in 2016 .

Example 27:
titleEntities: {'Subject': ['Southwest Airlines'], 'Date': ['2019']}
title: Revenue passenger miles ( RPMs ) of Southwest Airlines 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['RPMs', 'in', 'billions']: ['131.35', '133.32', '129.04', '124.8', '117.5', '108.04', '104.35', '102.87', '97.58', '78.05']

gold: In the fiscal year of 2019 , Southwest Airlines transported passengers on a total of over 131.3 billion miles . The leading low-cost carrier had a capacity of 157.2 billion available seat miles in that same year , and as such was efficient in using its fleet to transport paying customers . Flying with Southwest Southwest Airlines ' main hub , Las Vegas McCarran International Airport , saw a traffic of 17.5 million Southwest passengers in 2018 .
gold_template: In the fiscal templateXLabel[0] of templateXValue[max] , templateTitleSubject[0] transported passengers on a total of over 131.3 templateYLabel[1] templateTitle[2] . The leading low-cost carrier had a capacity of 157.2 templateYLabel[1] 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 templateYLabel[1] templateTitleSubject[0] passengers in templateXValue[1] .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] platform templateYLabel[0] of global templateTitle[0] templateTitle[1] systems in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to per templateXLabel[0] . In the fiscal templateXLabel[0] ending in templateXValue[min] , the templateTitle[0] templateTitle[1] templateXValue[min] to approximately templateYValue[0] templateXValue[max] British pounds . The Louis to one , the templateTitle[0] templateTitle[1] Republic templateYLabel[0] in the templateTitleSubject[0] is be of be as of 2019 .
generated: The statistic shows the Southwest Airlines passenger platform RPMs of global Revenue passenger systems in the Southwest Airlines ( ) from 2010 to per Year . In the fiscal Year ending in 2010 , the Revenue passenger 2010 to approximately 131.35 2019 British pounds . The Louis to one , the Revenue passenger Republic RPMs in the Southwest Airlines is be of as 2019 .

Example 28:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2008', '2019']}
title: Online purchasing penetration in Great Britain 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'respondents']: ['82', '78', '77', '77', '76', '74', '72', '67', '66', '62', '61', '53']

gold: This statistic displays a timeline of online purchasing take up in Great Britain from 2008 to 2019 . In 2019 , 82 percent of polled Britons made purchases online . This is greater penetration than in 2008 , when 53 percent of individuals made purchases online .
gold_template: This statistic displays a timeline of templateTitle[0] templateTitle[1] take up in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] percent of polled Britons made purchases templateTitle[0] . This is greater templateTitle[2] than in templateXValue[min] , when templateYValue[idxmin(X)] percent of individuals made purchases templateTitle[0] .

generated_template: There is no use denying templateYValue[idxmax(X)] the called people consume newspapers and magazines has changed . As of 2019 , more until two transportation of individuals in templateTitleSubject[0] were either reading or downloading templateTitle[0] templateTitle[1] , newspapers or magazines . The was more until three times the templateYLabel[0] of templateTitle[0] readers as compared to templateXValue[min] .
generated: There is no use denying 82 the called people consume newspapers and magazines has changed . As of 2019 , more until two transportation of individuals in Great Britain were either reading or downloading Online purchasing , newspapers or magazines . The was more until three times the Share of Online readers as compared to 2008 .

Example 29:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2010', '2018']}
title: University applicants in the United Kingdom ( UK ) 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Number', 'of', 'university', 'applicants']: ['636960', '649700', '674890', '673040', '659030', '635910', '616700', '668150', '658560']

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

generated_template: The statistic shows templateTitleSubject[0] that templateYLabel[0] and development and development from templateXValue[min] to templateXValue[6] . In templateXValue[4] , the company that templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] employed at approximately templateYValue[max] percent templateXValue[max] This . templateTitleSubject[0] is a globally inhabitants oil and gas company , and the favorable largest company generally worldwide as of templateXValue[1] , measured by templateTitle[4] .
generated: The statistic shows United Kingdom that Number and development from 2010 to 2012 . In 2014 , the company that University applicants United Number employed at approximately 674890 percent 2018 This . United Kingdom is a globally inhabitants oil and gas company , and the favorable largest company generally worldwide as of 2017 , measured by UK .

Example 30:
titleEntities: {'Subject': ['ITV', 'UK'], 'Date': ['2019']}
title: ITV viewers reached quarterly in the 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']: ['55196', '55302', '54443', '55327', '55277', '55014', '54013', '55559', '55443', '55628', '54636', '55195', '53997', '53716', '53457', '54227', '53928', '54201', '52843', '54150', '53038', '51936', '51546', '53303', '52906', '51628', '47762', '48375', '48236', '46376', '45308']

gold: This statistic shows the quarterly reach of the ITV television channel in the United Kingdom ( UK ) from the first quarter of 2012 to the third quarter of 2019 . In the quarter ending September 2014 , ITV reached 54.64 million viewers . The number of viewers fell to roughly 45.3 million in the last period in consideration .
gold_template: This statistic shows the templateTitle[3] reach of the templateTitleSubject[0] television channel in the United Kingdom ( templateTitleSubject[1] ) from the first templateXLabel[0] of templateXValue[0] to the third templateXLabel[0] of templateXValue[28] . In the templateXLabel[0] ending 2014 , templateTitleSubject[0] templateTitle[2] templateYValue[10] templateYLabel[1] templateYLabel[0] . The number of templateYLabel[0] fell to roughly templateYValue[min] templateYLabel[1] in the last period in consideration .

generated_template: The statistic shows a timeline with the global templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] and templateTitle[6] from the first templateXLabel[0] of templateTitle[6] to the fourth templateXLabel[0] of templateTitleDate[max] . In the fourth templateXLabel[0] of templateTitle[7] , templateTitleSubject[0] templateTitle[1] market templateYLabel[0] in the templateTitle[4] market was at templateYValue[0] percent .
generated: The statistic shows a timeline with the global Viewers of reached quarterly UK ITV and 2012-Q3 from the first Quarter of 2012-Q3 to the fourth Quarter of 2019 . In the fourth Quarter of 2019 , ITV viewers market in the UK market was at 55196 percent .

Example 31:
titleEntities: {'Subject': ['San Jose'], 'Date': ['2019']}
title: Player expenses ( payroll ) of San Jose Earthquakes 2019
X_Axis['Month']: ['Valeri_"Vako"_Qazaishvili', 'Chris_Wondolowski', 'Florian_Jungwirth', 'Guram_Kashia', 'Cristian_Espinoza', 'Danny_Hoesen', 'Anibal_Godoy', 'Magnus_Eriksson', 'Marcos_Lopez_Lanfranco', 'Harold_Cummings', 'Judson', 'Francois_Affolter', 'Shea_Salinas', 'Andrew_Tarbell', 'Nick_Lima', 'Daniel_Vega', 'Jackson_Yueill', 'Tommy_Thompson', 'JT_Marcinkowski', 'Eric_Calvillo', 'Siad_Haji', 'Gilbert_Fuentes', 'Matt_Bersano', 'Luis_Felipe', 'Jimmy_Ockford', 'Cade_Cowell', 'Jacob_Akanyirige', 'Kevin_Partida', 'Paul_Marie']
Y_Axis['Payroll', 'in', 'thousand', 'U.S.', 'dollars']: ['1604.04', '800.0', '616.68', '590.0', '550.0', '549.67', '498.13', '450.0', '387.75', '320.67', '305.0', '273.0', '250.0', '235.0', '218.44', '210.0', '190.0', '175.0', '147.0', '135.0', '104.0', '92.0', '71.67', '71.63', '70.88', '67.23', '64.23', '57.23', '57.23']

gold: The statistic shows the player expenses ( payroll ) of the San Jose Earthquakes club of Major League Soccer by player in 2019 . Valeri `` Vako '' Qazaishvili received a salary of 1.6 million U.S. dollars .
gold_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] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] Red templateTitle[6] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] received a salary of templateYValue[max] thousand templateXValue[max] This in templateYValue[idxmax(X)] year .
generated: statistic shows the Player expenses ( Payroll ) of the San Jose Red 2019 club of Major League Soccer by Player in 2019 . Valeri "Vako" Qazaishvili received a salary of 1604.04 thousand Valeri "Vako" Qazaishvili This in 57.23 year .

Example 32:
titleEntities: {'Subject': ['PepsiCo'], 'Date': ['2007', '2018']}
title: PepsiCo 's net revenue worldwide 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['64.66', '63.53', '62.8', '63.06', '66.68', '66.42', '65.49', '66.5', '57.84', '43.23', '43.25', '39.47']

gold: This timeline depicts PepsiCo 's net revenue worldwide from 2007 to 2018 . In 2018 , PepsiCo 's net revenue worldwide amounted to about 64.66 billion U.S. dollars . PepsiCoPepsiCo is an American food and beverage corporation , based in Purchase , NY .
gold_template: This timeline depicts templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[4] amounted to about templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] . PepsiCoPepsiCo is an American food and beverage corporation , based in Purchase , NY .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] Cheese templateTitle[4] templateTitle[5] from templateXValue[min] to templateTitleDate[max] . templateTitleSubject[0] Cheese templateTitle[4] generated approximately templateYValue[0] templateXValue[max] This in templateYLabel[0] in increase , up from templateYValue[1] at the previous templateXLabel[0] . templateTitle[0] templateTitleSubject[0] is a estimates of discount variety stores templateYValue[idxmax(X)] caused in 44 This in templateYValue[idxmax(X)] templateXLabel[0] .
generated: The statistic shows the Revenue of PepsiCo Cheese worldwide 2007 from to 2018 . PepsiCo Cheese worldwide generated approximately 64.66 2018 This in Revenue increase , up from 63.53 at the previous Year . PepsiCo is a estimates of discount variety stores 64.66 caused in 44 This in 64.66 Year .

Example 33:
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: The statistic shows the templateTitleSubject[0] video templateTitle[0] templateTitleSubject[0] templateTitle[5] as of October entertainment , templateTitleDate[0] . templateXValue[0] has hit the most reputation in templateTitleSubject[0] franchise history with an estimated templateYLabel[0] of templateYValue[max] templateXValue[max] This 's .
generated: The statistic shows the Oakland Athletics video Oakland Oakland Athletics leaders as of October entertainment , 2019 . Mark McGwire has hit the most reputation in Oakland Athletics franchise history with an estimated Number of 363 Mark McGwire This 's .

Example 34:
titleEntities: {'Subject': ['BuzzFeed'], 'Date': ['2015', '2016']}
title: BuzzFeed : monthly content views 2015 to 2016
X_Axis['Month']: ["Apr_'15", "Oct_'15", "May_'16"]
Y_Axis['Number', 'of', 'monthly', 'video', 'viewers', 'in', 'millions']: ['1000', '5000', '7000']

gold: This statistic presents the number of monthly BuzzFeed video viewers in 2013 and 2014 and as of May 2016 . In the most recent reported period , 7 billion viewers accessed content on the social sharing platform per month .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateTitleSubject[0] templateYLabel[2] templateYLabel[3] in 2013 and 2014 and as of templateXValue[last] templateTitleDate[max] . In the most recent reported period , templateYValue[max] templateYLabel[4] templateYLabel[3] accessed templateTitle[2] on the social sharing platform per templateXLabel[0] .

generated_template: The statistic been the templateYLabel[0] of templateTitle[1] in the templateTitle[0] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateYValue[0] templateYValue[max] templateXValue[max] templateTitle[1] and templateXValue[1] were they in the templateTitle[0] .
generated: The statistic been the Number of monthly in the BuzzFeed in 2015 , 2016 Month . In 2015 , 1000 7000 May '16 monthly and Oct '15 were they in the BuzzFeed .

Example 35:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000', '2018']}
title: Canada : number of robberies 2000 to 2018
X_Axis['Year']: ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Number', 'of', 'robberies']: ['30582', '30756', '30036', '32084', '30990', '32437', '34641', '34182', '32372', '32463', '30478', '29790', '27748', '23249', '20932', '22149', '21958', '22831', '22450']

gold: This statistic shows the number of robberies in Canada from 2000 to 2018 . There were 22,450 robberies in Canada in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . There were templateYValue[idxmax(X)] templateYLabel[1] in templateTitleSubject[0] in templateXValue[max] .

generated_template: The statistic shows the templateTitle[0] 913 templateTitle[1] in the templateTitle[3] from templateXValue[min] to templateTitleDate[max] . In templateXValue[14] , templateTitle[4] in the templateTitle[3] occupied a templateTitle[0] templateTitle[1] of approximately 899 templateXValue[max] . By recent period , than amount decreased to pictures templateXValue[max] .
generated: The statistic shows the Canada 913 number in the 2000 from to 2018 . In 2014 , 2018 in the 2000 occupied a Canada number of approximately 899 2018 . By recent period , than amount decreased to pictures 2018 .

Example 36:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of fresh citrus fruit 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']: ['23.95', '23.82', '24.07', '22.75', '23.28', '23.97', '23.54', '22.81', '21.59', '20.69', '20.62', '17.94', '21.64', '21.63', '22.7', '23.82', '23.37', '23.93', '23.54']

gold: This statistic presents the per capita consumption of fresh citrus fruit in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh citrus fruit amounted to about 23.95 pounds in 2018 .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] templateTitle[6] 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] templateTitle[6] amounted to about templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .

generated_template: The statistic depicts the templateYLabel[0] timeline templateXValue[max] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to per years . According to the report , the templateTitleSubject[0] templateYLabel[0] templateTitle[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] This in templateXValue[idxmin(Y)] . In the same templateXLabel[0] , the templateXValue[max] of templateTitle[4] fruit in the templateTitleSubject[0] amounted to about 115.14 This .
generated: The statistic depicts the Per timeline 2018 of fresh citrus in the U.S. from 2000 to per years . According to the report , the U.S. Per capita of fresh citrus amounted to approximately 23.95 This in 2007 . In the same Year , the 2018 of fresh fruit in the U.S. amounted to about 115.14 This .

Example 37:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2017']}
title: Car rental industry in the U.S. - revenue 2002 to 2017
X_Axis['Year']: ['2002', '2007', '2012', '2017']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['16.43', '21.49', '23.63', '28.63']

gold: This statistic represents the U.S. rental car industry 's total revenue between 2002 and 2017 . While the industry 's revenue grew to around 28.6 billion U.S. dollars in 2017 , about 1.59 million vehicles were purchased in 2017 by rental companies in the United States .
gold_template: This statistic represents 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] grew to around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] , about 1.59 templateYLabel[1] vehicles were purchased in templateXValue[max] by templateTitle[1] companies in the templateTitle[3] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateTitleDate[max] . In templateXValue[2] , there were have templateYValue[idxmax(X)] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] mobile templateTitle[5] templateYLabel[0] . In the same templateXLabel[0] , templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] based on templateTitle[4] business French by an templateYLabel[0] .
generated: The statistic shows the Revenue of U.S. industry revenue 2002 2017 from 2002 to 2017 . In 2012 , there were have 28.63 the industry U.S. revenue 2002 mobile Revenue . In the same Year , rental industry U.S. revenue based on revenue business French by an Revenue .

Example 38:
titleEntities: {'Subject': ['Google'], 'Date': ['2003']}
title: Google : quarterly net income 2003 to 2015
X_Axis['Financial', 'Quarter/Year']: ["Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10", "Q2_'10", "Q1_'10", "Q4_'09", "Q3_'09", "Q2_'09", "Q1_'09", "Q4_'08", "Q3_'08", "Q2_'08", "Q1_'08", "Q4_'07", "Q3_'07", "Q2_'07", "Q1_'07", "Q4_'06", "Q3_'06", "Q2_'06", "Q1_'06", "Q4_'05", "Q3_'05", "Q2_'05", "Q1_'05", "Q4_'04", "Q3_'04", "Q2_'04", "Q1_'04", "Q4_'03", "Q3_'03", "Q2_'03", "Q1_'03"]
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['3979.0', '3931.0', '3515.0', '4675.0', '2739.0', '3351.0', '3371.0', '3324.0', '2921.0', '3376.0', '2970.0', '2886.0', '2176.0', '2785.0', '2890.0', '2705.0', '2729.0', '2505.0', '1798.0', '2543.0', '2167.0', '1840.0', '1955.0', '1974.0', '1439.0', '1485.0', '1423.0', '382.4', '1289.9', '1247.5', '1307.1', '1206.4', '1070.0', '925.1', '1002.2', '1030.72', '733.36', '721.08', '592.29', '372.21', '381.18', '342.81', '369.19', '204.1', '51.98', '79.06', '63.97', '27.25', '20.43', '32.17', '25.8']

gold: This timeline shows Google 's quarterly net income from 2003 to 2015 . In the third quarter of 2015 , the California-based web company earned 3.98 billion US dollars , an increase from the 3.93 billion in the previous quarter . Due to restructuring under new parent company Alphabet , Google net income is not longer reported separately .
gold_template: This timeline shows templateTitleSubject[0] 's templateTitle[1] templateYLabel[0] templateYLabel[1] from templateTitleDate[0] to templateTitle[5] . In the third quarter of templateTitle[5] , the California-based web company earned templateYValue[0] templateYLabel[2] US templateYLabel[4] , an increase from the templateYValue[1] templateYLabel[2] in the previous quarter . Due to restructuring under new parent company Alphabet , templateTitleSubject[0] templateYLabel[0] templateYLabel[1] is not longer reported separately .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] in the templateTitle[4] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXLabel[0] of templateXValue[0] was ranked first with a templateYLabel[0] templateYLabel[1] of templateYValue[max] percent .
generated: The statistic shows the Net income of in the 2003 in , 2003 Financial . In 2003 , the Financial of Q3 '15 was ranked first with a Net income of 4675.0 percent .

Example 39:
titleEntities: {'Subject': ['Case Shiller National Home Price Index'], 'Date': ['2017', '2019']}
title: U.S. housing : Case Shiller National Home Price Index 2017 to 2019
X_Axis['Month']: ['Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17']
Y_Axis['Index', 'value']: ['212.06', '211.63', '210.87', '209.65', '207.96', '206.06', '204.72', '204.46', '204.94', '205.34', '205.59', '205.6', '205.55', '205.18', '204.27', '202.66', '200.8', '198.76', '197.08', '196.29', '196.02', '195.61', '195.24', '194.97', '194.5', '193.66', '192.4', '190.65', '188.65', '186.64', '185.14']

gold: This statistic presents the values of S & P Case Shiller U.S. National Home Price Index from February 2017 to August 2019 . The index value was equal to 100 as of January 2000 , so if the index value is equal to 130 in a given month , for example , it means that the house prices increased by 30 percent since 2000 . The value of the S & P Case Shiller National Home Price Index amounted to 212.06 in August 2019 .
gold_template: This statistic presents the values of S & P templateTitleSubject[0] U.S. templateTitleSubject[0] Price templateYLabel[0] from 2017 to 2019 . The templateYLabel[0] templateYLabel[1] was equal to 100 as of 2000 , so if the templateYLabel[0] templateYLabel[1] is equal to 130 in a given templateXLabel[0] , for example , it means that the house prices increased by 30 percent since 2000 . The templateYLabel[1] of the S & P templateTitleSubject[0] National templateTitleSubject[0] Index amounted to templateYValue[max] in 2019 .

generated_template: The statistic shows the templateTitle[3] templateYLabel[0] of the templateTitleSubject[0] Association ( templateTitleSubject[1] ) from templateTitleDate[min] to templateTitleDate[max] . In the last measured templateXLabel[0] , the social network had a templateYLabel[0] of templateYValue[max] percent .
generated: The statistic shows the Shiller Index of the Case Shiller National Home Price Index Association ( Case Shiller National Home Price Index ) from 2017 to 2019 . In the last measured Month , the social network had a Index of 212.06 percent .

Example 40:
titleEntities: {'Subject': ['Alibaba'], 'Date': ['2012', '2019']}
title: Alibaba : number of employees 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'employees']: ['101958', '66421', '50092', '36450', '34985', '22072', '20674', '21930']

gold: Alibaba Group Holding , China 's biggest e-commerce company , had nearly 102 thousand full-time employees by the end of March 2019 . While not the biggest employer in the country , Alibaba is the most valuable listed internet company in China and among the largest ones in terms of revenue . What is Alibaba Group ? The company was founded in Hangzhou , China in 1999 by a group of people led by a former English teacher Jack Ma .
gold_template: templateTitleSubject[0] Group Holding , China 's biggest e-commerce company , had nearly templateYValue[max] thousand full-time templateYLabel[1] by the end of 2019 templateXValue[idxmax(Y)] While not the biggest employer in the country , templateTitleSubject[0] is the most valuable listed internet company in China and among the largest ones in terms of revenue . What is templateTitleSubject[0] Group ? The company was founded in Hangzhou , China in 1999 by a group of people led by a former English teacher Jack Ma .

generated_template: The statistic illustrates the templateYLabel[0] of people 100,000 by the templateTitleSubject[1] ( templateTitleSubject[0] ) increased in the United Kingdom ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[1] . As of templateXValue[1] , templateTitleSubject[0] up a total templateYLabel[0] of not thousand British pounds .
generated: The statistic illustrates the Number of people 100,000 by the Alibaba ( ) increased in the United Kingdom ( Alibaba ) from 2012 to 2018 . As of 2018 , Alibaba up a total Number of not thousand British pounds .

Example 41:
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)] templateYLabel[2] templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] , up from around templateYValue[5] templateYLabel[2] in templateXValue[5] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] used in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with a forecast up years at a forecast up years operating . In templateXValue[6] , there were around timeline templateXValue[max] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] .
generated: The statistic shows the Number of network users used in Australia from 2015 to 2016 , with a forecast up years at a forecast up years operating . In 2016 , there were around timeline 2022 network users Australia in .

Example 42:
titleEntities: {'Subject': ['Nicaragua'], 'Date': ['2019']}
title: Unemployment rate in Nicaragua 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']: ['4.87', '4.48', '4.21', '4.34', '4.4', '4.52', '5.28', '5.21', '6.38', '7.83', '8.16', '6.2', '4.89', '5.31', '5.37', '6.41', '7.6', '7.61', '7.44', '7.47', '7.35']

gold: This statistic shows the unemployment rate in Nicaragua from 1999 to 2019 . In 2019 , the unemployment rate in Nicaragua was at approximately 4.87 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)] percent .

generated_template: The statistic shows the templateYLabel[0] in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[min] to had , in templateYLabel[0] in templateTitleSubject[0] with at approximately templateYValue[0] percent . In templateXValue[3] , the estimated templateYLabel[0] in templateTitleSubject[0] and templateTitleSubject[1] an to approximately templateYValue[0] percent .
generated: The statistic shows the Unemployment in Nicaragua and from 1999 to had , in Unemployment Nicaragua with at approximately 4.87 percent . In 2016 , the estimated Unemployment in Nicaragua and an to approximately 4.87 percent .

Example 43:
titleEntities: {'Subject': ['France'], 'Date': ['2000', '2016']}
title: France : number of internet users 2000 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Number', 'of', 'internet', 'users', 'in', 'millions']: ['55.86', '55.1', '53.7', '52.3', '51.76', '48.23', '48.65', '44.84', '44.04', '40.95', '28.87', '26.25', '23.82', '21.85', '18.13']

gold: This statistic shows the number of people in France who access the Internet at home from 2000 to 2016 . It shows that in 2015 , over 55 million people in France had access to the internet , up from 53.7 million in the previous year .
gold_template: This statistic shows the templateYLabel[0] of people in templateTitleSubject[0] who access the templateYLabel[1] at home from templateTitleDate[min] to templateXValue[max] . It shows that in templateXValue[1] , over templateYValue[1] templateYLabel[3] people in templateTitleSubject[0] had access to the templateYLabel[1] , up from templateYValue[2] templateYLabel[3] in the previous templateXLabel[0] .

generated_template: The statistic provides information on the templateYLabel[0] of templateTitleSubject[0] templateXValue[max] in the templateTitle[4] from templateXValue[min] to templateTitleDate[max] . In templateXValue[10] , the templateYLabel[0] of templateTitleSubject[0] templateXValue[max] in the templateTitle[4] employed at templateYValue[max] This .
generated: The statistic provides information on the Number of France 2016 in the 2000 from 2002 to 2016 . In 2006 , the Number of France 2016 in the 2000 employed at 55.86 This .

Example 44:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Cities with the highest spending on parks and recreation in the U.S. 2018
X_Axis['State']: ['Minneapolis', 'Seattle', 'San_Francisco', 'Portland', 'Arlington', 'Irvine', 'Washington_D.C.', 'St._Paul', 'Plano', 'Boise', 'New_York', 'Long_Beach', 'Chicago', 'Cincinnati', 'Aurora']
Y_Axis['Spending', 'per', 'resident', 'U.S.', 'dollars']: ['346.97', '268.42', '259.59', '233.99', '232.59', '206.12', '203.21', '201.96', '201.02', '182.04', '176.0', '171.14', '162.87', '153.37', '146.33']

gold: This statistic shows the cities with the highest spending per resident on parks and recreation in the United States in 2018 . Seattle , Washington , spent around 268.42 U.S. dollars per resident on parks and recreation in 2018 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[4] and templateTitle[5] in the templateTitle[6] in templateTitleDate[0] . templateXValue[1] , templateXValue[6] , spent around templateYValue[1] templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] on templateTitle[4] and templateTitle[5] in templateTitleDate[0] .

generated_template: The statistic shows the largest 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] templateXValue[max] This 's .
generated: The statistic shows the largest U.S. parks recreation U.S. in 2018 . That year , the Seattle was the second leading importer of U.S. , with imports valued at approximately 268.42 Minneapolis This 's .

Example 45:
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] percent . The monthly figure of full-time employees in the templateTitleSubject[0] can be accessed here .

generated_template: The statistic shows the templateYLabel[0] of refugees admitted to the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the number of people who were 100,000 at templateYValue[min] percent .
generated: The statistic shows the Employment of refugees admitted to the U.S. from 1990 to 2019 . In 2019 , the number of people who were 100,000 at 63.7 percent .

Example 46:
titleEntities: {'Subject': ['U.S.from'], 'Date': ['1900', '2016']}
title: Fatality numbers from heat waves and cold waves in the U.S.from 1900 to 2016
X_Axis['Type,', 'Year,', 'Location']: ['Heat_wave_1980_Kansas_City_Missouri_St_Loius', 'Heat_wave_1936_Illinois', 'Heat_wave_1995_Missouri_Oklahoma_Illinois', 'Heat_wave_1999_Illinois_Missouri_Wisconsin', 'Heat_wave_1983', 'Heat_wave_1966_St_Louis_Missouri', 'Heat_wave_2006_California', 'Cold_wave_1963', 'Heat_wave_1998_Arizona_Florida_Colorado', 'Heat_wave_2012_Washington_DC_Iowa_Virginia_North_Carolina']
Y_Axis['Number', 'of', 'fatalities']: ['1260', '1193', '670', '257', '188', '182', '164', '150', '130', '107']

gold: This statistic shows a list of heat waves and cold waves experienced in the United States from 1900 to 2016 , by the number of fatalities that occurred as a result . In the deadliest event on record , around 1,260 people lost their lives as a result of a heat wave in 1980 in Missouri and Tennessee .
gold_template: This statistic shows a list of templateXValue[0] templateTitle[4] and templateXValue[7] templateTitle[4] experienced in the country templateTitleSubject[0] templateTitleDate[min] to templateTitleDate[max] , by the templateYLabel[0] of templateYLabel[1] that occurred as a result . In the deadliest event on record , around templateYValue[max] people lost their lives as a result of a templateXValue[0] in templateXValue[0] in templateXValue[0] and Tennessee .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] the world in templateTitleDate[0] , sorted templateTitle[5] templateXLabel[0] templateXLabel[1] . templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] with templateYValue[max] percent .
generated: This statistic shows the Number of fatalities from heat the world in 1900 , sorted cold Type, Year, . Heat wave 1980 Kansas City Missouri St Loius had the highest Number of fatalities from heat in 1900 with 1260 percent .

Example 47:
titleEntities: {'Subject': ['UK'], 'Date': ['2017', '2019']}
title: Amounts outstanding of notes and coin in circulation in the UK 2017 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"]
Y_Axis['Amounts', 'outstanding', 'in', 'million', 'GBP']: ['82648', '82820', '82980', '82933', '82920', '82806', '82764', '82649', '82546', '82536', '82471', '82318', '82117', '81945', '81817', '81812', '81704', '81698', '81637', '81561', '81503', '81345', '81610', '81804', '81938', '82069', '82139', '82114', '82088', '82132', '82156', '82170', '82037', '81627', '81526', '81210']

gold: As of December 2019 , the value of outstanding notes and coins in circulation in the United Kingdom reached approximately 82.65 billion British pounds . This was an increase of over 1.4 billion British pounds as compared to January 2017 . When broken down by denomination , the twenty-pound note accounted for the highest share of notes in circulation .
gold_template: As of 2019 , the value of templateYLabel[1] templateTitle[2] and coins in templateTitle[4] in the United Kingdom reached approximately templateYValue[0] templateYLabel[2] British pounds . This was an increase of over 1.4 templateYLabel[2] 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_template: The statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[min] to templateTitleDate[max] . As of templateYValue[idxmax(X)] templateXLabel[0] , the templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitle[4] amounted to templateYValue[0] percent .
generated: The statistic shows the Amounts of notes coin in the UK ( ) from 2017 to 2019 . As of 81210 Month , the Amounts of notes coin in the circulation amounted to 82648 percent .

Example 48:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: U.S. mobile payment app usage 2016
X_Axis['Payment', 'Method']: ['Android_Pay', 'Retailer_mobile_app', 'Apple_Pay', 'Samsung_Pay', 'MasterPass', 'CurrenC', 'Windows_Phone_Wallet']
Y_Axis['Share', 'of', 'respondents']: ['18', '12', '11', '3', '2', '1', '1']

gold: This statistic presents the most commonly used mobile payment apps according to a survey of internet users carried out in February 2016 in the United States . During the survey period , 11 percent of respondents reported having used Apple Pay in the previous year .
gold_template: This statistic presents the most commonly used templateXValue[1] templateXLabel[0] apps according to a survey of internet users carried out in 2016 in the templateTitle[0] . During the survey period , templateYValue[2] percent of templateYLabel[1] reported having used templateXValue[2] templateXValue[0] in the previous year .

generated_template: When it comes to labor produced , the messaging app templateXValue[0] reigns supreme among templateTitle[2] out users in templateTitleSubject[0] . While globally leading templateTitle[3] such as templateXValue[1] , templateXValue[2] or templateXValue[4] cost get their fair templateYLabel[0] of popularity within the Japanese templateTitle[2] media ranked , templateXValue[0] that labor produced is on average more until score as distribution as those of other property , peaking at approximately 80 It will actives users in templateTitleSubject[0] during the first quarter of 2019 . What makes templateXValue[0] so templateTitle[1] ? The success card of messaging least templateXValue[0] , a Japanese subsidiary of South 2000s across powerhouse Naver , started annually in 2011 during the currency East templateTitleSubject[0] Earthquake .
generated: When it comes to labor produced , the messaging app Android Pay reigns supreme among payment out users in U.S. . While globally leading app such as Retailer mobile app , Apple Pay or MasterPass cost get their fair Share of popularity within the Japanese payment media ranked , Android Pay that labor produced is on average more until score as distribution those of other property , peaking at approximately 80 It will actives users in U.S. during the first quarter of 2019 . What makes Android Pay so mobile ? The success card of messaging least Android Pay , a Japanese subsidiary of South 2000s across powerhouse Naver , started annually in 2011 during the currency East U.S. Earthquake .

Example 49:
titleEntities: {'Subject': ['Utah Jazz'], 'Date': ['2001', '2019']}
title: Utah Jazz 's revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['258', '243', '221', '164', '146', '142', '131', '111', '120', '121', '118', '119', '114', '96', '91', '88', '85', '87']

gold: The statistic shows the revenue of the Utah Jazz 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 258 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: the statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the templateXValue[7] Basketball Association franchise an to templateYValue[max] franchise . templateTitleSubject[0] - additional information templateTitleSubject[0] is a professional basketball franchise of the templateXValue[7] Basketball Association ( 900 ) , Peace the league as templateXValue[6] expansion team in 1968 .
generated: the statistic shows the Revenue of the Utah Jazz franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the 11/12 Basketball Association franchise an to 258 franchise . Utah Jazz - additional information Utah Jazz is a professional basketball franchise of the 11/12 Basketball Association ( 900 ) , Peace the league as 12/13 expansion team in 1968 .

Example 50:
titleEntities: {'Subject': ['MTV', 'United Kingdom', 'UK'], 'Date': ['2019']}
title: MTV 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']: ['10610', '9638', '10353', '9702', '9677', '10047', '10609', '11487', '9967', '10947', '9674', '9917', '10647', '9856', '9509', '8351', '10631', '10279', '10201', '9717', '8838', '8761', '8738', '8188', '8011', '7629', '8344', '6833', '6482', '5960', '6890']

gold: This statistic shows the quarterly reach of MTV in the United Kingdom ( UK ) from the first quarter of 2012 to the third quarter of 2019 . In the first quarter of 2016 , MTV reached 10.63 million viewers . In the second quarter of 2019 , MTV had its lowest reach during the period in consideration at nearly six million viewers .
gold_template: This statistic shows the templateTitle[3] reach of templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) from the first templateXLabel[0] of templateXValue[0] to the third templateXLabel[0] of templateXValue[28] . In the first templateXLabel[0] of templateXValue[16] , templateTitleSubject[0] templateTitle[2] templateYValue[16] templateYLabel[1] templateYLabel[0] . In the second templateXLabel[0] of templateXValue[28] , templateTitleSubject[0] had its lowest reach during the period in consideration at nearly templateYValue[28] templateYLabel[1] templateYLabel[0] .

generated_template: The statistic shows a timeline with the global templateYLabel[0] of the templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . During the most recently reported templateXLabel[0] , templateTitleSubject[0] had a total templateYLabel[0] templateTitle[3] of templateYValue[0] templateXValue[max] This .
generated: The statistic shows a timeline with the global Viewers of the MTV from the first Quarter of 2019 to the fourth Quarter of 2019 . During the most recently reported Quarter , MTV had a total Viewers quarterly of 10610 Q4 2018 This .

Example 51:
titleEntities: {'Subject': ['Americans'], 'Date': ['2019']}
title: Church attendance of Americans 2019
X_Axis['Frequency']: ['Every_week', 'Almost_every_week', 'About_once_a_month', 'Seldom', 'Never', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['23', '10', '12', '24', '29', '3']

gold: According to a 2019 survey , 29 percent of Americans never attend church or synagogue , compared to 23 percent of Americans who attend every week . Religiosity in the United States Despite only about a fifth of Americans attending church or synagogue on a weekly basis , almost 40 percent consider themselves to be very religious . Additionally , states in the Deep South such as Mississippi , Alabama , and Louisiana had the most residents identifying as very religious .
gold_template: According to a templateTitleDate[0] survey , templateYValue[max] percent of templateTitleSubject[0] templateXValue[4] attend templateTitle[0] or synagogue , compared to templateYValue[0] percent of templateTitleSubject[0] who attend templateXValue[0] . Religiosity in the country Despite only templateXValue[2] a fifth of templateTitleSubject[0] attending templateTitle[0] or synagogue on a weekly basis , templateXValue[1] 40 percent consider themselves to be very religious . Additionally , states in the Deep South such as Mississippi , Alabama , and Louisiana had the most residents identifying as very religious .

generated_template: The statistic presents the employed of online consumers in the templateTitle[0] who about templateTitleSubject[0] templateTitle[2] members . increased the most Major survey year in quarter , it with found as was percent of responding Millennials country had templateTitleSubject[0] templateTitle[2] templateTitle[3] .
generated: The statistic presents the employed of online consumers in the Church who about Americans members . increased the most Major survey year in quarter , it with found as was percent of responding Millennials country had Americans 2019 .

Example 52:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2000', '2018']}
title: Hospitals in Belgium 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', 'hospitals']: ['174', '175', '177', '178', '187', '191', '192', '195', '198', '203', '209', '210', '215', '216', '214', '218', '219', '225', '228']

gold: Since 2000 , the number of hospitals in Belgium has declined nearly year-on-year . There were 228 hospitals in Belgium in the year 2000 and by 2018 this figure had fallen to 174 . This is a drop of over 23 percent in the provided time period .
gold_template: Since templateXValue[min] , the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] has declined nearly year-on-year . There were templateYValue[idxmin(X)] templateYLabel[1] in templateTitleSubject[0] in the templateXLabel[0] templateXValue[idxmax(Y)] and by templateXValue[max] this figure had fallen to templateYValue[idxmax(X)] . This is a drop of over 23 percent in the provided time period .

generated_template: The statistic shows the templateYLabel[0] of Scottish related in the templateTitle[0] from templateXValue[min] to 5 period . According to the report , there were templateYValue[10] such in the templateTitle[2] in the templateTitleSubject[1] in templateXValue[idxmin(Y)] , templateTitle[1] increased by approximately was thousand in increase .
generated: The statistic shows the Number of Scottish related in the Hospitals from 2000 to 5 period . According to the report , there were 209 such in the 2000 in the Belgium in 2018 , Belgium increased by approximately was thousand in increase .

Example 53:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2011', '2016']}
title: Number of mobile-only monthly active Facebook users 2011 to 2016
X_Axis['Quarter']: ["Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11"]
Y_Axis['Number', 'of', 'mobile-only', 'users', 'in', 'millions']: ['1149', '1055', '967', '894', '823', '727', '655', '581', '526', '456', '399', '341', '296', '254', '219', '189', '157', '126', '102', '83', '58']

gold: The timeline shows the number of mobile-only monthly active Facebook users . As of the fourth quarter of 2016 , 1,149 million Facebook users accessed the social media site exclusively via mobile device every month , up from 823 million users in the corresponding quarter of the previous year .
gold_template: The timeline shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] templateYLabel[2] . As of the fourth templateXLabel[0] of templateTitleDate[max] , templateYValue[max] templateYLabel[3] templateTitleSubject[0] templateYLabel[2] accessed the social media site exclusively via mobile device every month , up from templateYValue[4] templateYLabel[3] templateYLabel[2] in the corresponding templateXLabel[0] of the previous year .

generated_template: The statistic gives information on templateTitleSubject[0] that templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] from the second templateXLabel[0] of 2011 to the second templateXLabel[0] of templateTitleDate[max] . As of the last reported templateXLabel[0] , the website that templateYLabel[0] cost amounted to templateYValue[0] templateXValue[max] This . templateTitleSubject[0] belongs to Alibaba , be of the leading e-commerce companies in China and worldwide .
generated: The statistic gives information on Facebook that Number monthly active Facebook from the second Quarter of 2011 to the second Quarter of 2016 . As of the last reported Quarter , the website that Number cost amounted to 1149 Q4 '16 This . Facebook belongs to Alibaba , be of the leading e-commerce companies in China and worldwide .

Example 54:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Most popular games with casino visitors in the U.S. in 2014
X_Axis['Response']: ['Slot_machines', 'Black_Jack', 'Poker', 'Roulette', 'Video_poker', 'Craps', 'Sports_book', 'Baccarat', 'No_favorite']
Y_Axis['Share', 'of', 'respondents']: ['48', '16', '6', '5', '4', '4', '2', '1', '14']

gold: This statistic shows the most popular games with casino visitors in the United States as of May 2014 . During the survey , six percent of respondents said that poker was their favorite game to play at casinos .
gold_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] percent of templateYLabel[1] said that templateXValue[2] was their templateXValue[last] game to play at casinos .

generated_template: A survey conducted in templateTitleDate[0] reveals templateYValue[idxmax(X)] 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] percent of people declared when templateXValue[6] for the templateTitle[0] time in templateYValue[idxmax(X)] templateTitle[6] all-time . favorite templateYValue[3] percent of rankings throughout templateXValue[6] their templateTitle[0] templateTitle[1] templateTitle[2] in their early 20s .
generated: A survey conducted in 2014 reveals 14 most Italians Sports book their Most popular games Black Jack the 2014 of Poker and . More specifically , about 48 percent of people declared when Sports book for the Most time in 14 2014 all-time . favorite 5 percent of rankings throughout Sports book their Most popular games in their early 20s .

Example 55:
titleEntities: {'Subject': ['Becton', 'Dickinson', 'Company'], 'Date': ['2011', '2018']}
title: Becton , Dickinson , and Company 's employees from 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'employees']: ['76032', '41900', '50900', '49500', '30600', '30000', '29600', '29400']

gold: The statistic shows the number of employees of Becton , Dickinson , and Company for the fiscal years 2011 to 2018 . The number of employees at Becton , Dickinson , and Company reached a high in 2016 with 50,900 employed at the company that year .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] , templateTitleSubject[1] , and templateTitleSubject[2] for the fiscal years templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] , templateTitleSubject[1] , and templateTitleSubject[2] reached a high in templateXValue[2] with templateYValue[2] employed at the templateTitleSubject[2] that templateXLabel[0] .

generated_template: The statistic illustrates the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] employees templateTitle[4] from templateXValue[min] to templateXValue[1] . According to the report , templateTitleSubject[0] had a templateTitle[0] of templateYValue[max] associates steadily the world , up from templateYValue[1] in templateXValue[1] .
generated: The statistic illustrates the Becton Number of Becton employees from 2011 to 2017 . According to the report , Becton had a Becton of 76032 associates steadily the world , up from 41900 in 2017 .

Example 56:
titleEntities: {'Subject': ['Norway'], 'Date': ['2008', '2018']}
title: Number of births in Norway 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'live', 'births']: ['55120', '56633', '58890', '59058', '59084', '58995', '60255', '60220', '61442', '61807', '60497']

gold: This statistic shows the number of live births in Norway from 2008 to 2018 . The number of births has declined during the period , from the peak of roughly 61.8 thousand in 2009 to around 55.1 thousand in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[2] has declined during the period , from the peak of roughly templateYValue[max] thousand in templateXValue[idxmax(Y)] to around templateYValue[min] thousand in templateXValue[idxmin(Y)] .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateTitleDate[max] and details the where of the dropped templateXValue[max] , living in templateTitleSubject[0] in cities . In templateXValue[min] , the templateTitle[0] of templateTitleSubject[0] an to approximately templateYValue[0] thousand templateXValue[max] 2016 in cities .
generated: The statistic shows the Number of births in Norway from 2008 to 2018 and details the where of the dropped 2018 , living in Norway cities . In 2008 , the Number of Norway an to approximately 55120 thousand 2018 2016 in cities .

Example 57:
titleEntities: {'Subject': ['HPE research development'], 'Date': ['2013', '2019']}
title: HPE : research and development spending 2013 to 2019
X_Axis['Fiscal', 'year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Expenditure', 'in', 'million', 'U.S.', 'dollars']: ['1842', '1667', '1486', '1714', '2338', '2197', '1956']

gold: This statistic shows Hewlett Packard Enterprise 's ( previously a part of Hewlett-Packard Company ) expenditure on research and development for each fiscal year from 2013 to 2019 . In 2019 , HPE 's R & D expenditure came to 1.84 billion U.S. dollars . This represented a small portion of HPE 's net revenue , which reached 29.1 billion U.S. dollars .
gold_template: This statistic shows Hewlett Packard Enterprise 's ( previously a part of Hewlett-Packard Company ) templateYLabel[0] on templateTitleSubject[0] and templateTitleSubject[0] for each templateXLabel[0] templateXLabel[1] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , templateTitleSubject[0] 's R & D templateYLabel[0] came to templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . This represented a small portion of templateTitleSubject[0] 's net revenue , which reached 29.1 templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: templateTitleSubject[0] , the Canadian shopping apparel company , favorite more until templateYValue[max] templateYLabel[1] U.S. ranking in the financial templateXLabel[0] of templateXValue[0] . templateTitleSubject[0] templateTitle[4] templateTitleSubject[0] is part of the several templateTitleSubject[0] SE . In templateXValue[max] , their templateYLabel[0] of templateTitleSubject[0] increased by nearly templateYValue[max] templateYLabel[1] This .
generated: HPE research development , the Canadian shopping apparel company , favorite more until 2338 million U.S. ranking in the financial Fiscal of 2019 . HPE research development 2013 is part of the several HPE research development SE . In 2019 , their Expenditure of HPE research development increased by nearly 2338 million This .

Example 58:
titleEntities: {'Subject': ['Colombia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Colombia 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']: ['8096.64', '7729.02', '7381.73', '7053.53', '6744.01', '6508.13', '6641.51', '6325.49', '5799.62', '6088.68', '7998.84', '8103.49', '7950.38', '7264.11', '6285.2', '5170.53', '5444.82', '4684.01', '3717.55', '3384.61', '2737.58', '2240.21', '2347.51', '2383.31', '2455.11', '2588.92', '2992.93', '3291.58', '3037.63', '2939.1', '2640.39', '2139.18', '1923.58', '1658.07', '1637.5', '1640.39', '1660.55', '1572.58', '1542.36', '1572.19', '1711.86']

gold: The statistic shows gross domestic product ( GDP ) per capita in Colombia 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: templateTitle[0] templateTitle[1] templateTitle[2] is the total value of all goods and services they 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] percent templateXValue[max] to the This templateXLabel[0] .
generated: Gross domestic product is the total value of all goods and services they in a country in a Year . It is considered an important indicator of the economic strength of a country . In 2018 , GDP in Colombia amounted to around 6641.51 percent 2024 to the This Year .

Example 59:
titleEntities: {'Subject': ['Morocco'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Morocco 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['4.53', '4.42', '4.3', '4.13', '3.69', '2.66', '2.96', '4.2', '1.06', '4.55', '2.67']

gold: The statistic shows the growth in real GDP in Morocco from 2014 to 2018 , with projections up until 2024 . In 2018 , Morocco '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 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] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: that statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[0] . In templateXValue[6] , templateTitleSubject[0] that real templateYLabel[0] was around templateYValue[6] percent templateXValue[max] to the This year.Economy of templateTitleSubject[0] As an indicator for the shape of a country that economy , there are not many factors as telling as templateYLabel[0] . templateYLabel[0] is the total market value of all final goods and services templateYValue[idxmax(X)] have been they within a country within a given period of time , usually a templateXLabel[0] .
generated: that statistic shows the Gross domestic product ( GDP ) of Morocco from 2014 to 2018 , with projections up until 2024 . In 2018 , Morocco that real GDP was around 2.96 percent 2024 to the This year.Economy of Morocco As an indicator for the shape of a country that economy , there are not many factors as telling GDP . is the total market value of all final goods and services 4.53 have been they within a country within a given period of time , usually a Year .

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 templateYLabel[2] templateYLabel[3] templateYLabel[4] . Airline industry In templateXValue[2] , the templateYLabel[3] airline industry generated total operating revenue of almost 240 templateYLabel[2] templateYLabel[3] templateYLabel[4] , making the country one of the largest markets for the airline industry templateTitle[3] .

generated_template: The statistic shows the templateTitle[1] templateYLabel[0] of the templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[2] and a forecast for templateXValue[1] and a projection for period . In templateXValue[12] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] was estimated to reach templateYValue[2] templateXValue[max] This 's .
generated: The statistic shows the profit Net of the Net in from 2006 to 2018 and a forecast for 2019 and a projection for period . In 2008 , the Net profit in Net was estimated to reach 27.3 2020 This 's .

Example 61:
titleEntities: {'Subject': ['PV'], 'Date': ['2018']}
title: U.S. residential sector annual solar PV capacity installations 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Capacity', 'in', 'megawatts']: ['2400', '2227', '2583', '2099', '1231', '792', '494', '304', '246', '164', '82', '58', '38', '27']

gold: This statistic represents solar photovoltaic capacity installations in the residential sector in the United States between 2005 and 2018 . In 2018 , residential sector PV installations reached a capacity of 2.4 gigawatts .
gold_template: This statistic represents templateTitle[4] photovoltaic templateYLabel[0] templateTitle[7] 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 2.4 gigawatts .

generated_template: In templateXValue[0] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] was approximately templateYValue[min] people templateXValue[max] This . The is a significant change over the previous templateXLabel[0] , when the company reported a templateYLabel[0] loss of 322.56 templateXValue[max] This 's . templateTitleSubject[0] is a Japanese several behind game developer and publisher , headquartered in Minato-ku , Tokyo .
generated: In 2018 , the annual of U.S. and calves in the PV was approximately 27 people 2018 This . The is a significant change over the previous Year , when the company reported a Capacity loss of 322.56 2018 This 's . PV is a Japanese several behind game developer and publisher , headquartered in Minato-ku , Tokyo .

Example 62:
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] templateYLabel[1] templateYLabel[2] in templateXValue[3] , down from templateYValue[4] templateYLabel[1] templateYLabel[2] the previous templateXLabel[0] .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitleSubject[1] ) from templateXValue[min] to templateTitleSubject[1] , with a templateTitle[0] for templateTitle[2] templateTitle[3] templateTitle[4] of the European Central Bank . There is a decrease in templateXValue[9] , but from templateXValue[8] to the end of the year , the calculation since the calculation based on many observations throughout the year in question , which is different until templateXValue[6] templateTitle[4] measure at point in time : than denotes concrete values as of the end of the templateXLabel[0] . Between the period of templateXValue[min] and templateXValue[12] , the templateTitle[3] templateTitle[4] templateYLabel[0] of the templateTitle[0] to the templateTitleSubject[0] templateTitle[2] templateTitle[3] automobiles templateXValue[6] overall one .
generated: The statistic shows the American imports of 2001 2019 in the United Kingdom ( American ) from 2001 to American , with a American for corn 2001 2019 of the European Central Bank . There is a decrease in 2010 , but from 2011 to the end of the year , the calculation since the calculation based on many observations throughout the year in question , which is different until 2013 2019 measure at point in time : than denotes concrete values as of the end of the Year . Between the period of 2001 and 2007 , the 2001 2019 Imports of the American to the American corn 2001 automobiles 2013 overall one .

Example 63:
titleEntities: {'Subject': ['Americans'], 'Date': ['2012']}
title: Share of Americans who have had a one-night-stand , as of 2012
X_Axis['Response']: ['No', 'Yes']
Y_Axis['Share', 'of', 'respondents']: ['41.9', '58.1']

gold: This statistic shows the results of a survey in the United States in 2012 on one-night-stands . 58.1 percent of respondents in the United States stated they have had a one-night-stand before .
gold_template: This statistic shows the results of a survey in the country in templateTitleDate[0] on one-night-stands . templateYValue[max] percent of templateYLabel[1] in the country stated they templateTitle[3] templateTitle[4] a templateTitle[5] before .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] in the templateTitle[1] templateTitle[3] were templateTitleSubject[0] users as of who , sorted this templateXLabel[0] . estimated templateYValue[idxmax(X)] period of who , templateYValue[max] percent of female and templateYValue[min] percent of male teens used the social networking app .
generated: The statistic shows the Share of who in the Americans have were Americans users as of who , sorted this Response . estimated 58.1 period of who , 58.1 percent of female and 41.9 percent of male teens used the social networking app .

Example 64:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Leading coupon website awareness according to U.S. users 2016
X_Axis['Response']: ['groupon.com', 'coupons.com', 'retailmenot.com', 'livingsocial.com', 'redplum.com', 'mycoupons.com', 'couponcabin.com', 'swagbugs.com', 'savingstar.com', 'slickdeals.net', 'fatwallet.com', 'us.toluna.com', 'travelzoo.com', 'shopathome.com', 'woot.com', 'hip2save.com', 'couponmountain.com', 'freeshipping.org', 'couponchief.com', 'yipit.com', 'shesaved.com', 'passionforsavings.com', 'fyvor.com', 'other']
Y_Axis['Share', 'of', 'respondents']: ['75', '64', '62', '48', '32', '31', '25', '23', '22', '19', '18', '14', '14', '13', '12', '11', '9', '6', '5', '4', '3', '3', '2', '2']

gold: This statistic gives information on the most popular discount and promo code websites according to users in the United States . During the August 2016 survey , 75 percent of respondents stated that they were aware of groupon.com . Coupons.com was ranked second with 64 percent awareness rate and retailmenot.com was in third place with 62 percent of respondents stating that they knew the platform .
gold_template: This statistic gives information on the most popular discount and promo code websites templateTitle[4] to templateTitle[6] in the templateTitle[5] . During the 2016 survey , templateYValue[max] percent of templateYLabel[1] stated that they were aware of templateXValue[idxmax(Y)] . templateXValue[1] was ranked second with templateYValue[1] percent templateTitle[3] rate and templateXValue[2] was in third place with templateYValue[2] percent of templateYLabel[1] stating that they knew the platform .

generated_template: The statistic shows the results of a survey , conducted in 2018 templateXValue[16] the country , on templateTitleSubject[1] ' templateXValue[8] templateTitleSubject[0] templateTitle[2] resolutions templateTitle[5] templateTitleDate[0] . During the survey , templateYValue[2] percent of individuals in the country stated templateYValue[idxmax(X)] when have a `` hot trend '' for templateXValue[0] .
generated: The statistic shows the results of a survey , conducted in 2018 couponmountain.com the country , on U.S. ' savingstar.com U.S. website resolutions U.S. 2016 . During the survey , 62 percent of individuals in the country stated 2 when have a `` hot trend '' for groupon.com .

Example 65:
titleEntities: {'Subject': ['Thailand'], 'Date': ['2017', '2023']}
title: Number of social network users Thailand 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['37.2', '36.3', '35.6', '34.7', '33.8', '33.0', '32.1']

gold: This statistic shows the number of social network users in Thailand from 2017 to 2023 . In 2023 , it is estimated that there will be around 32.2 million social network users in the country , up from 32.1 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 32.2 templateYLabel[2] templateTitle[1] templateTitle[2] templateYLabel[1] in the country , up from templateYValue[idxmin(X)] templateYLabel[2] in templateXValue[idxmin(Y)] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[1] immigrating to the templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[6] , with projections up years templateYValue[min] templateXValue[max] in templateTitleSubject[1] in templateTitleSubject[1] , up from templateYValue[idxmin(X)] templateXValue[max] in quarter . Further information With more until capacity templateXValue[max] will active worldwide in quarter , templateTitleSubject[1] is the most popular social out worldwide .
generated: The statistic shows the Number of Thailand immigrating to the Thailand in the Thailand ( ) from 2017 to , with projections up years 32.1 2023 in Thailand , up from 32.1 2023 in quarter . Further information With more until capacity 2023 will active worldwide in quarter , Thailand is the most popular social out worldwide .

Example 66:
titleEntities: {'Subject': ['Zambia'], 'Date': ['2019']}
title: Youth unemployment rate in Zambia in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['15.94', '16.09', '16.12', '16.39', '16.49', '16.92', '17.05', '17.03', '21.45', '26.83', '21.44', '16.02', '19.3', '23.17', '28.26', '26.75', '25.55', '24.6', '23.08', '21.34', '20.88']

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

generated_template: The statistic shows the templateYLabel[0] gross templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to had , with projections are the data are ILO estimates . In fiscal , the estimated templateYLabel[0] dropped in templateTitleSubject[0] was at approximately templateYValue[0] percent . In the same templateXLabel[0] , the estimated templateYLabel[0] and the templateTitle[1] . By comparison , the estimated templateYLabel[0] history in templateTitleSubject[0] was at approximately templateYValue[0] templateXValue[max] This that .
generated: The statistic shows the Youth gross unemployment rate in Zambia from 1999 to had , with projections are the data are ILO estimates . In fiscal , the estimated Youth dropped in Zambia was at approximately 15.94 percent . In the same Year , the estimated Youth and the unemployment . By comparison , the estimated Youth history in Zambia was at approximately 15.94 2019 This that .

Example 67:
titleEntities: {'Subject': ['Worldwide'], 'Date': ['2013', '2019']}
title: Worldwide shipments of headphones 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Unit', 'shipments', 'in', 'millions']: ['400.0', '380.7', '363.0', '349.0', '331.3', '309.5', '286.0']

gold: The statistic shows global headphone unit shipments from 2013 to 2019 . In 2018 , 380.7 million headphones were sold worldwide .
gold_template: The statistic shows global headphone templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateYValue[1] templateYLabel[2] templateTitle[2] were sold templateTitleSubject[0] .

generated_template: Over templateYValue[max] templateXValue[max] units of templateTitle[1] templateTitle[2] was consumed areas templateTitleSubject[0] in increase . The was a decrease compared to the previous templateXLabel[0] . There were OECD , consuming referred was equated with living standards , diet , livestock production and consumer prices .
generated: Over 400.0 2019 units of shipments headphones was consumed areas Worldwide in increase . The was a decrease compared to the previous Year . There were OECD , consuming referred was equated with living standards , diet livestock production and consumer prices .

Example 68:
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', '-']

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[6] percent lower than in 2018 . templateTitle[1] templateYLabel[1] templateYLabel[2] The templateYLabel[3] of templateTitle[1] templateYLabel[1] templateYLabel[2] per liter has decreased in many templateTitle[6] .

generated_template: The statistic firm the templateTitle[3] templateTitle[4] of templateTitleSubject[0] templateYLabel[0] worldwide in templateTitleDate[0] . In templateYValue[idxmax(X)] year , templateXValue[0] was the leading producer of templateTitleSubject[0] templateTitle[4] , templateTitle[2] approximately templateYValue[max] percent of templateTitleSubject[0] that templateYLabel[0] coming to the previous year .
generated: The statistic firm the oil price of Change worldwide in 2018 . In - year , Canada was the leading producer of Change price , heating approximately 7 percent of Change that coming to the previous year .

Example 69:
titleEntities: {'Subject': ['European Union'], 'Date': ['2018', '2018']}
title: Number of dogs in the European Union 2018 , by country
X_Axis['Country']: ['Germany', 'United_Kingdom', 'Poland', 'Italy', 'France', 'Spain', 'Romania', 'Portugal', 'Czechia', 'Netherlands', 'Belgium', 'Hungary', 'Slovakia', 'Sweden', 'Austria', 'Finland', 'Bulgaria', 'Greece', 'Denmark', 'Lithuania', 'Ireland', 'Slovenia', 'Latvia', 'Estonia']
Y_Axis['Number', 'of', 'dogs', 'in', 'thousands']: ['9400', '9000', '7600', '7002', '6950', '6270', '4000', '2100', '2000', '1520', '1315', '1180', '900', '880', '827', '810', '740', '660', '595', '550', '450', '290', '260', '210']

gold: This statistic presents the number of pet dogs in the European Union by country , as of 2018 . Germany ranked highest with a dog population of approximately 9.4 million in 2018 , followed by the United Kingdom ( UK ) with nine million . The number of dogs in Europe has seen a notable increase since 2010 , with the number of dogs significantly increasing by more than eleven million from 2010 to 2018 .
gold_template: This statistic presents the templateYLabel[0] of pet templateYLabel[1] in the templateTitleSubject[0] by templateXLabel[0] , as of templateTitle[4] . templateXValue[0] ranked highest with a dog population of approximately templateYValue[max] templateYLabel[2] in templateTitle[4] , followed templateTitle[5] the templateXValue[1] ( UK ) with templateYValue[max] templateYLabel[2] . The templateYLabel[0] of templateYLabel[1] in Europe has seen a notable increase since 2010 , with the templateYLabel[0] of templateYLabel[1] significantly increasing templateTitle[5] more than eleven templateYLabel[2] from 2010 to templateTitle[4] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[4] templateTitleSubject[0] templateTitle[2] templateTitle[1] in templateTitleDate[0] , by templateXLabel[0] . In the country , templateYValue[2] templateTitleSubject[0] templateTitle[4] templateTitleSubject[0] templateTitle[2] were templateTitle[1] in templateTitleDate[0] . templateTitleSubject[0] templateTitle[4] templateTitleSubject[0] is a Swedish clothing manufacturer and retailer , based in Stockholm , templateXValue[8] .
generated: The statistic shows the Number of European Union 2018 European dogs in 2018 , by Country . In the country , 7600 European Union 2018 European were dogs in 2018 . European Union 2018 is a Swedish clothing manufacturer and retailer , based in Stockholm , Czechia .

Example 70:
titleEntities: {'Subject': ['A U.S.'], 'Date': ['2018']}
title: People with hemophilia A in the U.S. 2018 , by age group
X_Axis['Age', 'group']: ['0-4_years', '5-13_years', '14-18_years', '19-44_years', 'Above_45_years']
Y_Axis['Percentage', 'of', 'people']: ['9', '24', '13', '34', '21']

gold: This statistic displays the percentage of people in the United States diagnosed with Hemophilia A , sorted by age group , as of 2018 . In that year , nine percent of all Americans diagnosed with hemophilia A were between 0 and 4 years of age .
gold_template: This statistic displays the templateYLabel[0] 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] percent of all Americans diagnosed templateTitle[1] templateTitle[2] A were between 0 and 4 templateXValue[0] of templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , templateYValue[max] percent of the templateYLabel[1] in the templateTitle[5] were templateXValue[0] .
generated: The statistic shows the Percentage people of U.S. 2018 by in the age in 2018 , group Age . In 2018 , 34 percent of the people in the age were 0-4 years .

Example 71:
titleEntities: {'Subject': ['Estonia'], 'Date': ['2010', '2017']}
title: Electricity prices for households in Estonia 2010 - 2017 , semi-annually
X_Axis['Year']: ['2010_S1', '2010_S2', '2011_S1', '2011_S2', '2012_S1', '2012_S2', '2013_S1', '2013_S2', '2014_S1', '2014_S2', '2015_S1', '2015_S2', '2016_S1', '2016_S2', '2017_S1']
Y_Axis['Euro', 'cents', 'per', 'kilowatt-hour']: ['9.7', '10.04', '9.73', '10.42', '10.96', '11.23', '13.51', '13.67', '13.07', '13.25', '13.02', '12.91', '12.08', '12.38', '12.07']

gold: This statistic shows the electricity prices for household end users in Estonia semi-annually from 2010 to 2017 . In the first half of 2017 , the average electricity price for households was 12.07 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[0] to templateXValue[last] . In the first half of templateXValue[last] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[last] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[0] to templateXValue[last] . In the first half of templateXValue[12] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was 17.6 templateYLabel[0] The templateXValue[max] Product and had increased to templateYValue[max] the following templateXLabel[0] .
generated: The statistic shows the Electricity prices for household end users in Estonia semi-annually from 2010 S1 to 2017 S1 . In the first half of 2016 S1 , the average Electricity price for households was 17.6 Euro The 2017 S1 Product and had increased to 13.67 the following Year .

Example 72:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2008', '2018']}
title: Crude death rate in Sweden 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Death', 'rate', 'per', 'thousand', 'mid-year', 'population']: ['9.1', '9.1', '9.2', '9.3', '9.2', '9.4', '9.7', '9.5', '9.6', '9.7', '9.9']

gold: The crude death rate in Sweden was decreasing over the period and was down at the lowest point in 2017 and 2018 , when 9.1 deaths occurred per thousand mid-year population . Just over 92 thousand individuals in Sweden died during 2018 , a number that increased over the last few years , even though it was relatively stable during the period from 2008 to 2018 . Circulatory system diseases caused the most deaths Diseases in the circulatory system ( cardiovascular diseases ) were the most common cause of death in 2018 , which little more than 31 thousand individuals died from during that year .
gold_template: The templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was decreasing over the period and was down at the lowest point in templateXValue[1] and templateXValue[max] , when templateYValue[idxmax(X)] deaths occurred templateYLabel[2] thousand templateYLabel[4] templateYLabel[5] . Just over 92 thousand individuals in templateTitleSubject[0] died during templateXValue[idxmin(Y)] , a number that increased over the last few years , even though it was relatively stable during the period from templateXValue[min] to templateXValue[max] . Circulatory system diseases caused the most deaths Diseases in the circulatory system ( cardiovascular diseases ) were the most common cause of templateYLabel[0] in templateXValue[max] , which little more than 31 thousand individuals died from during that templateXLabel[0] .

generated_template: Some people complain templateYValue[idxmax(X)] these days couples are not carmaker to invest in their sheet anymore , templateYValue[idxmax(X)] the first hurdle encountered is a Online for a separation , and templateYValue[idxmax(X)] 1988 1988 death cost us part ' does not really mean anything anymore . But whether templateYValue[idxmax(X)] is true or not , facts show templateYValue[idxmax(X)] people in the templateTitleSubject[0] fees wait September to file for a templateTitle[3] . In the last decade , the templateTitle[0] templateYLabel[0] of the templateTitleSubject[0] has increased by a templateTitle[3] has increased by nearly a templateXLabel[0] , from templateYValue[min] templateXValue[max] in templateXValue[idxmin(Y)] to templateYValue[0] in 2009 .
generated: Some people complain 9.1 these days couples are not carmaker to invest in their sheet anymore , 9.1 the first hurdle encountered is a Online for a separation , and 9.1 1988 death cost us part ' does not really mean anything anymore . But whether 9.1 is true or not , facts show 9.1 people in the Sweden fees wait September to file for a Sweden . In the last decade , the Crude Death of the Sweden has increased by a Sweden has increased by nearly a Year , from 9.1 2018 in to 9.1 in 2009 .

Example 73:
titleEntities: {'Subject': ['Tesco', 'United Kingdom', 'UK'], 'Date': ['2010']}
title: Edible grocery sales forecast for Tesco in the United Kingdom ( UK ) from 2010 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Sales', 'in', 'million', 'US', 'dollars']: ['52714.03', '50821.69', '49342.45', '47921.14', '46631.26', '45524.45', '47444.1', '44802.19', '45335.47', '44899.26', '40675.81']

gold: This statistic shows the edible grocery sales forecast for Tesco in the United Kingdom ( UK ) from 2010 forecast to 2020 . In 2010 , Tesco made 40.68 billion US dollars in sales , which is forecast to increase to 52.71 billion US dollars by 2020 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateTitle[4] templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) templateTitle[9] templateXValue[min] templateTitle[3] to templateXValue[max] . In templateXValue[min] , templateTitleSubject[0] made templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] , which is templateTitle[3] to increase to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] by templateXValue[idxmax(Y)] .

generated_template: The statistic shows the number of the templateTitle[0] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[2] and a forecast through the forecast , as in thousands of the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitleSubject[0] is estimated to amount to templateYValue[max] percent in increase . In templateXValue[3] , templateTitle[0] of the templateTitleSubject[0] had an average templateTitle[1] templateTitle[2] templateYLabel[0] .
generated: The statistic shows the number of the Edible in the Tesco from 2010 to 2018 and a forecast through the forecast , as in thousands of the Edible of grocery sales in the Tesco is estimated to amount 52714.03 percent in increase . In 2017 , Edible of the Tesco had an average grocery sales .

Example 74:
titleEntities: {'Subject': ['Tate Modern', 'London'], 'Date': ['2007', '2018']}
title: Number of visitors to the Tate Modern in London 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'visitors', 'in', 'millions']: ['5.83', '5.66', '5.84', '4.71', '5.79', '4.8', '5.3', '4.8', '5.06', '4.75', '4.95', '5.19']

gold: This statistic shows the number of visitors to the Tate Modern in London from 2007 to 2018 . Approximately 5.83 million people visited the Tate Modern art museum in London in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] to the templateTitleSubject[0] in templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . Approximately templateYValue[idxmax(X)] templateYLabel[2] people visited the templateTitleSubject[0] art museum in templateTitleSubject[1] in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of recreational due to the templateTitleSubject[0] II templateTitleSubject[0] in the templateTitle[6] from templateXValue[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] of the templateTitleSubject[0] II templateTitleSubject[0] amounted to approximately templateYValue[0] templateYValue[idxmin(X)] templateXValue[max] .
generated: The statistic shows the Number of recreational due to the Tate Modern II in the 2018 from 2007 to 2018 . In 2018 , the Number of the Tate Modern II amounted to approximately 5.83 5.19 2018 .

Example 75:
titleEntities: {'Subject': ['NFL'], 'Date': ['2010', '2018']}
title: NFL - New England Patriots revenue from ticket sales 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Ticket', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['104', '103', '103', '99', '100', '95', '94', '97', '93']

gold: The statistic depicts the annual ticket sales of the New England Patriots in the years 2010 to 2018 . The New England Patriots , a franchise of the National Football League , generated 104 million U.S. dollars in revenue from gate receipts in 2018 .
gold_template: The statistic depicts the annual templateYLabel[0] templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] in the years templateXValue[min] to templateXValue[max] . The templateTitle[1] templateTitle[2] templateTitle[3] , a franchise of the National Football League , generated templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitle[4] templateTitle[5] gate receipts in templateXValue[idxmax(Y)] .

generated_template: The statistic shows the templateTitle[3] of the global templateTitle[1] and templateTitle[2] templateYLabel[0] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[3] , templateXValue[max] a forecast for this forecast for this templateXLabel[0] . major this period , the templateYLabel[0] are templateTitle[1] in templateTitleSubject[0] amounted to approximately templateYValue[max] templateXValue[max] This 's .
generated: The statistic shows the Patriots of the global New and England Ticket from in NFL from 2010 to 2015 , 2018 a forecast for this forecast for this Year . major this period , the Ticket are New in NFL amounted to approximately 104 2018 This 's .

Example 76:
titleEntities: {'Subject': ['Finland'], 'Date': ['2010/11', '2017/18']}
title: Ice hockey players in Finland 2010/11 - 2017/18
X_Axis['Year']: ['2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Number', 'of', 'players']: ['73374', '76387', '74150', '75871', '73682', '66636', '56626', '65251']

gold: The statistics shows the number of registered ice hockey players in Finland from 2010/11 to 2017/18 . The number of registered ice hockey players in 2017/18 amounted to nearly 73.4 thousand . The highest player number was reported in the previous season ( 2016/17 ) with over 76 thousand players .
gold_template: The statistics shows the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . The templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateXValue[0] amounted to nearly templateYValue[0] thousand . The highest player templateYLabel[0] was reported in the previous season ( templateXValue[1] ) with over templateYValue[max] thousand templateYLabel[1] .

generated_template: The statistic shows the templateTitle[3] of the templateTitle[0] templateTitle[1] templateYLabel[0] worldwide from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitleSubject[0] templateTitle[0] templateTitle[1] is estimated to reach templateYValue[max] templateXValue[max] This 's , up from templateYValue[min] templateXValue[max] in the previous templateXLabel[0] .
generated: The statistic shows the Finland of the Ice hockey Number worldwide from 2010/11 to 2017/18 . In the 2017/18 season , the Ice hockey Number of the Finland Ice hockey is estimated to reach 76387 2017/18 This 's , up from 56626 2017/18 in the previous Year .

Example 77:
titleEntities: {'Subject': ['Phoenix Suns', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Phoenix Suns ( 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']: ['1625', '1500', '1280', '1100', '1000', '910', '565', '474', '395', '411', '429', '452', '449', '410', '395', '356', '282', '272']

gold: This graph depicts the value of the Phoenix Suns franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the franchise had an estimated value of 1.625 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] franchise of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of 1.625 templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The graph depicts the estimated templateYLabel[0] of the templateTitleSubject[0] Lakers of the templateXValue[7] Basketball Association from templateXValue[min] to templateXValue[0] . In templateTitleDate[max] , the templateTitleSubject[0] templateYLabel[0] had an estimated to of templateYValue[max] templateXValue[max] This 's . templateTitleSubject[0] Lakers is a professional basketball basketball templateYLabel[0] of the templateXValue[7] Basketball Association ( templateTitleSubject[1] ) templateYValue[idxmax(X)] is part of the league that Western Conference .
generated: The graph depicts the estimated Franchise of the Phoenix Suns Lakers of the 2013 Basketball Association from 2003 to 2020 . In 2020 , the Phoenix Suns Franchise had an estimated to of 1625 2020 This 's . Phoenix Suns Lakers is a professional basketball Franchise of the 2013 Basketball Association ( NBA ) 1625 is part of the league that Western Conference .

Example 78:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. average retail electricity prices 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990']
Y_Axis['Average', 'electricity', 'price', 'in', 'U.S.', 'cents', 'per', 'kilowatt', 'hour']: ['10.58', '10.48', '10.27', '10.41', '10.44', '10.07', '9.84', '9.9', '9.83', '9.82', '9.74', '9.13', '8.9', '8.14', '6.81', '6.89', '6.57']

gold: This statistic reflects the average retail price of electricity in the United States between 1990 and 2018 . Here , the average retail price of electricity was at 10.58 cents per kilowatt hour in 2018 . A ranking of the largest electric utilities companies in the United States can be found here as well as a ranking of global electricity prices in select countries .
gold_template: This statistic reflects the templateYLabel[0] templateTitle[2] templateYLabel[2] of templateYLabel[1] in the templateTitle[0] between templateXValue[min] and templateXValue[max] . Here , the templateYLabel[0] templateTitle[2] templateYLabel[2] of templateYLabel[1] was at templateYValue[idxmax(X)] templateYLabel[4] templateYLabel[5] templateYLabel[6] templateYLabel[7] in templateXValue[idxmax(Y)] . A ranking of the largest electric utilities companies in the templateTitle[0] can be found here as well as a ranking of global templateYLabel[1] templateTitle[4] in select countries .

generated_template: The statistic shows the templateTitle[1] templateYLabel[0] of templateTitle[4] templateTitle[5] in the templateTitle[0] between templateXValue[min] and templateXValue[6] , with a forecast reason for templateXValue[1] . In templateXValue[min] , the templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[4] templateTitle[5] in the templateTitle[0] amounted to approximately templateYValue[0] thousand templateXValue[max] This .
generated: The statistic shows the average of prices 1990 in the U.S. between 1990 and 2012 , with a forecast reason for 2017 . In 1990 , the U.S. average of prices 1990 in the U.S. amounted to approximately 10.58 thousand 2018 This .

Example 79:
titleEntities: {'Subject': ['Middle East', 'Africa'], 'Date': ['2005']}
title: PC penetration in the Middle East and Africa 2005 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2011', '2012', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Penetration', 'in', 'percent']: ['7', '5', '3', '2', '2', '1', '1', '1', '1', '1', '1']

gold: The statistic depicts the consumer PC penetration per capita in the Middle East and Africa from 2005 to 2015 . In the Middle East and Africa , the consumer PC penetration per capita was 1 percent in 2006 . In 2015 , the PC penetration per capita is projected to reach 7 percent .
gold_template: The statistic depicts the consumer templateTitle[0] templateYLabel[0] per capita in the templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In the templateTitleSubject[0] and templateTitleSubject[1] , the consumer templateTitle[0] templateYLabel[0] per capita was templateYValue[min] templateYValue[idxmin(X)] in templateXValue[9] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] per capita is projected to reach templateYValue[idxmax(X)] templateYValue[idxmax(X)] .

generated_template: The statistic shows the number of metal and metal products templateYValue[idxmax(X)] are they from templateTitleSubject[0] templateTitle[1] and other low-grade residues worldwide from templateXValue[min] to templateTitleDate[max] . In templateXValue[6] , the templateTitle[2] templateYLabel[0] for templateTitleSubject[0] templateTitle[1] amounted to templateYValue[3] percent .
generated: The statistic shows the number of metal and products 7 are they from Middle East penetration and other low-grade residues worldwide from 2005 to . In 2009 , the Middle Penetration for Middle East penetration amounted to 2 percent .

Example 80:
titleEntities: {'Subject': ['SE annual'], 'Date': ['2009', '2016']}
title: Mast-Jägermeister SE - annual profit 2009 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Annual', 'profit', 'in', 'million', 'U.S.', 'dollars']: ['101.88', '106.95', '109.48', '135.87', '126.19', '132.32', '135.19', '135.21']

gold: The statistic shows the trend in profit at Mast-Jägermeister SE between 2009 and 2016 . In 2016 , Mast-Jägermeister SE generated a profit of around 101.88 million U.S. dollars . The spirits company is based in Wolfenbüttel , Germany .
gold_template: The statistic shows the trend in templateYLabel[1] at templateTitle[0] templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitleSubject[0] generated a templateYLabel[1] of around templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The spirits company is based in Wolfenbüttel , Germany .

generated_template: The statistic shows templateTitle[3] templateTitle[4] templateTitle[5] ' responses to a survey question tournament from templateXValue[min] to had , about increases in templateTitle[4] This . In templateXValue[1] , templateTitle[0] templateTitle[4] templateYLabel[0] was America at around templateYValue[min] templateXValue[max] This 's in templateTitleSubject[0] worldwide .
generated: The statistic shows profit 2009 2016 ' responses to a survey question tournament from 2009 to had , about increases in 2009 This . In 2015 , Mast-Jägermeister 2009 Annual was America at around 101.88 2016 This 's in SE annual worldwide .

Example 81:
titleEntities: {'Subject': ['Germany'], 'Date': ['2000', '2018']}
title: Revenue from used cars in Germany 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['Revenue', 'in', 'billion', 'euros']: ['84.7', '82.1', '84.62', '77.85', '69.78', '66.81', '62.98', '66.32', '56.54', '51.65', '53.11', '52.6', '55.95', '55.44', '52.22', '55.65', '54.03', '59.92', '59.0']

gold: This statistic shows the revenue of the used car market in Germany between 2000 and 2018 . In 2018 , the revenue amounted to roughly 84.7 billion euros .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[2] car market in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateYLabel[0] amounted to roughly templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] worldwide from templateXValue[min] to 20119 in the fiscal templateXLabel[0] of fiscal templateXLabel[0] . In the fiscal templateXLabel[0] of templateTitleSubject[0] templateTitle[2] templateYLabel[0] in the templateTitle[3] amounted to around templateYValue[0] measured . templateTitleSubject[0] templateTitle[2] key products include the rise of banks , but the company is ranked among the world templateTitle[2] largest tire manufacturers .
generated: The statistic shows the Revenue of Germany worldwide from 2000 to 20119 in the fiscal Year of fiscal Year . In the fiscal Year of Germany used Revenue in the cars amounted to around 84.7 measured . Germany used key products include the rise of banks , but the company is ranked among the world used largest tire manufacturers .

Example 82:
titleEntities: {'Subject': ['Number M A', 'Europe'], 'Date': ['2014', '2015']}
title: Number of M & A deals in Europe 2014 to 2015
X_Axis['Month']: ['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']
Y_Axis['Number', 'of', 'deals']: ['1222', '1030', '1107', '1166', '1097', '1076', '1235', '1152', '1167', '1222', '912', '1253', '1171', '1070', '1185', '1062']

gold: The statistic presents the number of merger and acquisition transactions in Europe from March 2014 to June 2015 . There were 1,222 M & A deals in Europe in June 2015 . The number of merger and acquisition transactions in Europe remained fairly steady between March 2014 and June 2015 .
gold_template: The statistic presents the templateYLabel[0] of merger and acquisition transactions in templateTitleSubject[1] from 2014 to 2015 . There were templateYValue[0] templateTitleSubject[0] templateTitle[2] A templateYLabel[1] in templateTitleSubject[1] in 2015 . The templateYLabel[0] of merger and acquisition transactions in templateTitleSubject[1] remained fairly steady between 2014 and 2015 .

generated_template: The statistic shows templateTitleSubject[0] that templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitle[4] from 2018 to 2019 . In the measured period , templateTitleSubject[0] had an templateYLabel[0] of templateYValue[max] templateXValue[max] This 's , up from templateYValue[1] templateXValue[max] This 's in 2014 .
generated: The statistic shows Number M A that M & Number in the Europe from 2018 to 2019 . In the measured period , Number M A had an Number of 1253 Jun 15 This 's , up from 1030 Jun 15 This 's in 2014 .

Example 83:
titleEntities: {'Subject': ['Bulgari'], 'Date': ['2011', '2017']}
title: Bulgari : turnover 2011 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Turnover', 'in', 'million', 'euros']: ['177.0', '194.9', '143.4', '121.6', '96.8', '104.7', '96.4']

gold: The turnover of the Italian high-end jewelry company Bulgari increased almost twofold from 2011 to 2017 . The peak was reached in 2016 , when Bulgari 's annual turnover amounted to approximately 195 million euros . In 2017 the turnover decreased to 177 million euros .
gold_template: The templateYLabel[0] of the Italian high-end jewelry company templateTitleSubject[0] increased 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] templateYLabel[1] templateYLabel[2] . In templateXValue[max] the templateYLabel[0] decreased to templateYValue[0] templateYLabel[1] templateYLabel[2] .

generated_template: In had , the templateYLabel[0] of templateTitleSubject[0] hotels ( WTI ) templateTitle[1] templateTitle[2] is expected to reach was thousand templateXValue[max] This that , a brand one from templateYValue[min] templateXValue[max] This that in templateXValue[idxmin(Y)] . major the By one in templateYLabel[0] during the year considered , the templateTitle[3] reported a net loss of approximately 25 templateXValue[max] This that . templateTitle[3] templateYValue[max] management Here in templateXValue[4] , templateTitle[3] templateYValue[max] 's is be of the leading retail relating to human purchases .
generated: In had , the Turnover of Bulgari hotels ( WTI ) turnover 2011 is expected to reach was thousand 2017 This that , a brand one from 96.4 2017 This that in 2011 . major the By one in Turnover during the year considered , the 2017 reported a net loss of approximately 25 2017 This that . 2017 194.9 management Here in 2013 , 2017 194.9 's is be of the leading retail relating to human purchases .

Example 84:
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: The graph depicts the templateYLabel[0] The templateXValue[max] for templateTitleSubject[0] templateTitle[4] templateTitle[6] of the templateTitle[6] Basketball internet from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateXValue[max] was templateYValue[min] This 's . With an templateYLabel[0] templateXValue[max] of more than 120 This 's , tickets for templateTitleSubject[0] Knicks templateTitle[7] are the most expensive in the league .
generated: The graph depicts the Average 2015/16 for Washington Wizards Wizards games of the games Basketball internet from 2006/07 to 2015/16 . In the 2006/07 season , the Average 2015/16 was 23.64 This 's . With an Average 2015/16 of more than 120 This 's , tickets for Washington Wizards Knicks 2015/16 are the most expensive in the league .

Example 85:
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: The statistic shows the templateTitle[3] templateTitle[4] of the templateTitle[0] templateTitle[1] in templateTitleDate[0] , based on templateTitle[7] volume . In templateTitleDate[0] , templateXValue[0] had the highest templateTitle[0] templateTitle[1] for templateYValue[max] percent of all templateTitle[0] templateTitle[1] .
generated: The statistic shows the working hours of the Employees ' in 2011 , based on worldwide volume . In 2011 , Singapore had the highest Employees ' for 44 percent of all Employees ' .

Example 86:
titleEntities: {'Subject': ['Nintendo'], 'Date': ['2011', '2018']}
title: Nintendo 3DS sales worldwide 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Sales', 'in', 'million', 'units']: ['6.4', '7.27', '6.79', '8.73', '12.24', '13.95', '13.53', '3.61']

gold: This statistic shows the number of Nintendo 3DS hardware units sold worldwide from 2011 to 2018 . In the 2018 financial year which ended in March 2018 , Nintendo sold 6.4 million 3DS consoles , down from 7.27 million units sold in the year prior . In terms of 3DS software , fiscal 2017 was the year of Pokemon , when more than 16 million copies of Pokemon X/Y for 3DS were sold .
gold_template: This statistic shows the number of templateTitleSubject[0] templateTitle[1] hardware templateYLabel[2] sold templateTitle[3] from templateXValue[min] to templateXValue[max] . In the templateXValue[max] financial templateXLabel[0] which ended in 2018 , templateTitleSubject[0] sold templateYValue[0] templateYValue[idxmax(X)] templateTitle[1] consoles , down from templateYValue[1] templateYLabel[1] templateYLabel[2] sold in the templateXLabel[0] prior . In terms of templateTitle[1] software , fiscal templateXValue[1] was the templateXLabel[0] of Pokemon , when more than 16 templateYLabel[1] copies of Pokemon X/Y for templateTitle[1] were sold .

generated_template: The statistic illustrates the templateYLabel[0] of templateTitle[1] and tourism templateTitle[2] per Product in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[0] . In templateXValue[3] , individuals saw the templateTitle[0] templateYLabel[0] of templateTitle[1] and templateTitle[2] , with templateXValue[6] value of templateYValue[max] each templateXLabel[0] . templateTitle[1] templateTitle[2] exists because the threats and them are great enough to warrant these measure .
generated: The statistic illustrates the Sales of 3DS and tourism sales per Product in the United Kingdom ( Nintendo ) from 2011 to 2018 . In 2015 , individuals saw the Nintendo Sales of 3DS and sales , with 2012 value of 13.95 each Year . 3DS sales exists because the threats and them are great enough to warrant these measure .

Example 87:
titleEntities: {'Subject': ['Japan'], 'Date': ['2017', '2023']}
title: Japan : mobile 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']: ['71.9', '71.5', '70.8', '69.9', '68.4', '66.6', '64.0']

gold: The statistic shows the number of mobile internet users in Japan from 2017 to 2023 . In 2018 , approximately 66.6 million people accessed the internet through mobile devices . In 2023 , this figure is projected to reach about 71.9 million mobile internet users .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , approximately templateYValue[5] templateYLabel[5] people accessed the templateYLabel[3] through templateYLabel[1] devices . In templateXValue[max] , this figure is projected to reach about templateYValue[idxmax(X)] templateYLabel[5] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of the This 's in templateTitleSubject[0] from templateXValue[min] to 2027 . In templateXValue[5] , timeline templateXValue[idxmax(Y)] people accessed the This through their templateYLabel[0] in templateTitleSubject[0] , and than figure is projected to amount to templateYValue[max] templateXValue[idxmax(Y)] in increase .
generated: The statistic shows the Number of the This 's in Japan from 2017 to 2027 . In 2018 , timeline 2023 people accessed the This through their Number in Japan , and than figure is projected to amount 71.9 2023 in increase .

Example 88:
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: The statistic shows the templateYLabel[0] of templateTitleSubject[0] employees from templateXValue[min] to 20119 worldwide . According to the report , templateTitleSubject[0] 100,000 templateYValue[0] thousand people worldwide . templateTitleSubject[0] – additional information templateTitleSubject[0] , or to give it its full name , StarCraft and other of Warcraft , headquartered in 1845 , and as of templateXValue[2] , its headquarters were located in New York City .
generated: The statistic shows the Number of Penguin Random House employees from 2005 to 20119 worldwide . According to the report , Penguin Random House 100,000 10351 thousand people worldwide . Penguin Random House – additional information Penguin Random House , or to give it its full name , StarCraft and other of Warcraft , headquartered in 1845 , and as of 2016 , its headquarters were located in New York City .

Example 89:
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] templateYLabel[1] British pounds . In templateXValue[max] , templateTitle[1] templateTitle[2] templateYLabel[0] exceeded templateYValue[0] templateYLabel[1] British pounds .

generated_template: British ownership losses templateTitleSubject[0] had a total templateYLabel[0] templateTitle[2] of over 1.86 templateXValue[max] This 's in the fiscal templateXLabel[0] of templateXValue[0] . The is a decrease from templateXValue[3] , when the UK that templateYLabel[0] increased by its peak . The was a decrease in the templateTitle[0] templateXValue[idxmin(Y)] , and the company has be of the highest templateYLabel[0] increased by the Planet of the British retailer .
generated: British ownership losses United Kingdom had a total Revenue office of over 1.86 2019 This 's in the fiscal Year of 2019 . The is a decrease from 2016 , when the UK that Revenue increased by its peak . The was a decrease in the Cinema 2000 , and the company has be of the highest Revenue increased by the Planet of the British retailer .

Example 90:
titleEntities: {'Subject': ['CPI UAE'], 'Date': ['2012']}
title: Average CPI in the UAE 2012 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Consumer', 'price', 'index']: ['107.83', '105.75', '125.75', '120.84', '118.07', '116.78']

gold: The statistic shows the average consumer price index ( CPI ) in the United Arab Emirates from 2012 to 2017 . The overall average for the 2017 consumer price index in the country was 107.83 , implying a 7.83 increase in the price level .
gold_template: The statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitleSubject[0] ) in the United Arab Emirates from templateXValue[min] to templateXValue[max] . The overall templateTitle[0] for the templateXValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the country was templateYValue[idxmax(X)] , implying a 7.83 increase in the templateYLabel[1] level .

generated_template: The statistic displays the templateTitle[0] of templateTitle[1] peer to peer templateYLabel[0] in the templateTitle[4] from templateXValue[min] to templateXValue[3] and a forecast reason years templateTitleDate[max] . In templateXValue[3] , the templateTitle[0] of templateTitle[1] templateTitleSubject[0] templateYLabel[0] an to not templateXValue[max] This and it was account to one to not templateXValue[max] This in increase .
generated: The statistic displays the Average of CPI peer to Consumer in the 2017 from 2012 to 2014 and a forecast reason years 2012 . In 2014 , the Average of CPI CPI UAE Consumer an to not 2017 This and it was account to one not 2017 This in increase .

Example 91:
titleEntities: {'Subject': ['Amazon'], 'Date': ['2019']}
title: Amazon 's Q4 income including seasonal sales 2009 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q4_'18", "Q4_'17", "Q4_'16", "Q4_'15", "Q4_'14", "Q4_'13", "Q4_'12", "Q4_'11", "Q4_'10", "Q4_'09"]
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['3268', '3000', '1900', '749', '482', '214', '239', '97', '177', '416', '384']

gold: This statistic shows Amazon.com 's net income in the fourth quarters from 2009 to 2019 , a comparison of the company 's quarterly income including the yearly Christmas sales . In 2019 , Amazon 's net income of that quarter amounted to 3.27 billion U.S. dollars .
gold_template: This statistic shows Amazon.com templateTitle[1] templateYLabel[0] templateYLabel[1] in the fourth quarters from templateTitle[7] to templateTitleDate[0] , a comparison of the company templateTitle[1] quarterly templateYLabel[1] templateTitle[4] the yearly Christmas templateTitle[6] . In templateTitleDate[0] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of that templateXLabel[0] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: During the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] had a templateYLabel[0] of templateYValue[0] templateXValue[max] This in the templateTitle[4] templateXLabel[0] of templateTitle[4] . In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] that templateYLabel[0] revenue amounted to over templateYValue[max] templateXValue[max] This in the previous templateXLabel[0] .
generated: During the fourth Quarter of 2019 , Amazon had a Net of 3268 Q4 '19 This in the including Quarter of including . In the fourth Quarter of 2019 , Amazon that Net revenue amounted to over 3268 Q4 '19 This in the previous Quarter .

Example 92:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2015']}
title: Fast food restaurant visit frequency in the United Kingdom ( UK ) 2015
X_Axis['Response']: ['Less_than_once_per_week', 'One_to_three_times_per_week', 'Four_to_six_times_per_week', 'Seven_to_nine_times_per_week', 'Ten_times_or_more_per_week', "I_don't_eat_at_fast_food_restaurants"]
Y_Axis['Share', 'of', 'respondents']: ['44', '27', '5', '2', '1', '24']

gold: This statistic shows the frequency of fast food ( any quick service ) restaurant visits in any given fast food restaurant in 2015 . In 2015 , 44 percent of respondents visited fast food restaurants less than once per week .
gold_template: This statistic shows the templateTitle[4] of templateXValue[last] ( any quick service ) templateTitle[2] visits in any given templateXValue[last] restaurant in templateTitleDate[0] . In templateTitleDate[0] , templateYValue[max] percent of templateYLabel[1] visited templateXValue[last] restaurants templateXValue[0] once templateXValue[0] .

generated_template: The statistic presents the templateTitle[3] templateTitle[4] templateTitle[5] according to throughout in the templateTitle[0] in templateTitleDate[0] . increased a survey , templateYValue[max] percent of throughout found the templateXValue[0] to FBI the templateTitle[3] templateTitle[4] templateTitle[5] to play .
generated: The statistic presents the visit frequency United according to throughout in the Fast in 2015 . increased a survey , 44 percent of throughout found the Less than once per week to FBI the visit frequency United to play .

Example 93:
titleEntities: {'Subject': ['Global'], 'Date': ['2017']}
title: Global papaya production 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Million', 'metric', 'tons']: ['13.02', '13.05', '12.08', '12.67', '12.36', '12.01', '11.3', '11.19', '10.73', '10.02', '9.53', '8.91', '8.03', '8.67', '8.16', '8.42', '8.55', '7.25']

gold: This statistic depicts the production of papaya worldwide from 2000 to 2017 . According to the report , global papaya production amounted to approximately 12.67 million metric tons in 2014 .
gold_template: This statistic depicts the templateTitle[2] of templateTitle[1] worldwide from templateXValue[min] to templateXValue[max] . According to the report , templateTitleSubject[0] templateTitle[1] templateTitle[2] amounted to approximately templateYValue[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[3] .

generated_template: Mangos are native to South and Southeast Asia and are now relevant all over the world . That templateXLabel[0] , templateTitle[2] up templateXValue[6] estimated templateYLabel[0] of about templateYValue[max] templateXValue[max] This that , compared to templateYValue[1] templateXValue[max] This that in the templateXLabel[0] .
generated: Mangos are native to South and Southeast Asia and are now relevant all over the world . That Year , production up 2011 estimated Million of about 13.05 2017 This that , compared to 13.05 2017 This that in the Year .

Example 94:
titleEntities: {'Subject': ['Italian'], 'Date': ['2011', '2018']}
title: Revenues of Italian company Guccio Gucci in 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Revenues', 'in', 'million', 'euros']: ['267.63', '214.28', '162.31', '181.03', '184.24', '182.72', '177.24', '157.41']

gold: In 2018 , the Italian fashion company Guccio Gucci S.p.A. , well known worldwide for designing and producing high-end clothing , footwear and accessories for women , men and kids , reported total revenues worth approximately 267 million euros . The figure represented an increase of over 50 million euros compared to the previous year , when the company registered a revenue of 214 million euros . An Italian success story Founded in 1921 in Florence as a leather goods manufacturer , Gucci has grown to become one of the leading players in the global luxury goods market and ranked third on the list of the most valuable luxury brands worldwide in 2018 .
gold_template: In templateXValue[max] , the templateTitleSubject[0] fashion templateTitle[2] templateTitle[3] templateTitle[4] S.p.A. , well known worldwide for designing and producing high-end clothing , footwear and accessories for women , men and kids , reported total templateYLabel[0] worth approximately 267 templateYLabel[1] templateYLabel[2] . The figure represented an increase of over 50 templateYLabel[1] templateYLabel[2] compared to the previous templateXLabel[0] , when the templateTitle[2] registered a revenue of templateYValue[1] templateYLabel[1] templateYLabel[2] . An templateTitleSubject[0] success story Founded in 1921 in Florence as a leather goods manufacturer , templateTitle[4] has grown to become one of the leading players in the global luxury goods market and ranked third on the list of the most valuable luxury brands worldwide in templateXValue[max] .

generated_template: The statistic illustrates the templateTitle[3] templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitle[5] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[1] , with an additional forecast for templateXValue[1] . Over than period , the templateTitle[4] of the templateTitle[0] and templateTitle[1] industry to templateTitle[5] in the templateTitleSubject[1] has increased , reaching a worldwide of approximately templateYValue[max] templateXValue[max] British pounds in increase .
generated: The statistic illustrates the Guccio Gucci of Revenues and Italian to 2011 in the Italian from 2011 to 2017 , with an additional forecast for 2017 . Over than period , the Gucci of the Revenues and Italian industry to 2011 in the Italian has increased , reaching a worldwide of approximately 267.63 2018 British pounds in increase .

Example 95:
titleEntities: {'Subject': ['Houston Rockets', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Houston Rockets ( 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']: ['2475', '2300', '2200', '1650', '1500', '1250', '775', '568', '453', '443', '470', '469', '462', '439', '422', '369', '278', '255']

gold: This graph depicts the franchise value of the Houston Rockets of the National Basketball Association from 2003 to 2020 . In 2020 , the Houston Rockets franchise had an estimated value of 2.475 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 2.475 templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The graph depicts the templateYLabel[0] 's of the templateTitleSubject[0] Knicks of the templateXValue[7] Basketball Association from templateXValue[min] to templateXValue[0] . In templateXValue[0] , the templateTitleSubject[0] Knicks templateYLabel[0] of the templateTitleSubject[0] Knicks was templateYValue[max] templateXValue[max] This 's . templateTitleSubject[0] Knicks - additional information One of the templateTitleSubject[0] Knicks is part of the Eastern Conference of the templateXValue[7] Basketball Association ( templateTitleSubject[1] ) .
generated: The graph depicts the Franchise 's of the Houston Rockets Knicks of the 2013 Basketball Association from 2003 to 2020 . In 2020 , the Houston Rockets Knicks Franchise of the Houston Rockets Knicks was 2475 2020 This 's . Houston Rockets Knicks - additional information One of the Houston Rockets Knicks is part of the Eastern Conference of the 2013 Basketball Association ( NBA ) .

Example 96:
titleEntities: {'Subject': ['Snapchat'], 'Date': ['2020', '2020']}
title: Countries with the most Snapchat users 2020
X_Axis['Country']: ['United_States', 'India', 'France', 'United_Kingdom', 'Saudi_Arabia', 'Mexico', 'Brazil', 'Germany', 'Canada', 'Russia']
Y_Axis['Audience', 'size', 'in', 'millions']: ['101.25', '22.95', '21.25', '18.7', '16.1', '14.8', '13.95', '12.15', '8.15', '7.75']

gold: As of January 2020 , the United States had the biggest Snapchat user base in the world , with an audience of 101.25 million users . India ranked in second place with a Snapchat audience base of 22.95 million users . The photo-sharing platform is projected to surpass 365 million global users in 2023 .
gold_template: As of 2020 , the templateXValue[0] had the biggest templateTitleSubject[0] user base in the world , templateTitle[1] an templateYLabel[0] of templateYValue[max] templateYLabel[2] templateTitle[4] . templateXValue[1] ranked in second place templateTitle[1] a templateTitleSubject[0] templateYLabel[0] base of templateYValue[1] templateYLabel[2] templateTitle[4] . The photo-sharing platform is projected to surpass 365 templateYLabel[2] global templateTitle[4] in 2023 .

generated_template: The statistic depicts a ranking of the templateTitle[0] templateTitle[1] the largest templateTitle[3] templateTitle[4] 's templateYLabel[0] in templateTitleDate[0] . According to the report , the templateXValue[0] Hospital approximately templateYValue[max] percent of templateTitleSubject[0] templateTitle[4] to the templateXValue[0] population .
generated: The statistic depicts a ranking of the Countries most the largest users 2020 's Audience in 2020 . According to the report , the United States Hospital approximately 101.25 percent of Snapchat 2020 to the United States population .

Example 97:
titleEntities: {'Subject': ['Minnesota Timberwolves'], 'Date': ['2001', '2018']}
title: Minnesota Timberwolves ' revenue 2001 to 2018
X_Axis['Year']: ['17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['223', '204', '154', '146', '128', '116', '96', '97', '95', '96', '100', '103', '103', '101', '97', '85', '85']

gold: The statistic shows the revenue of the Minnesota Timberwolves franchise from the 2001/02 season to the 2017/18 season . In 2017/18 , the estimated revenue of the National Basketball Association franchise amounted to 223 million U.S. dollars . Minnesota Timberwolves - additional information The Minnesota Timberwolves are a professional basketball team of the National Basketball Association ( NBA ) , joining the league as an expansion team in 1989 .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2017/18 season . In 2017/18 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] - additional information The templateTitleSubject[0] are a professional basketball team of the National Basketball Association ( NBA ) , joining the league as an expansion team in 1989 .

generated_template: The graph depicts the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2017/18 season . In 2017/18 , the estimated templateYLabel[0] of the templateXValue[7] Basketball Association franchise an to templateYValue[max] kilowatts This . templateTitleSubject[0] - additional information – additional information are a professional basketball team templateYValue[idxmax(X)] is part of the templateXValue[7] Basketball Association ( 900 ) .
generated: The graph depicts the Revenue of the Minnesota Timberwolves franchise from the 2001/02 season to the 2017/18 season . In 2017/18 , the estimated Revenue of the 10/11 Basketball Association franchise an to 223 kilowatts This . Minnesota Timberwolves - additional information – additional information are a professional basketball team 85 is part of the 10/11 Basketball Association ( 900 ) .

Example 98:
titleEntities: {'Subject': ['NASDAQ'], 'Date': ['1999', '2013']}
title: Market capitalization of the NASDAQ exchange in 1999 and 2013
X_Axis['Year']: ['1999', '2013']
Y_Axis['Market', 'cap', 'in', 'billion', 'U.S.', 'dollars']: ['1.16', '2.54']

gold: This statistic presents the average market capitalization of component listings of the NASDAQ in 1999 and 2013 . It was reported that the average market capitalization of companies listed on the exchange rose from 1.16 billion US dollars in 1999 to 2.54 billion US dollars in 2013 .
gold_template: This statistic presents the average templateYLabel[0] templateTitle[1] of component listings of the templateTitleSubject[0] in templateXValue[min] and templateXValue[max] . It was reported that the average templateYLabel[0] templateTitle[1] of companies listed on the templateTitle[3] rose from templateYValue[idxmin(X)] templateYLabel[2] US templateYLabel[4] in templateXValue[idxmin(Y)] to templateYValue[idxmax(X)] templateYLabel[2] US templateYLabel[4] in templateXValue[idxmax(Y)] .

generated_template: The statistic been the streaming of months templateXValue[max] a This 's born installed in templateXValue[min] and This 's . In the templateTitleSubject[1] Baseball previous , the streaming of templateTitle[0] templateTitle[3] was value to grow from templateYValue[idxmin(X)] percent to templateYValue[max] percent .
generated: The statistic been the streaming of months 2013 a This 's born installed in 1999 and This 's . In the NASDAQ Baseball previous , the streaming of Market exchange was value to grow from 1.16 percent to 2.54 percent .

Example 99:
titleEntities: {'Subject': ['Armenia'], 'Date': ['2019']}
title: Unemployment rate in Armenia 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']: ['17.71', '17.71', '17.83', '17.62', '18.26', '17.5', '16.18', '17.31', '18.44', '19.01', '18.74', '16.37', '9.81', '10.28', '10.8', '11.22', '11.23', '11.52', '11.44', '11.25', '11.2']

gold: This statistic shows the unemployment rate in Armenia from 1999 to 2019 . In 2019 , the unemployment rate in Armenia was estimated to be 17.71 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 estimated to be templateYValue[idxmax(X)] percent .

generated_template: The statistic shows the templateYLabel[0] The in templateTitleSubject[0] from templateXValue[min] to had , projections country years the percentage of the labor force in templateTitleSubject[0] with at approximately templateYValue[min] percent . In the year of templateTitleSubject[0] , the templateYLabel[0] in templateTitleSubject[0] has experienced a low , and the templateXLabel[0] of the population remaining relatively intended over the past decade , and be of the division much amounting to the percentage of the total labor force is made country of the rapidly in the will , which reflects concrete percent in comparison to the end of the templateXLabel[0] . Today , the templateTitle[3] templateYValue[max] templateYLabel[0] rates in the templateTitle[0] has been emerging emerging Central , and it after the templateTitle[0] to the EU , the percentage of the templateTitleSubject[0] templateYValue[idxmax(X)] 20 to the templateXValue[4] , there , there , the templateTitle[3] has been country to the percentage of the highest a trend as has been country to the highest templateTitle[3] has been ongoing . templateTitleSubject[0] templateYValue[idxmax(X)] 20 country to the next a Light templateTitleSubject[1] , the templateTitle[3] has been ongoing a Light templateTitleSubject[1] , the templateTitleSubject[0] templateYValue[idxmax(X)] 20 . The The is a Light ongoing . The The is a trend . The The The The is a trend . The The The is a templateTitle[3] has many The The The The is a trend The The The The is a trend is The The The The is has has has many The The The The The is has has has The The The The is is has many The The The The The The The The The The The The The The The The is has has is The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The is is is The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The
generated: The statistic shows the Unemployment in Armenia from 1999 to had , projections country years the percentage of the labor force in Armenia with at approximately 9.81 percent . In the year of Armenia , the Unemployment in Armenia has experienced a low , and the Year of the population remaining relatively intended over the past decade , and be of the division much amounting to the percentage of the total labor force is made country of the rapidly in the will , which reflects concrete percent in comparison to the end of the Year . Today , the 2019 19.01 Unemployment rates in the Unemployment has been emerging Central , and it after the Unemployment to the EU , the percentage of the Armenia 17.71 20 to the 2015 , there the 2019 has been country to the percentage of the highest a trend as has been country to the highest 2019 has been ongoing . Armenia 17.71 20 country to the next a Light Armenia , the 2019 has been ongoing a Light Armenia , the Armenia 17.71 20 . The is a Light ongoing . The is a trend . The is a trend . The is a 2019 has many The is a trend The is a trend is The has many The is has The is has many The is has The is

Example 100:
titleEntities: {'Subject': ['Peru'], 'Date': ['2015']}
title: Median age of the population in Peru 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']: ['39.4', '38.0', '36.8', '35.6', '34.2', '32.6', '31.0', '27.5', '25.5', '24.1', '22.7', '21.2', '20.1', '19.2', '18.5', '17.9', '17.6', '17.7', '18.2', '18.7', '19.2']

gold: This statistic shows the median age of the population in Peru 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 . In 2015 , the median age of the population of Peru was 27.5 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] 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] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of the templateTitle[2] of templateTitleSubject[0] was templateYValue[7] templateYLabel[2] .

generated_template: The statistic shows the templateYLabel[0] The of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to had , with projections templateYLabel[0] is the average value of a templateTitle[2] into two numerically equal groups lived of the templateTitle[2] is older until than is older until the templateYLabel[0] and the other half younger . In fiscal , the templateYLabel[0] of templateTitleSubject[0] templateYValue[idxmax(X)] templateTitle[2] was templateYValue[7] templateXValue[max] .
generated: The statistic shows the Median of the population in Peru from 1950 to had , with projections Median is the average value of a population into two numerically equal groups lived of the population is older until than is older until the Median and the other half younger . In fiscal , the Median of Peru 39.4 population was 27.5 2050 .

Example 101:
titleEntities: {'Subject': ['Americans'], 'Date': ['2017']}
title: Americans on the concept of the American Dream in 2017
X_Axis['Month']: ['Personal_freedom', 'Religious_freedom', 'Equality', 'Security', 'The_pursuit_of_happiness', 'Economic_freedom', 'Freedom_of_justice', 'Political_freedom', 'Common_good', 'Diversity', 'Freedom_of_the_press', 'Progress_and_change', 'Patriotism', 'Scientific_progress', 'Separation_of_powers', 'Individualism', 'Action_and_achievement', 'Competition', 'Capitalism', 'Solidarity', 'Volunteerism', 'None_of_the_above']
Y_Axis['Share', 'of', 'respondents']: ['66', '56', '55', '54', '53', '51', '48', '45', '44', '42', '40', '39', '37', '36', '36', '34', '34', '30', '28', '21', '19', '4']

gold: This statistic shows the results of a 2017 survey among Americans on the concepts essential for the American Dream . During the survey , 34 percent of respondents stated that action and achievement is essential for the American Dream .
gold_template: This statistic shows the results of a templateTitleDate[0] survey among templateTitleSubject[0] on the concepts essential for the templateTitleSubject[0] templateTitle[3] . During the survey , templateYValue[15] percent of templateYLabel[1] stated that templateXValue[16] and templateXValue[16] is essential for the templateTitleSubject[0] templateTitle[3] .

generated_template: Following two consecutive to templateXValue[1] , templateXValue[0] is the produced areas in the United Kingdom ( UK ) in the templateTitleSubject[0] , with templateYValue[max] percent of the people . The marked a new record , and continued the streak of either stability or growth . More users gaining between templateXValue[0] and quarter , the number of templateTitle[1] users rose from 1.02 billion users in templateXValue[14] .
generated: Following two consecutive to Religious freedom , Personal freedom is the produced areas in the United Kingdom ( UK ) in the Americans , with 66 percent of the people . The marked a new record , and continued the streak of either stability or growth . More users gaining between Personal freedom and quarter , the number of concept users rose from 1.02 billion users in Separation of powers .

Example 102:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2003', '2013']}
title: Attendance at performing arts events in the U.S. 2003 to 2013
X_Axis['Year']: ['2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Attendance', 'in', 'millions']: ['73.54', '74.94', '77.33', '74.98', '77.49', '76.68', '80.13', '82.28', '84.48', '85.6', '86.38']

gold: The statistic above shows the attendance at performing arts events in the United States from 2003 to 2013 . In year 2013 , approximately 73.54 million people attended a performing arts event at least once . Performing arts – additional information Performing arts are any type of art form in which a performer physically conveys an artistic piece ; this includes dance , opera , symphony and theatre performances .
gold_template: The statistic above shows the templateYLabel[0] at templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXLabel[0] templateXValue[max] , approximately templateYValue[idxmax(X)] templateYLabel[1] people attended a templateTitle[1] templateTitle[2] event at least once . templateTitle[1] templateTitle[2] – additional information templateTitle[1] templateTitle[2] are any type of art form in which a performer physically conveys an artistic piece ; this includes dance , opera , symphony and theatre performances .

generated_template: The statistic shows the templateTitle[3] templateYLabel[0] at templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[5] from templateXValue[min] to templateTitleDate[max] . In templateXValue[8] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitleSubject[0] was at some templateYValue[0] points .
generated: The statistic shows the events Attendance at performing arts in the 2003 from to 2013 . In 2005 , the Attendance performing arts Attendance in the U.S. was at some 73.54 points .

Example 103:
titleEntities: {'Subject': ['British Telecommunications BT', 'ARPU'], 'Date': []}
title: British Telecommunications ( BT ) : consumer ARPU Q1 2014/15-Q1 2019/20
X_Axis['Quarter']: ['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']
Y_Axis['Average', 'revenue', 'per', 'user', 'in', 'GBP', 'per', 'month']: ['37.9', '38.8', '39.6', '38.3', '37.9', '38.9', '37.7', '37.8', '37.6', '36.7', '37.3', '37.4', '35.0', '37.1', '36.6', '35.6', '34.9', '34.6', '34.2', '33.6', '33.2']

gold: This statistic shows the Consumer ARPU of British Telecommunications ( BT Consumer ) from the first quarter of 2014/15 to the first quarter of 2019/20 . In the first quarter of 2019/20 ending June 30 , the ARPU was 37.9 British pounds per month .
gold_template: This statistic shows the templateTitle[3] templateTitleSubject[1] of templateTitleSubject[0] ( templateTitleSubject[0] templateTitle[3] ) from the first templateXLabel[0] of 2014/15 to the first templateXLabel[0] of templateTitle[7] . In the first templateXLabel[0] of templateTitle[7] ending 30 , the templateTitleSubject[1] was templateYValue[0] templateTitleSubject[0] pounds templateYLabel[2] templateYLabel[6] .

generated_template: In the fourth templateXLabel[0] of templateXValue[0] , California-based web company templateTitleSubject[0] had an templateYLabel[0] templateYValue[idxmax(X)] of almost templateYValue[max] templateXValue[max] This 's , up from 10.89 templateXValue[max] This 's in the preceding fiscal templateXLabel[0] . templateTitleSubject[0] operates under the parent company Alphabet Inc .
generated: In the fourth Quarter of Q1 2019 , California-based web company British Telecommunications BT had an Average 33.2 of almost 39.6 Q4 2018 This 's , up from 10.89 Q4 2018 This 's in the preceding fiscal Quarter . British Telecommunications BT operates under the parent company Alphabet Inc .

Example 104:
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)] templateYLabel[2] U.S templateYLabel[4] .

generated_template: templateTitleSubject[0] that templateYLabel[0] from printers and copiers reached about templateYValue[0] templateXValue[max] This 's in templateXValue[idxmin(Y)] to templateYValue[max] templateXValue[max] 's in increase . templateTitleSubject[0] is an amalgamation of Rams Vuitton , Moët , and Hennessy , is the leading selling luxury personal goods companies in the world . the company caused globally selling a diverse all-time of products .
generated: Regal Entertainment Group that Net from printers and copiers reached about 112.3 2017 This 's in 2011 to 360.4 2017 's in increase . Regal Entertainment Group is an amalgamation of Rams Vuitton , Moët and Hennessy , is the leading selling luxury personal goods companies in the world . the company caused globally selling a diverse all-time of products .

Example 105:
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] grew by around templateYValue[2] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: templateTitleSubject[0] that real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) has been produces positive dollars for the past five years National templateXValue[min] , and is projected to continue to cost so through such as economic growth . In templateXValue[6] , templateTitleSubject[0] that real templateYLabel[0] is expected to reach templateYValue[max] percent . Learning from real templateYLabel[0] Real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) is a measure of the value of all goods and services templateYValue[idxmax(X)] are an economy arrived within a given templateXLabel[0] .
generated: Peru that real Gross domestic product ( GDP ) has been produces positive dollars for the past five years National 2014 , and is projected to continue cost so through such as economic growth . In 2018 , Peru that real GDP is expected to reach 4.05 percent . Learning from real GDP Gross domestic product ( GDP ) is a measure of the value of all goods and services 3.76 are an economy arrived within a given Year .

Example 106:
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] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateTitle[2] templateXValue[2] templateTitle[4] templateXLabel[0] the templateTitle[0] .

generated_template: The 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] percent of the female 1991 templateXValue[0] was the templateTitle[1] templateTitle[2] templateXValue[last] templateXLabel[0] in templateTitleSubject[0] .
generated: The statistic displays a Most on the used garage None of these openers in U.S. as of 2018 . During the Most period , 50 percent of the female 1991 Liftmaster was the used garage None of these Brand in U.S. .

Example 107:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2024']}
title: Inflation rate in Argentina 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['16.98', '22.21', '26.87', '32.26', '51', '54.44', '34.28', '25.68', '10.62', '10.04', '9.78', '10.46', '6.27', '8.59', '8.83', '10.9', '9.64', '4.42']

gold: Inflation in Argentina was 34 percent in 2018 , expected to rise to nearly 44 percent the following year before declining to 6 percent by 2024 . A major factor in this is the legacy of poor fiscal discipline left by the economic depression from 1998 to 2002 . What causes inflation ? Inflation is a rise in price levels for all goods .
gold_template: templateYLabel[0] in templateTitleSubject[0] was templateYValue[6] percent in templateXValue[6] , expected to rise to nearly 44 percent the following templateXLabel[0] before declining to templateYValue[12] percent by templateXValue[max] . A major factor in this is the legacy of poor fiscal discipline left by the economic depression from 1998 to 2002 . What causes templateYLabel[0] ? templateYLabel[0] is a rise in price levels for all goods .

generated_template: The statistic shows the average templateYLabel[0] average in templateTitleSubject[0] from templateXValue[min] to had , projections country years templateXValue[0] . In had , the average templateYLabel[0] average in templateTitleSubject[0] an to about templateYValue[6] percent templateXValue[max] to the This templateXLabel[0] .
generated: The statistic shows the average Inflation in Argentina from 2004 to had , projections country years 2024 . In had , the average Inflation in Argentina an to about 34.28 percent 2024 to the This Year .

Example 108:
titleEntities: {'Subject': ['Tourism'], 'Date': ['2018']}
title: Tourism industry growth expectations according to travel experts 2018
X_Axis['Response']: ['Significantly_decline', 'Slightly_decline', 'No_change', 'Slightly_grow', 'Significantly_grow']
Y_Axis['Share', 'of', 'respondents']: ['1', '11', '14', '56', '18']

gold: This statistic shows the results of a survey asking travel industry representatives and stakeholders at the London WTM exhibition ( UK ) to what extent do they expect the tourism industry to grow or decline in 2018 . Of respondents , 56 percent believed that the tourism industry will grow slightly in 2018 .
gold_template: This statistic shows the results of a survey asking templateTitle[5] templateTitle[1] representatives and stakeholders at the London WTM exhibition ( UK ) to what extent do they expect the templateTitleSubject[0] templateTitle[1] to templateXValue[3] or templateXValue[0] in templateTitleDate[0] . Of templateYLabel[1] , templateYValue[max] percent believed that the templateTitleSubject[0] templateTitle[1] will templateXValue[3] templateXValue[1] in templateTitleDate[0] .

generated_template: The statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . As of October templateTitleDate[0] , templateXValue[0] programs were templateTitle[0] templateTitle[1] among survey and templateYValue[max] percent .
generated: The statistic presents the Tourism industry growth expectations in the Tourism ( ) in 2018 . As of October 2018 , Significantly decline programs were Tourism industry among survey and 56 percent .

Example 109:
titleEntities: {'Subject': ['Chile'], 'Date': ['2018']}
title: Chile : public perception on the country 's main problems in 2018
X_Axis['Response']: ['Crime', 'Unemployment', 'Corruption', 'Health', 'Education', 'Low_salaries', 'Violation_of_human_rights', 'Immigrants', 'Income_distribution', 'Lack_of_care_for_the_elderly', 'Political_situation', 'The_economy', 'Poverty', 'Drug_consumption']
Y_Axis['Share', 'of', 'respondents']: ['38.2', '8.8', '6.2', '6', '5.8', '4.4', '3.9', '3.2', '2.8', '2.8', '2.7', '2.3', '1.8', '1.6']

gold: During a survey conducted in 2018 , over 38 percent of respondents in Chile mentioned criminality as one of the most important problems affecting the South American country . Furthermore , 3.2 percent of the people interviewed quoted immigrants as one of the main problems in their country , leaving behind income distribution , the political situation and the economy.Up to 45 percent of the people surveyed in Chile claim that most religious leaders are involved in acts of corruption .
gold_template: During a survey conducted in templateTitleDate[0] , over templateYValue[max] percent of templateYLabel[1] in templateTitleSubject[0] mentioned criminality as one of the most important templateTitle[6] affecting the South American templateTitle[3] . Furthermore , templateYValue[7] percent of the people interviewed quoted templateXValue[7] as one of the templateTitle[5] templateTitle[6] in their templateTitle[3] , leaving behind templateXValue[8] , the templateXValue[10] and the economy.Up to 45 percent of the people surveyed in templateTitleSubject[0] claim that most religious leaders are involved in acts of templateXValue[2] .

generated_template: The statistic shows the results of a survey about the templateTitle[2] templateTitle[3] of entire individual templateTitle[0] templateXValue[last] in templateTitleSubject[0] in 2015 , phrased templateXValue[1] the question : `` except reading books , which of the following templateTitle[0] templateXValue[last] cost templateXValue[2] cost in your free time ? '' . It was found templateYValue[idxmax(X)] listening to templateXValue[0] was the most practiced templateTitle[0] activity templateXValue[1] a templateTitle[2] templateTitle[3] of templateYValue[max] percent of households .
generated: The statistic shows the results of a survey about the perception country of entire individual Chile Drug consumption in Chile 2015 , phrased Unemployment the question : `` except reading books , which of the following Chile Drug consumption cost Corruption in your free time ? '' . It was found 1.6 listening to Crime was the most practiced Chile activity Unemployment a perception country of 38.2 percent of households .

Example 110:
titleEntities: {'Subject': ['Los Angeles Chargers'], 'Date': ['2006', '2019']}
title: Regular season home attendance of the Los Angeles Chargers 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['177755', '262145', '202687', '456197', '534180', '523457', '513641', '479716', '523143', '524240', '540344', '545104', '524016', '531024']

gold: This graph depicts the total regular season home attendance of the Los Angeles Chargers franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 177,755 .
gold_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Chargers franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[idxmax(X)] .

generated_template: The graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitleSubject[0] franchise of the National Football League from templateXValue[min] to fiscal templateXLabel[0] . In the fiscal templateXLabel[0] of the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitleSubject[0] amounted to templateYValue[max] templateXValue[max] This 's .
generated: The graph depicts the total Regular season Home in the Los Angeles Chargers franchise of the National Football League from 2006 to fiscal Year . In the fiscal Year of the Regular season Home in the Los Angeles Chargers amounted to 545104 2019 This 's .

Example 111:
titleEntities: {'Subject': ['CAC'], 'Date': ['1995', '2019']}
title: Annual CAC 40 performance 1995 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['Index', 'points']: ['5978.06', '4730.69', '5312.56', '4862.31', '4637.06', '4272.75', '4295.95', '3641.07', '3159.81', '3804.78', '3936.33', '3217.97', '5614.08', '5541.76', '4715.23', '3821.16', '3557.9', '3063.91', '4624.58', '5926.42', '5958.32', '3942.66', '2998.9', '2315.7', '1872.0']

gold: The statistic presents the annual development of the CAC 40 index from 1995 to 2019 . CAC 40 is the leading indicator of the French stock market . The year-end value of the CAC 40 index amounted to 1,872 in 1995 and reached 5,978.05 in 2019 .
gold_template: The statistic presents the templateTitle[0] development of the templateTitleSubject[0] templateTitle[2] templateYLabel[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] templateTitle[2] is the leading indicator of the French stock market . The year-end value of the templateTitleSubject[0] templateTitle[2] templateYLabel[0] amounted to templateYValue[min] in templateXValue[idxmin(Y)] and reached 5,978.05 in templateXValue[max] .

generated_template: According to recent data , the rate of reported templateYLabel[0] of Chancroids in the templateTitle[4] has dropped to very few templateYLabel[0] in recent period . There has been a significant decline in recent history with a distribution rate considering of almost templateYValue[max] templateYLabel[0] on a group in increase . After the first templateXLabel[0] in the templateYLabel[0] of templateTitle[0] in the templateTitleSubject[0] was Unite , reaching a value of templateYValue[min] templateXValue[max] This .
generated: According to recent data , the rate of reported Index of Chancroids in the 1995 has dropped to very few Index in recent period . There has been a significant decline in recent history with a distribution rate considering of almost 5978.06 Index on a group in increase . After the first Year in the Index of Annual in the CAC was Unite , reaching a value of 1872.0 2019 This .

Example 112:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Video game systems : U.S. installed base in 2017 , by platform
X_Axis['Console']: ['Xbox_One_S', 'Xbox_One', 'Xbox_360', 'Xbox', 'PlayStation_4', 'PlayStation_3', 'PlayStation_2', 'Wii_U', 'Wii']
Y_Axis['Installed', 'base', 'in', 'millions']: ['9', '12', '21', '5', '10', '13', '14', '6', '16']

gold: The statistic depicts the installed base of video game systems in the United States in 2017 , by platform . The installed base of Microsoft 's Xbox 360 was 21 million .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[7] templateTitle[8] . The templateYLabel[0] templateYLabel[1] of Microsoft 's templateXValue[0] templateXValue[2] was templateYValue[max] templateYLabel[2] .

generated_template: The statistic depicts the templateTitle[3] templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitle[5] in the templateTitleSubject[0] in templateTitleDate[0] . In templateXValue[1] , templateYValue[5] percent of respondents in the templateTitle[4] stated templateYValue[idxmax(X)] they sector .
generated: The statistic depicts the U.S. installed of Video and game to base in the U.S. in 2017 . In Xbox One , 13 percent of respondents in the installed stated 16 they sector .

Example 113:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2004', '2018']}
title: Capital expenditure of the U.S. chemical industry 2004 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Capital', 'expenditure', 'in', 'million', 'U.S.', 'dollars']: ['33200', '31900', '31100', '31000', '26700', '28900', '28800', '25758', '20339', '23625', '23839', '27412', '18151', '18056', '18857']

gold: This statistic shows the capital expenditure level for the United States chemical industry from 2004 to 2018 . In 2018 , the U.S. chemical industry capital expenditure reached approximately 33.2 billion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] level for the templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[3] templateTitle[3] templateTitle[4] templateYLabel[0] templateYLabel[1] reached approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] gross domestic product ( templateTitle[1] ) units units in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[6] with a forecast reason for the templateTitle[0] templateTitle[1] period , as well as the end of people . In the same templateXLabel[0] , the death of the templateTitleSubject[0] goods was at a value of around templateYValue[max] templateXValue[max] This 's .
generated: The statistic shows the Capital gross domestic product ( expenditure ) units in the U.S. ( ) from 2004 to 2012 with a forecast reason for the Capital expenditure period , as well the end of people . In the same Year , the death of the U.S. goods was at a value of around 33200 2018 This 's .

Example 114:
titleEntities: {'Subject': ['Number'], 'Date': ['2006', '2010']}
title: Number of poker players who play for money
X_Axis['Month']: ['September_2006', 'January_2007', 'Spring_2008', 'Spring_2009', 'Spring_2010']
Y_Axis['Number', 'of', 'players', '(in', 'millions)']: ['17.8', '15.2', '20.0', '20.8', '22.2']

gold: This statistic shows the number of poker players worldwide who play for money , from 2006 to 2010 ( in millions ) . In Spring 2008 , 20 million people played poker online for money .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] worldwide templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] , from templateXValue[0] to templateXValue[last] ( in millions ) . In templateXValue[2] , templateYValue[2] million people played templateTitle[1] online templateTitle[5] templateTitle[6] .

generated_template: The statistic illustrates the number of templateTitleSubject[0] templateYLabel[0] farm projects templateXValue[max] from 2014 to quarter . As of templateYValue[idxmax(X)] templateXLabel[0] , the British messenger hat templateYValue[max] This templateTitle[3] templateTitle[4] templateXValue[max] worldwide .
generated: The statistic illustrates the number of farm projects Spring 2010 from 2014 to quarter . As of 22.2 Month , the British messenger hat 22.2 This who play Spring 2010 worldwide .

Example 115:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009', '2014']}
title: Average price for a full set of gel toenails in nail salons in the U.S. 2009 to 2014
X_Axis['Year']: ['2009', '2010', '2011', '2012', '2013', '2014']
Y_Axis['Average', 'price', 'in', 'U.S.', 'dollars']: ['33.95', '37.03', '36.63', '39.16', '34.02', '34.01']

gold: This statistic shows the average price for a full set of gel toenails in nail salons in the United States from 2009 to 2014 . Nail salons charged an average price of 34.01 U.S. dollars for a full set of gel toenails in 2014 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] a templateTitle[3] templateTitle[4] of templateTitle[5] templateTitle[6] in templateTitle[7] templateTitle[8] in the templateTitle[9] from templateXValue[min] to templateXValue[max] . templateTitle[7] templateTitle[8] charged an templateYLabel[0] templateYLabel[1] of templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateTitle[2] a templateTitle[3] templateTitle[4] of templateTitle[5] templateTitle[6] in templateXValue[max] .

generated_template: The statistic shows the templateTitle[3] templateYLabel[0] held by templateTitle[0] templateTitle[1] ( excluding exports ) in the templateTitleSubject[0] from templateXValue[min] to templateXValue[4] and a forecast reason for templateXValue[1] and a forecast for the same templateXLabel[0] . In templateXValue[1] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to not templateXValue[max] This .
generated: The statistic shows the full Average held by Average price ( excluding exports ) in the U.S. from 2009 to 2013 and a forecast reason for 2010 and a forecast for the same Year . In 2010 , Average price for Average amounted to not 2014 This .

Example 116:
titleEntities: {'Subject': ['Most'], 'Date': ['2019']}
title: Most popular global mobile messaging apps 2019
X_Axis['Platform']: ['WhatsApp', 'Facebook_Messenger', 'WeChat', 'QQ_Mobile', 'Snapchat', 'Telegram']
Y_Axis['Monthly', 'active', 'users', 'in', 'millions']: ['1600', '1300', '1133', '808', '314', '200']

gold: As of October 2019 , 1.6 billion users were accessing the WhatsApp messenger on a monthly basis . The app 's reach is usage penetration is particularly strong in markets outside of the United States and it is one of the most popular mobile social apps worldwide . In February 2014 , social network Facebook acquired the mobile app for 19 billion U.S. dollars .
gold_template: As of October templateTitleDate[0] , templateYValue[max] templateYLabel[3] templateYLabel[2] were accessing the templateXValue[0] templateXValue[1] on a templateYLabel[0] basis . The app 's reach is usage penetration is particularly strong in markets outside of the country and it is one of the templateTitleSubject[0] templateTitle[1] templateXValue[3] social templateTitle[5] worldwide . In 2014 , social network templateXValue[1] acquired the templateXValue[3] app for 19 templateYLabel[3] U.S. dollars .

generated_template: The statistic gives information on the most popular templateTitle[1] networks used as of 2017 , based on the templateYLabel[0] of templateXValue[max] in templateTitleDate[0] . increased the survey period , it was found templateYValue[idxmax(X)] when up an templateYLabel[0] of templateYValue[max] percent .
generated: The statistic gives information on the most popular networks used as of 2017 , based on the Monthly of WhatsApp in 2019 . increased the survey period , it was found 200 when up an Monthly of 1600 percent .

Example 117:
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] percent , 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 November of templateTitleDate[min] , when an templateYLabel[0] templateYLabel[1] of templateYValue[max] percent was recorded .

generated_template: The statistic shows the templateXValue[max] This in the previous templateYLabel[0] of the templateTitle[1] templateTitle[0] in templateTitleSubject[0] from 2018 to 2020 . In 2020 templateYValue[min] the templateTitle[1] templateTitle[0] in templateTitleSubject[0] increased by approximately 1.14 percent . 2018 saw the highest templateTitle[1] in this period , with an templateTitle[1] templateTitle[0] of nearly templateYValue[max] percent in comparison to the previous templateYLabel[0] .
generated: The statistic shows the Nov '19 This in the previous Inflation of the rate Inflation in United Kingdom from 2018 to 2020 . In 2020 1.5 the rate Inflation in United Kingdom increased by approximately 1.14 percent . 2018 saw the highest rate in this period , with an rate Inflation of nearly 3.1 percent in comparison to the previous Inflation .

Example 118:
titleEntities: {'Subject': ['Global'], 'Date': ['2011', '2017']}
title: Global spending on motorsports sponsorships 2011 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Spending', 'in', 'billion', 'U.S.', 'dollars']: ['5.75', '5.58', '5.43', '5.26', '5.12', '4.97', '4.83']

gold: This statistic shows the worldwide spending for motorsports sponsorship from 2011 to 2017 . In the year 2013 , the global motorsports sponsorship spending amounted to 5.12 billion U.S. dollars .
gold_template: This statistic shows the worldwide templateYLabel[0] for templateTitle[2] sponsorship from templateXValue[min] to templateXValue[max] . In the templateXLabel[0] templateXValue[4] , the templateTitleSubject[0] templateTitle[2] sponsorship templateYLabel[0] amounted to templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[1] templateYLabel[0] for enterprise resource planning ( templateTitleSubject[0] ) templateTitle[3] from templateXValue[min] to templateXValue[4] , in templateXValue[max] This 's . In templateXValue[1] , templateTitle[1] templateTitleSubject[0] templateTitle[3] templateYLabel[0] an to around templateYValue[min] templateXValue[max] This 's .
generated: The statistic shows the spending for enterprise resource planning ( Global ) sponsorships from 2011 to 2013 , in 2017 This 's . In 2016 , spending Global sponsorships Spending an to around 4.83 2017 This 's .

Example 119:
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)] templateYLabel[2] . All in all , the templateYLabel[0] of templateTitle[2] players in the templateTitle[3] seems to be on the rise .

generated_template: The statistic shows the templateYLabel[0] of recreational percent in templateTitleSubject[0] from templateXValue[min] to templateXValue[idxmin(Y)] . According to the report , the templateYLabel[0] of templateTitle[1] templateTitle[2] in templateTitleSubject[0] amounted to approximately templateYValue[8] templateXValue[max] in templateXValue[idxmin(Y)] .
generated: The statistic shows the Number of recreational percent in U.S. from 2006 to 2007 . According to the report , the Number of indoor soccer in U.S. amounted to approximately 4.93 2018 in 2007 .

Example 120:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of fresh cucumbers 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']: ['8.0', '7.4', '8.1', '7.5', '7.4', '7.3', '7.1', '6.4', '6.7', '6.8', '6.4', '6.4', '6.1', '6.2', '6.4', '6.2', '6.6', '6.3', '6.4']

gold: The timeline shows the per capita consumption of fresh cucumbers in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh cucumbers amounted to approximately eight 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[0] templateYLabel[3] in templateXValue[max] .

generated_template: templateXValue[max] of templateTitle[4] templateTitle[5] in the templateYLabel[0] in the templateTitle[0] has increased by templateYValue[max] This in increase . The was a decrease from the previous templateXLabel[0] in the previous templateXLabel[0] . Global seafood market the templateYLabel[0] volume of the templateXValue[max] of the templateTitle[0] amounted to about templateYValue[max] This in increase .
generated: 2018 of fresh cucumbers in the Per in the U.S. has increased by 8.1 This in increase . The was a decrease from the previous Year in the previous Year . Global seafood market the Per volume of the 2018 of the U.S. amounted to about 8.1 This in increase .

Example 121:
titleEntities: {'Subject': ['Mattel'], 'Date': ['2013', '2019']}
title: Global ad spending of Mattel from 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Ad', 'expenditure', 'in', 'million', 'U.S.', 'dollars']: ['551.5', '524.3', '642.3', '634.9', '717.85', '733.2', '750.2']

gold: This statistic shows the advertising and promotion expenditure of Mattel worldwide from 2013 to 2019 . In 2019 , Mattel spent approximately 551.5 million U.S. dollars on advertising and promotion .
gold_template: This statistic shows the advertising and promotion templateYLabel[1] of templateTitleSubject[0] worldwide templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] spent approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateYLabel[4] on advertising and promotion .

generated_template: templateTitle[0] templateTitleSubject[0] accomplished an impressive feat with its templateYLabel[0] figures in the past few countries . As of templateXValue[min] , the British retailer that annual templateYLabel[0] of approximately templateYValue[max] thousand templateXValue[max] This in increase . The figure is due to one to nearly templateYValue[max] templateXValue[max] This in increase .
generated: Global Mattel accomplished an impressive feat with its Ad figures in the past few countries . As of 2013 , the British retailer that annual Ad of approximately 750.2 thousand 2019 This in increase . The figure is due to one nearly 750.2 2019 This in increase .

Example 122:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Origin of illegal immigrants in the U.S. 2015
X_Axis['Country']: ['Mexico', 'El_Salvador', 'Guatemala', 'India', 'Honduras', 'Philippines', 'China', 'Korea', 'Vietnam', 'Ecuador', 'Other']
Y_Axis['Illegal', 'immigrants', 'in', 'thousands']: ['6580', '750', '620', '470', '440', '370', '320', '230', '170', '150', '1870']

gold: The statistic shows the estimated number of illegal immigrants in the U.S. in 2015 , by country of origin . As of January 2015 , about 6.58 million illegal immigrants from Mexico were living in the United States .
gold_template: The statistic shows the estimated number of templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] in templateTitleDate[0] , by templateXLabel[0] of templateTitle[0] . As of 2015 , about templateYValue[max] templateYLabel[2] templateYLabel[0] templateYLabel[1] from templateXValue[0] were living in the templateTitle[3] .

generated_template: The statistic shows the number of templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] of templateXValue[max] salary 's n't activities in templateTitleDate[0] . templateXValue[0] up the smallest templateYLabel[0] of templateTitleSubject[0] templateTitle[1] in templateTitleDate[0] with templateYValue[max] percent .
generated: The statistic shows the number of U.S. illegal the immigrants U.S. of Mexico salary 's n't activities in 2015 . Mexico up the smallest Illegal of U.S. illegal in 2015 with 6580 percent .

Example 123:
titleEntities: {'Subject': ['Amazon'], 'Date': ['2018']}
title: U.S. Amazon Prime membership penetration 2018 , by income
X_Axis['Year']: ['Less_than_35K_USD', '35000_-_74999_USD', '75000_USD_and_over', '100000_USD_and_over', '150000_USD_and_over']
Y_Axis['Share', 'of', 'respondents']: ['31.7', '41.6', '55.7', '58.5', '63.3']

gold: This statistic presents the percentage of online consumers in the United States who are Amazon Prime members . During the measured period in August 2018 , it was found that 55.7 percent of consumers with an income of 75,000 U.S. dollars and more had an Amazon Prime membership .
gold_template: This statistic presents the percentage of online consumers in the templateTitle[0] who are templateTitleSubject[0] templateTitle[2] members . During the measured period in 2018 , it was found that templateYValue[2] percent of consumers with an templateTitle[7] of templateXValue[2] templateTitle[0] dollars and more had an templateTitleSubject[0] templateTitle[2] templateTitle[3] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] users who accessed templateTitle[2] through templateTitle[3] templateTitle[4] as of templateTitleDate[0] , sorted templateTitle[6] templateTitle[7] group . second the survey period , templateYValue[1] percent of templateTitleSubject[0] templateTitle[2] aged templateXValue[0] to 34 years used the social networking fluctuated .
generated: The statistic shows the Share of Amazon users who accessed Prime through membership penetration as of 2018 , sorted by income group . second the survey period , 41.6 percent of Amazon Prime aged Less than 35K USD to 34 years used the social networking fluctuated .

Example 124:
titleEntities: {'Subject': ['Texas'], 'Date': ['1992', '2018']}
title: Texas - 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', '4.3', '4.6', '4.4', '5.1', '6.3', '6.7', '7.8', '8.1', '7.6', '4.8', '4.3', '4.9', '5.4', '5.9', '6.7', '6.4', '5', '4.3', '4.7', '4.9', '5.3', '5.7', '6.1', '6.5', '7.2', '7.6']

gold: This statistic displays the unemployment rate in Texas from 1992 to 2018 . In 2018 , unemployment in Texas was 3.9 percent .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[min] percent .

generated_template: The statistic displays the templateYLabel[0] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[0] percent .
generated: The statistic displays the Unemployment in Texas from 1992 to 2018 . In 2018 , Unemployment in Texas was 3.9 percent .

Example 125:
titleEntities: {'Subject': ['USA'], 'Date': ['1990', '2018']}
title: USA - number of arrests for all offenses 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', 'arrests']: ['10310960', '10554985', '10662252', '10797088', '11205833', '11302102', '12196959', '12408899', '13120947', '13687241', '14005615', '14209365', '14380370', '14094186', '13938071', '13639500', '13741400', '13699300', '13980300', '14031100', '14528300', '15284300', '15168100', '15119800', '14648700', '14036300', '14075100', '14211900', '14195100']

gold: There were over 10.3 million arrests for all offenses in the United States in 2018 . This figure is a decrease from 1990 levels , when the number of arrests was over 14.1 million . Arrest rate in the U.S .
gold_template: There were over templateYValue[min] million templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] in the country in templateXValue[max] templateXValue[idxmin(Y)] This figure is a decrease from templateXValue[min] levels , when the templateYLabel[0] of templateYLabel[1] was over templateYValue[13] million . Arrest rate in the U.S .

generated_template: The statistic shows the templateYLabel[0] of templateXValue[max] This in the templateTitleSubject[0] who live below the templateTitle[0] level from templateXValue[min] to templateYValue[min] percent in templateXValue[idxmin(Y)] . In templateXValue[min] , this figure decreased to templateYValue[idxmin(X)] percent of the templateTitle[0] in the templateTitle[4] . templateTitle[0] is the state of be who lacks a certain amount of material possessions or money .
generated: The statistic shows the Number of 2018 This in the USA who live below the USA level from 1990 to 10310960 percent in 2018 . In 1990 , this figure decreased to 14195100 percent of the USA in the all . USA is the state of be who lacks a certain amount of material possessions or money .

Example 126:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Number of women murdered by men in the U.S. by state 2017
X_Axis['U.S.', 'states']: ['California', 'Texas', 'Georgia', 'Pennsylvania', 'Ohio', 'New_York', 'Virginia', 'Tennessee', 'Arizona', 'North_Carolina', 'Louisiana', 'Michigan', 'South_Carolina', 'Missouri', 'Illinois', 'Washington', 'New_Jersey', 'Kentucky', 'Maryland', 'Indiana', 'Arkansas', 'Colorado', 'Nevada', 'Wisconsin', 'Oklahoma', 'Massachusetts', 'Mississippi', 'Oregon', 'Minnesota', 'Kansas', 'West_Virginia', 'New_Mexico', 'Connecticut', 'Alaska', 'Idaho', 'Iowa', 'Utah', 'Montana', 'Delaware', 'Nebraska', 'Hawaii', 'Vermont', 'South_Dakota', 'Rhode_Island', 'Maine', 'North_Dakoda', 'New_Hampshire', 'Wyoming']
Y_Axis['Number', 'of', 'homicide', 'victims']: ['232', '221', '96', '82', '78', '74', '73', '69', '68', '65', '63', '58', '52', '51', '49', '42', '41', '40', '39', '38', '34', '32', '30', '30', '28', '23', '22', '21', '21', '16', '15', '15', '15', '14', '13', '12', '12', '10', '7', '7', '6', '6', '6', '5', '5', '4', '4', '4']

gold: This statistic shows the number of female homicide victims in single offender homicides per state in the United States in 2017 . In 2017 , there were 221 women killed by male single offenders in the state of Texas . Texas was the state with the highest number of women murdered by men in single offender homicides .
gold_template: This statistic shows the templateYLabel[0] of female templateYLabel[1] templateYLabel[2] in single offender homicides per templateTitle[7] in the templateTitle[5] in templateTitleDate[0] . In templateTitleDate[0] , there were templateYValue[1] templateTitle[1] killed templateTitle[3] male single offenders in the templateTitle[7] of templateXValue[1] . templateXValue[1] was the templateTitle[7] with the highest templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in single offender homicides .

generated_template: The statistic shows a ranking of the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] , based on the templateTitle[5] scores templateTitle[7] for admission in the templateTitleDate[min] - templateTitleDate[max] academic year . In templateTitleDate[min] - templateTitleDate[max] , the templateTitle[5] templateYLabel[1] based on the 25th percentile for admission to templateXValue[1] stood at templateYValue[1] percent .
generated: The statistic shows a ranking of the Number women murdered in the U.S. , based on the U.S. scores state for admission in the 2017 - academic year . In 2017 - , the U.S. homicide based on the 25th percentile for admission to Texas stood at 221 percent .

Example 127:
titleEntities: {'Subject': ['Canada', 'Facebook'], 'Date': ['2015']}
title: Canada : Facebook user penetration 2015 , by age
X_Axis['Year']: ['18-34', '35-44', '45-54', '55-64', '65+', 'Total']
Y_Axis['Share', 'of', 'population']: ['75', '68', '63', '47', '32', '59']

gold: This statistic shows the responses on a study about social network usage in Canada as of January 2015 . During the reported period , Facebook reached 75 percent of Canadians aged 18 to 34 , while among Canadians aged 65 and older Facebook had a 32 percent penetration rate . In general the platform reached 59 percent of people in Canada .
gold_template: This statistic shows the responses on a study about social network usage in templateTitleSubject[0] as of 2015 . During the reported period , templateTitleSubject[1] reached templateYValue[max] percent of Canadians aged 18 to 34 , while among Canadians aged 65 and older templateTitleSubject[1] had a templateYValue[min] percent templateTitle[3] rate . In general the platform reached templateYValue[last] percent of people in templateTitleSubject[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitleSubject[0] as of 2018 , sorted templateTitle[5] templateTitle[6] this . increased the survey period , it was found templateYValue[idxmax(X)] templateYValue[max] percent of across users were aged between 25 and 34 templateXValue[1] .
generated: The statistic shows the Share of Canada Facebook in the Canada as of 2018 , sorted by age this . increased the survey period , it was found 59 75 percent of across users were aged between 25 and 34 35-44 .

Example 128:
titleEntities: {'Subject': ['Current'], 'Date': []}
title: Current year in various historical and world calendars 2020
X_Axis['Month']: ['Assyrian', 'Hebrew', 'Chinese', 'Julian', 'Buddhist', 'Gregorian', 'Hindu', 'Islamic', 'Iranian', 'French_Revolutionary']
Y_Axis['Current', 'year', '(as', 'of', 'January', '25,', '2020)']: ['6770', '5780', '4718', '2773', '2563', '2020', '1941', '1441', '1398', '228']

gold: Today , the vast majority of the world uses what is known as the Gregorian calendar , Named after Pope Gregory XIII , who introduced it in 1582 . The Gregorian calendar replaced the Julian calendar , which had been the most used calendar in Europe until this point . The Gregorian calendar lasts for approximately 365.24 days ; this means that most years have 365 days , with one extra day being added every fourth year , unless the year is divisible by 100 but not 400 ( for example , the year 2000 was a leap year , whereas 2100 and 2200 will not be ) .
gold_template: Today , the vast majority of the templateTitle[4] uses what is known as the templateXValue[5] calendar , Named after Pope Gregory XIII , who introduced it in 1582 . The templateXValue[5] calendar replaced the templateXValue[3] calendar , which had been the most used calendar in Europe until this point . The templateXValue[5] calendar lasts for approximately 365.24 days ; this means that most years have 365 days , with one extra day being added every fourth templateYLabel[1] , unless the templateYLabel[1] is divisible by 100 but not 400 ( for example , the templateYLabel[1] 2000 was a leap templateYLabel[1] , whereas 2100 and 2200 will not be ) .

generated_template: The statistic gives a templateTitle[0] of the templateTitle[2] popular templateTitle[0] on templateTitleSubject[0] , ranked by templateTitle[1] of templateTitle[2] on the social revenues . As of 2020 , personal care templateYLabel[0] templateXValue[0] Body templateXValue[0] was ranked first with close to templateYValue[max] templateXValue[max] British pounds .
generated: The statistic gives a Current of the various popular Current on , ranked by year of various on the social revenues . As of 2020 , personal care Current Assyrian Body was ranked first with close to 6770 Assyrian British pounds .

Example 129:
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: The statistic illustrates the templateTitle[3] of templateYLabel[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with a forecast for templateXValue[1] and a forecast for templateXValue[1] , with a forecast for templateXValue[1] , with a forecast for the period , based on templateYLabel[0] templateTitle[1] and the industry is ‘ to reach templateYValue[min] templateXValue[max] This 's in templateXValue[2] . According to the source , worldwide are expected to one to over 40 templateXValue[max] British pounds in templateTitleSubject[0] in increase .
generated: The statistic illustrates the Coast of Number duty in Coast Guard from 1995 to 2000 , with a forecast for 2009 and a forecast for 2009 , with a forecast for 2009 , with a forecast for the period , based on Number duty and the industry is ‘ to reach 34804 2010 This 's in 2008 . According to the source , worldwide are expected to one over 40 2010 British pounds in Coast Guard increase .

Example 130:
titleEntities: {'Subject': ['Physicians'], 'Date': ['2013']}
title: Physicians density worldwide by region 2013
X_Axis['Country']: ['Europe', 'Americas', 'Western_Pacific', 'World', 'Eastern_Mediterranean', 'Southeast_Asia', 'Africa']
Y_Axis['Physicians', 'per', '10,000', 'population']: ['32.1', '21.5', '15.5', '13.9', '12.7', '5.9', '2.7']

gold: This statistic depicts the physicians density worldwide as of 2013 , by WHO region . As of that year , the average density in Southeast Asia stood at 5.9 physicians per 10,000 inhabitants . The world average was around 14 physicians per 10,000 inhabitants .
gold_template: This statistic depicts the templateYLabel[0] templateTitle[1] templateTitle[2] as of templateTitleDate[0] , templateTitle[3] WHO templateTitle[4] . As of that year , the average templateTitle[1] in templateXValue[5] stood at templateYValue[5] templateYLabel[0] templateYLabel[1] 10,000 inhabitants . The templateXValue[3] average was around templateYValue[3] templateYLabel[0] templateYLabel[1] 10,000 inhabitants .

generated_template: The statistic shows the templateTitle[3] templateTitle[4] of templateYLabel[0] worldwide in selected countries in templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] has the highest levels of templateYLabel[0] templateTitle[4] , with a templateTitle[3] templateTitle[4] of templateYValue[max] templateXValue[max] This 's .
generated: The statistic shows the by region of Physicians worldwide in selected countries in Physicians 2013 . Europe has the highest levels of Physicians region , with a by region of 32.1 Europe This 's .

Example 131:
titleEntities: {'Subject': ['Wayfair'], 'Date': ['2013', '2018']}
title: Wayfair active customers 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Number', 'of', 'active', 'customers', 'in', 'millions']: ['15.16', '10.99', '8.25', '5.36', '3.22', '2.09']

gold: The timeline shows e-retailer Wayfair 's active customer base from 2013 to 2018 . During the last reported fiscal period , the online furniture retailer had 15.16 million active customers , up from 10.99 million customers in the previous year .
gold_template: The timeline shows e-retailer templateTitleSubject[0] 's templateYLabel[1] customer base from templateXValue[min] to templateXValue[max] . During the last reported fiscal period , the online furniture retailer had templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[1] templateYLabel[2] , up from templateYValue[1] templateYLabel[3] templateYLabel[2] in the previous templateXLabel[0] .

generated_template: templateTitleSubject[0] , the Canadian athletic apparel company , favorite templateYValue[max] templateXValue[max] This that in increase . The is a significant decrease from templateXValue[min] since templateYValue[idxmin(X)] templateXValue[max] This that in templateXValue[idxmin(Y)] .
generated: Wayfair , the Canadian athletic apparel company , favorite 15.16 2018 This that in increase . The is a significant decrease from 2013 since 2.09 2018 This that in 2013 .

Example 132:
titleEntities: {'Subject': ['Italians'], 'Date': ['2017', '2017']}
title: Persons with whom Italians cheat on their partners 2017
X_Axis['Response']: ['Friend', 'Colleague_from_work', 'Stranger_met_in_a_particular_context_(disco_gym_holidays_etc.)', 'Neighbor', 'Does_not_reply', 'Partner_of_a_friend_of_mine', 'Stranger_met_by_chance', 'Escort', 'Family_member']
Y_Axis['Share', 'of', 'respondents']: ['25.4', '22.6', '17.5', '10.7', '9.3', '5.3', '4.4', '3.5', '1.3']

gold: A survey conducted in 2017 reveals that the largest groups of Italian respondents cheated on their partner either with a friend or with a colleague . In particular , 25.4 percent of interviewees in Italy stated that they betrayed their partner with a friend , while 22.6 percent did so with a colleague . However , it was common to cheat with strangers as well .
gold_template: A survey conducted in templateTitle[7] reveals that the largest groups of Italian templateYLabel[1] cheated on templateTitle[5] templateXValue[5] either templateTitle[1] a templateXValue[0] or templateTitle[1] a templateXValue[1] . In templateXValue[2] , templateYValue[max] percent of interviewees in Italy stated that they betrayed templateTitle[5] templateXValue[5] templateTitle[1] a templateXValue[0] , while templateYValue[1] percent did so templateTitle[1] a templateXValue[1] . However , it was common to templateTitle[4] templateTitle[1] strangers as well .

generated_template: A survey conducted in templateTitleDate[0] reveals templateYValue[idxmax(X)] 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] percent of people declared when templateXValue[6] for the templateTitle[0] time in templateYValue[idxmax(X)] templateTitle[6] all-time . favorite templateYValue[3] percent of rankings throughout templateXValue[6] their templateTitle[0] templateTitle[1] templateTitle[2] in their early 20s .
generated: A survey conducted in 2017 reveals 1.3 most Italians Stranger met by chance their Persons whom Italians Colleague from work the 2017 of Stranger met in a particular context (disco gym holidays etc.) and . More specifically , about 25.4 percent of people declared when Stranger met by chance for the Persons time in 1.3 2017 all-time . favorite 10.7 percent of rankings throughout Stranger met by chance their Persons whom Italians in their early 20s .

Example 133:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2012']}
title: Car rental in the U.S. - total cars in service 2002 to 2012
X_Axis['Year']: ['2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012']
Y_Axis['Cars', 'in', 'service', 'in', 'thousands']: ['1643', '1617', '1665', '1714', '1768', '1861', '1813', '1637', '1629', '1761', '1857']

gold: This statistic represents the US rental car industry 's total number of cars in service between 2002 and 2012 . In 2012 , there were about 1.86 million rental cars in service , while the industry 's revenue grew to around 23.6 billion US dollars .
gold_template: This statistic represents the US templateTitle[1] templateTitle[0] industry 's templateTitle[3] number of templateYLabel[0] in templateYLabel[1] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were about templateYValue[max] templateYLabel[2] templateTitle[1] templateYLabel[0] in templateYLabel[1] , while the industry 's revenue grew to around 23.6 templateYLabel[2] US dollars .

generated_template: The statistic shows the number of metal and metal products templateYValue[idxmax(X)] are they from templateTitleSubject[0] templateTitle[1] and other low-grade residues worldwide from templateXValue[min] to templateTitleDate[max] . In templateXValue[6] , the templateTitle[2] templateYLabel[0] for templateTitleSubject[0] templateTitle[1] amounted to templateYValue[3] percent .
generated: The statistic shows the number of metal and products 1857 are they from U.S. rental and other low-grade residues worldwide from 2002 to 2012 . In 2008 , the U.S. Cars for U.S. rental amounted to 1714 percent .

Example 134:
titleEntities: {'Subject': ['Russia'], 'Date': ['2015']}
title: Largest cities in Russia 2015
X_Axis['Month']: ['Moscow', 'St._Petersburg', 'Novosibirsk', 'Jekaterinburg', 'Nižnij_Novgorod', "Kazan'", "Čel'abinsk", 'Samara', 'Omsk', 'Rostov-na-Donu']
Y_Axis['Residents', 'in', 'million']: ['12.05', '5.19', '1.57', '1.43', '1.27', '1.21', '1.18', '1.17', '1.17', '1.11']

gold: The statistic depicts the ten largest cities in Russia in 2015 . In 2015 , Moscow had around 12.1 million residents , making it the largest city in Russia . Population and workforce of Russia Russia is the largest country in the world ; with a total population of around 144 million people ( 263767 ) , it is the ninth most populous nation worldwide .
gold_template: The statistic depicts the ten templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] had around templateYValue[max] templateYLabel[1] templateYLabel[0] , making it the templateTitle[0] city in templateTitleSubject[0] . Population and workforce of templateTitleSubject[0] is the templateTitle[0] country in the world ; with a total population of around 144 templateYLabel[1] people ( 263767 ) , it is the ninth most populous nation worldwide .

generated_template: The statistic shows the service templateTitle[0] templateTitle[1] in templateTitleSubject[0] templateXValue[0] in templateTitleDate[0] . In templateTitleDate[0] , around was thousand people 2016 in templateXValue[0] , making it the templateTitle[0] includes in templateTitleSubject[0] .
generated: The statistic shows the service Largest cities in Russia Moscow in 2015 . In 2015 , around was thousand people 2016 in Moscow , making it the Largest includes in Russia .

Example 135:
titleEntities: {'Subject': ['Spain'], 'Date': ['2006', '2017']}
title: Number of arrivals in tourist accommodation Spain 2006 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['129.4', '123.5', '114.4', '107.5', '101.7', '100.4', '103.1', '98.8', '93.7', '100.1', '101.8', '99.9']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Spain from 2006 to 2017 . There were over 129 million arrivals at accommodation establishments in Spain in 2017 , a rise since the previous year .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . There were over templateYValue[max] templateYLabel[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] in templateXValue[idxmax(Y)] , a rise since the previous templateXLabel[0] .

generated_template: In templateTitleDate[0] over templateYValue[max] templateXValue[max] templateTitle[2] templateTitle[3] were recorded at templateTitle[3] 29 in templateTitleSubject[0] . Since templateXValue[8] , figures have been steadily rising , with more and more domestic as well as international in Romanian templateTitle[2] templateTitle[3] .
generated: In 2006 over 129.4 2017 tourist accommodation were recorded at accommodation 29 in Spain . Since 2009 , figures have been steadily rising , with more and domestic as well international in Romanian tourist accommodation .

Example 136:
titleEntities: {'Subject': ['Europe'], 'Date': ['2014']}
title: Car sharing users in Europe 2014 , by country
X_Axis['Country']: ['Germany', 'United_Kingdom', 'France', 'Italy', 'Switzerland', 'Austria', 'Netherlands', 'Sweden', 'Spain', 'Belgium', 'Norway', 'Denmark']
Y_Axis['Number', 'of', 'users']: ['757000', '163000', '153000', '130000', '105000', '75000', '51000', '21000', '20000', '16000', '8500', '7800']

gold: This statistic displays the number of car sharing users in selected countries in Europe in 2014 . Germany had by far the highest number of people using car sharing companies in Europe and almost five times as many as France . In Italy there were an estimated 130,000 car share users .
gold_template: This statistic displays the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in selected countries in templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] had templateTitle[5] far the highest templateYLabel[0] of people using templateTitle[0] templateTitle[1] companies in templateTitleSubject[0] and almost five times as many as templateXValue[2] . In templateXValue[3] there were an estimated templateYValue[3] templateTitle[0] share templateYLabel[1] .

generated_template: The statistic displays the mobile of templateTitleSubject[0] templateYLabel[0] as of templateTitleDate[0] , templateTitle[5] templateTitle[6] . In templateYValue[idxmax(X)] year , the templateXValue[0] accounted for more until be third of the total templateTitleSubject[0] templateYLabel[0] , amounting to almost templateYValue[max] templateXValue[max] This . H templateXValue[5] M templateTitle[3] second , with revenues of approximately templateYValue[1] templateXValue[max] This .
generated: The statistic displays the mobile of Europe Number as of 2014 , by country . In 7800 year , the Germany accounted for more until be third of the total Europe Number , amounting to almost 757000 Germany This . H Austria M Europe second , with revenues of approximately 163000 Germany This .

Example 137:
titleEntities: {'Subject': ['Share'], 'Date': ['2015']}
title: Share of the global agricultural machinery market , by region 2015
X_Axis['Country']: ['European_Union', 'NAFTA', 'China', 'South_America', 'India', 'Commonwealth_of_Independent_States', 'Japan', 'Turkey', 'Rest_of_World']
Y_Axis['Share', 'of', 'market']: ['26', '22', '15', '8', '6', '6', '4', '3', '10']

gold: This statistic shows the share of the global farming machinery market in 2015 , by region . The European Union and NAFTA accounted for 48 percent of the agricultural machinery market in this year , though China in third place is one of the fastest growing markets .
gold_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 percent of the templateTitle[2] templateTitle[3] templateYLabel[1] in this year , though templateXValue[2] in third place is one of the fastest growing markets .

generated_template: The statistic shows the templateTitle[3] templateTitle[4] templateTitle[5] templateYLabel[0] worldwide in templateTitleDate[0] , templateTitle[6] templateTitle[7] templateXLabel[0] . templateXValue[0] has the highest levels of templateTitle[3] templateYLabel[0] and templateXValue[5] amounted to templateYValue[max] percent of templateTitleSubject[0] templateTitle[4] templateYLabel[0] in templateYValue[idxmax(X)] year .
generated: The statistic shows the machinery market by Share worldwide in 2015 , region 2015 Country . European Union has the highest levels of machinery Share and Commonwealth of Independent States amounted to 26 percent of Share market in 10 year .

Example 138:
titleEntities: {'Subject': ['Tonga'], 'Date': ['2019']}
title: Unemployment rate in Tonga 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['1.02', '1.03', '1', '1.06', '1.08', '1.11', '1.14', '1.11', '1.13', '1.15', '1.13', '0.96', '0.99', '1.09', '2.14', '3.49', '5.18', '4.74', '4.19', '3.8', '3.43']

gold: This statistic shows the unemployment rate in Tonga from 1999 to 2019 . In 2019 , the unemployment rate in Tonga was at approximately 1.02 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] percent .

generated_template: The statistic shows the templateYLabel[0] The in templateTitleSubject[0] from templateXValue[min] to had , in projections country years the percentage , the data are ILO estimates . In templateXValue[min] , the templateYLabel[0] in templateTitleSubject[0] with at approximately templateYValue[0] percent . In the Asia-Pacific templateXLabel[0] , the templateYLabel[0] in templateTitleSubject[0] has experienced the exception of templateXValue[15] .
generated: The statistic shows the Unemployment in Tonga from 1999 to had , in projections country years the percentage , the data are ILO estimates . In 1999 , the Unemployment in Tonga with at approximately 1.02 percent . In the Asia-Pacific Year , the Unemployment in Tonga has experienced the exception of 2004 .

Example 139:
titleEntities: {'Subject': ['Major'], 'Date': ['2017']}
title: Major species groups in global aquaculture production worldwide 2017
X_Axis['Month']: ['Carps_barbels_and_other_cyprinids', 'Miscellaneous_freshwater_fishes', 'Tilapias_and_other_cichlids', 'Oysters', 'Clams_cockles_arkshells', 'Shrimps_prawns', 'Salmons_trouts_smelts', 'Freshwater_crustaceans', 'Scallops_pectens', 'Mussels', 'Marine_fishes_not_identified']
Y_Axis['Production', 'in', 'thousand', 'metric', 'tons']: ['28345', '10433', '5881', '5711', '5658', '5512', '3477', '2526', '2185', '2164', '990']

gold: This statistic shows the top 10 species groups for aquaculture production worldwide in 2017 . In that year , with over 3.5 million metric tons , salmons , trouts and smelts represented one of the world 's most produced species groups in aquaculture .
gold_template: This statistic shows the top 10 templateTitle[1] templateTitle[2] for templateTitle[4] templateYLabel[0] templateTitle[6] in templateTitleDate[0] . In that year , with over templateYValue[6] templateYLabel[1] templateYLabel[2] templateYLabel[3] , templateXValue[6] , templateXValue[6] and templateXValue[6] represented one of the world 's most produced templateTitle[1] templateTitle[2] in templateTitle[4] .

generated_template: Over the past decade the templateYLabel[0] from off-course templateTitle[2] templateTitle[3] templateTitle[4] in the United Kingdom ( UK ) has significantly decreased from templateYValue[max] templateXValue[max] British pounds in 2009 to templateYValue[min] templateXValue[max] This in templateXValue[0] in templateTitleDate[0] . A ranking of templateTitleDate[0] , the volume of templateXValue[0] templateYLabel[0] in templateTitleSubject[0] was templateYValue[min] templateXValue[max] .
generated: Over the past decade the Production from off-course groups global aquaculture in the United Kingdom ( UK ) has significantly decreased from 28345 Carps barbels and other cyprinids British pounds in 2009 to 990 Carps barbels and other cyprinids This in Carps barbels and other cyprinids 2017 . A ranking of 2017 , the volume of Carps barbels and other cyprinids Production in Major was 990 Carps barbels and other cyprinids .

Example 140:
titleEntities: {'Subject': ['Germany'], 'Date': ['2013', '2018']}
title: Forecast : Online retail sales value in Germany 2013 to 2018
X_Axis['Year']: ['2013', '2018']
Y_Axis['Sales', 'in', 'billion', 'euros']: ['25', '41']

gold: This statistic shows the value of online retail sales in Germany in 2013 and with a forecast for 2018 . Online sales were measured at 25 billion euros in Germany in 2013 , but were expected to grow to 41 billion euros in 2018 .
gold_template: This statistic shows the templateTitle[4] of templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] in templateXValue[min] and with a templateTitle[0] for templateXValue[max] . templateTitle[1] templateYLabel[0] were measured at templateYValue[idxmin(X)] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateXValue[idxmin(Y)] , but were expected to grow to templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] in templateXValue[idxmax(Y)] .

generated_template: The statistic shows the templateYLabel[0] of the entering food templateTitle[3] worldwide in templateXValue[min] and templateXValue[1] . In templateXValue[min] , the templateTitleSubject[0] entering food templateTitle[3] was America at some templateYValue[idxmin(X)] templateXValue[max] This 's . Baby food is foods food leading templateTitle[3] little foods 19 for the giant templateTitle[2] of earned and infants .
generated: The statistic shows the Sales of the entering food sales worldwide in 2013 and 2018 . In 2013 , the Germany entering food sales was America at some 25 2018 This 's . Baby food is foods food leading sales little foods 19 for the giant retail of earned and infants .

Example 141:
titleEntities: {'Subject': ['Nuclear'], 'Date': ['2005', '2019']}
title: Nuclear power plants : permanent shutdowns 2005 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Number', 'of', 'shutdowns']: ['3', '7', '5', '4', '7', '1', '6', '3', '13', '1', '3', '2', '1', '8', '2']

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

generated_template: The statistic shows the number of templateTitle[0] templateTitle[2] by templateTitle[1] in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[min] to 20119 , based on volume . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] Spanish templateYLabel[0] in templateTitleSubject[0] was estimated to reach templateYValue[max] templateXValue[max] This .
generated: The statistic shows the number of Nuclear plants by power in Nuclear and from 2005 to 20119 , based on volume . In 2016 , the Nuclear power plants Spanish Number in Nuclear was estimated to reach 13 2019 This .

Example 142:
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] percent 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 decrease of 23 percent since templateXValue[min] , when some templateYValue[idxmin(X)] percent of templateYLabel[1] were planning to do the majority of their templateTitle[3] on templateTitleSubject[0] .

generated_template: The statistic presents the templateTitle[0] Association size of universities templateTitleSubject[0] users in the templateTitle[1] . second the health survey , it was found templateYValue[idxmax(X)] templateYValue[min] percent of the templateTitle[1] templateTitle[2] . The templateYLabel[0] is projected to grow to templateYValue[max] percent in increase .
generated: The statistic presents the Share Association size of universities U.S. Black Friday users in the U.S. . second the health survey , it was found 36 35 percent of the U.S. consumers . The Share is projected to grow 59 percent in increase .

Example 143:
titleEntities: {'Subject': ['BMW Group'], 'Date': ['2010', '2018']}
title: BMW Group - motorcycle production 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Production', 'in', 'units']: ['162687', '185682', '145555', '151004', '133615', '110127', '113811', '110360', '99236']

gold: This statistic represents the number of motorcycles produced by BMW Group from the fiscal year of 2010 to the fiscal year of 2018 . BMW Group produced around 162,700 motorcycles in the fiscal year of 2018 .
gold_template: This statistic represents the number of motorcycles produced by templateTitleSubject[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . templateTitleSubject[0] produced around 162,700 motorcycles in the fiscal templateXLabel[0] of templateXValue[max] .

generated_template: The statistic shows templateTitleSubject[0] that templateYLabel[0] and development and development from templateXValue[min] to had . In fiscal , the company that templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] employed at approximately templateYValue[max] percent templateXValue[max] This . templateTitleSubject[0] is a globally inhabitants oil and gas company , and the favorable largest company generally worldwide as of templateXValue[1] , measured by monthly .
generated: The statistic shows BMW Group that Production and development from 2010 to had . In fiscal , the company that BMW Group motorcycle Production employed at approximately 185682 percent 2018 This . BMW Group is a globally inhabitants oil and gas company , and the favorable largest company generally worldwide as of 2017 , measured by monthly .

Example 144:
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] templateYLabel[4] 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: The statistic shows a timeline with the amount of monthly templateXValue[max] templateTitle[0] templateTitleSubject[0] This worldwide as of 2020 . As of templateYValue[idxmax(X)] templateXLabel[0] , the stood messaging app announced more than templateYValue[max] 's This 's , up from over templateYValue[1] 's in the previous year .
generated: The statistic shows a timeline with the amount of monthly Sep '17 Facebook Messenger This worldwide as of 2020 . As of 200 Month , the stood messaging app announced more than 1300 's This , up from over 1200 's in the previous year .

Example 145:
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)] percent of the templateYLabel[1] in the country accessed templateTitle[1] templateTitle[2] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] percent .

generated_template: The statistic provides information on the templateTitle[1] templateTitle[3] rate in templateTitleSubject[0] from templateXValue[min] to templateXValue[1] . In templateXValue[min] , templateYValue[idxmin(X)] percent of the Indonesian population accessed the templateTitle[1] . In templateXValue[4] , than figure is projected to grow to templateYValue[4] percent .
generated: The statistic provides information on the social user rate in Vietnam from 2017 to 2022 . In 2017 , 46 percent of the Indonesian population accessed the social . In 2019 , than figure is projected to grow 49 percent .

Example 146:
titleEntities: {'Subject': ['Easton Bell Sports'], 'Date': ['2013', '2013']}
title: Sporting goods industry : Revenue of Easton Bell Sports 2013
X_Axis['Year']: ['2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Revenue', '(in', 'million', 'U.S.', 'dollars)']: ['780.4', '827.2', '834.9', '772.8', '716.3', '775.5', '724.7', '639.0']

gold: This graph depicts the revenue of the sporting goods company Easton-Bell Sports from 2006 to 2013 . In 2006 , Easton-Bell Sports generated revenues of 639 million U.S. dollars . Easton-Bell Sports makes sports equipment and clothing under the brands Bell Sports , Blackburn , Easton , Giro , and Riddell .
gold_template: This graph depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] company Easton-Bell templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , Easton-Bell templateTitleSubject[0] generated revenues of templateYValue[min] templateYLabel[2] templateYLabel[3] dollars . Easton-Bell templateTitleSubject[0] makes templateTitleSubject[0] equipment and clothing under the brands templateTitleSubject[0] , Blackburn , templateTitleSubject[0] , Giro , and Riddell .

generated_template: The statistic illustrates the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] amounted to templateYValue[max] templateXValue[max] This 's .
generated: The statistic illustrates the Revenue of goods industry Revenue Easton Bell in the Sports from 2006 to 2013 . In 2013 , the Revenue of goods industry Revenue Easton Bell in the Sports amounted to 834.9 2013 This 's .

Example 147:
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: The statistic shows the templateXValue[max] This templateTitle[2] of templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] . In than year , templateXValue[0] was the leading market for the templateTitle[2] of templateTitle[0] templateTitle[1] with templateYValue[max] liters of templateTitle[0] templateTitle[1] a templateTitle[3] . templateTitle[0] templateTitle[1] is the second largest templateXLabel[0] of the world templateTitle[1] with templateYValue[2] liters in the world .
generated: The statistic shows the Nissan This vehicle of Mexico Light in Mexico 2019 . In than year , Nissan was the leading market for the vehicle of Mexico Light with 174706 liters of Mexico Light a sales . Mexico Light is the second largest Automaker of the world Light with 117045 liters in the world .

Example 148:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2017', '2023']}
title: Mexico : 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']: ['73.0', '71.1', '69.1', '66.7', '64.1', '61.7', '58.8']

gold: This statistic shows the number of social network users in Mexico from 2017 to 2023 . In 2018 , there were around 62 million social network users in Mexico , up from 59 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[5] , there were around templateYValue[5] templateYLabel[2] templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] , up from templateYValue[min] templateYLabel[2] in templateXValue[idxmin(Y)] .

generated_template: In templateXValue[min] , approximately templateYValue[idxmin(X)] templateXValue[max] people had could to the templateTitle[2] in templateTitleSubject[0] . The figure is projected to grow to about templateYValue[max] templateXValue[max] in increase . As the figures suggest , the templateYLabel[0] of templateTitle[2] templateTitle[3] has been gradually increasing National health .
generated: In 2017 , approximately 58.8 2023 people had could to the social in Mexico . The figure is projected to grow about 73.0 2023 in increase . As the figures suggest , the Number of social network has been gradually increasing National health .

Example 149:
titleEntities: {'Subject': ['Bombardier'], 'Date': ['2007', '2018']}
title: Bombardier - transportation revenue 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['8.91', '8.55', '7.6', '8.3', '9.6', '8.8', '7.8', '9.3', '10.0', '9.8', '7.8', '6.6']

gold: The timeline shows Bombardier 's transportation revenues between the 2007 and 2018 fiscal years . Bombardier Inc. is a Canadian conglomerate , founded by Joseph-Armand Bombardier as L'Auto-Neige Bombardier Limitée in 1942 , at Valcourt in the Eastern Townships , Quebec . Over the years it has been a large manufacturer of regional aircraft , business jets , mass transportation equipment , recreational equipment and a financial services provider .
gold_template: The timeline shows templateTitleSubject[0] 's templateTitle[1] revenues between the templateXValue[min] and templateXValue[max] fiscal years . templateTitleSubject[0] Inc. is a Canadian conglomerate , founded by Joseph-Armand templateTitleSubject[0] as L'Auto-Neige templateTitleSubject[0] Limitée in 1942 , at Valcourt in the Eastern Townships , Quebec . Over the years it has been a large manufacturer of regional aircraft , business jets , mass templateTitle[1] equipment , recreational equipment and a financial services provider .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] Cheese templateTitle[4] 's from templateXValue[min] to million period . templateTitleSubject[0] Cheese templateTitle[4] generated approximately templateYValue[0] templateXValue[max] This in templateYLabel[0] in increase , a Global distribution of . templateTitleSubject[0] is headquartered in Joseph Manhattan , New York City and was steady in 1946 by CDs templateTitleSubject[0] and his wife templateTitleSubject[0] .
generated: The statistic shows the Revenue of Bombardier Cheese 2018 's from 2007 to million period . Bombardier Cheese 2018 generated approximately 8.91 2018 This in Revenue increase , a Global distribution of . Bombardier is headquartered in Joseph Manhattan , New York City and was steady in 1946 by CDs Bombardier and his wife Bombardier .

Example 150:
titleEntities: {'Subject': ['Lauder'], 'Date': ['2012', '2024']}
title: Estée Lauder 's share of the makeup 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']: ['24.4', '23.4', '22.4', '21.4', '20.4', '18.9', '17.4', '16.4', '13.6', '12.9', '14.2', '13.8', '12']

gold: This statistic shows Estée Lauder 's share of the makeup products market worldwide from 2012 to 2024 . In 2018 , Estée Lauder 's share of the global makeup products market is estimated to be 17.4 percent .
gold_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] percent .

generated_template: The statistic shows templateTitle[0] templateTitleSubject[0] templateTitle[2] devices for the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] templateTitle[8] from templateXValue[min] to templateTitleDate[max] . In templateXValue[6] , templateTitle[0] templateTitleSubject[0] templateTitle[2] ( templateYLabel[0] ) was at templateYValue[7] percent . templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] - additional information templateYLabel[0] 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 billion U.S. dollars .
generated: The statistic shows Estée Lauder 's devices for the makeup products market 2012 from to 2024 . In 2018 , Estée Lauder 's ( Market ) was at 16.4 percent . Lauder 's Market - additional information Market is a manufacturer and marketer of prestige makeup products , makeup fragrance and hair products market , with global net sales of over 11.2 billion U.S. dollars .

Example 151:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2015', '2022']}
title: South Korea : number of social network users 2015 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['28.16', '27.66', '27.1', '26.32', '25.53', '24.77', '23.99', '23.07']

gold: This statistic shows the number of social network users in South Korea from 2015 to 2022 . In 2022 , it is estimated that there will be around 28.16 million social network users in South Korea , up from 24.77 million in 2017 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[3] templateTitle[4] 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)] templateYLabel[2] templateTitle[3] templateTitle[4] templateYLabel[1] in templateTitleSubject[0] , up from templateYValue[5] templateYLabel[2] in templateXValue[5] .

generated_template: The statistic shows the global templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with a forecast for until templateTitleDate[max] . In templateXValue[5] , there were around templateYValue[min] templateXValue[max] templateTitle[1] templateTitle[2] templateTitle[3] . The figure is estimated to one to a templateXValue[max] in increase .
generated: The statistic shows the global Number of Korea number social in South Korea from 2015 to 2016 , with a forecast for until 2022 . In 2017 , there were around 23.07 2022 Korea number social . The figure is estimated to one a 2022 in increase .

Example 152:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2015', '2050']}
title: Projected number of people with dementia in Malaysia 2015 to 2050
X_Axis['Year']: ['2050', '2030', '2015']
Y_Axis['Number', 'of', 'people', 'in', 'thousands']: ['590', '261', '123']

gold: This statistic displays the projected number of people with dementia in Malaysia in 2015 , 2030 and 2050 . According to the source , approximately 590 thousand people are projected to have dementia by 2050 in Malaysia .
gold_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 templateYValue[idxmax(X)] thousand templateYLabel[1] are templateTitle[0] to have templateTitle[4] by templateXValue[idxmax(Y)] in templateTitleSubject[0] .

generated_template: The statistic displays the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[1] and a stated for templateXValue[1] and a projection for templateXValue[1] . In templateXValue[min] , templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] amounted to 1,880 It 2018 pounds . The The The The are expected to one to over grown It templateTitleSubject[0] UK in templateXValue[min] .
generated: The statistic displays the Projected Number of people dementia Malaysia in from 2015 to 2030 and a stated for 2030 and a projection for 2030 . In 2015 , number people dementia Malaysia in amounted to 1,880 It 2018 pounds . The are expected to one over grown It Malaysia UK in 2015 .

Example 153:
titleEntities: {'Subject': ['Qatar'], 'Date': ['2019']}
title: Unemployment rate in Qatar 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']: ['0.15', '0.14', '0.14', '0.14', '0.16', '0.19', '0.27', '0.48', '0.56', '0.45', '0.31', '0.31', '0.52', '0.87', '1.21', '1.48', '1.53', '1.51', '1.46', '1.51', '1.57']

gold: This statistic shows the unemployment rate in Qatar from 1999 to 2019 . In 2019 , 0.15 percent of the total labor force in Qatar was unemployed .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] percent of the total labor force in templateTitleSubject[0] was unemployed .

generated_template: The statistic shows the templateYLabel[0] in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[min] to half in the templateTitle[0] templateTitle[1] is the average labor force in the state , the average templateYLabel[0] rates of templateTitleSubject[0] with at approximately templateYValue[0] percent in the templateXLabel[0] . In templateXValue[1] , templateTitleSubject[0] templateYValue[idxmax(X)] templateYLabel[0] in templateTitleSubject[0] has experienced templateTitle[0] at around templateYValue[0] percent . By comparison to the same templateXLabel[0] of templateTitleSubject[0] templateYValue[idxmax(X)] population live to the templateTitleSubject[0] is be of the most popular economies in the world and be of the total population .
generated: The statistic shows the Unemployment in Qatar and from 1999 to half in the Unemployment rate is the average labor force in the state , the average Unemployment rates of Qatar with at approximately 0.15 percent in the Year . In 2018 , Qatar 0.15 Unemployment in Qatar has experienced Unemployment at around 0.15 percent . By comparison to the same Year of Qatar 0.15 population live to the Qatar is be of the most popular economies in the world and be of the total population .

Example 154:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2008', '2019']}
title: Twitter : number of employees 2008 to 2019
X_Axis['Month']: ["Dec_'19", "Dec_'18", "Dec_'17", "Dec_'16", "Dec_'15", "Dec_'14", "Dec_'13", "Jan_'11", "Jan_'10", "Jan_'09", "Jan_'08"]
Y_Axis['Number', 'of', 'employees']: ['4900', '3920', '3372', '3583', '3900', '3638', '2712', '350', '130', '29', '8']

gold: The statistic provides the number of employees of Twitter from 2008 to 2019 . At the end of the most recently reported year , the microblogging company employed 4,900 people , up from 3,920 people in the previous year . Twitter 's corporate demography In 2018 , the majority of Twitter 's employees were male with a share of 59.8 percent and of a white ethnicity with 42.3 percent .
gold_template: The statistic provides the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . At the end of the most recently reported year , the microblogging company employed templateYValue[max] people , up from templateYValue[1] people in the previous year . templateTitleSubject[0] 's corporate demography In 2018 , the majority of templateTitleSubject[0] 's templateYLabel[1] were male with a share of 59.8 percent and of a white ethnicity with 42.3 percent .

generated_template: templateTitleSubject[0] , be of the world templateTitle[1] largest oil market providers , 100,000 templateYValue[0] people in the European Kingdom . The is templateXValue[6] American several corporation steady in 1919 . Headquarted in both converted , Texas , and Dubai , it is now be of the largest oil field in the world .
generated: Twitter , be of the world number largest oil market providers , 100,000 4900 people in the European Kingdom . The is Dec '13 American several corporation steady in 1919 . Headquarted in both converted , Texas and Dubai , it is now be of the largest oil field in the world .

Example 155:
titleEntities: {'Subject': ['Americans', 'American'], 'Date': ['2018']}
title: Americans ' level of pride to be an American 2018
X_Axis['Response']: ['Extremely', 'Very', 'Moderately', 'Only_a_little', 'Not_at_all', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['47', '25', '16', '7', '3', '1']

gold: This statistic shows the results of a 2018 survey regarding patriotism in the United States . The respondents were asked how proud they are to be an American . In 2018 , some 47 percent of survey respondents stated they were extremely proud to be an American .
gold_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] percent of survey templateYLabel[1] stated they were templateXValue[0] proud to be an templateTitleSubject[0] .

generated_template: The statistic presents the templateTitle[3] templateTitle[4] templateTitle[5] according to throughout in the templateTitle[0] in templateTitleDate[0] . increased a survey , templateYValue[max] percent of throughout found the templateXValue[0] to FBI the templateTitle[3] templateTitle[4] templateTitle[5] to play .
generated: The statistic presents the pride American 2018 according to throughout in the Americans in 2018 . increased a survey , 47 percent of throughout found the Extremely to FBI the pride American 2018 to play .

Example 156:
titleEntities: {'Subject': ['Bulgaria'], 'Date': ['2019']}
title: Unemployment rate in Bulgaria 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']: ['4.82', '5.26', '6.16', '7.58', '9.14', '11.42', '12.94', '12.27', '11.26', '10.28', '6.82', '5.61', '6.88', '8.95', '10.08', '12.04', '13.73', '18.11', '19.92', '16.22', '14.1']

gold: This statistic shows the unemployment rate in Bulgaria from 1999 to 2019 . In 2019 , the unemployment rate in Bulgaria was at approximately 4.82 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)] percent .

generated_template: The statistic shows the templateYLabel[0] As of the templateTitleSubject[0] from templateXValue[min] to 20119 , in templateYLabel[0] in templateTitleSubject[0] with at approximately templateYValue[0] percent . In 2018 , templateTitleSubject[0] has the second-highest templateYLabel[0] in the United EU states . templateTitleSubject[0] templateYValue[idxmax(X)] economy templateYLabel[0] in all EU , most notably of inflation for templateXValue[8] , templateTitleSubject[0] is be of the G-20 major major economies by the world templateYValue[idxmax(X)] population in the BRIC period , templateXValue[6] association as is made country of rapidly growing economies .
generated: The statistic shows the Unemployment As of the Bulgaria from 1999 to 20119 , in Unemployment Bulgaria with at approximately 4.82 percent . In 2018 , Bulgaria has the second-highest Unemployment in the United EU states . Bulgaria 4.82 economy Unemployment in all EU , most notably of inflation for 2011 , Bulgaria is be of the G-20 major economies by the world 4.82 population in the BRIC period , 2013 association as is made country of rapidly growing economies .

Example 157:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2009', '2018']}
title: Retail sales value of yogurt & sour milk products in the United Kingdom 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']: ['2984.2', '2997.6', '3011.1', '3033.8', '3063.4', '3030.9', '2941.6', '2856.4', '2773.4', '2633.5']

gold: This statistic presents data on the retail sales value of yogurt and sour milk products in the United Kingdom ( UK ) from 2009 to 2018 . In 2013 , the retail sales value of yogurt and sour milk products amounted to approximately 3 billion U.S. dollars , according to historic retail figures . This is expected to decrease by 2018 .
gold_template: This statistic presents data on the templateYLabel[0] templateYLabel[2] templateYLabel[1] of templateTitle[3] and templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitleSubject[0] ( UK ) from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateYLabel[0] templateYLabel[2] templateYLabel[1] of templateTitle[3] and templateTitle[5] templateTitle[6] templateTitle[7] amounted to approximately templateYValue[0] templateYLabel[3] templateYLabel[4] templateYLabel[5] , according to historic templateYLabel[0] figures . This is expected to decrease by templateXValue[max] .

generated_template: The statistic shows the templateTitle[3] of templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[3] and corresponding templateXValue[max] forecasts for the years templateXValue[4] to keep in templateYLabel[0] in templateXValue[5] and templateTitleSubject[1] amounted to the following years . In templateXValue[min] , the value of the templateTitleSubject[0] in the country amounted to approximately templateYValue[max] percent of templateTitle[0] templateTitle[1] in the increase .
generated: The statistic shows the yogurt of Retail sales in the United Kingdom from 2009 to 2015 and corresponding 2018 forecasts for the years 2014 to keep in Retail 2013 and United Kingdom amounted to the following years . In 2009 , the value of the United Kingdom in the country amounted to approximately 3063.4 percent of Retail sales in the increase .

Example 158:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2010', '2019']}
title: Ice hockey players in Sweden 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']: ['55431', '62701', '63901', '60408', '60089', '56839', '64214', '69921', '62003']

gold: The statistics depicts the number of registered ice hockey players in Sweden from 2010/11 to 2018/19 . In the 2018/19 season , there were a total of 55,431 registered ice hockey players in Sweden 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[min] registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] according to the International templateTitle[0] templateTitle[1] Federation .

generated_template: In the academic templateXLabel[0] templateXValue[0] there were approximately templateYValue[min] thousand templateTitle[1] recorded in the templateTitleSubject[0] , around difference fewer until there were in the templateXValue[last] academic templateXLabel[0] . During templateXValue[1] templateTitle[1] in the templateTitle[3] has been a By decline in the UK , with around counted children for single be teacher .
generated: In the academic Year 2018/19 there were approximately 55431 thousand hockey recorded in the Sweden , around difference fewer until there were in the 2010/11 academic Year . During 2017/18 hockey in the Sweden has been a By decline in the UK , with around counted children for single be teacher .

Example 159:
titleEntities: {'Subject': ['Guatemala'], 'Date': ['2015']}
title: Median age of the population in Guatemala 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']: ['33.0', '31.3', '29.6', '28.0', '26.3', '24.5', '22.9', '21.3', '19.7', '18.5', '17.7', '17.2', '16.9', '16.6', '16.8', '17.0', '16.9', '16.8', '17.2', '17.8', '18.4']

gold: This statistic shows the median age of the population in Guatemala 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 statistic shows the templateYLabel[0] The of the templateTitle[2] in templateTitleSubject[0] , also known as Burma , from templateXValue[min] to had , with projections estimates . In had , the templateYLabel[0] of the templateTitle[2] in templateTitleSubject[0] was at templateYValue[7] templateXValue[max] .
generated: The statistic shows the Median of the population in Guatemala , also known as Burma , from 1950 to had , with projections estimates . In had , the Median of the population in Guatemala was at 21.3 2050 .

Example 160:
titleEntities: {'Subject': ['Obamacare U.S.'], 'Date': ['2019']}
title: Number of sign-ups during 2019 Obamacare open enrollment , by U.S. state
X_Axis['State']: ['Florida', 'California', 'Texas', 'North_Carolina', 'Georgia', 'Pennsylvania', 'Virginia', 'Illinois', 'Massachusetts', 'Michigan', 'New_York', 'New_Jersey', 'Washington', 'Tennessee', 'Missouri', 'South_Carolina', 'Ohio', 'Wisconsin', 'Utah', 'Colorado', 'Alabama', 'Arizona', 'Maryland', 'Oklahoma', 'Indiana', 'Oregon', 'Minnesota', 'Connecticut', 'Idaho', 'Louisiana', 'Kansas', 'Mississippi', 'Nebraska', 'Kentucky', 'Nevada', 'Maine', 'Arkansas', 'Iowa', 'Montana', 'New_Mexico', 'New_Hampshire', 'Rhode_Island', 'South_Dakota', 'Vermont', 'Wyoming', 'West_Virginia', 'Delaware', 'North_Dakota', 'District_of_Columbia', 'Hawaii', 'Alaska']
Y_Axis['Number', 'of', 'signups']: ['1783304', '1513883', '1087240', '501271', '458437', '365888', '328020', '312280', '300085', '274058', '271873', '255246', '222636', '221533', '220461', '214956', '206871', '205118', '194570', '169672', '166128', '160456', '156963', '150759', '148404', '148180', '123731', '111066', '103154', '92948', '89993', '88542', '87416', '84620', '83449', '70987', '67413', '49210', '45374', '45001', '44581', '34533', '29069', '25223', '24852', '22599', '22562', '21820', '20894', '20193', '17805']

gold: This statistic displays the number of Affordable Care Act ( Obamacare ) sign-ups during the 2019 open enrollment period as of February 2019 , by U.S. state . Until February 2019 , there were around 1.51 million sign-ups in California . Open enrollment allows U.S. citizens to enroll , switch plans , and get subsidies on various plans under the Affordable Care Act .
gold_template: This statistic displays the templateYLabel[0] of Affordable Care Act ( templateTitleSubject[0] ) templateTitle[1] templateTitle[2] the templateTitleDate[0] templateTitle[5] templateTitle[6] period as of 2019 , templateTitle[7] templateTitleSubject[0] templateXLabel[0] . Until 2019 , there were around templateYValue[1] million templateTitle[1] in templateXValue[1] . templateTitle[5] templateTitle[6] allows templateTitleSubject[0] citizens to enroll , switch plans , and get subsidies on various plans under the Affordable Care Act .

generated_template: The statistic shows the percentage of templateTitle[1] in the templateTitle[0] with templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in 2017 templateYValue[38] templateTitle[7] templateXLabel[0] . During the survey period , it was found templateYValue[idxmax(X)] templateYValue[1] percent of the templateTitle[1] population in templateXValue[1] used a templateTitle[2] templateTitle[4] to access the templateTitle[3] at home .
generated: The statistic shows the percentage of sign-ups in the Number with during 2019 Obamacare open in 2017 45374 by State . During the survey period , it was found 17805 1513883 percent of the sign-ups population in California used a during Obamacare to access the 2019 at home .

Example 161:
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] templateYLabel[3] templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the total state templateTitle[1] .

generated_template: The graph shows the templateXValue[0] templateXValue[max] to the Gross Domestic penetration ( templateTitle[1] ) of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the had templateXLabel[0] templateXValue[max] around templateYValue[9] This 's chain lowest From of templateTitle[0] templateTitle[1] .
generated: The graph shows the Manufacturing to the Gross Domestic penetration ( GDP ) of Michigan in 2018 , by Industry . In 2018 , the had Industry Manufacturing around 14.24 This 's chain lowest From of Real GDP .

Example 162:
titleEntities: {'Subject': ['American'], 'Date': ['2012']}
title: Influence of friends and family on American teenagers ' decisions regarding sex in 2012
X_Axis['Response']: ['Parents', 'Friends', 'The_media', 'Religious_leaders', 'Siblings', 'Teachers_and_educators', 'Someone_else']
Y_Axis['Share', 'of', 'respondents']: ['38', '22', '9', '6', '6', '4', '10']

gold: This statistic shows the results of a 2012 survey among American teenagers aged 12 to 19 regarding the influence of their social environment on their decisions about sex . Some 38 percent of surveyed teenagers stated that parents most influenced their decisions about sex .
gold_template: This statistic shows the results of a templateTitleDate[0] survey among templateTitleSubject[0] templateTitle[4] aged 12 to 19 templateTitle[7] the templateTitle[0] of their social environment on their templateTitle[6] about templateTitle[8] . Some templateYValue[max] percent of surveyed templateTitle[4] stated that templateXValue[0] most influenced their templateTitle[6] about templateTitle[8] .

generated_template: The statistic depicts the results of a survey , conducted in quarter in Canada , on templateXValue[5] templateTitle[3] templateTitle[4] templateTitle[5] . According to templateYValue[max] percent of surveyed templateTitleSubject[0] , their top resolution templateTitle[6] templateTitleDate[0] was to templateXValue[0] fitness and templateXValue[0] .
generated: The statistic depicts the results of a survey , conducted in quarter Canada , on Teachers and educators American teenagers ' . According to 38 percent of surveyed American , their top resolution decisions 2012 was to Parents fitness and Parents .

Example 163:
titleEntities: {'Subject': ['IT'], 'Date': ['2017']}
title: IT functions outsourced worldwide 2017
X_Axis['Response']: ['Software_application_development', 'Software_application_maintenance', 'Data_centers', 'IT_infrastructure', 'Service_desk_/_help_desk', 'Networks', 'Systems_integration', 'HR_BPO', 'IT_department', 'IT_BPO', 'KPO']
Y_Axis['Share', 'of', 'respondents']: ['64', '51', '40', '32', '32', '29', '29', '12', '12', '12', '6']

gold: The statistic reveals the IT functions most commonly outsourced by IT leaders worldwide , as of 2017 . In 2017 , software application development was outsourced by 64 percent of respondents .
gold_template: The statistic reveals the IT templateTitle[0] most commonly templateTitle[1] by IT leaders templateTitle[2] , as of templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] development was templateTitle[1] by templateYValue[max] percent of templateYLabel[1] .

generated_template: The statistic presents a templateTitle[0] on the templateTitle[1] templateTitle[2] templateXValue[last] templateTitle[4] in templateTitleSubject[0] as of 2018 . During the templateTitle[0] year , templateYValue[max] percent of the female 1991 templateXValue[0] as their templateTitle[0] preferred activity .
generated: The statistic presents a functions on the outsourced worldwide KPO 2017 in IT as of 2018 . During the functions year , 64 percent of the female 1991 Software application development as their functions preferred activity .

Example 164:
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)] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic displays the streaming of months templateXValue[max] a This 's born installed in templateXValue[min] and This 's . In the templateTitle[0] , templateYValue[max] percent of the templateTitle[3] templateTitle[4] was templateXValue[0] and templateYValue[min] percent were most .
generated: The statistic displays the streaming of months 2011 a This 's born installed in 2010 and This 's . In the Camping , 210.38 percent of the U.S. sleeping was 2010 and 191.76 percent were most .

Example 165:
titleEntities: {'Subject': ['Outback Steakhouse'], 'Date': ['2015', '2018']}
title: Average sales per unit of Outback Steakhouse restaurants in the U.S. 2015 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015']
Y_Axis['Average', 'sales', 'in', 'thousand', 'U.S.', 'dollars']: ['3580', '3542', '3354', '3430']

gold: This statistic shows the average sales per unit of Outback Steakhouse restaurants in the United States from 2015 to 2018 . Average sales per Outback Steakhouse restaurant amounted to approximately 3.58 million U.S. dollars in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] restaurants in the templateTitle[7] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitleSubject[0] restaurant amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to . In templateXValue[min] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] up a templateYLabel[0] of templateYValue[idxmin(X)] templateXValue[max] This .
generated: The statistic shows the Outback Steakhouse sales per unit Outback size Steakhouse from 2015 to . In 2015 , the Outback Steakhouse sales per unit Outback up a Average of 3430 2018 This .

Example 166:
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] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitleSubject[0] franchise of the templateXValue[7] Football League from templateXValue[min] to templateTitleSubject[1] , with templateXValue[6] templateYLabel[0] templateTitle[0] templateTitle[1] an to templateYValue[max] templateXValue[max] This 's .
generated: The graph depicts the total Franchise value in the Chicago Blackhawks franchise of the 2012 Football League from 2006 to Chicago Blackhawks , with 2013 Franchise value an to 1085 2019 This 's .

Example 167:
titleEntities: {'Subject': ['APAC'], 'Date': ['2018', '2018']}
title: Corruption perception index APAC 2018 , by country
X_Axis['Country']: ['New_Zealand', 'Singapore', 'Australia', 'Hong_Kong', 'Japan', 'Bhutan', 'Taiwan', 'South_Korea', 'Malaysia', 'India', 'China', 'Sri_Lanka', 'Indonesia', 'Mongolia', 'Philippines', 'Thailand', 'Timor-Leste', 'Vietnam', 'Pakistan', 'Nepal', 'Myanmar', 'Laos', 'Papua_New_Guinea', 'Bangladesh', 'Cambodia', 'Afghanistan', 'North_Korea']
Y_Axis['Index', 'score']: ['87', '85', '77', '76', '73', '68', '63', '57', '47', '41', '39', '38', '38', '37', '36', '36', '35', '33', '33', '31', '29', '29', '28', '26', '20', '16', '14']

gold: This statistic shows the perceived corruption of the countries in the Asia Pacific region , as calculated by the Corruption Perception Index in 2018 . In that year , New Zealand was perceived as the least corrupt country with an index score of 87 out of 100 .
gold_template: This statistic shows the perceived templateTitle[0] of the countries in the Asia Pacific region , as calculated templateTitle[5] the templateTitle[0] templateTitle[1] templateYLabel[0] in templateTitle[4] . In that year , templateXValue[0] was perceived as the least corrupt templateXLabel[0] with an templateYLabel[0] templateYLabel[1] of templateYValue[max] out of 100 .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] the highest templateYLabel[0] of templateTitle[3] templateTitle[4] in templateTitle[5] templateTitle[6] this in templateTitleDate[0] . In the templateXValue[0] ( UK ) , approximately templateYValue[max] percent of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] .
generated: The statistic shows the APAC perception the highest Index of APAC 2018 in by country this in 2018 . In the New Zealand ( UK ) , approximately 87 percent of APAC 2018 by country .

Example 168:
titleEntities: {'Subject': ['Metro Group'], 'Date': []}
title: Metro Group 's sales worldwide 2018/2019 , by region
X_Axis['Country']: ['Western_Europe_(excluding_Germany)', 'Eastern_Europe_(exluding_Russia)', 'Germany', 'Russia', 'Asia']
Y_Axis['Sales', 'in', 'million', 'euros']: ['8885', '5986', '4075', '2406', '1097']

gold: This statistic shows the global sales of the Metro Group in 2018/2019 , by region . In that year , the sales of the Metro Group in Germany amounted to approximately 2.25 billion euros . The company discontinued its hypermarket sales and its operations in China .
gold_template: This statistic shows the global templateYLabel[0] of the templateTitleSubject[0] in templateTitle[5] , templateTitle[6] templateTitle[7] . In that year , the templateYLabel[0] of the templateTitleSubject[0] in templateXValue[2] amounted to approximately 2.25 templateYLabel[1] templateYLabel[2] . The company discontinued its hypermarket templateYLabel[0] and its operations in China .

generated_template: The statistic illustrates the templateTitle[5] of templateTitleSubject[0] templateYLabel[0] farm projects templateXValue[max] This in leading templateYLabel[0] out countries as of October templateTitleDate[0] . There were templateYValue[1] templateYLabel[0] farm projects templateXValue[max] This in templateXValue[2] . Higher templateYLabel[0] speeds templateYValue[idxmax(X)] occur templateTitleSubject[0] in comparison to land , allows for a higher supply of electricity from templateTitleSubject[0] templateYLabel[0] templateTitleDate[0] .
generated: The statistic illustrates the 2018/2019 of Metro Group Sales farm projects Western Europe (excluding Germany) This in leading Sales out countries as of October . There were 5986 Sales farm projects Western Europe (excluding Germany) This in Germany . Higher Sales speeds 1097 occur Metro Group in comparison to land , allows for a higher supply of electricity from Metro Group Sales .

Example 169:
titleEntities: {'Subject': ['Nigeria'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Nigeria 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['3346.39', '3074.98', '3830.0', '2602.02', '2400.45', '2222.01', '2032.86', '1971.79', '2180.27', '2726.34', '3222.68', '3042.05', '2797.86', '2582.57', '2365.01', '1958.58', '2234.36', '1822.79', '1591.33', '1245.07', '982.98', '797.64', '748.31', '598.29', '570.17', '496.5', '1861.07', '1713.38', '1618.29', '1273.27', '792.81']

gold: The statistic shows gross domestic product ( GDP ) per capita in Nigeria from 1994 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] 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: templateTitle[0] templateTitle[1] templateTitle[2] is the total value of all goods and services they 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] percent of the economy .
generated: Gross domestic product is the total value of all goods and services they in a country in a Year . It is considered an important indicator of the economic strength of a country . In 2018 , GDP in Nigeria amounted to around 2032.86 percent of the economy .

Example 170:
titleEntities: {'Subject': ['Latin America', 'Caribbean'], 'Date': ['2024', '2024']}
title: Gross domestic product of Latin America and the Caribbean 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'in', 'billion', 'U.S.', 'dollars']: ['6546.54', '6234.32', '5922.28', '5634.42', '5352.4', '5188.25', '5249.66', '5459.78', '5057.11', '5271.94', '5988.65']

gold: This statistic shows gross domestic product ( GDP ) of Latin America and the Caribbean from 2014 to 2024 in billion U.S. dollars . In 2018 , Latin America and the Caribbean 's GDP amounted to about 5.25 trillion U.S. dollars .
gold_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of templateTitleSubject[0] and the templateTitleSubject[1] from templateXValue[min] to templateXValue[max] in templateYLabel[1] templateYLabel[2] templateYLabel[3] . In templateXValue[6] , templateTitleSubject[0] and the templateTitleSubject[1] 's templateYLabel[0] amounted to about templateYValue[6] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up years templateTitleDate[max] . In templateXValue[6] , the templateYLabel[0] of the European Union in templateTitleSubject[0] was around templateYValue[6] percent templateXValue[max] to the This templateXLabel[0] .
generated: The statistic shows Gross domestic product ( GDP ) of the Latin America from 2014 to 2018 , with projections up years 2024 . In 2018 , the GDP of the European Union in Latin America was around 5249.66 percent 2024 to the This Year .

Example 171:
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: The statistic gives a provided of templateTitleSubject[0] that FY templateTitleDate[0] templateTitle[0] templateYLabel[0] templateTitle[3] templateTitle[4] . Some their percent of H-D that templateTitle[0] templateYLabel[0] came from slight and the Middle East . templateTitleSubject[0] , Inc. is a publicly traded US-based company ( NYSE : HOG ) , which is active in two main business distribution : the motorbike and motorbike accessories sector and the financial services sector .
generated: The statistic gives a provided of Terrorism that FY Terrorism Number taken by . Some their percent of H-D that Terrorism Number came from slight and the Middle East . Terrorism , Inc. is a publicly traded US-based company ( NYSE : HOG ) , which is active in two main business distribution : the motorbike and accessories sector and the financial services sector .

Example 172:
titleEntities: {'Subject': ['Luxury'], 'Date': ['2017']}
title: Luxury brand social media engagement generated by top influencers 2017
X_Axis['Brand', '(top', 'influencer)']: ['Valentino_(Demi_Lovato)', 'Tory_Burch_(Shay_Mitchell)', "Tod's_(Naomi_Campbell)", 'Tiffany_&_Co._(Reese_Witherspoon)', 'Salvatore_Ferragamo_(Nina_Dobrev)', 'Saint_Laurent_(J_Balvin)', 'Prada_(Chiara_Ferragni)', 'Michael_Kors_(Blake_Lively)', 'Louis_Vuitton_(Kevin_Ma)', 'Hugo_Boss_(Mariano_Di_Vaio)', 'Hermes_(Xenia_Tchoumi)', 'Gucci_(Nina_Dobrev)', 'Givenchy_(Nicki_Minaj)', 'Fendi_(Gigi_Hadid)', 'Dolce_&_Gabbana_(Cameron_Dallas)', 'Dior_(Rihanna)', 'Chanel_(Cara_Delevigne)', 'Celine_(Kim_Kardashian)', 'Cartier_(Nikkie_Tutorials)', 'Burberry_(Dove_Cameron)', 'Bulgari_(Bella_Hadid)', 'Bottega_Veneta_(Kris_Jenner)', 'Balenciaga_(Nicki_Minaj)', 'Average']
Y_Axis['Number', 'of', 'social', 'media', 'actions', 'per', 'post']: ['1385467', '134751', '40647', '77643', '601316', '181475', '116169', '759670', '26689', '91041', '27692', '458444', '629753', '653272', '742342', '629179', '662894', '1182087', '290287', '447287', '591423', '102457', '341862', '442341']

gold: This statistic presents the number of social media actions generated per post by top luxury brand influencer . During the first quarter of 2017 , an average social media post by Kim Kardashian generated 1.2 million social media actions . Kardashian is the top influencer for luxury brand Celine .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[5] templateYLabel[4] templateYLabel[5] templateTitle[6] templateTitle[7] templateTitleSubject[0] templateXLabel[0] influencer . During the first quarter of templateTitleDate[0] , an templateXValue[last] templateYLabel[1] templateYLabel[2] templateYLabel[5] templateTitle[6] Kim Kardashian templateTitle[5] templateYValue[17] million templateYLabel[1] templateYLabel[2] templateYLabel[3] . Kardashian is the templateTitle[7] influencer for templateTitleSubject[0] templateXLabel[0] templateXValue[17] .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] at least one templateTitle[0] in the templateTitleSubject[0] by templateXLabel[0] in templateTitle[4] . templateXValue[0] and templateXValue[1] ranked highest with templateYValue[max] percent of templateYLabel[1] templateYLabel[2] considering that they on a templateTitle[3] .
generated: This statistic presents the Number of social media at least one Luxury in the Luxury by Brand in engagement . Valentino (Demi Lovato) and Tory Burch (Shay Mitchell) ranked highest with 1385467 percent of social media considering that they on a media .

Example 173:
titleEntities: {'Subject': ['Euro'], 'Date': ['2019', '2019']}
title: Monthly inflation rate in Euro area countries June 2019
X_Axis['Country']: ['Latvia', 'Slowakia', 'Netherlands', 'Estonia', 'Lithuania', 'Slovenia', 'Malta', 'Austria', 'Luxembourg', 'Germany', 'France', 'Belgium', 'Euro_area', 'Ireland', 'Finland', 'Italy', 'Portugal', 'Spain', 'Cyprus', 'Greece']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['3.1', '2.7', '2.7', '2.6', '2.4', '1.9', '1.8', '1.6', '1.5', '1.5', '1.4', '1.3', '1.3', '1.1', '1.1', '0.8', '0.7', '0.6', '0.3', '0.2']

gold: The statistic shows the inflation rate in the Euro area countries in June 2019 . Inflation or currency devaluation ( drop in the value of money ) , is characterized by a steady rise in the prices of finished products ( consumer goods , capital goods ) . The consumer price index tracks price trends of private consumption expenditure , and shows an increase in the index 's current level of inflation .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateXValue[12] countries in templateTitle[6] templateTitle[7] . templateYLabel[0] or currency devaluation ( drop in the value of money ) , is characterized by a steady rise in the prices of finished products ( consumer goods , capital goods ) . The consumer price index tracks price trends of private consumption expenditure , and shows an increase in the index 's current level of templateYLabel[0] .

generated_template: The statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] ranked 1st templateTitle[1] a negative templateYLabel[0] of about 27.6 percent templateXValue[max] to the This 's . templateYLabel[0] rates and the financial crisis Due to relatively intended worker worker wages as well as a hesitation from banks to so easily distribute loans to the ordinary citizen , templateYLabel[0] has remained considerably low .
generated: The statistic shows the 20 Euro inflation the rate Euro area in 2019 . In 2019 , Latvia ranked 1st inflation a negative Inflation of about 27.6 percent Latvia to the This 's . Inflation rates and the financial crisis Due to relatively intended worker wages as well a hesitation from banks to so easily distribute loans to the ordinary citizen , Inflation has remained considerably low .

Example 174:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1995', '2019']}
title: U.S. retail price of grapes 1995 to 2019
X_Axis['Year']: ['1995', '1997', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'pound']: ['1.86', '2.19', '2.4', '2.36', '2.31', '2.27', '2.25', '3.09', '2.76', '2.89', '2.72', '2.46', '3.14', '2.87', '2.86', '3.09', '2.57', '2.96', '2.83', '2.88', '2.61', '2.28', '2.39']

gold: The price of one pound of seedless grapes ( Thompson ) in the United States was 2.39 U.S. dollars in 2019 . U.S. seedless grape prices peaked in 2009 at 3.14 U.S. dollars per pound . Grape Production in the United States Grapes have the highest production volume of any fruit in the United States .
gold_template: The templateYLabel[0] of one templateYLabel[4] of seedless templateTitle[3] ( Thompson ) in the templateTitle[0] was templateYValue[last] templateYLabel[1] templateYLabel[2] in templateXValue[max] . templateYLabel[1] seedless grape prices peaked in templateXValue[12] at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Grape Production in the templateTitle[0] templateTitle[3] have the highest production volume of any fruit in the templateTitle[0] .

generated_template: The statistic shows the templateTitle[0] templateYLabel[0] templateTitle[1] of templateTitle[2] in the templateTitleSubject[0] from templateXValue[min] to templateTitleDate[max] . In templateXValue[2] , German templateTitle[1] templateTitle[2] was valued at a templateTitle[1] templateTitle[0] of roughly templateYValue[1] templateXValue[max] This . templateYValue[10] period later , templateTitle[0] figures an to less until templateYValue[8] templateXValue[max] This .
generated: The statistic shows the U.S. Price retail of price in the U.S. from 1995 to 2019 . In 1999 , German retail price was valued at a retail U.S. of roughly 2.19 2019 This . 2.72 period later , U.S. figures an to less until 2.76 2019 This .

Example 175:
titleEntities: {'Subject': ['Bolivia'], 'Date': ['2000']}
title: Bolivia : internet penetration 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'individuals', 'using', 'internet']: ['43.83', '39.7', '35.56', '34.6', '36.99', '35.34', '30', '22.4', '16.8', '12.5', '10.5', '6.2', '5.23', '4.44', '3.51', '3.12', '2.12', '1.44']

gold: This statistic gives information on the internet penetration in Bolivia from 2000 to 2017 . In 2017 , 43.83 percent of the Bolivian population accessed the internet , up from 6.2 percent in 2006 .
gold_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] percent of the Bolivian population accessed the templateYLabel[3] , up from templateYValue[11] percent in templateXValue[11] .

generated_template: templateYValue[max] percent of the population of the templateTitleSubject[0] ( templateTitleSubject[1] ) used the This in templateTitleDate[0] increase according to figures from Eurostat and the International match Union . The figure is unchanged from templateXValue[1] , marking the first templateXLabel[0] ‘ growth National templateXValue[10] . This users in Great Britain44.1 This daily used the This daily in Great occurring during 2018 according to the templateTitleSubject[0] templateYValue[idxmax(X)] ( templateTitleSubject[1] ) Office for templateXValue[7] Statistics ( ONS ) , breaking the record set in templateXValue[1] .
generated: 43.83 percent of the population of the Bolivia ( ) used the This in 2000 increase according to figures from Eurostat and the International match Union . The figure is unchanged from 2016 , marking the first Year ‘ growth National 2007 . This users in Great Britain44.1 This daily used the This daily in Great occurring during 2018 according to the Bolivia 43.83 ( Bolivia ) Office for 2010 Statistics ( ONS ) , breaking the record set in 2016 .

Example 176:
titleEntities: {'Subject': ['China'], 'Date': ['1960', '2017']}
title: Female life expectancy in China 1960 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984', '1983', '1982', '1981', '1980', '1979', '1978', '1977', '1976', '1975', '1974', '1973', '1972', '1971', '1970', '1969', '1968', '1967', '1966', '1965', '1964', '1963', '1962', '1961', '1960']
Y_Axis['Life', 'expectancy', 'at', 'birth', 'in', 'years']: ['78.83', '78.57', '78.29', '77.97', '77.64', '77.28', '76.93', '76.57', '76.21', '75.86', '75.51', '75.17', '74.85', '74.54', '74.24', '73.95', '73.67', '73.41', '73.14', '72.88', '72.62', '72.36', '72.1', '71.86', '71.63', '71.41', '71.2', '71.0', '70.82', '70.64', '70.45', '70.25', '70.02', '69.77', '69.47', '69.13', '68.74', '68.3', '67.8', '67.26', '66.66', '66.02', '65.32', '64.58', '63.8', '62.96', '62.02', '60.9', '59.48', '57.75', '55.73', '53.51', '51.26', '49.19', '47.49', '46.24', '45.5', '45.19']

gold: The graph shows the life expectancy of women in China from 1960 until 2017 . In 2017 , the average life expectancy of women at birth in China was about 78.8 years .
gold_template: The graph shows the templateYLabel[0] templateYLabel[1] of women in templateTitleSubject[0] from templateXValue[min] until templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of women at templateYLabel[2] in templateTitleSubject[0] was about templateYValue[max] templateYLabel[3] .

generated_template: The statistic shows the degree of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateXValue[min] to had , with a forecast up years to templateTitleDate[max] . In had , the templateTitle[0] of templateYLabel[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] was at around templateYValue[max] templateXValue[max] This 's .
generated: The statistic shows the degree of Female life expectancy in the China from 1960 to had , with a forecast up years to 2017 . In had , the Female of Life expectancy in the China was at around 78.83 2017 This 's .

Example 177:
titleEntities: {'Subject': ['China'], 'Date': ['2007', '2017']}
title: Fertility rate in China 2007 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['1.6', '1.6', '1.57', '1.56', '1.56', '1.55', '1.54', '1.54', '1.54', '1.53', '1.53']

gold: The statistic shows the fertility rate in China from 2007 to 2017 . In 2017 , the fertility rate in China was 1.6 children per woman . Overall , the number of births in China reached about 17.23 million that year .
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] was templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[3] templateYLabel[4] . Overall , the templateYLabel[0] of births in templateTitleSubject[0] reached about 17.23 million that templateXLabel[0] .

generated_template: templateTitle[0] templateTitle[1] in templateTitleSubject[0] reached the highest point in the past decade , with templateXValue[6] average of templateYValue[0] templateXValue[max] This that . The is templateXValue[6] one of about influenced percent compared to the previous templateXLabel[0] . templateTitleSubject[0] templateTitle[0] templateTitle[1] templateYLabel[0] increased by the economic templateTitle[1] in the world , the templateTitleSubject[0] is be of the largest templateTitle[0] templateTitle[1] in the world .
generated: Fertility rate in China reached the highest point in the past decade , with 2011 average of 1.6 2017 This that . The is 2011 one of about influenced percent compared to the previous Year . China Fertility rate Number increased by the economic rate in the world , the China is be of the largest Fertility rate in the world .

Example 178:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['16', '24']}
title: Most important things for young people ( 16 - 24 ) in Great Britain 2013
X_Axis['Response']: ['Family', 'Friends', 'Having_a_good_education', 'Living_a_healthy_active_lifestyle', 'Having_money', 'Doing_good_things_for_the_community', 'The_environment', 'None_of_the_above']
Y_Axis['Share', 'of', 'respondents']: ['51', '14', '11', '8', '6', '3', '1', '5']

gold: This statistic shows the result of a survey question designed to find out what is most important to young people ( 16 - 24 ) in Great Britain , as of November 2013 . The majority of respondents said that their family is the most important thing to them .
gold_template: This statistic shows the result 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_template: The statistic displays the templateTitle[2] of templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] as of 2018 . During an online survey conducted by Are Flint 180 , it was found templateYValue[idxmax(X)] templateYValue[0] percent of templateTitleSubject[0] users in the templateTitleSubject[1] accessed the messenger app templateXValue[1] templateXValue[2] days .
generated: The statistic displays the things of Great Britain important in the Great Britain as of 2018 . During an online survey conducted by Are Flint 180 , it was found 5 51 percent of Great Britain users in the Great Britain accessed the messenger app Friends Having a good education days .

Example 179:
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 increased its research and development templateYLabel[1] since templateXValue[3] , with templateYValue[max] templateYLabel[2] templateYLabel[3] spent by the department in templateXValue[idxmax(Y)] – an increase of 7.7 templateYLabel[2] 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: Over templateYValue[max] thousand templateXValue[max] This of templateTitle[1] was consumed in templateTitleSubject[0] in increase . The was a decrease compared to the previous templateXLabel[0] . According to OECD , consuming referred was equated with living standards , diet , livestock production and consumer prices .
generated: Over 61.7 thousand 2019 This of R was consumed in Puma increase . The was a decrease compared to the previous Year . According to OECD , consuming referred was equated with living standards , diet livestock production and consumer prices .

Example 180:
titleEntities: {'Subject': ['Azerbaijan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Azerbaijan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.42', '2.35', '2.21', '2.15', '2.11', '2.7', '0.97', '0.15', '-3.06', '1.05', '2.8']

gold: The statistic shows the growth in real GDP in Azerbaijan from 2014 to 2018 , with projections up until 2024 . In 2018 , Azerbaijan 's real gross domestic product increased by around 0.97 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] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: that statistic shows the real templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[0] . In templateXValue[6] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateXValue[max] to the This templateXLabel[0] .
generated: that statistic shows the real Gross domestic in Azerbaijan from 2014 to 2018 , with projections up until 2024 . In 2018 , the Gross domestic in Azerbaijan amounted to about 0.97 percent 2024 to the This Year .

Example 181:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2024']}
title: Argentina 's budget balance in relation to GDP 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'in', 'relation', 'to', 'GDP']: ['-2.93', '-2.72', '-2.99', '-3.13', '-2.68', '-3.97', '-5.23', '-6.7', '-6.66', '-6', '-4.25']

gold: The statistic shows Argentina 's budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 . A positive value indicates a budget surplus , a negative value indicates a deficit . In 2018 , Argentina 's deficit amounted to around 5.23 percent of GDP .
gold_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] deficit amounted to around 5.23 percent of templateYLabel[3] .

generated_template: The statistic shows templateTitleSubject[0] that templateTitle[0] of templateTitle[1] templateTitle[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( This ) from templateXValue[min] to templateXValue[6] , with projections up years templateTitleDate[max] . In templateXValue[6] , templateTitleSubject[0] that templateTitle[0] of templateTitle[1] templateTitle[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( This ) amounted to templateYValue[0] percent .
generated: The statistic shows Argentina that of 's budget to balance relation GDP ( This ) from 2014 to 2018 , with projections up years 2024 . In 2018 , Argentina that of 's budget to balance relation GDP ( This ) amounted to -2.93 percent .

Example 182:
titleEntities: {'Subject': ['Value'], 'Date': ['2018']}
title: Value of the leading 10 textile importers worldwide 2018 , by country
X_Axis['Country']: ['European_Union_(28)', 'United_States', 'China', 'Viet_Nam', 'Bangladesh', 'Japan', 'Hong_Kong_China', 'Indonesia', 'Mexico_', 'Turkey']
Y_Axis['Import', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['77', '30', '18', '18', '11', '9', '7', '7', '7', '6']

gold: The European Union ( EU28 ) was the leading importer of textiles worldwide , with textile imports valuing approximately 77 billion U.S. dollars in 2018 . That year , the United States and China were the next two largest importers of textiles , with imports of 30 billion U.S. dollars and 18 billion U.S. dollars respectively . What are textiles ? Textiles are the materials which are made from natural or synthetic fibers , thin threads or filaments .
gold_template: The templateXValue[0] ( EU28 ) was the templateTitle[1] importer of textiles templateTitle[5] , with templateTitle[3] imports valuing approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitleDate[0] . That year , the templateXValue[1] and templateXValue[2] were the next two largest templateTitle[4] of textiles , with imports of templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] and templateYValue[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] respectively . What are textiles ? Textiles are the materials which are made from natural or synthetic fibers , thin threads or filaments .

generated_template: templateXValue[0] was the largest templateTitle[1] of templateYLabel[0] tourists arriving to templateTitleSubject[0] in templateTitleDate[0] at over 2.25 This visitors . Bangladeshi tourists have been outnumbering the tourists templateYValue[idxmax(X)] FBI the templateXLabel[0] from other European and American templateTitle[2] for the past few years . In templateTitleDate[0] , citizens from templateXValue[0] , the templateXValue[1] of America , and the templateXValue[1] templateXValue[2] had the highest number of tourists visiting templateTitleSubject[0] , while about templateYValue[3] This tourists came from templateXValue[3] , templateXValue[4] and templateXValue[5] .
generated: European Union (28) was the largest leading of Import tourists arriving to Value in 2018 at over 2.25 This visitors . Bangladeshi tourists have been outnumbering the tourists 6 FBI the Country from other European and American 10 for the past few years . In 2018 , citizens from European Union (28) , the United States of America , and the United States China had the highest number of tourists visiting Value , while about 18 This tourists came from Viet Nam , Bangladesh and Japan .

Example 183:
titleEntities: {'Subject': ['Los Angeles Angels'], 'Date': ['2002', '2019']}
title: Franchise value of the Los Angeles Angels 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', '1800', '1750', '1340', '1300', '775', '718', '656', '554', '521', '509', '500', '431', '368', '294', '241', '225', '195']

gold: This graph depicts the value of the Los Angeles Angels franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 1.9 billion U.S. dollars . The Los Angeles Angels are owned by Arturo Moreno , who bought the franchise for 184 million U.S. dollars in 2003 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Angels 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] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Angels are owned by Arturo Moreno , who bought the templateYLabel[0] for 184 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[16] .

generated_template: The graph depicts the American templateYLabel[0] of the templateTitleSubject[0] Yankees from the templateXValue[7] Football League from templateXValue[min] to the fiscal templateXLabel[0] . In the fiscal templateXLabel[0] season , the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[1] was templateYValue[min] templateXValue[max] This 's . The is a professional basketball team of the templateXValue[7] Football League ( templateTitleSubject[1] ) .
generated: The graph depicts the American Franchise of the Los Angeles Angels Yankees from the 2012 Football League from 2002 to the fiscal Year . In the fiscal Year season , the Franchise of the Los Angeles Angels was 195 2019 This 's . The is a professional basketball team of the 2012 Football League ( Los Angeles Angels ) .

Example 184:
titleEntities: {'Subject': ['Detroit Pistons', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Detroit Pistons ( 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']: ['31.42', '38.8', '40.1', '40.1', '41.26', '42.76', '47.5', '47.5', '47.5', '46.23']

gold: This graph depicts the average ticket price for Detroit Pistons games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 46.23 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: The graph depicts the templateYLabel[0] The templateXValue[max] for templateTitleSubject[0] games of the Basketball Basketball internet from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateXValue[max] was templateYValue[min] This 's .
generated: The graph depicts the Average 2015/16 for Detroit Pistons games of the Basketball internet from 2006/07 to 2015/16 . In the 2006/07 season , the Average 2015/16 was 31.42 This 's .

Example 185:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1991', '2015']}
title: Median age of first alcohol use among U.S. youth 1991 to 2015
X_Axis['Year']: ['2013–2015', '2012–2014', '2011–2013', '2010–2012', '2009–2011', '2008–2010', '2007–2009', '2006–2008', '2005–2007', '2004–2006', '2003–2005', '2002–2004', '2001–2003', '2000–2002', '1999–2001', '1998–2000', '1997–1999', '1996–1998', '1995–1997', '1994–1996', '1993–1995', '1992–1994', '1991–1993']
Y_Axis['Average', 'age', 'in', 'years']: ['14.64', '14.56', '14.47', '14.4', '14.35', '14.29', '14.22', '14.14', '14.06', '13.97', '13.9', '13.85', '13.82', '13.81', '13.81', '13.88', '13.91', '13.94', '13.85', '13.8', '13.76', '13.76', '13.65']

gold: This statistic shows the average age youths aged 12 to 20 years first drank alcohol in the U.S. from 1991 to 2015 . In the period 2013 to 2015 , on average those aged 12 to 20 years first drank alcohol when they were 14.64 years old .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] youths aged 12 to 20 templateYLabel[2] templateTitle[2] drank templateTitle[3] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In the period 2013 to templateTitleDate[max] , on templateYLabel[0] those aged 12 to 20 templateYLabel[2] templateTitle[2] drank templateTitle[3] when they were templateYValue[max] templateYLabel[2] old .

generated_template: In templateXValue[0] , the average templateYLabel[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[0] templateXValue[max] . According to the source , templateYLabel[0] in templateTitleSubject[0] was templateYValue[min] templateXValue[max] .
generated: In 2013–2015 , the average age in U.S. amounted to about 14.64 2013–2015 . According to the source , Average in U.S. was 13.65 2013–2015 .

Example 186:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2004', '2017']}
title: Companies on the insurance market in the United Kingdom ( UK ) 2004 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Number', 'of', 'companies']: ['436', '464', '496', '526', '613', '528', '540', '521', '534', '549', '576', '594', '640', '673']

gold: As of 2017 , The United Kingdom ( UK ) had the largest insurance market in Europe , and fourth largest globally . Direct premiums written by UK companies , increased between 2016 to 2017 , with claims and benefits paid reaching nearly 290 billion euros . Leading insurance companies As of 2016 , Prudential Plc was the leading insurance company in the United Kingdom in terms of total assets and market value ( market capitalization ) .
gold_template: As of templateXValue[max] , The templateTitleSubject[0] ( templateTitleSubject[1] ) had the largest templateTitle[1] templateTitle[2] in Europe , and fourth largest globally . Direct premiums written by templateTitleSubject[1] templateYLabel[1] , increased between templateXValue[1] to templateXValue[max] , with claims and benefits paid reaching nearly 290 billion euros . Leading templateTitle[1] templateYLabel[1] As of templateXValue[1] , Prudential Plc was the leading templateTitle[1] company in the templateTitleSubject[0] in terms of total assets and templateTitle[2] value ( templateTitle[2] capitalization ) .

generated_template: The statistic shows the percentage of conceptions of under 16 utilized templateXValue[max] in This in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[min] to templateTitleDate[max] . most templateYLabel[0] of terminated in this age group has remained around the same amount over the provided time period . In templateXValue[12] , approximately templateYValue[12] templateXValue[max] This 's in templateTitleSubject[0] and templateTitleSubject[1] .
generated: The statistic shows the percentage of conceptions under 16 utilized 2017 in This United Kingdom and UK from 2004 to 2017 . most Number of terminated in this age group has remained around the same amount over the provided time period . In 2005 , approximately 640 2017 This 's in United Kingdom and UK .

Example 187:
titleEntities: {'Subject': ['Los Angeles Rams', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Los Angeles Rams ( 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']: ['3800', '3200', '3000', '2900', '1450', '930', '875', '780', '775', '779', '913', '929', '908', '841', '757', '708', '602', '544']

gold: This graph depicts the franchise value of the Los Angeles Rams of the National Football League from 2002 to 2019 . In 2019 , the franchise value amounted to around 3.8 billion U.S. dollars . The Los Angeles Rams are owned by Stan Kroenke , who bought the franchise for 750 million U.S. dollars in 2010 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Rams of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] amounted to around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Rams are owned by Stan Kroenke , who bought the templateYLabel[0] for 750 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[9] .

generated_template: The graph depicts the templateYLabel[0] 's of the templateTitleSubject[0] of the Major League Baseball from templateXValue[min] to templateXValue[0] . In the report , the templateYLabel[0] had an estimated to templateYValue[max] templateXValue[max] This 's . Since watching , the templateTitleSubject[0] crime have been owned by Gayle Benson , wife of the policy Thomas Benson , who bought the templateYLabel[0] for 70.2 templateXValue[max] This 's in watching .
generated: The graph depicts the Franchise 's of the Los Angeles Rams of the Major League Baseball from 2002 to 2019 . In the report , the Franchise had an estimated to 3800 2019 This 's . Since watching , the Los Angeles Rams crime have been owned by Gayle Benson , wife of the policy Thomas Benson , who bought the Franchise for 70.2 2019 This 's in watching .

Example 188:
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 templateYLabel[2] of templateYLabel[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , this sector accounted for templateYValue[idxmax(X)] % of the nation 's Gross Domestic Product , making it one of the largest sectors of the U.S. economy .

generated_template: In fiscal templateXLabel[0] the templateYLabel[0] of templateTitleSubject[0] in the templateTitle[4] has been shrinking , with a templateTitle[3] of approximately templateYValue[0] templateXValue[max] This 's in templateYLabel[0] in increase . The figure represents the highest templateXValue[max] in over a decade . Purchasing behavior in the templateTitle[4] market is downward , with the templateYLabel[0] of templateTitleSubject[0] in the templateTitle[4] has been steadily over the past few templateXValue[max] , from templateYValue[min] percent to templateYValue[max] templateXValue[max] This 's in increase .
generated: In fiscal Year the Value of GDP in the GDP has been shrinking , with a percentage of approximately 11.4 2018 This 's in Value increase . The figure represents the highest 2018 in over a decade . Purchasing behavior in the GDP market is downward , with the Value of GDP in the GDP has been steadily over the past few 2018 , from 11.1 percent to 12.8 2018 This 's in increase .

Example 189:
titleEntities: {'Subject': ['Search'], 'Date': ['2013']}
title: Search engines : regular usage penetration 2013 , by country
X_Axis['Country']: ['Belgium', 'Norway', 'South_Africa', 'Turkey', 'Germany', 'Great_Britain', 'Australia', 'Poland', 'Sweden', 'France', 'Canada', 'Argentina', 'Italy', 'Total', 'Spain', 'Brazil', 'South_Korea', 'India', 'Mexico', 'Saudi_Arabia', 'China', 'United_States', 'Hungary', 'Russia', 'Japan', 'Indonesia']
Y_Axis['Share', 'of', 'respondents']: ['85', '85', '85', '83', '82', '82', '82', '80', '79', '78', '78', '78', '76', '74', '74', '73', '71', '70', '69', '69', '69', '68', '67', '57', '56', '40']

gold: This statistic displays the share of internet users in selected countries visiting search engines regularly as of August 2013 . Penetration was 85 percent in Belgium , Norway and South Africa .
gold_template: This statistic displays the templateYLabel[0] of internet users in selected countries visiting templateTitleSubject[0] templateTitle[1] regularly as of 2013 . templateTitle[4] was templateYValue[max] percent in templateXValue[0] , templateXValue[1] and templateXValue[2] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] produced or selected templateTitleSubject[0] in templateTitle[6] , split templateTitle[7] templateXLabel[0] . In the survey period , templateXValue[0] had the highest streaming of templateTitle[1] in the EU with templateYValue[max] percent . Cigarette prices differ across templateTitleSubject[0] : the most expensive templateXLabel[0] in which to purchase cigarettes is the compound ( UK ) .
generated: The statistic shows the Share of Search engines usage penetration 2013 by country produced or selected Search in by , split country . In the survey period , Belgium had the highest streaming of engines in the EU with 85 percent . Cigarette prices differ across Search : the most expensive Country in which to purchase cigarettes is the compound ( UK ) .

Example 190:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: U.S. parental digital monitoring on teen online behavior 2015
X_Axis['Response']: ['What_sites_he/she_can_access', 'What_online_accounts_he/she_can_have', 'What_he/she_can_post_online_for_others_to_see', 'The_time_of_day_he/she_can_use_technology', 'May_only_download_apps_with_age_ratings', 'Amount_of_time_he/she_can_use_technology_per_day_or_per_week', 'Must_check_devices_with_parents/leave_in_common_area_before_going_to_bed', 'When_in_home_he/she_can_use_or_be_online']
Y_Axis['Share', 'of', 'respondents']: ['79', '77', '75', '74', '67', '65', '60', '59']

gold: This statistic presents the most popular rules of parents in the United States to monitor their teen 's activities online . As of October 2015 , 77 percent of responding parents stated that they had rules about what online accounts their children were allowed to have .
gold_template: This statistic presents the most popular rules of parents in the templateTitle[0] to monitor their templateTitle[4] 's activities templateXValue[1] . As of October templateTitleDate[0] , templateYValue[1] percent of responding parents stated that they had rules about templateXValue[0] templateXValue[1] their children were allowed to templateXValue[1] .

generated_template: The statistic presents the templateYLabel[0] of templateTitleSubject[0] templateTitle[3] users who are concerned about governments censoring the templateTitle[3] as of 2014 , sorted templateTitle[6] templateTitle[7] . During the survey period it was found templateYValue[idxmax(X)] templateYValue[min] percent of templateTitle[3] users in templateXValue[last] felt concerned about their governments censoring the templateTitle[3] . Overall , templateYValue[0] percent of templateTitleSubject[0] templateTitle[3] users agreed with the statement .
generated: The statistic presents the Share of U.S. monitoring users who are concerned about governments censoring the monitoring as of 2014 , sorted behavior 2015 . During the survey period it was found 59 percent of monitoring users in When in home he/she can use or be online felt concerned about their governments censoring the monitoring . Overall , 79 percent of U.S. monitoring users agreed with the statement .

Example 191:
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 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 .

generated_template: The statistic shows the 12 templateTitleSubject[0] templateTitle[3] on the templateXValue[18] gaming platform Raptr in 2015 , by templateYLabel[0] of total value . In the measured period , the company that templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[max] percent , with the highest templateYLabel[0] forecast given for templateXValue[max] .
generated: The statistic shows the 12 Chained index on the 2001 gaming platform Raptr in 2015 , by Chained of total value . In the measured period , the company that Chained Consumer amounted to approximately 144.73 percent , with the highest Chained forecast given for 2019 .

Example 192:
titleEntities: {'Subject': ['Texas'], 'Date': ['1990', '2018']}
title: Texas - 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']: ['59785', '59295', '58146', '56473', '53875', '53027', '51926', '49047', '47266', '47475', '46490', '46053', '43307', '41422', '41397', '39271', '40149', '40860', '38609', '38688', '35783', '35075', '33072', '32039', '30755', '28727', '27953', '27733', '28228']

gold: This statistic shows the median household income in Texas from 1990 to 2018 . In 2018 , the median household income in Texas amounted to 59,785 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: The statistic shows the templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to had , with a forecast up years templateTitleDate[max] . In had , the templateTitle[2] templateYLabel[0] in templateTitleSubject[0] an to about templateYValue[0] percent templateXValue[max] to the This templateXLabel[0] .
generated: The statistic shows the household in Texas from 1990 to had , with a forecast up years 2018 . In had , the household in Texas an to about 59785 percent 2018 to the This Year .

Example 193:
titleEntities: {'Subject': ['UK'], 'Date': ['2019']}
title: Public expenditure on recreational and sporting services in the UK 2013 to 2019
X_Axis['Year']: ['2018//19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14']
Y_Axis['Expenditure', 'in', 'million', 'GBP']: ['2947', '3048', '3230', '3268', '3997', '3472']

gold: This statistic shows public sector expenditure on recreational and sporting services in the United Kingdom from 2013/14 to 2018/19 . In the year 2014/15 , the United Kingdom spent the most during this period with approximately 4 billion British pounds going toward recreational and sporting services . In 2017/18 , 2.9 billion British pounds were spent on recreational and sporting services in the UK .
gold_template: This statistic shows templateTitle[0] sector templateYLabel[0] on templateTitle[2] and templateTitle[3] templateTitle[4] in the United Kingdom from templateXValue[last] to 2018/19 . In the templateXLabel[0] templateXValue[4] , the United Kingdom spent the most during this period with approximately templateYValue[max] templateYLabel[1] British pounds going toward templateTitle[2] and templateTitle[3] templateTitle[4] . In templateXValue[1] , templateYValue[min] templateYLabel[1] British pounds were spent on templateTitle[2] and templateTitle[3] templateTitle[4] in the templateTitleSubject[0] .

generated_template: The statistic shows templateTitle[0] sector templateYLabel[0] on templateTitle[2] templateTitle[3] in the templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . templateYLabel[0] on templateTitle[2] templateTitle[3] has fees decreased during this period .
generated: The statistic shows Public sector Expenditure on recreational sporting in the UK from 2013/14 to 2018//19 . Expenditure on recreational sporting has fees decreased during this period .

Example 194:
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: The statistic shows the templateYLabel[0] gross domestic product in templateTitleSubject[0] from templateXValue[min] to templateTitleSubject[1] , with projections with projections are , with projections . In templateXValue[min] , the templateYLabel[0] in templateTitleSubject[0] was at approximately templateYValue[min] percent of the total labor force . The is a very important indicator of the economic strength of a country and a be of approximately be of the economic strength of a group in urban will and a traditional is templateXValue[6] indicator of the rapidly in a given year of the world templateYValue[idxmax(X)] number of the world templateYValue[idxmax(X)] live and the world , the templateTitle[3] is made by the templateTitle[0] is used a result of a Light one . . . . . It , the world templateYValue[idxmax(X)] trend and it is , there is used to templateXValue[15] , there were more until the average , templateTitleSubject[0] templateYValue[idxmax(X)] templateYLabel[0] rates in recent a trend is templateXValue[6] association templateXValue[6] ongoing for the highest templateTitle[3] of a trend is projected to the templateTitleSubject[0] templateYValue[idxmax(X)] trend is templateXValue[6] association . It is templateXValue[6] one of the estimated to the The The is , the trend The The The is , the templateYLabel[0] The The is a trend is The The The The would a trend is The The The is than trend is is The The The The is is is The The The The is is is is The The The The The The The The The The The The The The is is is is The The The The The of templateYLabel[0] The The The The The The The The The The The The The The The The is is is is The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The The
generated: The statistic shows the Unemployment gross domestic product in Czech Republic from 1999 to Czech Republic , with projections are , with projections . In 1999 , the Unemployment in Czech Republic was at approximately 2.4 percent of the total labor force . The is a very important indicator of the economic strength of a country and a be of approximately be of the economic strength of a group in urban will and a traditional is 2013 indicator of the rapidly in a given year of the world 2.47 number of the world 2.47 live and the world , the Republic is made by the Unemployment is used a result of a Light one . It , the world 2.47 trend and it is , there is used to 2004 , there were more until the average , Czech Republic 2.47 Unemployment rates in recent a trend is 2013 association ongoing for the highest Republic of a trend is projected to the Czech Republic 2.47 trend is 2013 association . It is 2013 one of the estimated to the is , the trend is , the Unemployment is a trend is The would a trend is The than trend is The of Unemployment The is

Example 195:
titleEntities: {'Subject': ['Italy'], 'Date': ['2024']}
title: Budget balance in Italy 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'in', 'billion', 'euros']: ['-51.31', '-49.15', '-48.57', '-47.61', '-45.51', '-35.77', '-37.51', '-41.29', '-42.66', '-43.14', '-49.34']

gold: The statistic shows the budget balance in Italy 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 Italy was around 37.51 billion euros .
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] . A positive value indicates a state surplus ; a negative value , a state deficit . In templateXValue[6] , the state deficit of templateTitleSubject[0] was around 37.51 templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the European Union in the templateXValue[max] This that templateXValue[idxmax(Y)] based on purchasing-power-parity from templateXValue[min] to had , in templateYLabel[0] in templateTitleSubject[0] with around templateYValue[6] percent . In had , the templateTitleSubject[0] Emirates ' templateYLabel[0] an to approximately templateYValue[0] percent of the This templateXLabel[0] . See the templateTitleSubject[0] This for further information .
generated: The statistic shows the Budget of the European Union in the 2024 This that 2019 based on purchasing-power-parity from 2014 to had , in Budget Italy with around -37.51 percent . In had , the Italy Emirates ' Budget an to approximately -51.31 percent of the This Year . See the Italy This for further information .

Example 196:
titleEntities: {'Subject': ['Germany'], 'Date': ['2010', '2019']}
title: Electricity prices for households in Germany 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']: ['30.88', '30.0', '29.87', '30.48', '30.48', '29.77', '29.69', '29.46', '29.51', '29.74', '29.81', '29.21', '29.19', '26.76', '25.95', '25.31', '25.28', '24.38', '23.75']

gold: Between 2010 and 2013 , there was a noticeable increase in the price of electricity for households in Germany . At the start of the decade households paid less than 24 euro cents per kilowatt-hour . However , this increased by more than five euro cents by the second half of 2013 .
gold_template: Between templateXValue[17] and templateXValue[11] , there was a noticeable increase in the price of templateTitle[0] templateTitle[2] templateTitle[3] in templateTitleSubject[0] . At the start of the decade templateTitle[3] paid less than templateYValue[17] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . However , this increased by more than five templateYLabel[0] templateYLabel[1] by the second half of templateXValue[11] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the first half of templateXValue[0] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[min] templateYLabel[0] price from the previous templateXLabel[0] .
generated: The statistic shows the Electricity prices for household end users in Germany semi-annually from 2010 S2 to 2019 S1 . In the first half of 2019 S1 , the average Electricity price for households was 23.75 Euro price from the previous Year .

Example 197:
titleEntities: {'Subject': ['Cyprus'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Cyprus 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.51', '2.46', '2.61', '2.71', '2.89', '3.11', '3.88', '4.47', '4.81', '1.96', '-1.31']

gold: The statistic shows the growth in real GDP in Cyprus from 2014 to 2018 , with projections up to 2024 . In 2018 , Cyprus 's real gross domestic product increased by around 3.88 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 to templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: templateTitleSubject[0] that real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) has been produces positive percentage for the past five years National templateXValue[min] , and is projected to continue to cost so through templateYValue[min] percent . In templateXValue[6] , templateTitleSubject[0] that real templateYLabel[0] increased by around templateYValue[0] percent templateXValue[max] to the This templateXLabel[0] . Learning from real templateYLabel[0] Real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) is a measure templateYValue[idxmax(X)] reflects the value of all goods and services an economy arrived within a given templateXLabel[0] .
generated: Cyprus that real Gross domestic product ( GDP ) has been produces positive percentage for the past five years National 2014 , and is projected to continue cost so through -1.31 percent . In 2018 , Cyprus that real GDP increased by around 2.51 percent 2024 to the This Year . Learning from real GDP Gross domestic product ( GDP ) is a measure 2.51 reflects the value of all goods and services an economy arrived within a given Year .

Example 198:
titleEntities: {'Subject': ['NFL'], 'Date': ['2019']}
title: NFL Thursday Night Football average viewers 2015 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015']
Y_Axis['Viewers', 'in', 'millions']: ['15.4', '14.9', '10.94', '12.44', '12.43']

gold: An average of 15.4 million viewers tuned in to watch Thursday Night Football in the United States during the 2019 NFL season , an increase of half a million on the previous year . Ratings for Monday Night Football also increased in 2019 , with the average number of viewers rising to 12.6 million . Prime-time packages receive ratings boost Thursday Night Football is part of the prime-time television schedule that also includes Monday Night Football and Sunday Night Football – these NFL games are broadcast across the entire United States by one major national network .
gold_template: An templateTitle[4] of templateYValue[max] templateYLabel[1] templateYLabel[0] tuned in to watch templateTitle[1] templateTitle[2] templateTitle[3] in the country during the templateXValue[max] templateXValue[idxmax(Y)] season , an increase of half a templateYLabel[1] on the previous templateXLabel[0] . Ratings for Monday templateTitle[2] templateTitle[3] also increased in templateXValue[max] , with the templateTitle[4] number of templateYLabel[0] rising to 12.6 templateYLabel[1] . Prime-time packages receive ratings boost templateTitle[1] templateTitle[2] templateTitle[3] is part of the prime-time television schedule that also includes Monday templateTitle[2] templateTitle[3] and Sunday templateTitle[2] templateTitle[3] – these templateTitleSubject[0] games are broadcast across the entire country by one major national network .

generated_template: The statistic represents the templateYLabel[0] of templateTitle[1] , templateXValue[max] and This in the United owned ( templateTitleSubject[0] ) from templateXValue[min] to 20119 , templateYLabel[0] in the million templateXLabel[0] of templateXValue[4] . goods is expected to grow by templateYValue[max] percent in increase .
generated: The statistic represents the Viewers of Thursday , 2019 and This in the United owned ( NFL ) from 2015 to 20119 , Viewers in the million Year of 2015 . goods is expected to grow by 15.4 percent in increase .

Example 199:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2004', '2018']}
title: U.S. airlines - domestic passenger enplanements 2004 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Passenger', 'enplanements', '(in', 'millions)']: ['777.91', '741.73', '719.99', '696.01', '662.82', '645.68', '642.29', '638.25', '629.54', '618.05', '651.71', '679.17', '658.36', '657.26', '629.77']

gold: This statistic represents the number of domestic passengers on U.S. airlines between 2004 and 2018 . U.S. airlines carried about 777.9 million passengers on domestic flights across the United States in 2018 .
gold_template: This statistic represents the number of templateTitle[2] passengers on templateTitleSubject[0] templateTitle[1] between templateXValue[min] and templateXValue[max] . templateTitleSubject[0] templateTitle[1] carried about templateYValue[max] million passengers on templateTitle[2] flights across the templateTitle[0] in templateXValue[max] templateXValue[idxmax(Y)]

generated_template: The statistic shows the templateTitle[3] templateYLabel[0] generated by templateTitle[0] and furnishings ( templateTitle[0] ) in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[12] , with projections up years templateYValue[min] percent of all goods . In templateXValue[12] , the templateTitle[2] templateTitle[3] templateYLabel[0] in the templateTitleSubject[0] is templateTitle[0] to grow by approximately templateYValue[max] percent .
generated: The statistic shows the passenger generated by U.S. and furnishings ( U.S. ) in the U.S. ( ) from 2004 to 2006 , with projections up years 618.05 percent of all goods . In 2006 , the domestic passenger in the U.S. is to grow by approximately 777.91 percent .

Example 200:
titleEntities: {'Subject': ['Bloomin Brands'], 'Date': ['2009', '2018']}
title: Revenue of Bloomin ' Brands , Inc. worldwide from 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['4.06', '4.21', '4.25', '4.38', '4.44', '4.13', '3.99', '3.84', '3.63', '3.6']

gold: This statistic shows the revenue of Bloomin ' Brands , Inc. worldwide from 2009 to 2018 . In 2018 , Bloomin ' Brands generated approximately 4.06 billion U.S. dollars in revenue . Bloomin ' Brands , Inc. is a U.S.-based restaurant company that owns and operates the casual dining chains Bonefish Grill , Carrabba 's Italian Grill , Fleming 's Prime Steakhouse & Wine Bar , Outback Steakhouse and Roy 's .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] , templateTitle[4] templateTitle[5] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] generated approximately templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] . templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] , templateTitle[4] is a U.S.-based restaurant company that owns and operates the casual dining chains Bonefish Grill , Carrabba 's Italian Grill , Fleming 's Prime Steakhouse & Wine Bar , Outback Steakhouse and Roy 's .

generated_template: favorite appliance restaurant estimates templateTitleSubject[0] generated approximately templateYValue[max] templateXValue[max] This in templateYLabel[0] in increase , up from templateYValue[1] templateXValue[max] the previous templateXLabel[0] . The figure has become a templateYLabel[0] of over templateYValue[max] templateXValue[max] This in templateXValue[idxmin(Y)] . Here in templateYLabel[0] is a company of templateTitleSubject[0] but .
generated: favorite appliance restaurant estimates Bloomin Brands generated approximately 4.44 2018 This in Revenue increase , up from 4.21 2018 the previous Year . The figure has become a Revenue of over 4.44 2018 This in 2009 . Here in Revenue is a company of Bloomin Brands but .

Example 201:
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] templateYLabel[2] templateYLabel[3] templateYLabel[4] in an optimistic scenario .

generated_template: The statistic shows the templateXValue[max] This on the global templateXLabel[0] of templateTitle[0] prices in the templateTitleSubject[0] in templateXValue[min] with a templateTitle[3] for templateXValue[1] . In templateXValue[1] , templateTitle[0] prices in the templateTitleSubject[0] are expected to reach templateYValue[min] percent .
generated: The statistic shows the 2030 This on the global Year of Autonomous prices in the Autonomous in 2015 with a components for 2025 . In 2025 , Autonomous prices in the Autonomous are expected to reach 0.4 percent .

Example 202:
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 statistic depicts the templateYLabel[0] templateTitle[4] of the templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to 2018/2019 . According to the report , the templateTitleSubject[0] templateYLabel[0] templateXValue[max] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[max] This in templateXValue[idxmin(Y)] . In the same templateXLabel[0] , the templateXValue[max] of templateTitle[4] fruit in the templateTitleSubject[0] amounted to about 115.14 This .
generated: The statistic depicts the Per oat of the consumption oat products in the U.S. from 2000 to 2018/2019 . According to the report , the U.S. Per 2018 of oat products amounted to approximately 4.8 This in 2000 . In the same Year , the 2018 of oat fruit in the U.S. amounted to about 115.14 This .

Example 203:
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: The statistic shows a ranking of the templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] on templateYLabel[0] in templateTitleDate[0] . According to the source , the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[1] , generating a templateYLabel[0] of templateYValue[max] templateXValue[max] This .
generated: The statistic shows a ranking of the Wealth U.S. the UHNW super rich population on Number in 2014 . According to the source , the State U.S. the UHNW super rich in the U.S. , generating a Number of 8655 New York This .

Example 204:
titleEntities: {'Subject': ['North America'], 'Date': ['2019']}
title: The most successful music tours in North America in 2019
X_Axis['Artist', '/', 'Band']: ['The_Rolling_Stones', 'Elton_John', 'Bob_Seger_&_The_Silver_Bullet_Band', 'Pink', 'Ariana_Grande', 'Jonas_Brothers', 'Kiss', 'Fleetwood_Mac', 'Garth_Brooks', 'Justin_Timberlake', 'Billy_Joel', 'Dead_&_Company', 'Eric_Church', 'Michael_Buble', 'Trans-Siberian_Orchesta']
Y_Axis['Gross', 'revenue', 'in', 'million', 'U.S.', 'dollars']: ['177.8', '157.4', '97.0', '87.8', '82.6', '81.7', '81.6', '77.5', '76.1', '75.6', '70.4', '68.6', '68.6', '66.7', '65.7']

gold: In 2019 , the most successful music tour in North America based on gross revenue was that of The Rolling Stones , which generated 177.8 million U.S. dollars . Following closely behind was Elton John 's tour , which made 157.4 million dollars in revenue and sold over 1.15 million tickets .
gold_template: In templateTitleDate[0] , the templateTitle[0] templateTitle[1] templateTitle[2] tour in templateTitleSubject[0] based on templateYLabel[0] templateYLabel[1] was that of The templateXValue[0] , which generated templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Following closely behind was templateXValue[1] 's tour , which made templateYValue[1] templateYLabel[2] templateYLabel[4] in templateYLabel[1] and sold over 1.15 templateYLabel[2] tickets .

generated_template: This statistic shows the templateTitle[1] with the ; templateYLabel[1] of official disasters templateTitle[2] to the brand 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 official hazards and social , political and incurred factors .
generated: This statistic shows the successful with the ; revenue of official disasters music to the brand revenue million in 2019 . At this time , The Rolling Stones with an million value of 65.7 , was the most Artist in the Gross . In the framework of the WorldRiskIndex , disaster revenue is analyzed as a complex interplay of official hazards and social , political and incurred factors .

Example 205:
titleEntities: {'Subject': ['Samoa'], 'Date': ['2019']}
title: Unemployment rate in Samoa 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']: ['8.46', '8.33', '8.32', '8.61', '8.62', '8.72', '8.8', '8.75', '5.68', '5.52', '5.35', '4.77', '4.75', '4.96', '5.14', '5.14', '5.15', '5.11', '4.96', '4.72', '4.43']

gold: This statistic shows the unemployment rate in Samoa from 1999 to 2019 . In 2019 , the unemployment rate in Samoa was at approximately 8.46 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] percent .

generated_template: The statistic shows the templateYLabel[0] up in templateTitleSubject[0] from templateXValue[min] to 2027 . In templateTitleSubject[1] , the templateYLabel[0] in templateTitleSubject[0] with at approximately templateYValue[0] percent . Population of templateTitleSubject[0] A member of the Group templateYValue[min] and G20 , templateTitleSubject[0] is be of the worlds most powerful , declines and emerging economies .
generated: The statistic shows the Unemployment up in Samoa from 1999 to 2027 . In Samoa , the Unemployment in Samoa with at approximately 8.46 percent . Population of Samoa A member of the Group 4.43 and G20 , Samoa is be of the worlds most powerful , declines and emerging economies .

Example 206:
titleEntities: {'Subject': ['H M'], 'Date': ['2019']}
title: Number of H & M store openings worldwide by country in 2019
X_Axis['Country']: ['Total', 'Franchise', 'USA', 'China', 'Japan', 'UK', 'Sweden', 'Germany', 'Italy', 'Russia', 'France', 'South_Korea', 'India', 'Mexico', 'Philippines', 'Netherlands', 'Belgium', 'Canada', 'Australia', 'Finland', 'Luxembourg', 'Poland', 'New_Zealand', 'Spain', 'South_Africa', 'Norway', 'Iceland', 'Switzerland', 'Colombia', 'Kazakhstan', 'Austria', 'Belarus', 'Uruguay', 'Malaysia', 'Chile', 'Peru', 'Hong_Kong', 'Vietnam', 'Singapore', 'Slovakia', 'Latvia', 'Lithuania', 'Denmark', 'Georgia', 'Bosnia-Herzegovina', 'Turkey', 'Romania', 'Estonia', 'Serbia', 'Ukraine', 'Portugal', 'Greece', 'Puerto_Rico', 'Slovenia', 'Hungary', 'Taiwan', 'Bulgaria', 'Cyprus', 'Macau', 'Croatia', 'Ireland', 'Czech_Republic']
Y_Axis['Number', 'of', 'H&M', 'store', 'openings']: ['375', '28', '28', '21', '15', '13', '12', '11', '10', '10', '10', '8', '8', '7', '6', '6', '6', '5', '5', '5', '5', '4', '4', '4', '4', '3', '3', '3', '3', '3', '3', '2', '2', '2', '2', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0']

gold: This statistic shows the number of new openings of the H & M Group stores in 2019 , by country . In 2019 , 28 new H & M stores were opened in the United States . H & M is a Swedish clothing manufacturer and retailer , based in Stockholm , Sweden .
gold_template: This statistic shows the templateYLabel[0] of templateXValue[22] templateYLabel[3] of the templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] Group stores in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , templateYValue[1] templateXValue[22] templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] stores were opened in the country . templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] is a Swedish clothing manufacturer and retailer , based in Stockholm , templateXValue[6] .

generated_template: The statistic shows the templateTitle[3] 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] percent of templateTitle[0] templateTitle[1] templateTitle[2] .
generated: The statistic shows the M of Number H & in H M 2019 , distinguished openings Country . In 2019 , H investors from the Total invested 375 percent of Number H & .

Example 207:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2003', '2016']}
title: Plywood and veneer production in the U.S. 2003 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Production', 'in', 'million', 'cubic', 'feet']: ['683', '693', '693', '703', '679', '653', '655', '616', '754', '898', '989', '1068', '1082', '1052']

gold: This statistic depicts the plywood and veneer production in the United States from 2001 to 2016 . In 2015 , the total U.S. plywood and veneer production amounted to some 683 million cubic feet .
gold_template: This statistic depicts the templateTitle[0] and templateTitle[1] templateYLabel[0] in the templateTitle[3] from 2001 to templateXValue[max] . In templateXValue[1] , the total templateTitleSubject[0] templateTitle[0] and templateTitle[1] templateYLabel[0] amounted to some templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[3] of conceptions of under 16 utilized templateXValue[max] in This in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[min] to had , with a forecast for the following templateXLabel[0] . templateTitleSubject[0] templateTitle[1] in templateTitleSubject[1] - additional information According to the fact of templateXValue[12] and is ‘ to grow by the largest amount of each templateXLabel[0] .
generated: The statistic shows the U.S. of conceptions under 16 utilized 2016 in This U.S. and from 2003 to had , with a forecast for the following Year . U.S. veneer in U.S. - additional information According to the fact of 2004 and is ‘ to grow by the largest amount of each Year .

Example 208:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016']}
title: Number of mobile internet users in the United Kingdom ( UK ) Q3 2013-Q2 2016
X_Axis['Quarter']: ['Q2_2016', 'Q1_2016', 'Q4_2015', 'Q3_2015', 'Q2_2015', 'Q1_2015', 'Q4_2014', 'Q3_2014', 'Q2_2014', 'Q1_2014', 'Q4_2013', 'Q3_2013']
Y_Axis['Users', 'in', 'millions']: ['42.77', '42.03', '41.27', '40.36', '39.56', '38.7', '37.82', '36.73', '35.78', '34.75', '33.69', '32.57']

gold: This statistic displays a forecast of the number of mobile internet users in the UK from third quarter 2013 to second quarter 2016 . It is forecast that there will be 43 million mobile internet users as of quarter two 2016 .
gold_template: This statistic displays 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] templateYLabel[1] templateTitle[1] templateTitle[2] templateYLabel[0] as of templateXLabel[0] two templateXValue[0] .

generated_template: The statistic illustrates the templateTitle[1] of templateTitle[2] templateTitleSubject[0] as of the fourth templateXLabel[0] of of fourth templateXLabel[0] . During this period , templateTitleSubject[0] had paid a templateTitle[0] of templateYValue[6] percent templateTitle[1] templateYLabel[0] in templateTitleSubject[0] .
generated: The statistic illustrates the mobile of internet United Kingdom as of the fourth Quarter of fourth Quarter . During this period , United Kingdom had paid a Number of 37.82 percent mobile Users in United Kingdom .

Example 209:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Number of participants in sailing in the U.S. 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']: ['3.75', '3.97', '4.1', '4.1', '3.92', '3.92', '3.96', '3.73', '3.87', '4.34', '4.23', '3.79', '3.39']

gold: This statistic shows the number of participants in sailing in the United States from 2006 to 2018 . In 2018 , there were approximately 3.75 million participants in sailing in the U.S. , down from 3.97 million the previous year .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[1] in templateTitle[2] in the templateTitleSubject[0] , down from templateYValue[1] templateYLabel[2] the previous templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] in the templateTitle[3] of templateTitleSubject[0] from templateXValue[min] to templateXValue[9] , with a forecast basis . In templateXValue[12] , the country of templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] an to approximately templateYValue[max] templateXValue[max] . templateTitle[1] is a popular recreational activity with more until templateYValue[min] templateXValue[max] people partaking in templateTitle[1] activities in the templateTitle[2] each templateXLabel[0] .
generated: The statistic shows the Number of sailing in the U.S. of from 2006 to 2009 , with a forecast basis . In 2006 , the country of U.S. participants in the U.S. an to approximately 4.34 2018 . participants is a popular recreational activity with more until 3.39 2018 people partaking in participants activities in the sailing each Year .

Example 210:
titleEntities: {'Subject': ['France'], 'Date': ['2012', '2028']}
title: Domestic travel spending in France 2012 to 2028
X_Axis['Year']: ['2028', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Spending', 'in', 'billion', 'euros']: ['131.4', '114.9', '112.3', '110.9', '108.1', '108.3', '107.5', '107.2']

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

generated_template: The statistic shows the templateTitle[3] templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitle[5] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[1] , with templateXValue[6] additional forecast for templateXValue[1] . Over than templateXLabel[0] , the templateTitle[4] of the templateTitle[0] and templateTitle[1] industry to templateTitle[5] in the templateTitleSubject[1] has increased , reaching templateYValue[2] and templateYValue[2] period respectively .
generated: The statistic shows the France 2012 of Domestic and travel to 2028 in the France from 2012 to 2018 , with 2013 additional forecast for 2018 . Over than Year , the 2012 of the Domestic and travel industry to 2028 in the France has increased , reaching 112.3 and period respectively .

Example 211:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1950', '2017']}
title: Deaths by unintentional drug overdose in the U.S. 1950 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', '1990', '1980', '1970', '1960', '1950']
Y_Axis['Death', 'rate', 'per', '100,000', 'population']: ['20.1', '18.2', '14.8', '13.1', '12.2', '11.5', '11.6', '10.6', '10.3', '10.2', '9.9', '9.2', '8.0', '7.2', '6.7', '6.1', '4.9', '4.5', '4.4', '3.9', '3.7', '3.5', '3.4', '2.3', '1.9', '2.8', '1.7', '2.5']

gold: This statistic shows the death rate due to unintentional drug overdose in the United States from 1950 to 2017 . In 2017 , there were 20.1 deaths by unintentional poisoning per 100,000 people in the United States .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] due to templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitle[0] templateTitle[1] templateTitle[2] poisoning templateYLabel[2] 100,000 people in the templateTitle[5] .

generated_template: The statistic shows the average templateYLabel[0] of templateXValue[max] This to the templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[5] , with a forecast up to templateTitleDate[max] . In templateXValue[min] , templateYValue[idxmin(X)] percent of all goods and services for the templateTitle[3] was templateYValue[min] percent .
generated: The statistic shows the average Death of 2017 This to the U.S. drug overdose U.S. from 1950 to 2012 , with a forecast up to 2017 . In 1950 , 2.5 percent of all goods and services for the drug was 1.7 percent .

Example 212:
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] million people .

generated_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 2015 sales classes .
generated: China (1928) was by far the leading Number Country in terms of total Number of deaths , with about 250 thousand deaths by the end of 2016 . Bangladesh (1943) was ranked second , among the leading ten caused , with 1900000 thousand deaths as of 2016 . deaths classification In the deaths are classified by on the farm income and government payments into 2015 sales classes .

Example 213:
titleEntities: {'Subject': ['Alibaba'], 'Date': ['2016']}
title: Alibaba : mobile share of gross merchandise volume Q2 2012-Q2 2016
X_Axis['Quarter']: ["Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12"]
Y_Axis['Percentage', 'of', 'mobile', 'GMV']: ['75', '73', '68', '62', '55', '51', '42', '36', '33', '27', '20', '15', '12', '10.7', '7.4', '5.6', '4.6']

gold: This statistic gives information on the mobile share of Alibaba 's gross merchandise volume from the second quarter of 2012 to the second quarter of 2016 . Last reported quarter , mobile sales accounted for 75 percent of the group 's China retail marketplaces GMV .
gold_template: This statistic gives information on the templateYLabel[1] templateTitle[2] of templateTitleSubject[0] 's templateTitle[3] templateTitle[4] templateTitle[5] from the second templateXLabel[0] of 2012 to the second templateXLabel[0] of templateTitleDate[0] . Last reported templateXLabel[0] , templateYLabel[1] sales accounted for templateYValue[max] percent of the group 's China retail marketplaces templateYLabel[2] .

generated_template: The statistic shows templateTitleSubject[0] that templateTitle[1] templateTitle[2] templateYLabel[0] figures from the second templateXLabel[0] of templateTitleDate[min] to the second templateXLabel[0] of templateTitleDate[max] . As of the last reported templateXLabel[0] , templateYValue[0] percent of the group that templateTitle[1] visits thirds to purchases .
generated: The statistic shows Alibaba that mobile share Percentage figures from the second Quarter of 2016 to the second Quarter of 2016 . As of the last reported Quarter , 75 percent of the group that mobile visits thirds to purchases .

Example 214:
titleEntities: {'Subject': ['Latvia'], 'Date': ['2010', '2019']}
title: Electricity prices for households in Latvia 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']: ['16.29', '15.11', '15.31', '15.82', '15.86', '16.24', '16.28', '16.5', '16.35', '13.01', '13.65', '13.58', '13.78', '13.69', '13.82', '13.42', '11.68', '10.48', '10.49']

gold: This statistic shows the electricity prices for household end users in Latvia semi-annually from 2010 to 2019 . In the second half of 2018 , the average electricity price for households was 15.11 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: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the first half of templateXValue[0] , the average templateTitle[0] price templateTitle[2] templateTitle[3] increased from the previous templateXLabel[0] to templateYValue[max] templateYLabel[0] templateXValue[max] kWh .
generated: The statistic shows the Electricity prices for household end users in Latvia semi-annually from 2010 S2 to 2019 S1 . In the first half of 2019 S1 , the average Electricity price for households increased from the previous Year to 16.5 Euro 2019 S1 kWh .

Example 215:
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: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the first half of templateXValue[0] , the average templateTitle[0] price templateTitle[2] templateTitle[3] increased from the previous templateXLabel[0] to templateYValue[max] templateYLabel[0] templateXValue[max] kWh .
generated: The statistic shows the Electricity prices for household end users in Luxembourg semi-annually from 2010 S2 to 2019 S1 . In the first half of 2019 S1 , the average Electricity price for households increased from the previous Year to 17.98 Euro 2019 S1 kWh .

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

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

generated_template: There were templateYValue[0] reported templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateXValue[idxmin(Y)] . The was a Global one compared to the previous templateXLabel[0] , but templateXValue[6] one of 14 incidents compared to the low reported in templateXValue[4] . Nutrition European European island state is known for enforcing a strict templateTitle[1] safety lost in order to ensure the security of its residents and tourists in the country .
generated: There were 233 reported road deaths in Switzerland 2016 . The was a Global one compared to the previous Year , but 2012 one of 14 incidents compared to the low reported in 2014 . Nutrition European island state is known for enforcing a strict road safety lost in order to ensure the security of its residents and tourists in the country .

Example 217:
titleEntities: {'Subject': ['Walmart'], 'Date': ['2006', '2019']}
title: Walmart 's operating income worldwide 2006 to 2019
X_Axis['Fiscal', 'year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Operating', 'income', 'in', 'billion', 'U.S.', 'dollars']: ['21.96', '20.44', '22.76', '24.11', '27.15', '26.87', '27.73', '26.49', '25.51', '23.97', '22.77', '21.92', '20.5', '18.69']

gold: The timeline shows Walmart 's operating income worldwide from 2006 to 2019 . In 2016 , Walmart 's global operating income amounted to about 24.11 billion U.S. dollars . Walmart , founded in 1962 , is an American multinational retailer corporation that runs chains of large discount department stores and warehouse stores .
gold_template: The timeline shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitle[4] from templateXValue[last] to templateXValue[0] . In templateXValue[3] , templateTitleSubject[0] templateTitle[1] global templateYLabel[0] templateYLabel[1] amounted to about templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] , founded in 1962 , is an American multinational retailer corporation that runs chains of large discount department stores and warehouse stores .

generated_template: templateTitleSubject[0] experienced a templateYLabel[0] loss of seven templateYLabel[1] U.S. This in templateXValue[max] , getting closer for the profit of the previous templateXLabel[0] . The Finnish company once known for cell terrorist has shifted its focus to providing telecommunication customers in the world . The company has a templateYLabel[0] of how templateYValue[max] templateYLabel[1] templateTitleSubject[0] dollars in the templateTitleSubject[1] in templateXValue[max] .
generated: Walmart experienced a Operating loss of seven income U.S. This in 2019 , getting closer for the profit of the previous Fiscal . The Finnish company once known for cell terrorist has shifted its focus to providing telecommunication customers in the world . The company has a Operating of how 27.73 income Walmart dollars in the Walmart in 2019 .

Example 218:
titleEntities: {'Subject': ['BNP Paribas'], 'Date': ['2003', '2018']}
title: Return on equity of BNP Paribas 2003 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Return', 'on', 'equity']: ['8.2', '8.9', '9.3', '8.3', '7.7', '6.1', '8.9', '8.8', '12.3', '10.8', '6.6', '19.6', '21.2', '20.2', '16.8', '15.3']

gold: The statistic shows the return on equity of BNP Paribas from 2003 to 2018 . In 2018 , the return on equity of the global banking group headquartered in Paris was equal to 8.2 percent .
gold_template: The statistic shows the templateYLabel[0] on templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] on templateYLabel[1] of the global banking group headquartered in Paris was equal to templateYValue[idxmax(X)] percent .

generated_template: In had , the templateTitle[2] templateTitle[3] in templateTitleSubject[0] generated at templateYValue[0] percent . The was a Global decrease from templateXValue[min] to templateXValue[10] , but still well above the slumps in templateXValue[11] and templateXValue[8] due to the Financial Crisis and its aftermath . What does the templateYLabel[0] of templateTitle[2] in templateTitleSubject[0] has been relatively higher until a handful of around be percent .
generated: In had , the BNP Paribas in BNP Paribas generated at 8.2 percent . The was a Global decrease from 2003 to 2008 , but still well above the slumps in 2007 and 2010 due to the Financial Crisis and its aftermath . What does the Return of BNP in BNP Paribas has been relatively higher until a handful of around be percent .

Example 219:
titleEntities: {'Subject': ['Slovakia'], 'Date': ['2024']}
title: Inflation rate in Slovakia 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']: ['2.01', '1.97', '1.97', '2.09', '2.06', '2.6', '2.53', '1.39', '-0.47', '-0.33', '-0.1', '1.46', '3.73', '4.07', '0.7', '0.94', '3.95', '1.9', '4.27', '2.78', '7.45', '8.42', '3.49', '7.14', '12.16', '10.46', '6.69', '6.03', '5.78', '9.9', '13.46']

gold: This statistic shows the average inflation rate in Slovakia from 1994 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Slovakia increased about 2.53 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] increased about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the average templateYLabel[0] In had , officially the templateTitleSubject[0] of templateTitleSubject[0] , from templateXValue[min] to had , projections country years about templateYValue[6] percent templateXValue[max] to the This templateXLabel[0] . In had , the templateTitleSubject[0] ' templateYLabel[0] an to about templateYValue[6] percent templateXValue[max] to the This templateXLabel[0] .
generated: The statistic shows the average Inflation In had , officially the Slovakia of , from 1994 to had , projections country years about 2.53 percent 2024 to the This Year . In had , the Slovakia ' Inflation an to about 2.53 percent 2024 to the This Year .

Example 220:
titleEntities: {'Subject': ['Ethiopia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Ethiopia 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']: ['1411.77', '1328.18', '1248.69', '1161.57', '1066.2', '953.18', '852.88', '817.49', '777.29', '702.77', '613.1', '535.26', '493.32', '379.38', '360.83', '397.81', '334.15', '249.21', '198.34', '165.22', '138.55', '120.87', '116.79', '125.72', '129.68', '128.21', '134.23', '152.17', '155.12', '152.66', '151.97', '176.47', '293.35', '287.65', '269.08', '262.23', '257.56', '256.7', '247.98', '246.51', '217.42']

gold: The statistic shows gross domestic product ( GDP ) per capita in Ethiopia 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: templateTitle[0] templateTitle[1] templateTitle[2] is the total value of all goods and services they 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] percent of the This templateXLabel[0] .
generated: Gross domestic product is the total value of all goods and services they in a country in a Year . It is considered an important indicator of the economic strength of a country . In 2018 , GDP in Ethiopia amounted to around 852.88 percent of the This Year .

Example 221:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019']}
title: Marijuana and cannabis consumption in the past three months Canada by gender 2019
X_Axis['Sex']: ['Male', 'Female']
Y_Axis['Share', 'of', 'respondents']: ['18.4', '15.1']

gold: As of December 2019 , some 18.4 percent of surveyed males had consumed marijuana or cannabis in the past three months in Canada . In comparison , 15.1 percent of female respondents had used the recently legalized drug in the same time period . The legalization of cannabis in Canada Following a similar trend in the United States , the legalization of recreational marijuana in Canada has become a hot topic in politics and in the public realm .
gold_template: As of 2019 , some templateYValue[max] percent of surveyed males had consumed templateTitle[0] or templateTitle[1] in the templateTitle[3] three templateTitle[5] in templateTitleSubject[0] . In comparison , templateYValue[min] percent of templateXValue[last] templateYLabel[1] had used the recently legalized drug in the same time period . The legalization of templateTitle[1] in templateTitleSubject[0] Following a similar trend in the country , the legalization of recreational templateTitle[0] in templateTitleSubject[0] has become a hot topic in politics and in the public realm .

generated_template: The statistic been the stood of templateTitleSubject[0] templateTitle[1] templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . season to cases alcoholic templateTitle[5] templateTitleSubject[0] harassment support group WHOA , templateYValue[max] percent of reporting templateTitle[3] were templateXValue[0] .
generated: The statistic been the stood of Canada cannabis past in 2019 , months Canada . season to cases alcoholic months Canada harassment support group WHOA , 18.4 percent of reporting past were Male .

Example 222:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2019']}
title: Average price of an acrylic fill in nail salons in the U.S. 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Average', 'price', 'in', 'U.S.', 'dollars']: ['29.91', '29.71', '30.57', '27.86', '27.14', '26.18', '26.57', '26.99', '25.87', '25.85', '28.96', '27.63', '24.36']

gold: The average price of an acrylic fill in nail salons in the United States came at just under 30 U.S. dollars in 2019 . Many different types of manicures are offered at nail salons . One type of manicure is the acrylic nail .
gold_template: The templateYLabel[0] templateYLabel[1] of an templateTitle[2] templateTitle[3] in templateTitle[4] templateTitle[5] in the templateTitle[6] came at just under templateYValue[0] templateYLabel[2] templateYLabel[3] in templateXValue[max] . Many different types of manicures are offered at templateTitle[4] templateTitle[5] . One type of manicure is the templateTitle[2] templateTitle[4] .

generated_template: The statistic shows the templateYLabel[0] templateTitle[2] templateTitle[3] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to per years . templateTitleSubject[0] is a Swiss-based pharmaceutical company , and be of the leading pharma companies worldwide . In templateXValue[min] , the templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateYLabel[0] in the templateTitle[4] was downward at approximately templateYValue[max] templateXValue[max] This 's .
generated: The statistic shows the Average acrylic fill of nail salons in the Average from 2006 to per years . U.S. is a Swiss-based pharmaceutical company , and be of the leading pharma companies worldwide . In 2006 , the U.S. Average acrylic in the nail was downward at approximately 30.57 2019 This 's .

Example 223:
titleEntities: {'Subject': ['Italian'], 'Date': ['2011', '2018']}
title: Turnover of Italian fashion company Giorgio Armani 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Turnover', 'in', 'million', 'euros']: ['1360', '1515', '1676', '1702', '1644', '1650', '1584', '1275']

gold: The turnover of the Italian luxury fashion company Giorgio Armani S.p.A. amounted to 1.3 billion euros in 2018 . This figure represents a decrease compared to the peak reached by the company in 2015 , when the turnover reported amounted to 1.7 billion euros . The reduction in turnover coincided with lower profits for the company during the same period .
gold_template: The templateYLabel[0] of the templateTitleSubject[0] luxury templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] S.p.A. amounted to templateYValue[min] templateYLabel[1] templateYLabel[2] in templateXValue[max] . This figure represents a decrease compared to the peak reached by the templateTitle[3] in templateXValue[3] , when the templateYLabel[0] reported amounted to templateYValue[2] templateYLabel[1] templateYLabel[2] . The reduction in templateYLabel[0] coincided with lower profits for the templateTitle[3] during the same period .

generated_template: The statistic illustrates the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] from templateXValue[min] to templateXValue[3] , in templateXValue[max] This 's . In templateXValue[min] , the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] was forecasted to some templateYValue[idxmin(X)] templateXValue[max] This 's .
generated: The statistic illustrates the Turnover of fashion company Giorgio Armani in the Italian from 2011 to 2015 , in 2018 This 's . In 2011 , the Turnover of fashion company Giorgio Armani in the Italian was forecasted to some 1275 2018 This 's .

Example 224:
titleEntities: {'Subject': ['Latin American'], 'Date': ['2018', '2018']}
title: 2018 FIFA World Cup : most valued Latin American teams
X_Axis['Country']: ['Brazil', 'Argentina', 'Uruguay', 'Colombia', 'Mexico', 'Costa_Rica', 'Peru', 'Panama']
Y_Axis['Value', 'in', 'million', 'U.S.', 'dollars']: ['981.0', '699.0', '373.0', '251.1', '154.6', '40.15', '38.53', '8.23']

gold: The statistic presents the market value of all Latin American soccer teams participating in the 2018 FIFA World Cup in Russia . The Brazilian squad was the Latin American team with the highest market value , amounting to 981 million euros . Argentina ranked second , with a market value of 699 million euros .
gold_template: The statistic presents the market templateYLabel[0] of all templateTitleSubject[0] soccer templateTitle[8] participating in the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in Russia . The Brazilian squad was the templateTitleSubject[0] team with the highest market templateYLabel[0] , amounting to templateYValue[max] templateYLabel[1] euros . templateXValue[1] ranked second , with a market templateYLabel[0] of templateYValue[1] templateYLabel[1] euros .

generated_template: The statistic shows the countries with the largest templateTitle[1] templateYLabel[0] worldwide as estimated in templateTitleDate[0] . In templateYValue[idxmax(X)] year , it was estimated templateYValue[idxmax(X)] the templateXValue[4] had total templateTitle[1] templateYLabel[0] of approximately templateYValue[max] templateXValue[max] This . 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: The statistic shows the countries with the largest FIFA Value worldwide as estimated in 2018 . In 8.23 year , it was estimated 8.23 the Mexico had total FIFA Value of approximately 981.0 Brazil This . FIFA Value Cup Country FIFA is a soft , silver-white metal within the alkali metal group on the periodic table .

Example 225:
titleEntities: {'Subject': ['Vinci Group revenue'], 'Date': ['2009', '2018']}
title: Vinci Group - revenue 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['43.52', '40.25', '38.07', '38.5', '38.7', '40.34', '38.63', '36.96', '33.38', '30.74']

gold: The statistic represents Vinci 's total revenue from the fiscal year of 2009 to the fiscal year of 2018 . In the fiscal year of 2018 , the French concessions and construction company generated around 43.5 billion euros ( or about 47.8 billion US dollars ) in revenue .
gold_template: The statistic represents templateTitleSubject[0] 's total templateYLabel[0] 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 French concessions and construction company generated around templateYValue[max] templateYLabel[1] templateYLabel[2] ( or about 47.8 templateYLabel[1] US dollars ) in templateYLabel[0] .

generated_template: templateTitleSubject[0] generated about templateYValue[max] templateXValue[max] This in templateYLabel[0] in increase . The is a large one from their templateYLabel[0] in templateXValue[min] , which totaled templateYValue[min] templateXValue[max] This . templateTitleSubject[0] Inc. is a leading company in cleaning , sanitizing , pest elimination services , food safety , and infection prevention products and services worldwide .
generated: Vinci Group revenue generated about 43.52 2018 This in Revenue increase . The is a large one from their Revenue in 2009 , which totaled 30.74 2018 This . Vinci Group revenue Inc. is a leading company in cleaning , sanitizing pest elimination services , food safety , and infection prevention products and services worldwide .

Example 226:
titleEntities: {'Subject': ['Portugal'], 'Date': ['2012', '2028']}
title: International tourism spending in Portugal 2012 to 2028
X_Axis['Year']: ['2028', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Spending', 'in', 'billion', 'euros']: ['26.4', '19.4', '18.1', '15.6', '14.5', '13.9', '12.8', '12.2']

gold: This statistic shows the total annual spend of international tourists ( visitor exports ) in Portugal from 2012 to 2018 and a forecast for 2028 . In 2017 international visitors in Portugal spent a total of 18.1 billion euros . This is expected to increase in 2018 to 19.4 billion euros .
gold_template: This statistic shows the total annual spend of templateTitle[0] tourists ( visitor exports ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[1] and a forecast for templateXValue[max] . In templateXValue[2] templateTitle[0] visitors in templateTitleSubject[0] spent a total of templateYValue[2] templateYLabel[1] templateYLabel[2] . This is expected to increase in templateXValue[1] to templateYValue[1] templateYLabel[1] templateYLabel[2] .

generated_template: The statistic presents the templateTitle[3] templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[1] , with a forecast for additional forecast for than templateXLabel[0] . Over than time , the templateTitle[4] of the templateTitle[0] and templateTitle[1] industry to templateTitle[5] in templateTitleSubject[0] has increased , reaching templateXValue[6] estimated templateTitle[4] of approximately templateYValue[max] templateXValue[max] in increase .
generated: The statistic presents the Portugal 2012 of International and tourism to 2028 in Portugal from 2012 to 2018 , with a forecast for additional forecast for than Year . Over than time , the 2012 of the International and tourism industry to 2028 in Portugal has increased , reaching 2013 estimated 2012 of approximately 26.4 2028 in increase .

Example 227:
titleEntities: {'Subject': ['Johnson Johnson'], 'Date': ['2013']}
title: Johnson & Johnson 's share of the skin care products market worldwide 2013 to 2021
X_Axis['Year']: ['2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021']
Y_Axis['Market', 'share']: ['4.3', '4.2', '4.15', '4.1', '4.05', '3.95', '3.85', '3.75', '3.65']

gold: This statistic shows Johnson & Johnson 's share of the skin care products market worldwide from 2013 to 2021 . In 2015 , Johnson & Johnson 's share of the global skin care products market is estimated to be 4.15 percent .
gold_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateYLabel[1] of the templateTitle[5] templateTitle[6] templateTitle[7] templateYLabel[0] templateTitle[9] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateYLabel[1] of the global templateTitle[5] templateTitle[6] templateTitle[7] templateYLabel[0] is estimated to be templateYValue[2] percent .

generated_template: The statistic shows a templateTitleSubject[0] forecast for the global templateYLabel[0] of templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[1] , measured in templateXValue[max] This 's . In templateXValue[min] , the templateTitleSubject[0] templateYLabel[0] for templateTitle[1] templateTitle[2] templateTitle[3] was estimated to one by templateYValue[idxmin(X)] percent in templateXValue[idxmin(Y)] .
generated: The statistic shows a Johnson Johnson forecast for the global Market of Johnson 's from 2013 to 2014 , measured in 2021 This 's . In 2013 , the Johnson Johnson Market for & Johnson 's was estimated to one by 4.3 percent in 2021 .

Example 228:
titleEntities: {'Subject': ['Iran'], 'Date': ['2017']}
title: Main export partners of Iran 2017
X_Axis['Country']: ['Japan', 'Italy', 'Turkey', 'South_Korea', 'India', 'China']
Y_Axis['Share', 'in', 'total', 'exports']: ['5.3', '5.7', '11.1', '11.4', '15.1', '27.5']

gold: This statistic shows Iran 's main export partners in 2017 , sorted by their share in total exports . In 2017 , Iran 's main export partner was China with a share of 27.5 percent in all exports .
gold_template: This statistic shows templateTitleSubject[0] 's templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleDate[0] , sorted by their templateYLabel[0] in templateYLabel[1] templateYLabel[2] . In templateTitleDate[0] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] partner was templateXValue[last] with a templateYLabel[0] of templateYValue[max] percent in all templateYLabel[2] .

generated_template: The statistic shows the templateXValue[0] templateTitleSubject[0] templateYValue[idxmax(X)] templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] were the templateYValue[5] templateYValue[idxmax(X)] most important templateTitle[1] partner with a templateYLabel[0] of templateYValue[max] percent of across templateXValue[max] .
generated: The statistic shows the Japan Iran 27.5 Main export partners in 2017 . In 2017 , Japan were the 27.5 most important export partner with a Share of 27.5 percent of across Japan .

Example 229:
titleEntities: {'Subject': ['Jamaica'], 'Date': ['2018']}
title: Urbanization in Jamaica 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['55.67', '55.38', '55.1', '54.83', '54.58', '54.35', '54.13', '53.93', '53.74', '53.56', '53.37']

gold: This statistic shows the degree of urbanization in Jamaica from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 55.67 percent of Jamaica 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: The statistic in the degree of templateXValue[min] in templateTitleSubject[0] , also made templateXValue[1] Burma , from shows to cities . templateXValue[min] means the templateYLabel[0] of the total templateXValue[max] in the This templateXValue[max] of a approximately in the This templateXValue[max] of a approximately . goods is considered to a very important indicator of the economic strength of a approximately and a positive change is templateXValue[6] indicator of economic growth .
generated: The statistic in the degree of 2008 in Jamaica , also made 2017 Burma , from shows to cities . 2008 means the Share of the total 2018 in the This 2018 of a approximately in the This 2018 of a approximately . goods is considered to a very important indicator of the economic strength of a approximately and a positive change is 2012 indicator of economic growth .

Example 230:
titleEntities: {'Subject': ['Reddit.com'], 'Date': ['2019']}
title: Distribution of Reddit.com traffic 2019 , by country
X_Axis['Country']: ['United_States', 'United_Kingdom', 'Canada', 'Australia', 'Germany']
Y_Axis['Share', 'of', 'traffic']: ['49.57', '7.79', '7.75', '4.28', '3.27']

gold: Reddit is one of the world 's most popular websites and as of October 2019 , the United States generated 49.57 percent of desktop traffic to the forum site . Reddit in the United States In August 2019 , Reddit accounted for over less than one percent of social media website traffic in the United States , still ranking the platform one of the leading social networks based on visits . Founded in 2005 , Reddit is a discussion website which enables users to aggregate news by posting links and let other users vote and comment on them .
gold_template: Reddit is one of the world 's most popular websites and as of October templateTitleDate[0] , the templateXValue[0] generated templateYValue[max] percent of desktop templateYLabel[1] to the forum site . Reddit in the templateXValue[0] In 2019 , Reddit accounted for over less than one percent of social media website templateYLabel[1] in the templateXValue[0] , still ranking the platform one of the leading social networks based on visits . Founded in 2005 , Reddit is a discussion website which enables users to aggregate news templateTitle[4] posting links and let other users vote and comment on them .

generated_template: The statistic represents the regional templateTitle[0] of templateTitleSubject[0] Company in the last decline numerically . As of October templateTitleDate[0] , the templateXValue[0] accounted for templateYValue[max] percent of desktop website to the blogging owner fluctuated .
generated: The statistic represents the regional Distribution of Reddit.com Company in the last decline numerically . As of October 2019 , the United States accounted for 49.57 percent of desktop website to the blogging owner fluctuated .

Example 231:
titleEntities: {'Subject': ['Green Bay Packers', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Green Bay Packers ( 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']: ['456', '434', '421', '391', '347', '299', '282', '276', '259', '242', '232', '218', '197', '194', '189', '138', '152', '132']

gold: The statistic depicts the revenue of the Green Bay Packers , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Green Bay Packers was 456 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Packers , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] Packers was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .

generated_template: statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the templateXValue[7] Football League , from templateXValue[min] to 2027 . In the fiscal templateXLabel[0] season , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateXValue[max] This . templateTitleSubject[0] Jays is a professional basketball team of the templateXValue[7] Football League ( templateTitleSubject[1] ) .
generated: statistic depicts the Revenue of the Green Bay Packers , a franchise of the 2011 Football League , from 2001 to 2027 . In the fiscal Year season , the Revenue of the Green Bay Packers was 456 2018 This . Green Bay Packers Jays is a professional basketball team of the 2011 Football League ( NFL ) .

Example 232:
titleEntities: {'Subject': ['NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Baltimore Ravens ( 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']: ['438', '417', '403', '378', '345', '304', '292', '279', '262', '255', '240', '226', '205', '201', '192', '172', '155', '148']

gold: The statistic depicts the revenue of the Baltimore Ravens , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Baltimore Ravens was 438 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitle[1] templateTitle[2] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[1] templateTitle[2] was templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , franchise of the templateXValue[7] Football League , from templateXValue[min] to templateTitleDate[max] . In 2019 , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] an to templateYValue[max] templateXValue[max] This . templateTitleSubject[0] are a professional basketball team which is a professional basketball team templateYValue[idxmax(X)] is part of the templateXValue[7] Basketball Association ( 900 ) .
generated: statistic depicts the Revenue of the NFL , franchise of the 2011 Football League , from 2001 to 2018 . In 2019 , the Revenue of the NFL was 438 an to 438 2018 This . NFL are a professional basketball team which is a professional basketball team 438 is part of the 2011 Basketball Association ( 900 ) .

Example 233:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Gross domestic product ( GDP ) at current prices in Denmark 2008 to 2018
X_Axis['Year']: ['2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['GDP', 'in', 'million', 'Danish', 'kroner']: ['1801470', '1722143', '1810926', '1846854', '1895002', '1929677', '1981165', '2036356', '2107808', '2175106', '2245954']

gold: The statistic shows the gross domestic product ( GDP ) in Denmark from 2008 to 2018 . From 2009 and onwards , the GDP increased on an annual basis . As of 2018 , it amounted to around 2,246 billion Danish kroner .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . From templateXValue[1] and onwards , the templateYLabel[0] increased on an annual basis . As of templateXValue[max] , it amounted to around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[8] , with projections up until templateTitleDate[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[7] percent .
generated: The statistic shows the Gross domestic in Denmark from 2008 to 2016 , with projections up until 2018 . In 2014 , the Gross domestic of Denmark amounted to approximately 2036356 percent .

Example 234:
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)] percent of the population accessed the templateYLabel[3] , up from templateYValue[idxmin(X)] percent in templateXValue[idxmin(Y)] . In templateXValue[2] , templateTitleSubject[0] 's population amounted to 18.11 million inhabitants .

generated_template: In the U.S. state of templateTitleSubject[0] , there were approximately templateYValue[0] thousand templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] 's amounted in this in templateXValue[idxmin(Y)] . Since templateXValue[min] , than figure up been steadily increasing increasing to the templateXLabel[0] templateXValue[10] in which it also reached its peak with around 213 thousand templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] 's amounted in this . leader templateXValue[10] on , the templateYLabel[0] of casino 's amounted in templateTitleSubject[0] has been Founded down down .
generated: In the U.S. state of Malawi , there were approximately 13.78 thousand internet penetration and 2000 2017 's amounted in this 2000 . Since 2000 , than figure up been steadily increasing to the Year 2007 in which it also reached its peak with around 213 thousand internet penetration and 2000 2017 's amounted in this . leader 2007 on , the Percentage of casino 's amounted in Malawi has been Founded down .

Example 235:
titleEntities: {'Subject': ['South Sudan'], 'Date': ['2018']}
title: Infant mortality rate in South Sudan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['63.7', '63.7', '63.7', '63.7', '63.7', '64.0', '64.9', '66.9', '69.7', '72.8', '76.1']

gold: The statistic shows the infant mortality rate in South Sudan from 2008 to 2018 . In 2018 , the infant mortality rate in South Sudan was at about 63.7 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[min] templateYValue[idxmax(X)] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: templateTitle[0] templateTitle[1] has been best in templateTitleSubject[0] in the past decade , from templateYValue[max] templateYLabel[0] per 1,000 This 's in increase to templateYValue[min] in templateXValue[idxmin(Y)] . The figure helps to assess the overall healthcare system that efficacy , because childbirth and templateTitle[0] care require more direct patient care until any other distribution of life . Similarly , measures wage to combat templateTitle[0] templateTitle[1] often have spillover effects , improving the dropped healthcare system .
generated: Infant mortality has been best in South Sudan the past decade , from 76.1 Deaths per 1,000 This 's in increase to 63.7 in 2018 . The figure helps to assess the overall healthcare system that efficacy , because childbirth and Infant care require more direct patient care until any other distribution of life . Similarly , measures wage to combat Infant mortality often have spillover effects , improving the dropped healthcare system .

Example 236:
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] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . In templateXValue[max] , total templateYLabel[0] dropped just above templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[2] and net income of biotechnology company templateTitleSubject[0] from templateXValue[min] to 20119 with a forecast reason for templateXValue[1] . It is estimated templateYValue[idxmax(X)] the templateTitle[2] templateYLabel[0] for the templateTitleSubject[0] increased to reach some templateYValue[max] templateXValue[max] This in increase .
generated: The statistic shows the production and net income of biotechnology company Global from 2007 to 20119 with a forecast reason for 2018 . It is estimated 1100 the production for the Global increased to reach some 2200 2019 This in increase .

Example 237:
titleEntities: {'Subject': ['Netflix'], 'Date': ['2017']}
title: Netflix content watching worldwide by device 2017
X_Axis['Platform']: ['Television', 'PC/laptop', 'Mobile', 'Tablet']
Y_Axis['Share', 'of', 'time', 'spent']: ['70', '15', '10', '5']

gold: The figure shows the share of time spent watching Netflix worldwide in 2017 , by device . According to the source , 70 percent of content streamed on Netflix worldwide in 2017 was via a connected TV .
gold_template: The figure shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] templateTitleSubject[0] templateTitle[3] in templateTitleDate[0] , templateTitle[4] templateTitle[5] . According to the source , templateYValue[max] percent of templateTitle[1] streamed on templateTitleSubject[0] templateTitle[3] in templateTitleDate[0] was via a connected TV .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] users in the templateTitle[5] who were using templateTitleSubject[0] as of 2019 , sorted templateTitle[6] templateXLabel[0] templateXLabel[1] . second templateYValue[idxmax(X)] period of League , templateYValue[max] percent of templateTitleSubject[0] internet up vans a templateXValue[1] degree used the photo sharing app .
generated: The statistic shows the Share of Netflix users in the device who were using Netflix as of 2019 , sorted 2017 Platform . second 5 period of League , 70 percent of Netflix internet up vans a PC/laptop degree used the photo sharing app .

Example 238:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Book readers in the U.S. 2019 , by age group
X_Axis['Year']: ['18-29', '30-49', '50-64', '65+']
Y_Axis['Share', 'of', 'respondents']: ['81', '72', '67', '68']

gold: During a survey held in early 2019 , it was found that 81 percent of adults aged between 18 and 29 years old had read a book in any format in the previous year . The survey results showed that adults within this age category were more likely than older respondents to have read a book within the last twelve months . Book readers in the U.S. – additional information While it is mostly believed that book reading is a vanishing pastime , particularly among Millennials , surveys among consumers in the U.S. have shown the opposite .
gold_template: During a survey held in early templateTitleDate[0] , it was found that templateYValue[max] percent of adults aged between 18 and 29 years old had read a templateTitle[0] in any format in the previous templateXLabel[0] . The survey results showed that adults within this templateTitle[5] category were more likely than older templateYLabel[1] to have read a templateTitle[0] within the last twelve months . templateTitle[0] templateTitle[1] in the templateTitleSubject[0] – additional information While it is mostly believed that templateTitle[0] reading is a vanishing pastime , particularly among Millennials , surveys among consumers in the templateTitleSubject[0] have shown the opposite .

generated_template: The statistic shows the templateYLabel[0] of throughout in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] this produced . second templateYValue[idxmax(X)] period of League , templateYValue[max] percent of survey between his and actively templateXLabel[0] used the social networking fluctuated .
generated: The statistic shows the Share of throughout in the U.S. who were using Book as of 2019 , sorted group this produced . second 68 period of League , 81 percent of survey between his and actively Year used the social networking fluctuated .

Example 239:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Daily engagement rate of U.S. mobile social users 2019
X_Axis['Platform']: ['Facebook', 'Instagram', 'Snapchat', 'Facebook_Messenger', 'WhatsApp', 'Reddit', 'TikTok', 'Twitter', 'Tumblr', 'Pinterest']
Y_Axis['Share', 'of', 'daily', 'active', 'users']: ['63.7', '58.8', '55.3', '54.9', '46.2', '43.8', '40', '38.3', '23.6', '19.3']

gold: As of December 2019 , it was found that 46.2 percent of U.S. Android users who had installed WhatsApp were also daily active users . According to App Ape , mobile Facebook app audiences in the United States showed the highest daily app engagement rate with almost 64 percent .
gold_template: As of 2019 , it was found that templateYValue[4] percent of templateTitleSubject[0] Android templateYLabel[3] who had installed templateXValue[4] were also templateYLabel[1] templateYLabel[2] templateYLabel[3] . According to App Ape , templateTitle[4] templateXValue[0] app audiences in the templateTitle[3] showed the highest templateYLabel[1] app templateTitle[1] templateTitle[2] with almost templateYValue[max] percent .

generated_template: What is the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateXLabel[0] ? Indian templateXValue[7] revenues templateXValue[0] had the templateTitle[1] templateTitleSubject[0] templateXLabel[0] in the world in templateTitleDate[0] , with almost templateYValue[max] templateXValue[max] users following the templateXLabel[0] . Swedish behind game commentator templateXValue[1] ranked second with over templateYValue[1] templateXValue[max] political . templateTitleSubject[0] content and impact templateTitleSubject[0] was launched in 2005 as a platform for sharing user-generated videos such as vlogs , tutorials or original series .
generated: What is the engagement rate U.S. Platform ? Indian Twitter revenues Facebook had the engagement U.S. Platform in the world in 2019 , with almost 63.7 Facebook users following the Platform . Swedish behind game commentator Instagram ranked second with over 58.8 Facebook political . U.S. content and impact U.S. was launched in 2005 as a platform for sharing user-generated videos such as vlogs , tutorials or original series .

Example 240:
titleEntities: {'Subject': ['Most'], 'Date': ['2019']}
title: Most utilized frameworks among developers worldwide 2019
X_Axis['Response']: ['Node.js', '.NET', '.NET_Core', 'Pandas', 'Unity_3D', 'React_Native', 'TensorFlow', 'Ansible', 'Cordova', 'Xamarin', 'Apache_Spark', 'Hadoop', 'Unreal_Engine', 'Flutter', 'Torch/PyTorch', 'Puppet', 'Chef', 'CryEngine']
Y_Axis['Share', 'of', 'respondents']: ['49.9', '37.4', '23.7', '12.7', '11.3', '10.5', '10.3', '9.4', '7.1', '6.5', '5.8', '4.9', '3.5', '3.4', '3.3', '2.7', '2.5', '0.6']

gold: The statistic shows the most used libraries , frameworks , and tools among software developers worldwide , as of early 2019 . According to the survey , 49.9 percent of respondents used Node.js , while 37.4 percent used .NET . The least used framework was CryEngine with only 0.6 percent of respondents reporting to use it .
gold_template: The statistic shows the templateTitleSubject[0] used libraries , templateTitle[2] , and tools templateTitle[3] software templateTitle[4] templateTitle[5] , as of early templateTitleDate[0] . According to the survey , templateYValue[max] percent of templateYLabel[1] used templateXValue[0] , while templateYValue[1] percent used templateXValue[1] . The least used framework was templateXValue[last] with only templateYValue[min] percent of templateYLabel[1] reporting to use it .

generated_template: The statistic shows the results of an opinion poll conducted in templateTitleDate[0] attract Americans to describe templateTitle[0] templateTitleSubject[0] in be templateTitle[3] . In templateTitleDate[0] , templateYValue[max] percent of Americans stays the words `` templateXValue[0] '' to describe templateTitle[0] templateTitleSubject[0] , templateYValue[1] percent stays `` templateXValue[1] '' and templateYValue[2] percent stays `` templateXValue[2] '' . templateTitleSubject[0] that perception as a presidential candidate In templateTitleDate[0] , since Americans who were tournament to describe templateTitle[0] templateTitleSubject[0] in be templateTitle[3] , close to templateYValue[max] percent of Americans stays the words `` templateXValue[0] '' .
generated: The statistic shows the results of an opinion poll conducted in 2019 attract Americans to describe Most in be among . In 2019 , 49.9 percent of Americans stays the words `` Node.js '' to describe Most , 37.4 percent stays `` .NET '' and 23.7 percent stays `` .NET Core '' . Most that perception as a presidential candidate In 2019 , since Americans who were tournament to describe Most in be among , close to 49.9 percent of Americans stays the words `` Node.js '' .

Example 241:
titleEntities: {'Subject': ['SAP'], 'Date': ['2006', '2018']}
title: SAP 's net profit 2006 to 2018
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Net', 'profit', 'in', 'million', 'euros']: ['3321', '4083', '4008', '3646', '3064', '3280', '3326', '2803', '3437', '1813', '1750', '1847', '1906', '1871']

gold: This statistic shows the net profit generated by software company SAP from 2006 to 2019 . In 2019 , the profit attributable to the SAP parent company came to approximately 3.3 billion euros . That same year , SAP generated 27.55 billion euros in revenue worldwide .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] generated by software company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] attributable to the templateTitleSubject[0] parent company came to approximately templateYValue[0] templateYLabel[2] templateYLabel[3] . That same templateXLabel[0] , templateTitleSubject[0] generated 27.55 templateYLabel[2] templateYLabel[3] in revenue worldwide .

generated_template: The statistic shows the median templateYLabel[0] establishments for templateTitle[0] templateTitle[1] ( excluding overtime ) of the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to 20119 , with a forecast of reaching over templateYValue[0] templateXValue[max] This 's . The is due to the templateYLabel[0] of templateTitle[0] templateTitle[1] increased by templateYValue[12] templateXValue[max] This 's .
generated: The statistic shows the median Net establishments for SAP 's ( excluding overtime ) of the SAP ( ) from 2006 to 20119 , with a forecast of reaching over 3321 2019 This 's . The is due to the Net of SAP 's increased by 1906 2019 This 's .

Example 242:
titleEntities: {'Subject': ['Texas'], 'Date': ['2000', '2018']}
title: Texas - poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['14.9', '14.7', '15.6', '15.9', '17.2', '17.5', '17.9', '18.5', '17.9', '17.2', '15.8', '16.3', '16.9', '17.6', '16.6', '16.3', '15.6', '15', '15.1']

gold: This statistic shows the poverty rate in Texas from 2000 to 2018 . For instance , 14.9 percent of Texas 's population lived below the poverty line in 2018 .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . For instance , templateYValue[idxmax(X)] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line in templateXValue[max] .

generated_template: The statistic gives information on the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateTitleDate[max] . In the most recently measured year , the templateTitle[1] templateTitle[2] in templateTitleSubject[0] increased to approximately templateYValue[0] percent in templateXValue[idxmin(Y)] .
generated: The statistic gives information on the poverty rate in Texas from 2000 to 2018 . In the most recently measured year , the poverty rate in Texas increased to approximately 14.9 percent in 2017 .

Example 243:
titleEntities: {'Subject': ['France'], 'Date': ['2018']}
title: Best selling video games on all gaming platforms in France 2018 , by sales volume
X_Axis['Month']: ['FIFA_19', 'Red_Dead_Redemption_2', 'Call_of_Duty:_Black_Ops_4', 'Mario_Kart_8_Deluxe', 'Super_Mario_Party', 'Spider-Man', 'Super_Smash_Bros._Ultimate', 'Super_Mario_Odyssey', 'Assassin’s_Creed_Odyssey', 'God_of_War']
Y_Axis['Number', 'of', 'units', 'sold', 'in', 'thousands']: ['1353.4', '1011.0', '565.0', '542.4', '380.3', '345.3', '335.6', '328.7', '322.8', '301.4']

gold: This statistic represents the ten best selling video games across all gaming platforms combined in France in 2018 , by number of physical software units sold . With around 1.4 million units sold , FIFA 19 was the most sold video game in France that year .
gold_template: This statistic represents the ten templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] across templateTitle[4] templateTitle[5] templateTitle[6] combined in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[9] templateYLabel[0] of physical software templateYLabel[1] templateYLabel[2] . With around templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] , templateXValue[0] was the most templateYLabel[2] templateTitle[2] game in templateTitleSubject[0] that year .

generated_template: The statistic gives a ranking of the templateTitle[2] popular templateTitle[0] on templateTitleSubject[0] , ranked by templateTitle[1] of templateTitle[2] on the social revenues . As of 2020 , personal care templateYLabel[0] templateXValue[0] Body templateXValue[0] was ranked first with close to templateYValue[max] templateXValue[max] British pounds .
generated: The statistic gives a ranking of the video popular Best on France , ranked by selling of video on the social revenues . As of 2020 , personal care Number FIFA 19 Body was ranked first with close to 1353.4 FIFA 19 British pounds .

Example 244:
titleEntities: {'Subject': ['Taiwan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Taiwan 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']: ['31483.8', '29870.22', '28324.43', '26861.07', '25525.81', '24827.9', '25007.75', '24389.68', '22572.7', '22373.56', '22638.92', '21887.99', '21269.61', '20911.64', '19261.67', '16959.78', '18102.95', '17780.93', '16984.54', '16503.31', '15360.72', '14094.37', '13715.53', '13408.38', '14876.88', '13768.27', '12787.26', '13968.1', '13597.25', '13706.01', '12108.75', '11200.81', '10725.7', '9092.11', '8178.15', '7577.05', '6337.5', '5325.22', '4010.11', '3295.11', '3203.47']

gold: The statistic shows gross domestic product ( GDP ) per capita in Taiwan 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: templateTitle[0] templateTitle[1] templateTitle[2] is the total value of all goods and services they 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] percent templateXValue[max] to the This templateXLabel[0] .
generated: Gross domestic product is the total value of all goods and services they in a country in a Year . It is considered an important indicator of the economic strength of a country . In 2018 , GDP in Taiwan amounted to around 25007.75 percent 2024 to the This Year .

Example 245:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1992', '2017']}
title: Luggage and leather goods store sales in the U.S. from 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']: ['33.92', '33.97', '34.14', '33.83', '33.21', '32.32', '31.25', '27.78', '27.46', '30.91', '32.77', '31.96', '30.13', '29.28', '27.17', '26.27', '25.2', '26.7', '25.38', '22.79', '21.12', '21.63', '20.28', '19.02', '17.59', '16.12']

gold: This timeline shows jewelry , luggage , and leather goods store sales in the United States from 1992 to 2017 . In 2017 , U.S. jewelry , luggage , and leather goods store sales amounted to about 33.92 billion U.S. dollars .
gold_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] templateYLabel[0] templateYLabel[1] templateYLabel[2] .

generated_template: The 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] , templateXValue[max] templateTitle[0] , templateTitle[1] , and templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[0] templateYLabel[0] dollars.Additional information on the templateTitle[0] templateTitle[1] and gifts market in the USThe statistic above shows how templateTitle[4] in the templateTitle[0] , templateTitle[1] and templateTitle[2] sector dropped 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 templateYValue[idxmax(X)] younger people are choosing travel and entertainment over more traditional gifts than their older counterparts .
generated: The timeline shows Luggage , leather and goods store sales in the U.S. from 1992 to 2017 . In 2017 , 99 Luggage , leather and goods store sales amounted to about 33.92 Billion dollars.Additional information on the Luggage leather and gifts market in the USThe statistic above shows how sales in the Luggage , leather and goods sector dropped following the market crash in 2008 , as did retail in the U.S. overall . A comparison of Luggage purchases in 2015 between millennials and consumers aged 35 and over shows 25.38 younger people are choosing travel and entertainment over more traditional gifts than their older counterparts .

Example 246:
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 decreased from templateXValue[min] to templateXValue[max] , sinking from templateYValue[idxmin(X)] to templateYValue[idxmax(X)] units . Such a decrease 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 decreased steadily , and , as a consequence , the population size per bank branch increased from 1,734 in 2008 to 2,067 in templateXValue[2] .

generated_template: templateTitleSubject[1] templateTitle[4] templateTitleSubject[1] is a luxury templateTitleSubject[0] month First steady in a either town cose to watching in winning by the two templateTitleSubject[0] designers Domenico templateTitleSubject[1] and Stefano templateTitleSubject[1] . The recent illustrates the turrnover of the templateTitleSubject[0] luxury month templateTitle[2] templateTitleSubject[1] templateTitle[4] templateTitleSubject[1] between templateXValue[min] and templateXValue[3] , the total templateYLabel[0] of the templateTitleSubject[0] luxury templateTitleSubject[1] templateTitle[4] templateTitleSubject[1] an to over templateYValue[0] templateXValue[max] in increase . In the same templateXLabel[0] earlier , the total templateYLabel[0] of the templateTitleSubject[0] templateYValue[idxmax(X)] net decrease in than chart in comparison , with a decrease of over templateYValue[0] templateXValue[max] in templateXValue[idxmin(Y)] .
generated: Italy 2018 Italy is a luxury Italy month First steady in a either town cose to watching in winning by the two Italy designers Domenico Italy and Stefano Italy . The recent illustrates the turrnover of the Italy luxury month banks Italy 2018 between 2011 and 2015 , the total Number of the Italy luxury 2018 Italy an to over 505 2018 in increase . In the same Year earlier , the total Number of the Italy 505 net decrease in than chart in comparison , with a decrease of over 505 2018 in .

Example 247:
titleEntities: {'Subject': ['Trade'], 'Date': ['1950', '2018']}
title: Trade : export volume worldwide 1950 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Export', 'volume', 'in', 'billion', 'U.S.', 'dollars']: ['19453.36', '17731.08', '16021.98', '16539.16', '18986.54', '18950.64', '18513.19', '18339.81', '15302.68', '12556.2', '16149.3', '14021.16', '12128.03', '10502.74', '6452.32', '5176.2', '3495.69', '1964.84', '2049.41', '876.89', '318.02', '189.62', '130.09', '93.92', '61.81']

gold: In 2018 , the global trade value of goods exported throughout the world amounted to approximately 19.5 trillion U.S. dollars . In comparison , this figure stood at around 6.45 trillion U.S. dollars in 2000 . The rise in the value of goods exported around the world reflects developments in international trade , globalization , and advances in technology .
gold_template: In templateXValue[max] , the global templateTitleSubject[0] value of goods exported throughout the world amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . In comparison , this figure stood at around templateYValue[14] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[14] . The rise in the value of goods exported around the world reflects developments in international templateTitleSubject[0] , globalization , and advances in technology .

generated_template: The statistic shows the templateXValue[max] This in the United templateYLabel[0] of templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateTitleDate[max] , in This 's . In templateXValue[13] , the templateTitle[0] of templateTitle[0] templateYLabel[0] templateTitle[1] in templateTitleSubject[0] was valued at 4 templateXValue[max] This 's .
generated: The statistic shows the 2018 This in the United Export of Trade export in Trade from 1950 to 2018 , in This 's . In 2005 , the Trade of Export in Trade was valued at 4 2018 This 's .

Example 248:
titleEntities: {'Subject': ['Research'], 'Date': ['2010', '2019']}
title: Research and development spending of 3M from 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Spending', 'in', 'million', 'U.S.', 'dollars']: ['1911', '1821', '1870', '1735', '1763', '1770', '1715', '1634', '1570', '1434']

gold: This statistic shows 3M 's spending on research , development , and related expenses from 2010 to 2019 . 3M Company , formerly known as the Minnesota Mining and Manufacturing Company , is a U.S.-based multinational company that produces some 55 thousand products , ranging from adhesives to electronic materials . 3M 's spending research and development in 2019 came to around 1.9 billion U.S. dollars .
gold_template: This statistic shows templateTitle[3] 's templateYLabel[0] on templateTitleSubject[0] , templateTitle[1] , and related expenses templateTitle[4] templateXValue[min] to templateXValue[max] . templateTitle[3] Company , formerly known as the Minnesota Mining and Manufacturing Company , is a U.S.-based multinational company that produces some 55 thousand products , ranging templateTitle[4] adhesives to electronic materials . templateTitle[3] 's templateYLabel[0] templateTitleSubject[0] and templateTitle[1] in templateXValue[max] came to around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[3] templateYLabel[0] of templateTitleSubject[0] Holdings in the fiscal templateXLabel[0] templateXValue[min] to the fiscal templateXLabel[0] of fiscal templateXLabel[0] . In the fiscal templateXLabel[0] ending 2019 , the British retailer generated templateYLabel[0] of approximately templateYValue[5] templateXValue[max] This . The is a decrease of over 1 and be .
generated: The statistic shows the 3M Spending of Research Holdings in the fiscal Year 2010 to the fiscal Year of fiscal Year . In the fiscal Year ending 2019 , the British retailer generated Spending of approximately 1770 2019 This . The is a decrease of over 1 and be .

Example 249:
titleEntities: {'Subject': ['Latin America'], 'Date': ['2020']}
title: Latin America : gender gap index 2020 , by country
X_Axis['Country']: ['Nicaragua', 'Costa_Rica', 'Colombia', 'Trinidad_and_Tobago', 'Mexico', 'Barbados', 'Argentina', 'Cuba', 'Uruguay', 'Jamaica', 'Bolivia', 'Panama', 'Ecuador', 'Chile', 'Honduras', 'Bahamas', 'Peru', 'Venezuela', 'Suriname', 'El_Salvador', 'Dominican_Republic', 'Brazil', 'Paraguay', 'Belize', 'Guatemala']
Y_Axis['Index', 'score']: ['0.81', '0.78', '0.76', '0.76', '0.75', '0.75', '0.75', '0.75', '0.74', '0.74', '0.73', '0.73', '0.73', '0.72', '0.72', '0.72', '0.71', '0.71', '0.71', '0.71', '0.7', '0.69', '0.68', '0.67', '0.67']

gold: In 2020 , Nicaragua was the Latin American country with the highest gender gap index , with 0.81 points . Guatemala , on the other hand , had the worst score in the region with 0.67 points , which shows a gender pay gap of 33 percent ( on average , women had 33 percent less opportunities than men in Guatemala ) .
gold_template: In templateTitleDate[0] , templateXValue[0] was the templateTitleSubject[0] American templateXLabel[0] with the highest templateTitle[2] templateTitle[3] templateYLabel[0] , with templateYValue[max] points . templateXValue[last] , on the other hand , had the worst templateYLabel[1] in the region with templateYValue[min] points , which shows a templateTitle[2] pay templateTitle[3] of 33 percent ( on average , women had 33 percent less opportunities than men in templateXValue[last] ) .

generated_template: The statistic shows the top 25 templateTitle[0] in the world templateTitle[1] the templateTitle[2] number of their templateYLabel[0] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] of templateXValue[max] in the templateTitleSubject[1] with templateYValue[max] percent .
generated: The statistic shows the top 25 Latin in the world America the gender number of their Index in 2020 . In 2020 , Nicaragua had the highest Index of Nicaragua in the Latin America with 0.81 percent .

Example 250:
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: templateTitle[0] templateTitle[1] in templateTitleSubject[0] reached the ; point in the templateTitle[0] , templateXValue[6] average in the last five period . In the most recent period , the templateTitle[0] templateTitle[1] in the templateTitle[3] has declined at templateYValue[0] templateXValue[max] This . The with at the ; templateTitle[0] templateTitle[1] in the most recent period .
generated: Fertility rate in Nepal reached the ; point in the Fertility , 2011 average in the last five period . In the most recent period , the Fertility rate in the 2017 has declined at 1.97 2017 This . The with at the ; Fertility rate in the most recent period .

Example 251:
titleEntities: {'Subject': ['earth', 'gadolinium'], 'Date': ['2009']}
title: Forecast of rare earth oxide gadolinium oxide price globally 2009 to 2025
X_Axis['Year']: ['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022', '2023', '2024', '2025']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'metric', 'ton']: ['10707', '62724', '24167', '24033', '25485', '26046', '26447', '28055', '28473', '23916', '19700', '19700', '19982', '20603', '21907', '23437']

gold: This statistic shows the price development of rare earth oxide gadolinium oxide globally from 2009 to 2025 . It is expected that the price of gadolinium oxide will reach some 28,055 U.S. dollars per metric ton in 2017 .
gold_template: This statistic shows the templateYLabel[0] development of templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitleSubject[1] templateTitle[3] templateTitle[7] from templateTitleDate[0] to templateXValue[max] . It is expected that the templateYLabel[0] of templateTitleSubject[1] templateTitle[3] will reach some templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[7] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] per annum between templateXValue[min] and templateXValue[9] , and a forecast reason for templateXValue[1] and a forecast for the economic production for the templateXLabel[0] templateXValue[8] . In templateXValue[12] , the templateTitle[2] templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] was expected to reach templateYValue[7] thousand templateXValue[max] This 's .
generated: The statistic shows the Price of Forecast rare earth oxide per annum between 2010 and 2019 , and a forecast reason for 2011 and a forecast for the economic production for the Year 2018 . In 2022 , the earth Price of Forecast rare earth oxide in the earth was expected to reach 28055 thousand 2025 This 's .

Example 252:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009']}
title: U.S. households - average hours per day of computer usage in 2009
X_Axis['Hours', 'used', 'per', 'day']: ['Less_than_1_hour', '1_to_3_hours', '3_to_6_hours', '6_to_10_hours', 'More_than_10_hours', 'No_computers']
Y_Axis['Housing', 'units', 'in', 'millions']: ['16.6', '32.0', '17.5', '7.7', '12.5', '27.4']

gold: This statistic shows the hours per day a computer is used in a U.S. home in 2009 . In 17.5 million U.S. households , a computer is used 3 to 6 hours per day .
gold_template: This statistic shows the templateXValue[1] templateXLabel[2] templateXLabel[3] a templateTitle[6] is templateXLabel[1] in a templateTitleSubject[0] home in templateTitleDate[0] . In templateYValue[2] templateYLabel[2] templateTitleSubject[0] templateTitle[1] , a templateTitle[6] is templateXLabel[1] templateXValue[1] to templateXValue[2] templateXValue[1] templateXLabel[2] templateXLabel[3] .

generated_template: The statistic depicts the templateXLabel[0] of templateTitle[1] in templateTitle[6] homes in templateTitleDate[0] . In that year , templateYValue[max] templateYLabel[2] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] was templateXValue[2] templateTitle[1] .
generated: The statistic depicts the Hours of households in computer homes in 2009 . In that year , 32.0 millions Housing units in the U.S. was 3 to 6 hours households .

Example 253:
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: The statistic shows the templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to had , with a forecast up years templateTitleDate[max] . In had , the templateTitle[2] templateYLabel[0] in templateTitleSubject[0] an to templateYValue[max] percent templateXValue[max] to the This templateXLabel[0] .
generated: The statistic shows the household in Illinois from 1990 to had , with a forecast up years 2018 . In had , the household in Illinois an to 70145 percent 2018 to the This Year .

Example 254:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Number of professionals at the leading accounting firms in the U.S. 2019
X_Axis['Accounting', 'firm']: ['Deloitte', 'PwC', 'Ernst_&_Young', 'KPMG', 'RSM_US', 'Grant_Thornton', 'BDO_USA', 'CliftonLarsonAllen', 'Crowe_Horwath', 'CBIZ_/_Mayer_Hoffman_McCann', 'Moss_Adams', 'CohnReznick', 'BKD', 'Baker_Tilly_Virchow_Krause', 'Marcum', 'Plante_Moran', 'Dixon_Hughes_Goodman', 'EisnerAmper', 'Wipfli', 'Carr_Riggs_&_Ingram', 'Eide_Bailly', 'Citrin_Cooperman_&_Co.', 'Armanino', 'Withum', 'Mazars_USA']
Y_Axis['Number', 'of', 'professionals']: ['73855', '35350', '33600', '26447', '7252', '6616', '4958', '4056', '3402', '2470', '2066', '1908', '1824', '2095', '1219', '1796', '1385', '979', '1229', '1346', '1386', '675', '900', '720', '601']

gold: This statistic shows the number of professionals at the leading accounting firms in the United States as of March 2019 . The largest accounting firm in the U.S. , Deloitte , employed 73,855 professionals at the end of their fiscal year in June 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] at the templateTitle[2] templateXLabel[0] templateTitle[4] in the templateTitle[5] as of 2019 . The largest templateXLabel[0] templateXLabel[1] in the templateTitleSubject[0] , templateXValue[0] , employed templateYValue[max] templateYLabel[1] at the end of their fiscal year in 2018 .

generated_template: The statistic shows a ranking of templateTitle[0] of the templateTitleSubject[0] templateTitle[6] this templateTitle[1] templateYLabel[0] ( templateTitle[3] ) templateYLabel[1] in templateTitleDate[0] . templateXValue[3] of FC templateXValue[1] Munich has a templateYLabel[0] templateYLabel[1] of templateYValue[max] points .
generated: The statistic shows a ranking of Number the U.S. 2019 this professionals Number ( accounting ) professionals in 2019 . KPMG of FC PwC Munich has a Number professionals of 73855 points .

Example 255:
titleEntities: {'Subject': ['Ireland'], 'Date': ['2018']}
title: Urbanization in Ireland 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['63.17', '62.95', '62.74', '62.54', '62.34', '62.14', '61.94', '61.74', '61.54', '61.34', '61.14']

gold: This statistic shows the degree of urbanization in Ireland from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 63.17 percent of Ireland '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)] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: The statistic in the templateXValue[min] of templateTitle[1] in templateTitleSubject[0] from shows to cities . templateTitle[1] is defined templateXValue[1] the templateYLabel[0] of the total templateXValue[max] in the This templateXValue[max] of a approximately . In the This templateXValue[max] of the This templateXValue[max] of the This templateXValue[max] of the This templateXValue[max] of a approximately .
generated: The statistic in the 2008 of Ireland in from shows to cities . Ireland is defined 2017 the Share of the total 2018 in the This 2018 of a approximately . In the This 2018 of the This 2018 of the This 2018 of the This 2018 of a approximately .

Example 256:
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)] templateYLabel[5] people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[idxmax(X)] templateYLabel[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of the This 's in templateTitleSubject[0] from templateXValue[min] to 2027 . In templateXValue[min] , templateYValue[idxmin(X)] templateXValue[idxmax(Y)] people accessed the This through their thousand , up from templateYValue[idxmin(X)] templateXValue[idxmax(Y)] in templateXValue[idxmin(Y)] .
generated: The statistic shows the Number of the This 's in Singapore from 2017 to 2027 . In 2017 , 4.3 2023 people accessed the This through their thousand , up from 4.3 2023 in 2017 .

Example 257:
titleEntities: {'Subject': ['India'], 'Date': ['2024']}
title: National debt of India in relation to gross domestic product ( GDP ) 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'to', 'GDP', 'ratio']: ['65.61', '66.19', '66.93', '67.75', '68.52', '69.04', '68.05', '67.83', '67.67', '68.78', '66.83']

gold: The statistic shows the national debt in India from 2014 to 2018 in relation to gross domestic product ( GDP ) , with projections up until 2024 . In 2018 , the national debt of India amounted to about 68.05 percent of the gross domestic product . India 's economy on the rise India is one of the most populous countries in the world , and although a large share of inhabitants are living below the poverty line – or probably due to this fact – , the country 's economy is growing steadily .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to about templateYValue[6] percent of the templateTitle[4] templateTitle[5] templateTitle[6] . templateTitleSubject[0] 's economy on the rise templateTitleSubject[0] is one of the most populous countries in the world , and although a large share of inhabitants are living below the poverty line – or probably due to this fact – , the country 's economy is growing steadily .

generated_template: The statistic shows the templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] ( templateXValue[max] ) , with projections up until years . In templateXValue[6] , the templateYLabel[0] in templateTitleSubject[0] amounted to about templateYValue[6] percent of the templateTitle[4] templateTitle[5] templateTitle[6] .
generated: The statistic shows the National in India from 2014 to 2018 in relation to gross domestic product GDP ( 2024 ) , with projections up until years . In 2018 , the National in India amounted to about 68.05 percent of the gross domestic product .

Example 258:
titleEntities: {'Subject': ['Terrorism'], 'Date': []}
title: Terrorism - kidnappings grouped by country
X_Axis['Country']: ['Somalia', 'Afghanistan', 'Pakistan', 'India', 'Colombia', 'Congo_Democratic_Republic', 'Sudan', 'Yemen', 'Central_African_Republic', 'Iraq', 'Gaza_Strip', 'Philippines', 'Turkey', 'Burma', 'Nigeria']
Y_Axis['Number', 'of', 'kidnappings']: ['2527', '902', '430', '341', '285', '189', '159', '117', '115', '111', '107', '102', '63', '25', '17']

gold: The statistic shows the number of kidnappings due to terrorism grouped by country in 2011 . 2,527 people werde kidnapped in Somalia .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] due to templateTitleSubject[0] templateTitle[2] templateTitle[3] templateXLabel[0] in 2011 . templateYValue[max] people werde kidnapped in templateXValue[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] in templateXValue[0] between tend and October templateTitleDate[0] , templateTitle[4] templateXLabel[0] of amounted . According to the source , templateXValue[0] up the largest active templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitleSubject[0] , with over templateYValue[max] thousand people in templateXValue[1] .
generated: The statistic shows the Number of Terrorism kidnappings in Somalia between tend and October , country of amounted . According to the source , Somalia up the largest active Number of Terrorism kidnappings in the Terrorism , with over 2527 thousand people in Afghanistan .

Example 259:
titleEntities: {'Subject': ['Pakistan'], 'Date': ['2024']}
title: National debt of Pakistan in relation to gross domestic product ( GDP ) 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'relation', 'to', 'GDP']: ['65.41', '68.98', '72.48', '76.11', '78.65', '76.73', '71.69', '67.05', '67.63', '63.32', '63.47']

gold: This statistic shows the national debt of Pakistan from 2014 to 2018 , in relation to the gross domestic product ( GDP ) , with projections up until 2024 . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In 2018 , the national debt of Pakistan amounted to approximately 71.69 percent of the GDP .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , in templateYLabel[2] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] percent of the templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] government of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateXValue[max] to the templateTitle[4] templateTitle[5] templateTitle[6] ( This ) , with projections up until domestic years . In templateXValue[6] , templateTitleSubject[0] that templateYLabel[0] amounted to approximately templateYValue[min] percent of the This .
generated: The statistic shows the National government of Pakistan from 2014 to 2018 in 2024 to the gross domestic product ( This ) , with projections up until domestic years . In 2018 , Pakistan that National amounted to approximately 63.32 percent of the This .

Example 260:
titleEntities: {'Subject': ['Ukraine'], 'Date': ['2010', '2024']}
title: National debt of Ukraine in relation to gross domestic product 2010 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['National', 'debt', 'as', 'percent', 'of', 'GDP']: ['43.72', '46.24', '49.77', '53.83', '57.89', '62.03', '63.86', '71.92', '81.18', '79.5', '70.32', '40.52', '37.54', '36.88', '40.63']

gold: This statistic shows the national debt of Ukraine from 2010 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In 2018 , the national debt of Ukraine amounted to an estimate of approximately 63.86 percent of the GDP .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to an estimate of approximately templateYValue[6] templateYLabel[2] of the templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] The of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( This ) , with projections up until templateTitleDate[max] . In templateXValue[6] , the annual templateYLabel[0] of templateTitleSubject[0] amounted to about templateYValue[6] percent of the templateTitle[4] templateTitle[5] templateTitle[6] .
generated: The statistic shows the National of Ukraine from 2010 to 2018 in relation to the gross domestic product ( This ) , with projections up until 2024 . In 2018 , the annual National of Ukraine amounted to about 63.86 percent of the gross domestic product .

Example 261:
titleEntities: {'Subject': ['Azerbaijan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) 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['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['57.13', '54.15', '51.52', '49.4', '48.04', '47.17', '46.94', '41.38', '37.83', '50.84', '75.24', '74.16', '69.69', '65.99', '52.91', '44.29', '48.98', '33.09', '21.03', '13.27', '8.68', '7.28', '6.23', '5.48', '5.27', '4.58', '4.28', '3.96', '3.18', '2.42', '2.26']

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

generated_template: In had , templateTitleSubject[0] that templateYLabel[0] templateYValue[0] templateXValue[max] ( templateTitle[3] ) an to around templateYValue[6] This 's templateXValue[idxmax(Y)] , and is expected to one to almost templateYValue[max] This 's templateXValue[idxmax(Y)] by increase . templateYLabel[0] is the total value of all services and services they within a country in any given templateXLabel[0] . templateTitle[3] is templateXValue[6] important indicator of a country that economic power .
generated: In had , Azerbaijan that Gross 57.13 2024 ( GDP ) an to around 46.94 This 's 2014 , and is expected to one almost 75.24 This 's 2014 by increase . Gross is the total value of all services and they within a country in any given Year . GDP is 2018 important indicator of a country that economic power .

Example 262:
titleEntities: {'Subject': ['Europe'], 'Date': ['2018']}
title: Production volume of tomatoes in Europe 2018
X_Axis['Country']: ['Turkey', 'Italy', 'Spain', 'Portugal', 'Poland', 'Netherlands', 'Greece', 'France', 'Romania', 'Albania_', 'Belgium', 'Hungary', 'Serbia_', 'North_Macedonia', 'Bulgaria', 'Germany', 'United_Kingdom', 'Austria', 'Switzerland', 'Bosnia_and_Herzegovina_', 'Finland', 'Croatia', 'Slovakia', 'Kosovo', 'Sweden', 'Cyprus_', 'Lithuania', 'Denmark', 'Malta', 'Norway', 'Czechia', 'Slovenia', 'Latvia', 'Ireland', 'Montenegro_', 'Iceland', 'Estonia', 'Luxembourg']
Y_Axis['Volume', 'in', 'thousand', 'tonnes']: ['12150.0', '6055.43', '4768.82', '1329.76', '928.83', '910.0', '885.15', '712.02', '451.76', '286.8', '258.68', '204.0', '170.76', '161.62', '148.08', '103.27', '66.9', '58.15', '48.24', '43.92', '39.32', '22.59', '22.29', '18.8', '18.23', '15.7', '12.19', '11.76', '11.19', '10.57', '10.08', '8.39', '5.2', '3.92', '3.27', '1.21', '0.29', '0.01']

gold: This statistic displays the volume of harvested tomato production in Europe in 2018 , by country . In this year , Turkey produced the largest volume of tomatoes at over 12.15 million tonnes , followed by Italy with 6.06 million tonnes produced .
gold_template: This statistic displays 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 templateTitle[2] at over templateYValue[max] templateYLabel[1] templateYLabel[2] , followed by templateXValue[1] with templateYValue[1] templateYLabel[1] templateYLabel[2] produced .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateXValue[max] in templateTitleDate[0] , 's templateXLabel[0] . In templateTitleDate[0] , the majority of templateTitle[1] templateTitle[2] were based in the templateXValue[0] and Europe . In templateTitleDate[0] , there were templateYValue[max] templateTitle[1] templateTitle[2] in the templateXValue[0] .
generated: The statistic shows the Volume of tomatoes Turkey in 2018 , 's Country . In 2018 , the majority of volume tomatoes were based in the Turkey and Europe . In 2018 , there were 12150.0 volume tomatoes in the Turkey .

Example 263:
titleEntities: {'Subject': ['Growth'], 'Date': ['2008', '2012']}
title: Growth of crowdfunding platforms worldwide 2008 to 2012
X_Axis['Year']: ['2012', '2011', '2010', '2009', '2008']
Y_Axis['Growth', 'in', 'the', 'number', 'of', 'CFPs']: ['60', '54', '47', '45', '38']

gold: The statistic shows the percentage growth in the number of crowdfunding platforms worldwide from 2008 to 2012 . In 2008 , the number of crowdfunding platforms worldwide increased by 38 percent in comparison to the previous year . The rate of crowdfunding platform growth continued in all of the following years and the growth rate reached 60 percent in 2012 .
gold_template: The statistic shows the percentage templateYLabel[0] in the templateYLabel[1] of templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[1] of templateTitle[1] templateTitle[2] templateTitle[3] increased by templateYValue[idxmin(X)] percent in comparison to the previous templateXLabel[0] . The rate of templateTitle[1] platform templateYLabel[0] continued in all of the following years and the templateYLabel[0] rate reached templateYValue[idxmax(X)] percent in templateXValue[idxmax(Y)] .

generated_template: The statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per Product in the templateTitleSubject[0] between amounted and this , produced high . In than templateYValue[min] templateXLabel[0] old , individuals aged between year-on-year and World templateXValue[0] old templateTitle[3] templateYValue[max] percent of templateTitle[1] templateTitle[2] per Product .
generated: The statistic displays the Growth of crowdfunding platforms worldwide per Product in the Growth between amounted and this , produced high . In than 38 Year old , individuals aged between year-on-year and World 2012 old worldwide 60 percent of crowdfunding platforms per Product .

Example 264:
titleEntities: {'Subject': ['Fresh'], 'Date': ['1990', '2018']}
title: Fresh fruit production worldwide 1990 to 2018
X_Axis['Year']: ['1990', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Production', 'in', 'million', 'metric', 'tons']: ['401.61', '576.65', '591.63', '609.53', '621.92', '645.87', '661.62', '687.42', '702.61', '718.82', '736.13', '740.54', '766.21', '778.94', '810.44', '822.81', '833.74', '835.34', '842.84', '868.09']

gold: This statistic illustrates the total global production of fresh fruit from 1990 to 2018 . In 2018 , the global production of fresh fruit amounted to some 868.1 million metric tons , increasing from 576.65 million metric tons in 2000 .
gold_template: This statistic illustrates the total global templateYLabel[0] of templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateYLabel[0] of templateTitleSubject[0] templateTitle[1] amounted to some templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , increasing from templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[1] .

generated_template: The statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateTitleDate[max] . In crop templateXLabel[0] templateXValue[1] , templateTitle[2] templateTitle[3] templateYLabel[0] in the templateTitle[0] came to approximately templateYValue[min] fifth templateXValue[max] This .
generated: The statistic shows the Fresh Production of worldwide in the 1990 from to 2018 . In crop Year 2000 , production worldwide in the Fresh came to approximately 401.61 fifth 2018 This .

Example 265:
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] increased by templateYValue[0] percent .

generated_template: Between 2014 and 2019 , the ; templateYLabel[0] of templateTitle[0] raindays occurred in 2014 , when just templateYValue[18] thousand 1mm 1mm or more of templateTitle[2] . The is a respiratory disease templateYValue[idxmax(X)] in January of the same year , at templateYValue[max] . Fourth , or point the wettest decades of the year tend to the start of the start and the end .
generated: Between 2014 and 2019 , the ; Percentage of Internet raindays occurred in 2014 , when just 13.6 thousand 1mm or more of sales . The is a respiratory disease 12.8 in January of the same year , at 30.1 . Fourth , or point the wettest decades of the year tend to the start of the start and the end .

